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Impact of institutional quality on the capital structure of firms in developing countries
Accepted Manuscript Impact of institutional quality on the capital structure of firms in developing countries
Bolaji Tunde Matemilola, A.N. Bany-Ariffin, W.N.W. AzmanSaini, Annuar Md Nassir PII: DOI: Reference:
S1566-0141(16)30145-5 https://doi.org/10.1016/j.ememar.2019.04.003 EMEMAR 613
To appear in:
Emerging Markets Review
Received date: Revised date: Accepted date:
27 November 2016 28 March 2019 23 April 2019
Please cite this article as: B.T. Matemilola, A.N. Bany-Ariffin, W.N.W. Azman-Saini, et al., Impact of institutional quality on the capital structure of firms in developing countries, Emerging Markets Review, https://doi.org/10.1016/j.ememar.2019.04.003
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ACCEPTED MANUSCRIPT Impact of institutional quality on the capital structure of firms in developing countries Bolaji Tunde Matemilolaa, Bany-Ariffin A.N b,* , Azman-Saini W.N.Wc, and Annuar Md Nassird abd Department of Accounting and Finance, Universiti Putra Malaysia, 43400 Serdang, Malaysia c Department of Economics, Universiti Putra Malaysia, 43400 Serdang, Malaysia
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Abstract
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This paper investigates the effects of institutional quality on firms’ capital structure for a panel of
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3,891 firms from 23 developing countries. Our main findings for the full panel show institutional quality has a significantly positive effect on firms’ capital structure. At the regional level, based
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on the panel data for 2,187 firms from Asian countries and 1,091 firms from Latin American and Eastern European countries, institutional quality has a significantly positive effect on capital
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structure. However, for the 613 firms from African countries, institutional quality is mostly insignificant. Additional analysis reveals positive effects of legal enforcement on capital
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structure.
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JEL classification– G32, G37
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Keywords: Capital structure, debt, institutional factors, international evidence, System GMM
* Corresponding author. Email address: [email protected] (Bany-Ariffin A.N)
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ACCEPTED MANUSCRIPT Impact of institutional quality on the capital structure of firms in developing countries Abstract This paper investigates the effects of institutional quality on firms’ capital structure for a panel of 3,891 firms from 23 developing countries. Our main findings for the full panel show institutional
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quality has a significantly positive effect on firms’ capital structure. At the regional level, based
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on the panel data for 2,187 firms from Asian countries and 1,091 firms from Latin American and
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Eastern European countries, institutional quality has a significantly positive effect on capital structure. However, for the 613 firms from African countries, institutional quality is mostly insignificant. Additional analysis reveals positive effects of legal enforcement on capital
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structure.
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Keywords: Capital structure, debt, institutional factors, international evidence, System GMM
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JEL classification– G32, G37
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I. Introduction
A general consensus holds that low institutional quality is a major constraint that hinders
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some firms in developing countries from easily obtaining the external capital that is vital for funding profitable investments that enhance returns. Low institutional quality in developing
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countries seem to reduce loan availability because lenders are unwilling to provide credit to firms for fear of inadequate protections (Qian and Strahan, 2007). Prior studies (e.g., Awartani et al. (2016) , Oztekin and Flannery (2012), Fan et al. (2012), De Jong et al. (2008), Demirguc-
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Kunt and Maksimovic (1999) in areas of finance have recognized the importance of institutional quality in international samples covering firms from different countries. Specifically, Awartani et al. (2016) investigate debt maturity structure and its firm and institutional determinants using a large sample of listed firms in the MENA region. The authors find that better institutional quality lead to the usage of more long-term debt and limited use of long-term debt by MENA firms. Oztekin and Flannery (2012) compare firms’ capital structure adjustments across countries and investigate if institutional differences explain the variance in estimated speeds of adjustment in 37 countries (25 are developed countries) using a partial 2
ACCEPTED MANUSCRIPT adjustment model. The authors find that institutional features influence adjustment speeds. Fan et al. (2012) examine how the institutional environment affects capital structure and debt maturity choices of firms in 39 countries (25 are from developed countries). Fan et al.’s (2012) findings reveal that a country’s legal and tax system, corruption, and preferences of creditors explain a significant portion of the variation in debt maturity and debt ratios. This study builds on this line of research in three important ways. First, this paper
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innovates using aggregated (single-index) as the main measure of institutional quality because
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it is a broader measure of overall institutional quality, and the disaggregated measure of
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institutional quality assembled by Kaufmann et al. (2009) to investigate the effects of institutional quality on the capital structure of firms in developing countries, within the
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framework of Myers’ (1984) trade-off theory. As developing countries’ institutional quality and legal enforcement improve, lenders are more willing to grant credit to firms. Thus, holding
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firms’ assets and investment plans constant, firms in developing countries with strong institutions are more likely to increase debt because better institutional quality encourages
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lenders to lend money and it lowers bankruptcy costs, resulting in firms using more debt to take advantage of tax-shield benefits of debt interest to increase returns.
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Secondly, our paper accounts for regional factor effects on capital structure by segregating the data into three regions – Asian countries’ listed firms, African countries’ listed firms, and
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Latin American and Eastern European countries’ listed firms. Although, regional sample segregation has some limitation, it enables us to compare the likely effects of institutional factors
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on firms’ capital structure at the regional level. Third, the indices in prior studies used to measure institutional quality proxy for the
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existence of the laws and regulations. However, enforcement of laws is key to creating strong institutions and an effective business environment. Weak enforcement is a general problem in most developing countries, and it affects firms seeking external financing. A weak enforcement environment makes is more difficult for firms to make commitments to contractual agreement. As legal enforcement seems important in developing countries, this paper conduct additional analysis to investigate the effects of legal enforcement on capital structure of firms’ in developing countries. The paper reports new findings that legal enforcement has positive effects on capital structure of firms in developing countries. Specifically, legal enforcement variable is positively related to book value measure of capital structure. Likewise, legal enforcement is 3
ACCEPTED MANUSCRIPT positively related to market value measure of capital structure. Fourth, more recent and adequately large firm-level and country-level datasets of developing countries are used to enhance the robustness of our conclusion. Specifically, the full sample consisted of annual firmlevel and country-level data of 3,891 listed firms from 23 developing countries covering the 2006 to 2014 time period. Our main results can be summarized as follows. In the case of the full sample of 3,891
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listed firms from 23 developing countries, this paper finds that a single aggregated index
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measure of institutional quality is significantly and positively related to both the book and
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market value measures of capital structure (debt ratios). This positive relationship between institutional quality and debt ratios remains unchanged using six disaggregated measures of
voice and accountability, and control of corruption.
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institutional quality: rule of law, regulatory quality, governance effectiveness, political stability,
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Similar results are found using 2,187 listed firms from 7 Asian countries and the 1,091 listed firms from 6 Latin American and Eastern European countries. The single aggregated
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index measure of institutional quality is significantly and positively related to both the book and market value measures of capital structure (debt ratios). The positive relationship between
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institutional quality and debt ratios remains unchanged using the six disaggregated measures of institutional quality. These results support our argument that holding firms’ assets and
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investment plans constant, firms in developing countries with strong institutions appear to increase debt because better institutional quality encourages lenders to lend money, resulting in
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firms using more debt to take advantage of tax-shield benefits of debt interest. Conversely, the results are different using the 613 listed firms from 10 African countries.
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The results reveal that a single aggregated index measure of institutional quality is not significantly and positively related to either the book or market value measures of capital structure (debt ratios). The results remain insignificant in four disaggregated measures of institutional quality (i.e., rule of law, regulatory quality, governance effectiveness, and control of corruption). Specifically, among the six components of institutional quality, only political stability (negative sign) and voice and accountability (positive sign) are significantly related to debt ratios in the African sample. Political stability measures perceptions of the likelihood that a government will be destabilized by violent means. The likelihood that a government will be destabilized by violence is high in most African countries, which may explain the negative 4
ACCEPTED MANUSCRIPT effects of political stability on debt ratios. Unstable environment discourages creditors to provide debt capital to firms, which reduces their debt ratios. However, voice and accountability have a positive effect on debt ratios in the African sample. Voice and accountability measures perceptions of the likelihood or extent to which a country’s citizen are able to participate in selecting a government. Awareness of the need for more citizens to participate in selecting government and elect credible leaders (democracy) is
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increasing in several African countries, which could explain the positive effect of voice and
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accountability on debt ratios. As the awareness to embrace democracy increases, investors
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appear more confident to provide debt capital to firms, which increases their debt ratios. Moreover, variations in quality of law are central to understanding why firms raise more capital
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in some countries than in others. Rule of law, regulatory quality, and control of corruption are subcomponents of institutional quality that influences availability of debt capital. Firms that
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operate in countries where there are a strong rule of law, regulatory quality, and control of corruption have easy access to debt capital compared with countries that are weak in these
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subcomponents of institutional quality. Most African countries have a weak rule of law and corruption control as well as poor regulatory quality, which may explain why these
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subcomponents of institutional quality have insignificant effects on debt ratios in the African sample.
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Contrariwise, the six components of institutional quality (e.g., rule of law, regulatory quality, control of corruption, governance effectiveness, political stability, and voice and
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accountability) have positive effects on debt ratios in the Latin-American and Eastern European sample. Over the years, Latin-American countries have gone through reforms in terms of laws citizens’
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and institutions that promote transparency, accountability, political stability, and encourage participation
in selecting government.
Most countries in Latin America have
incorporated basic democratic principles of the rule of law into their constitutions, but more compliance to the rule of law is needed (Zurbriggen, 2014). Likewise, over the years, there are improvements in institution reform in Eastern European countries that promote rule of law, regulatory quality, government quality, and control of corruption (Quality of Government Institute, 2017). These law and institution reforms may partly explain why these six components of institutional quality have positive effects on debt ratios in Latin-American and Eastern
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ACCEPTED MANUSCRIPT European countries. Improvement in these six components of institutional quality encourages investors to provide debt capital to firms, which increase their debt ratios. Additionally, we find more evidence of indirect effects of institutional quality (via interaction of institutional quality and some firm-specific factors) on debt in both Asian region and Latin American and Eastern European region. Moreover, our results reveal that some firmspecific factors (e.g., size, fixed assets, profits, and price-to-book ratio) and macroeconomic
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factors such as banking credit (ratio of the domestic credit provided by the banking sector to
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gross domestic product) and market capitalization (ratio of stock market capitalization of listed
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firms to gross domestic product) consistently affect the debt ratio with correct signs similar to previous findings in the literature. Specifically, size and fixed assets are positively related to
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both the book and market value measures of capital structure (debt ratios) while profits and growth opportunity are negatively related to both the book and market value measures of
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capital structure (debt ratios) in all the results (both the full sample results and regional subsample results).
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The rest of the paper is organized as follows: Section 2 reviews the relevant literature. Section 3 describes the data and methodology. Section 4 discusses the results, and Section 5
Literature Review
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2.
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concludes the paper.
This section explains the theoretical framework to establish the relationship between
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institutional quality and debt ratios and reviews the most relevant empirical literature. Moreover, this section briefly explains relevant capital structure theories, but the study’s main
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focus is on the trade-off theory of capital structure. Our paper does not focus on broad determinants of capital structure. 2.1.
Theoretical Framework Low institutional quality in developing countries has made it difficult for firms to raise
the external debt capital (Agca et al., 2013) needed for growth. Institutions and laws are believed to shape financial contracts with respect to banks. Strong institutions and laws that protect creditors’ rights improve loan availability and encourage lenders to provide debt capital to firms (Qian and Strahan, 2007). Likewise, well-developed legal institutions are important for
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ACCEPTED MANUSCRIPT firm growth, and firms that operate in countries with strong institutions can obtain external capital and grow faster (Demirguc-Kunt and Maksimovic, 1999). However, countries with weak institutions in terms of legal rules, low investor protections, and low quality of law enforcement are likely to be characterized by narrower capital markets (La Porta et al., 1997), which limits the capital available to firms to fund profitable investments that increase shareholders’ returns. Similarly, weak institutions, legal inefficiencies, weak
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protection of property rights, and high risk of expropriation are identified as the main factors
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limiting the growth of firms’ capital (Papaionannou, 2009). Weak institutions distort lenders’
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ability to channel resources to fund profitable investments efficiently (Law et al., 2014). A subset of institutional quality is property rights. Institutional quality significantly
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increases firm value because of its impacts on both firm-level investment and financing decisions, which confirms the importance of property rights protections (Berkowitz et al.,
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2015). In an earlier study, Beck et al. (2005) also show that a subset of institutional quality, property rights protection, enhances firm value.
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According to the supply side theory which is largely embraced in the law and finance literature, strong creditor protection which is a subset of institutional quality, induces lenders to
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provide credit on more favorable terms (La Porta et al., 1997), resulting in firms using more debt. As developing countries’ institutional quality improves, lenders are more willing to grant
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credit (e.g., debt capital) to firms. Moreover, the trade-off theory maintains that firms’ decision to use more debt is the outcome of the trade-off between the costs (e.g. bankruptcy costs) and
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the benefits (interest tax-shield) of debt (Myers, 1984). Based on the trade-off theory, more debt in institutional settings, with higher tax-shield and lower bankruptcy costs, would be
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expected. Thus, holding firms’ assets and investment plans constant, firms in developing countries with strong institutions and legal enforcement are more likely to increase debt in their capital structure because better institutional quality encourages lenders to lend money and it lowers bankruptcy costs, resulting in firms using more debt to take advantage of tax-shield benefits of debt interest. Capital structure research is usually conducted within the framework of trade-off theory, agency theory and pecking order theory. The trade-off theory argues for the existence of an optimal capital structure that balances the interest tax-shield benefits of debt against the bankruptcy costs of debt (Frank and Goyal, 2009). Similarly, agency theory argues for the 7
ACCEPTED MANUSCRIPT existence of an optimal capital structure that minimizes total agency costs (Jensen, 1986). Conversely, the pecking order theory argues that there is no clearly defined optimal capital structure, rather due to information asymmetry costs, firms follow the hierarchy of financing, first using retained earnings, followed by debt, and then equity issues (Frank and Goyal, 2009). This current paper investigates the effects of institutional quality on capital structure within the framework of Myers (1984) trade-off theory. This paper argues that better institutional quality
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and legal enforcement encourages lender to lend money and it lowers bankruptcy costs,
2.2.
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resulting in firms using more debt to take advantage of debt interest tax-shield benefits. Empirical Review
Turning to empirical studies, research to date on capital structure within the Myers (1984)
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tradeoff theory framework has given inadequate attention to the importance of institutional factors (at aggregate level) on capital structure. On the importance of institutional quality,
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researchers (e.g. Belkhir et al., 2016; Fan et al., 2012; Oztekin and Flannery, 2012; Jong et al., 2008) provide argument supporting the need to focus on institutional factors as a determinant of
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firms’ capital structure. Although several studies (e.g. Demirguc-Kunt and Maksimovic, 1999
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and Bancel and Mittoo, 2004) have investigated the effects of macroeconomic factors on capital structure, they focus on limited aspects of institutions measures. This study extends this line of research using aggregated (single index) and disaggregated measures of institutional
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quality to thoroughly disentangle the effects of institutio nal quality on capital structure. Belkhir et al. (2016) examine firm-level and country-level determinants of firm capital
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structure decisions in Middle East and North Africa (MENA) region. Using 444 listed firms from 10 countries over the 2003 to 2011 period, they find that MENA firms have target debt
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towards which they adjust over time. Moreover, they examine the indirect effects of institutional quality (institutional quality * size and fixed assets) on debt but find that the interaction of institutional quality and fixed assets is significantly and negatively related to debt. Conversely, the interaction of institutional quality (except regulatory effectiveness * size) and size has insignificantly effects on debt. Belkhir et al. (2016) research focuses on MENA region where forces of demand and supply may not fully determine the prices of financial assets (due to regulatory authorities’ intervention) in some countries (e.g. Middle Eastern countries). Conversely, we focus on large sample of listed firms (3891) from 23 developing
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ACCEPTED MANUSCRIPT countries where forces of demand and supply better determine the prices of financial assets. Moreover, we account for regional factor effects on capital structure by segregating the data into three regions – Asian region (with 2187 listed firms from 7 countries), African region (with 613 listed firms from 10 countries), and Latin American and Eastern European region (with 2187 listed firms from 6 countries), and report new findings that the effects of institutional quality on debt ratios is different among the regions. Only political stability and
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voice and accountability affects debt ratios in the African region, while all the six components
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and single index measures of institutional quality affects debt ratios in the Asian and Latin
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American and Eastern European regions.
Fan et al. (2012) examine the effects of institutional environment (e.g. legal system, tax
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treatment of debt and equity, and importance of regulation of financial institutions) on the capital structure and debt maturity of firms in 39 countries (25 developed and 14 developing countries).
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The authors’ findings reveal that a country’s legal and tax system, corruption, and preferences of capital suppliers explain the substantial variation in debt and debt maturity ratios. Fan et al.
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(2012) also find that more debt is used in countries where there is greater tax gain from leverage, which is in accordance with our argument that firms operating in countries with strong
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institutional quality use more debt in order to maximize the tax-shield benefits of debt. Oztekin and Flannery (2012) compare firms’ capital structure adjustments across 37 countries (25
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developed and 12 developing countries); they investigate whether differences in disaggregated measure of institutional characteristics (i.e. legal origin, shareholder rights, creditor rights, financial emphasis, efficiency) explain the variation in estimated
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corporate transparency,
adjustment speeds. Applying the system generalized method of moments, they find that legal and
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financial factors significantly affect firms’ adjustment speeds. Oztekin and Flannery (2012) conclude that their results support the theory that better institutional quality reduces transaction costs, which is linked to firms’ adjustment of their debt. Our work is different from Fan et al (2012) and Oztekin and Flannery (2012) in significant ways. Firstly, we innovate using an aggregate index as the main measure of institutional quality to investigate the relationship between debt ratios and institutional quality using large sample of 3,891 listed firms from 23 developing countries, and report new findings that the aggregate index measure of institutional quality is positively related debt ratios in developing countries. Aggregate or single index is a comprehensive measure, and it eliminates multicollinearity 9
ACCEPTED MANUSCRIPT problem associated with the use of disaggregated measures in same model specification. This aggregate index approach has been used to investigate the relationship between economic growth and institutional quality. Secondly, we account for regional factor effects on capital structure by segregating the data into three regions. Our results reveal that only two sub-components of institutional quality affect debt ratios in the African region. Specifically, political stability negatively affects debt ratios while voice and accountability positively affects debt ratios in the
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African region. Conversely, the six components of institutional quality (e.g. rule of law,
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regulatory quality, control of corruption, governance effectiveness, political stability, and voice
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and accountability) have positive effects on debt ratios in the Latin-American and Eastern European region and the Asian region.
quality,
governance effectiveness,
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Furthermore, our disaggregated measures of institutional quality (rule of law, regulatory political stability,
voice and
accountability,
control of
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corruption) is different from disaggregated measures used by Oztekin and Flannery (2012). Moreover, we also conduct additional analysis and explore the indirect effects of institutional
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quality (via combining some firm-specific factors and institutional quality variables) on capital structure. Additional analysis investigates the effects of legal enforcement on capital structure of
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firms, and we report new findings that legal enforcement has positive effects on both the book debt and market debt measures of capital structure.
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Focusing on the indirect effects of institutional and macroeconomic factors, Jong et al. (2008) use ordinary least squares method to analyze the importance of country-specific factors
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in the capital structure choices of firms from 42 developed and developing countries. They acknowledge that country-specific factors have direct effects on capital structure, but their
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analysis focuses mainly on the indirect effects of macroeconomic and institutional factors on the firm-specific determinants of capital structure. The findings by Jong et al. (2008) indicate that macroeconomic and institutional factors affect the roles of firm-specific determinants of capital structure. They conclude that it is necessary for future research to take into account the impact of macroeconomics and institutional factors in the analysis of firm capital structure. Motivated by the possibility that capital structure decisions may be influenced differently by institutional factors, Bancel and Mittoo (2004) use a literature survey approach to examine the roles of legal institutions in explaining the capital structure decisions of firms from 16 countries. More specifically, they use a literature survey approach to investigate whether capital 10
ACCEPTED MANUSCRIPT structure decisions are determined largely by the legal institutions of the home country or are the result of a complex interaction of several institutions in a country. Bancel and Mittoo (2004) findings show that firms’ capital structure decisions are influenced positively by their institutional environment. Moreover, their results suggest that firms determine their optimal capital structures by trading off benefits and costs of debt financing. Rather than a survey approach, this study uses empirical approach and investigates the effects of institutional quality
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(using aggregated and disaggregated measures of institutional quality) on capital structure
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within the Myers (1984) tradeoff theory that postulate that firms balance the benefits and costs
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of debt to determine their optimal capital structure. Moreover, we separate the data into regions and report new findings that the effects of aggregated and disaggregated measures of
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institutional quality are different across regions.
In an early study, Demirguc-Kunt and Maksimovic (1999) compare capital structure of firms
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from 11 developing and 19 developed countries. They find that institutional differences between developed and developing countries explain large fraction of the variation in firms’ capital
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structure. Institutional factors also affect the capital structure of large and small firms differently. Likewise, Booth et al. (2001) investigate if capital structure theory is applicable to developing
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countries with different level of stock market and banking sector development. They conclude that capital structure in developing countries appear to be affected by similar factors (firm-
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specific and macroeconomic factors) identified in developed countries. Unlike Booth et al. (2001) and Demirguc-Kunt and Maksimovic (1999), we segregate the data into 3 regions and
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report new findings that only two components of institutional quality, namely, political stability (negative effect) and voice and accountability (positive effect) affect debt ratios in the African
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region while the six components of institutional quality have positive effects on debt ratios in the Latin American and Eastern European region and Asian region. Furthermore, this paper investigates the effects of institutional quality on firms’ capital structure in developing countries using large annual firm-level and country-level data of 3,891 listed firms from 23 developing countries covering the 2006 to 2014 time period. Moreover, in an additional analysis, this paper investigates the effects of legal enforcement on capital structure of firms’ in developing countries and report new findings that legal enforcement is positively related to both the book and market value measures of capital structure.
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ACCEPTED MANUSCRIPT 3.
Data and Method
This section describes the data, justification for each variable, and hypothesis development. It then explains the method employed in this study. Moreover, the study’s main focus is on the trade-off theory of capital structure. 3.1.
Data and Descriptive Statistics
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The full sample data consist of 3,891 listed firms from 23 developing countries. The paper
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defines developing countries based on their income level following World Bank classification.
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For the regional sample, the number of firms is 2,187 listed firms from 7 Asian countries, 1,091 listed firms from 6 Latin American and Eastern European countries, and 613 listed firms from
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10 African countries. The years covered are 2006 to 2014. The data start from 2006 and end in 2014 due to data availability for capital structure (debt) determinants. In a robustness analysis,
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this paper focuses on 2010 to 2014 (periods after the crisis) periods to remove the impact of the financial crisis years on debt and institutional quality relationship. The financial crisis years
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may have some influence on capital structure decisions (Adomako, 2014). Debt and capital structure are used interchangeably following conventional practice in the literature. Institutional
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quality is our main independent variable and it is obtained from the World Governance Indicators (WGI). Other macroeconomic data such as interest rate, inflation rate, bank credit to
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the private sector, market capitalization, and gross domestic product growth rate are obtained from the World Development Indicators (World Bank database) and are unbalanced panel data.
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The other firm-specific data were extracted from Datastream databases and are also unbalanced panel data. As part of the data-sampling process, financial firms are excluded
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because their financial statement differs significantly from that of non-financial listed firms. Furthermore, the paper excludes regulated firms (e.g. real estate investment trusts) because their debt ratio is usually higher than in other non-financial firms (Rajan and Zingales, 1995). The final full sample comprises 33,971 firm-year observations. The paper applies the winsorization technique as in Lemmon et al. (2008) to mitigate the effects of extreme values of some data on the estimated parameters. All the firm-level data used as control variables (e.g., fixed assets, profits, size, price-to-book ratio, non-debt tax-shield) are the traditional firm-level determinants of capital structure with clear predicted signs based on trade-off theory.
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ACCEPTED MANUSCRIPT Moreover, the paper controls for firm age, ownership structure, dividend pay-out ratio, and other macroeconomic determinants of capital structure.
Unit of M easurement
M ean
Std dev.
M ini.
M axi.
the ratio of short-term debt plus long-term debt to total assets (property, plant and equipment). the ratio of book value of total debt to market value of equity plus book value to total debt. Aggregate Institutions Index [(Rol + Regq + GE + PS + VA + CC)/6]
0.2741
1.2588
0.0000
1.0000
0.3467
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Table 1a Descriptive statistics for 3, 891 listed firms from 23 developing countries, 2006-2014
0.0000
1.0000
59.1932
14.7141
5.3734
84.8161
53.7313
15.2705
0.9390
89.4737
55.6094
16.4995
1.4563
93.3014
59.8978
16.7616
2.8708
87.3786
32.4812
22.1718
0.4717
83.9623
49.6918
17.7269
7.1090
84.3602
49.7276
17.5084
3.3175
91.3876
Int
Rule of Law: reflects perceptions of the extent to which agents have confidence and abide by society rules (ranges from 0 to 100) Regulatory Quality: reflects perceptions of the ability of the government to formulate & implement sound policies (ranges from 0 to 100) Governance Effectiveness: reflects perceptions of the quality of public services and the degree of its independence from political pressure (from 0 to 100) Political S tability: reflects perceptions of the likelihood that the government will be destabilize by violent means (ranges from 0 to 100) Voice & Accountability: reflects perceptions of the extent to which a country’s citizen are able to participate in selecting government (ranges from 0 to 100) Control of Corruption: reflects perceptions of the extent to which public power is exercised for gain (ranges from 0 to 100). Interest rate: annual interest rate
4.6320
7.3936
-42.3102
41.3454
Inf
Inflation: annual inflation rate. Growth in consumer price index
5.8909
3.7363
-0.6782
26.2398
Gdpg
Annual growth in nominal gross domestic product (in percentage)
5.1081
2.7396
-17.6690
14.0460
BC
Banking Credit: ratio of the domestic credit provided by the banking sector to gross domestic products (in percentages) Market Capitalization: ratio of stock market capitalization of listed firms to gross domestic products (in percentage) the ratio of property, plant and equipment to book value of total assets
47.6357
40.1663
0.0010
123.8840
80.6793
54.0112
7.8273
387.8241
0.3542
0.2440
0.0001
1.8240
The ratio of earnings before interest, tax and depreciation to book value of total assets the log of total assets
0.0510
1.9451
-2.8818
24.0402
14.6175
3.1427
1.2011
31.4936
PB
the ratio of book value of debt plus market value of equity to book value of total assets
2.6314
5.7226
3.9100
43.0000
Ndts
Ndts is the ratio of depreciation to total assets
0.0259
0.0424
0.0000
5.4915
Age
Firm-age: natural log of (one plus firm-age)
3.2736
0.9604
0.0000
5.5174
DPO
Dividend pay-out: natural log of (one plus percentage of dividend pay -
0.4447
0.7455
0.0000
0.8151
0.5427
0.4988
0.0000
1.0000
Tdmv Inst
CR
Rol Regq
US
GE
PS
AN
VA
ED
M
CC
PT
MC FA
CE
PRF
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Size
0.2878
IP
Tdbv
out) OWS
Ownership structure: dummy variable equal to 1 if managers own more than 5 percent shares and zero otherwise a
Notes: N*T Total Observations (33971) is for listed firms from 23 developing countries. Number of firms for each country and the 23 Countries covered are included in appendix 1. b Descriptive statistics for the regional panel (Asian countries’ listed firms, African countries’ listed firms, and Latin America combined with Eastern European countries’ listed firms) are not reported to save space. c They are available upon request.
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ACCEPTED MANUSCRIPT Table 1b M ean value of institutional quality each year across regions, 2006-2014 2007
2008
2009
2010
2011
2012
2013
2014
Inst (African Region)
44.25
41.46
40.27
42.79
43.19
41.82
38.51
39.52
39.74
Inst (Asian Region)
57.56
50.43
51.90
58.91
50.42
45.71
45.66
46.91
49.63
Inst (Latin America & Eastern Europe Region
58.37
59.01
60.47
62.04
63.14
63.15
63.00
62.05
61.74
IP
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Note: Inst is the aggregate institutions index [Rol + Regq + GE + PS + VA + CC)/6]
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2006
US
Table 1a above shows the descriptive statistics. The single aggregate institutional quality index (Inst) has a minimum value of 5.3734 and a maximum value of 84.8161. As expected, the mean value of the single aggregate institutional quality index falls within the minimum and maximum
AN
values. Moreover, market capitalization has the highest standard deviation value suggesting that it is the most volatile variable. Conversely, non-debt tax-shield has the lowest standard deviation
M
value suggesting that it is the least volatile. Moreover, Table 1b shows that the aggregate
ED
institutional quality index each year is lower for the African region compare to the other two regions (Asian, Latin America & Eastern European regions). We conduct panel unit root test to
3.2.
PT
confirm if the variables are stationary1 .
Variables Justification and Hypotheses Development
CE
The dependent variable is debt ratios and the paper uses two measures of capital structure (debt ratios). The paper uses book total debt ratio as main dependent variable because it is not
AC
affected by stock price changes compared to the market total debt ratios. Ratio of total debt-tototal assets is the primary and the most utilized measure of capital structure (Frank and Goyal, 2009; Bany-Ariffin, 2010). Previous studies mostly use either book total debt ratio or market total debt ratio as proxy for capital structure. But this paper uses book total debt ratio as main proxy of capital structure and market total debt ratio as a robustness tests. Specifically, capital structure (debt ratio) is measured as the ratio of book value of total debt to book value of total 1
The paper adopts the Levin-Lin-Chu [LLC] (2002), the Im-Pesaran-Shin [IPS] (2003), the Augmented Dickey and Fuller (1979), and PP-Fisher Chi-square (Phillips and Perron, 1988). Based on the results of the stationary test of each variable, it is clear that the variables have stationary characteristics because the null of the unit root are rejected.
14
ACCEPTED MANUSCRIPT assets (TDBV), and the ratio of book value of total debt to market value of equity plus book value to total debt (TDMV). The main independent variable is institutional quality (aggregate institutional quality which is a single index). This institutional quality data set is based on information gathered through cross-country surveys and expert polls. Kaufmann et al. (2009) apply unobserved components model which allow them to measure institutional quality using six indicators (rule law,
regulatory
quality,
governance
effectiveness,
political
stability,
T
of
voice
and
IP
accountability, and control of corruption) for many countries. As a measure of institutional
CR
quality, this paper averages the six indicators to obtain a single broader index (aggregate institutional quality index) as in Langbein and Knack (2010). Langbein and Knack (2010) note
US
that World Governance Indicators (WGI) measure the same underlying governance or institutional quality concept; although, the six indicators are meant to capture different concept.
AN
They also argue that the six institutional quality indicators are highly correlated. Researchers (e.g. Fan et al., 2012; Oztekin and Flannery, 2012; Jong et al., 2008) provide argument supporting the need to focus on institutional factors as a determinant of firms’ capital
M
structure. Fan et al. (2012) findings reveal that institutional factors such as country’s legal
ED
system and control of corruption explain substantial variation in debt ratios. Oztekin and Flannery (2012) find that legal and financial factors significantly affect firms’ adjustment
PT
speeds. They conclude that their results support the theory that better institutional quality reduces transaction costs that is linked to firms’ adjusting their debt. Bancel and Mittoo (2004)
CE
survey findings show that firms’ capital structure decisions are influenced by their institutional environment. Moreover, their survey results suggest that firms determine their optimal capital
AC
structure (debt) by trading off benefits and costs of debt financing. This paper argues that as developing countries institutional quality improves; lenders are more willing to grant credit (e.g. debt capital) to firms. Moreover, the trade-off theory maintains that firms’ decision to use more debt is the outcome of the trade-off between the costs (e.g. bankruptcy costs) and the benefits (interest tax-shield) of debt (Myers, 1984). Thus, holding the firms’ assets and investment plans constant, firms in developing countries with strong institutions are likely to increase debt because better institutional quality encourages lenders to lend money and it lowers bankruptcy costs, resulting in firms using more debt to capitalize on tax-shield benefits of debt interest. Therefore, our alternative hypothesis (HAlternate) 15
ACCEPTED MANUSCRIPT is that institutional quality has positive direct effects on listed firms’ debt ratios in developing countries. The null hypothesis (H0 ) is that institutional quality has no direct effects on listed firms’ debt ratios in developing countries. A rejection of the null hypothesis would support our alternative hypothesis. Moreover, it is possible that the effects of institutional quality on firms’ debt ratios are different across regions of developing countries. Therefore, this paper divides the samples into
T
three regions. The division of the samples into regions enables us to compare the effects of
IP
institutional quality on firms’ debt ratios across regions of developing countries. These
CR
divisions also enable us to focus on countries having relatively homogenous macroeconomic factors (Narayan et al., 2011). Additionally, the paper controls for variables that are established
US
in the trade-off theory of capital structure. The control variables are fixed assets, profits, size, price-to-book ratio, and non-debt tax shield. Fixed assets (FA) are the ratio of property, plant
AN
and equipment to book value of total assets. Based on the trade-off theory, fixed assets are positively related to debt because fixed assets can be used as collateral to obtain debt capital
M
(Frank and Goyal, 2009). Fixed assets are positively related to debt in several empirical studies (e.g. Flannery and Hankins, 2013; Matemilola et al., 2013; Oztekin and Flannery, 2012; Frank
ED
and Goyal, 2009). This paper expects fixed assets to be positively related to debt because fixed assets serve as collateral to obtained more debt capital.
PT
Profits are the ratio of earnings before interest, tax and depreciation to book value of total assets. According to the trade-off theory, profits are positively related to debt because
CE
profitable firms are able to repay principal amount borrowed plus interest, resulting in usage of more debt (Matemilola et al., 2013; Rajan and Zingales, 1995). Conversely, according to the
AC
pecking order theory, profits are negatively related to debt. Due to information asymmetry costs, firms prefer internal finance (profits) to debt. Thus, firms that generate more profits use less debt (Flannery and Hankins, 2013; Lemmon et al., 2008). Based on the trade-off theory, this paper expects profits to be positively related to debt because profitable firms can easily repay principal amount borrowed plus interest. Size is log of total assets as in Oztekin and Flannery (2012). The trade-off theory predicts that size is positively related to debt because bigger firm are more stable and less likely to go bankrupt (Frank and Goyal, 2009). Available evidence on the effects of size on debt give mixed results. Frank and Goyal (2009), Gormley and Matsa (2013), Matemilola et al. (2013), and 16
ACCEPTED MANUSCRIPT Oino and Ukaegbu (2015) find a positive relationship between debt ratios and size. Conversely, some authors find a negative relationship between debt ratios and size (Hanousek and Shamshur, 2011; Lemmon et al., 2008; Chakraborty, 2010). This paper expects size to be positively related to debt because bigger firms are more stable and less likely to go bankrupt. Price-to-book ratio (PB) is the ratio of book value of debt plus market value of equity to book value of total assets. From the perspective of the trade-off theory, growth firms should use
T
less debt because growth opportunity is intangible assets without collateral value if firms face
IP
bankruptcy (Myers, 1984). From the perspective of pecking order theory, growth firms that
CR
need finance should issue security with less information asymmetry costs (Frank and Goyal, 2009). Flannery and Hankins (2013) find growth opportunity is positively related to debt.
US
Conversely, Gormley and Matsa (2013) and Lemmon et al. (2008) report a negative relationship between debt and growth opportunity. Based on the trade-off theory, this paper
AN
expects price-to-book ratio to have a negative relationship with debt because it captures growth opportunity which is an intangible asset without collateral value.
M
Non-debt tax shield (Ndts) is the ratio of depreciation to total assets. Although non-debt tax shield does not consist of debt related costs, it acts as substitutes for tax shields and should
ED
be negatively related to debt (Oztekin and Flannery, 2012; De-Angelo and Masulis, 1980). Oztekin and Flannery (2012) and De-Angelo and Masulis (1980) find negative relationship
PT
between non-debt tax shield and debt. Conversely, Chakraborty (2010) and Oino and Ukaegbu (2015) report a positive relationship between debt and non-debt tax shield. In accordance with
CE
the trade-off theory, this paper expects non-debt tax shield to be negatively related to debt because alternative tax-shield sources reduce attractiveness of debt.
AC
Additional control variables such as ownership structure, dividend payout ratio, and firm age are included in the models to substantiate the validity of our findings. Following prior literature, ownership structure is measured as a dummy variable equal to 1 if managers own more than 5 percent shares and zero otherwise. Little is known about the effects of ownership structure on debt (Driffield et al., 2007). Limited available studies predict positive, negative, and insignificant effects of ownership structure on debt. For example, Anderson and Reeb (2003) and King and Santor’s (2008) findings reveal that insider (managerial) ownership has insignificant effects on debt. Harvey et al. (2004) find that higher managerial ownership levels provide benefits to controlling shareholders that are shared with other stakeholders, thereby, 17
ACCEPTED MANUSCRIPT reducing the need for debt as an internal control mechanism. Harvey et al. (2004) interpretation is consistent with the view of the agency theory. Likewise, Wang et al. (2018) report negative effects of ownership structure on debt, which is consistent with the view of the agency theory. Conversely, based on the signaling theory, managerial ownership is positively related to debt because managers signal growth opportunity by increasing debt. Consistent with the signaling theory, Kim and Sorensen (1986) document positive effects of managerial ownership on debt,
T
suggesting that managers use debt to signal growth opportunity. In an early study, Stulz (1988)
IP
argues that controlling shareholders should increase debt because it increases their voting
CR
control for a given level of equity investment; further, it reduces the risk of hostile takeover. This study expects ownership structure to be positively related to debt because managers signal
US
growth opportunity by increasing debt.
In accordance with previous literature, dividend payout is measured as natural log of (one
AN
plus percentage of profits paid out as dividends). The existing capital structure theories have ambiguous prediction on the relationship between dividend pay-out ratio and debt (Frank and
M
Goyal, 2009). However, Frank and Goyal (2009) findings indicate that firms that pay dividends have less debt. Conversely, Cooper and Lambertides (2018) results reveal that large dividend
ED
increases are followed by a significant increase in debt, which suggest that management increases dividend to use up excess debt capacity. Similarly, Bhaduri (2002) suggests that a
PT
dividend-paying firm signals its quality to outsiders; further, it can avoid an information premium and likely gain access to external sources of capital. As dividend payments indicate a
CE
signal of strong financial health and therefore higher debt-issuing capacity, this study expects dividend payout ratio to be positively related to debt.
AC
Firm age is the time between the initial creation of a firm and the present time, measured as natural log of (one plus firm age). The empirical evidence on the effect of firm age on debt is mixed. Firm age has negative but insignificant effects on the book–debt ratio (King & Santor, 2008), for example. In addition, firm age is negatively related to debt, indicating that older firms seem to value financial flexibility or unused debt capacity (DeAngelo, 2015). Consistent with this reasoning, Kieschnick and Moussawi (2018) document that firm age is negatively related to debt. Conversely, firm age is positively related to debt, according to Hovakimian et al. (2001) and Sundaresan et al. (2015), suggesting that aging firms have more assets-in-pace
18
ACCEPTED MANUSCRIPT which justify taking on more debt. This study expects a negative effect of firm age on debt because older firms appear to value financial flexibility. Macroeconomic factors have been argued to affect firms’ capital structure decisions (e.g. Oztekin and Flannery, 2012; Fan et al., 2012; Jong et al., 2008; Bancel and Mittoo, 2004; Booth et al., 2001; Demirguc-Kunt and Maksimovic, 1999). As macroeconomic factors such as interest rate, inflation rate, GDP growth rate, bank credits, stock market capitalization changes,
T
the firm would adjust their capital structure accordingly in response to favorable or unfavorable
IP
changes in macroeconomic factors. For instance, higher interest rate increases the costs of debt
CR
financing and discourages the firms to use more debt. Inflation rate is usually considered as a proxy for a government’s ability to manage the economy (Demirgu¨c¸-Kunt and Maksimovic,
provide debt capital, resulting in lower debt usage.
US
1999). As inflation rate increases, risk of lending to firms increases and lenders are unwilling to
AN
The firms’ capital structure decisions may be affected by the economic growth rate. As economic growth is believed to be correlated with firm growth which is a proxy for firm’s
M
investment opportunity set and its financing needs (Demirgu¨c¸-Kunt and Maksimovic, 1999). As the economic growth rate increases, it signals good economic prospects encouraging firms
ED
to use more debt (Cheng and Shiu, 2007). Moreover, the stock markets and banking sector developments affect capital structure of the firms. The financing patterns (either through the
PT
capital market or banks) fit the governance system in the sense that those to whom the governance system gives most power to influence the policies of firms would also be the main
CE
providers of capital (Antoniou et al., 2008; Fan et al., 2012). As the stock markets develop, transaction costs reduce, and firms are likely to raise capital through the stock markets lowering
AC
debt usage (Fan et al., 2012). Conversely, as banking sector develops, costs of borrowing reduce, and firms are likely to raise debt through the banks which increase debt usage (Booth et al., 2001). 3.3.
Econometric Model Prior studies (e.g. Oztekin, 2015; Flannery and Hankins, 2013; Oztekin and Flannery,
2012) have concluded that adjustment costs are nontrivial and that firm-fixed effects are important to capture unobserved firm-specific heterogeneity. The paper follows Oztekin and Flannery et al. (2012) and applies the standard partial adjustment model to capture the dynamic 19
ACCEPTED MANUSCRIPT adjustment toward the target capital structure. Rather than estimate a static panel model based on contemporaneous debt ratios, we estimate a dynamic panel model that produce an estimate of the unobserved target debt as well as the adjustment speed to the target debt, that is: Debtij ,t Debtij ,t 1 ( Debt *ij ,t Debtij ,t 1 ) ij ,t
(1)
Where λ is the average speed of adjustment (SOA) to the target capital structure each
T
period for all the sample firms, Debt*ij,t is the target debt level while Debtij,t and Debtij,t-1 are the
IP
current and lagged 1 period debt ratios, respectively. The paper uses two measures of debt
CR
(book total debt ratio and market total debt ratio). The model assumes that firm has a target debt level and adjust if there is a deviation from the target debt level. Full adjustment occurs
US
when λ =1 while λ =0 means there is no adjustment. In the partial adjustment model, the actual adjustment of debt should be between 0 and 1. The target debt or capital structure is unobservable, so, we proxy it with the fitted values from a regression of observed debt on a set
AN
of firms’ specific determinants and macroeconomic determinants of the target capital structure (Flannery and Hankins, 2013; Oztekin and Flannery, 2012) shown in equation 2.
M
Debtij ,t X ij ,t i t ij ,t
(2)
ED
Where Xijt represents the firm specific and macroeconomic determinants of capital structure, ηi and αt are firm specific effects and year fixed effects, respectively. After we substitute the target
PT
debt or target capital structure from Equation (2) into the partial adjustment model in Equation (1) and rearranging the terms, the estimation in a single equation becomes:
CE
Debtij ,t (1 ) Debtij ,t 1 X ij ,t i t ij ,t
AC
Debtij ,t (1 ) Debtij ,t 1 ( 1 2 INST jt 3 FAijt 4 PRFij ,t 5 Sizeij ,t 6 PBij ,t 7 Ndtsij ,t
(3a) (3b)
Agei j ,t DPOi j ,t OWSi j ,t 8 INT jt 9 INFjt 10MC jt 11BC jt 12Gdpg jt ) i t ij ,t
Where: Debtij,t Debtij,t-1 ß1 INSTjt FAij,t
= debt for the i firm in country j and t time (using book debt and market debt ratios as proxy for capital structure) = lagged 1 period debt ratios for the i firm in country j and t time = the constant = institutional quality for the j country and t time (single institutional quality index is the main measure, and disaggregated measures of institutional quality are robust check) = fixed assets for the i firm in country j and t time 20
ACCEPTED MANUSCRIPT
CR
IP
T
= profits for the i firm in country j and t time = size for the i firm in country j and t time = price-to-book ratio for the i firm in country j and t time = non-debt tax shield for the i firm in country j and t time = firm-age for the i firm in country j and t time = dividend pay-out ratio for the i firm in country j and t time = ownership structure for the i firm in country j and t time = interest rate for the j country and t time = inflation rate for the j country and t time = stock market capitalization for the j country and t time = bank credit to private sector for the j country and t time = gross domestic product growth rate for the j country and t time = the unobservable firm-specific effects = the year fixed effects = speed of adjustment to target debt level = the residual term
US
PRFij,t Sizeij,t PBij,t Ndtsij,t Ageij,t DPO ij,t OWSij,t INTjt INFjt MCjt BCjt Gdpgjt ηi αt 1-λ µijt
AN
Subscript 'i' ‘j’ and‘t’ represents a firm, country and time period, respectively. The model is estimated with two-step system generalized method of moments (GMM) because debt displays persistence behaviour (Lemmon et al., 2008). This suggests that previous
M
year debt affect current year debt. Moreover, the paper uses two-step system generalized
ED
method of moments because there is possibility of reverse causality between debt and the explanatory variables. Application of traditional ordinary least squares methods to estimate
PT
parameters in a dynamic model that include firm-specific effects and lagged debt variable would produce biased coefficients (Flannery and Hankins, 2013). Therefore, this paper applies
CE
the two-step system generalized method of moments because it is recognized as one of the best methods to estimate parameters of the target debt in the presence of firm-specific effects and
AC
lagged debt variable (Flannery and Hankins, 2013). Two-steps system generalized method of moments correct for endogenous and reverse causality problems between variables using an efficient instrumental variable technique (Blundell and Bond, 1998). Blundell and Bond (1998) two-step system generalized method of moments combine level-equation and differenceequation, and it better addresses reverse causality or endogenous problem using efficient instrumental variable technique. The two-step system generalized method of moments combine the difference generalized method of moments’ conditions in equation (4) and additional moment condition in equation (5) to produce unbiased estimators. E [∆µij,t * Debtt-k ] = E [∆µij,t * Xt-k ] = 0 k > 1.
(4) 21
ACCEPTED MANUSCRIPT Where: Debtt-k is the higher order lags of debt (dependent variable) and Xt-k is the higher order lags of independent variables used as internal instrument. In words, the correlation between the difference error-term and lagged debt variable used as internal instruments as well as lagged independent variables used as internal instruments equals zero. The firm-specific factors and institutional quality variable are treated as endogenous variables and the two-steps system generalized method of moments internal instruments are used to resolve the endogenous
T
problem. The lagged levels of the dependent variable (debt) used as instruments in the difference
IP
generalized method of moments become weak instrument if it is persistent (Blundell and Bond,
CR
1998). Thus, the two-steps system generalized method of moments adds additional moment conditions:
US
E [∆Debtij,t * ηi] = 0
(5)
AN
In words, the correlation between the difference instruments (∆Debtij,t ), and unobservable firmspecific effects (ηi) in the level equation equals zero. In all estimations, the paper uses two-step
M
estimates because this method uses the first-step errors to construct heteroskedasticity-consistent
4.
ED
standard errors or optimal weighting matrices (Blundell and Bond, 1998)).
Empirical Results
PT
This section presents correlation results for the full sample and panel regressions (for the full sample and three regional subsamples, separately) that estimate the effects of aggregated
CE
(single-index) and disaggregated measures of institutional quality on capital structure (book debt and market debt ratios), controlling for firm-level and macroeconomic factors. The book
AC
total debt ratio is our main proxy for capital structure and market total debt ratio is used to check the robustness of our findings to alternative measures of capital structure. Additionally, an aggregated (single-index) measure of institutional quality is the main focus; the six disaggregated measures of institutional quality are used as robustness checks. Our estimation is based on the two-step system of generalized method of moments (GMM) that accounts for the possibility that the error terms are heteroskedastic and serially correlated across both firm-level and country-level observations. 4.1.
Full Sample Results
22
ACCEPTED MANUSCRIPT Tables 2a and 2b contain the correlation results. The correlation results reveal that the degree of association between most of the variables is weak because the correlation coefficients are generally lower among the independent variables and statistically significant. Thus, there is little risk of multi-collinearity among the independent variables. However, the correlation analysis confirms that the six institutional quality indicators are highly correlated (see Table 2a). Therefore, our main results average the six indicators to obtain a single index (aggregated Table 2a Correlations results for 3,891 listed firms from 23 developing countries, 2006-2014 Rol
Regq
GE
PS
1.00 0.14a -0.01c -0.01b -0.03a -0.02b -0.02b 0.02b -0.02b 0.01b 0.02b 0.01b 0.03b -0.02b
1.00 -0.07a -0.02a -0.13a -0.05a -0.10a 0.06a -0.09a 0.02a 0.11a 0.06a 0.13a -0.05a
1.00 0.84a 0.93a 0.87a 0.89a 0.52a 0.94a -0.04a -0.46 a -0.20 a 0.38 a 0.34 a
1.00 0.70a 0.79a 0.64a 0.34a 0.82a -0.09a -0.40a -0.08 a 0.37a 0.40a
1.00 0.84a 0.85a 0.34a 0.90a -0.05a -0.50a -0.28a 0.37 a 0.31a
1.00 0.72a 0.21a 0.83a -0.14a -0.44a -0.12a 0.39 a 0.40 a
1.00 0.38a 0.80a -0.07a -0.41a -0.29a 0.38a 0.27a
AN
M
VA
CC
CR
Inst
Int
Inf
Gdpg
BC
MC
1.00 0.17a -0.13a -0.04a -0.04a
1.00 0.23a -0.51a -0.34a
1.00 -0.33a -0.09a
1.00 -0.34a
1.00
US
Tdmv
1.00 0.36a 0.24a -0.08a -0.11a -0.24a 0.09a
1.00 0.07a -0.49a -0.24a 0.31a 0.34a
ED
Tdbv Tdmv Inst Rol Regq GE PS VA CC Int Inf Gdpg BC MC
Tdbv
IP
T
institutional quality index), as in Langbein and Knack (2010).
AC
CE
PT
Notes: See Table 1 for the definition of variables and measurements.
23
ACCEPTED MANUSCRIPT
Table 2b Correlations results for 3,891 listed firms from 23 developing countries, 2006-2014 Tdbv
Tdmv
Inst
Rol
Regq
GE
PS
VA
CC
FA
PRF
Size
PB
Ndts
Age
DPO
OWS
T P
Tdbv
1.00
Tdmv
0.14a
1.00
Inst
-0.01c
-0.07a
1.00
Rol
-0.01b
-0.02a
0.84a
1.00
Regq
-0.03a
-0.13a
0.93a
0.70a
1.00
GE
-0.02b
-0.05a
0.87a
0.79a
0.84a
1.00
PS
-0.02b
-0.10a
0.89a
0.64a
0.85a
0.72a
1.00
VA
0.02b
0.06a
0.52a
0.34a
0.34a
0.21a
0.38a
1.00
M
CC
-0.02b
-0.09a
0.94a
0.82a
0.90a
0.83a
0.80a
0.36a
1.00
FA
0.02b
0.10a
-0.08a
-0.07a
-0.06a
-0.06a
-0.07a
-0.06a
0.06a
1.00
PRF
0.06b
0.02a
-0.02 a
-0.02a
-0.02a
-0.03a
-0.02a
-0.01a
-0.01a
0.05a
1.00
Size
0.01b
0.11a
-0.22 a
-0.31 a
-0.22a
-0.24a
-0.23a
0.16a
-0.31a
0.06a
0.03a
1.00
PB
0.03b
0.12a
0.10 a
0.20a
0.17 a
0.04a
0.05a
-0.01a
0.03a
-0.02a
0.07a
0.05a
1.00
Ndts
-0.04b
-0.08a
0.12 a
0.18a
0.04a
0.03 a
0.08a
0.02a
0.09a
0.24a
-0.04a
0.03a
-0.07a
1.00
Age
0.01b
0.03a
-0.04a
-0.04a
-0.04a
-0.05a
-0.09a
0.06a
-0.03a
0.03a
0.00
0.18a
-0.02a
0.02a
1.00
DPO
-0.02b
-0.14a
-0.03a
-0.02a
-0.02a
-0.00
-0.03a
-0.05a
-0.00
0.00
0.01b
0.17a
0.00
0.00
0.13a
1.00
OWS
0.01b
0.06a
-0.07a
-0.03a
-0.12a
0.15a
-0.07a
-0.18a
-0.10a
0.04a
0.01b
0.13a
-0.02a
0.02a
0.05a
0.09a
I R
C S
E C
C A
A
D E
T P
U N
1.00
24
ACCEPTED MANUSCRIPT Notes: See Table 1 for the definition of variables and measurements. The correlation results reported is for the full sample, and ‘Inst’ is the single index measure of institutional quality. Correlation results for the regional subsamples (Asian countries’ listed firms, African countries’ listed firms, and Latin American and Eastern European countries’ listed firms) and disaggregated measures of institutional quality are not reported to save space. They are available upon request. a and b indicate correlation coefficient is significant at 1 and 5 per cent, respectivel
T P
I R
C S
U N
A
D E
M
T P
E C
C A
25
ACCEPTED MANUSCRIPT
Tables 3 and 4 report the main effects of a single aggregated institutional quality index and six disaggregated institutional quality measures for the full sample of 3,891 listed firms from 23 developing countries. The diagnostic checks on the two-steps system generalized method of moments reveal the following. The models passed the AR (2) tests, as indicated by the
T
insignificant p-values showing the absence of second-order serial correlation. Overall, the
IP
validity of the instruments and the additional instruments is confirmed, as indicated by the
CR
insignificant p-values of the difference in Hansen tests in the models. Moreover, in all estimations, the number of ‘N’ cross-sectional observations is greater than the number of
US
instruments, supporting the validity of the estimations. In the empirical results, the book total debt ratio is our main proxy for capital structure and the market total debt ratio is used to check
AN
the robustness of our findings to alternative measures of capital structure. The lagged dependent variable is statistically significant at the 1% level in all the models,
M
which supports the relevance of the dynamic model for capital structure research. Intuitively, the results suggest that if firms deviate from their target debt they make adjustments. The
ED
empirical results for the full sample show that the single aggregated institutional quality index is statistically significant and positively related to the book total debt ratio (see Table 3, Model
PT
1a). As a robustness test, the single aggregated institutional quality index is statistically significant and positively related to the market total debt ratio (see Table 4, Model 2a).
CE
Additionally, the six disaggregated measures of institutional quality are statistically significant and positively related to both the book total debt ratio (see Table 3, Models 1b to 1g) and the
AC
market total debt ratio (see Table 4, Models 2b to 2g). Furthermore, all of the six disaggregated measures of institutional quality are included together in a single model2 , and the results are broadly similar. But the coefficient of most of the institutional quality variables reduces. Moreover, the coefficient of the lagged debt (debtit-1 ), firm-specific and other macroeconomic variables changes (see Table 3, Model 1h and Table 4, Model 2h). The dynamic panel model specification and two-step system generalized method of moment enable us to model the dynamic behavior of firms’ capital structure. Specifically, the empirical results also show that 2
The Stata software automatically removes the aggregated (single index) measure of institutional quality from the single model during estimation because of multicollinearity. 26
ACCEPTED MANUSCRIPT firms make some adjustments to their target debt when there is a deviation from the target debt level, consistent with the dynamic version of trade-off theory. The speed of adjustment to the target debt level is calculated as 1-λ, where λ is the coefficient of the lagged debt variables. Previous researchers (e.g., Matemilola and Ahmad, 2015), Flannery and Hankins (2013), Oztekin and Flannery (2012)) find evidence that firms adjust to their target debt level. Our empirical results also show that firms make faster adjustments to their target debt, especially
T
the book debt ratio, when there is a deviation from the target debt level; this is consistent with
IP
the dynamic version of trade-off theory.
CR
Additionally, firm-specific factors (i.e. fixed assets, profits, size, firm age, and ownership structure) and macroeconomic factors (e.g. interest rate, inflation rate, banking credit, market
US
capitalization, and annual growth rate in nominal gross domestic product) consistently affect both the book and market debt ratios in the full sample, similar to previous findings in the
AN
literature.
M
Table 3 Direct effects of institutional quality on Book-debt models
System-GM M Two-step Estimation Results using Book-debt and 3,891 Listed Firms from 23 Developing Countries, 2006-2014
0.2797*** (67.63)
INST (Single Institutional 0.0014*** Quality Index) (3.68)
M odel 1e With PS
M odel 1f With VA
M odel 1g With CC
M odel 1h (Single Equation)
0.2859*** 0.2789*** (67.56) (67.91)
0.2759*** 0.2888*** (70.95) (61.93)
0.2907*** (66.14)
0.2759*** 0.666*** (70.17) (96.22)
0.0003** (2.27)
-
-
-
PT
TDBVit-1 (Lag Book-debt)
ED
M odel 1a With Single M odel 1b M odel 1c M odel 1d INST Index With ROL With REGQ With GE
0.0001** ROL (Rule of law) (2.02) 0.0014** 0.0007*** REGQ (Regulatory quality) (2.65) (2.81) 0.0010** 0.0007*** GE (Govt. Effectiveness) (2.51) (3.09) 0.0011*** 0.0005*** PS (Political Stability) (6.54) (3.80) 0.0021*** 0.0004*** VA (Voice & Account.) (2.92) (2.87) 0.0010*** 0.0003* CC (Control of Corruption) (6.10) (1.89) 0.0793*** 0.0719*** 0.0717*** 0.0686*** 0.0960*** 0.0587*** 0.0987*** 0.1072*** FA (Fixed Assets) (5.60) (5.16) (5.19) (4.91) (6.90) (4.35) (6.72) (9.05) -0.4930*** -0.0506*** -0.4922*** -0.4845*** -0.5129*** -0.5156*** -0.4839*** -0.4458*** PRF (Profits) (-9.89) (-9.56) (-10.11) (-6.51) (-5.13) (-8.57) (-6.65) (-16.11) 0.0070*** 0.0081*** 0.0084*** 0.0111*** 0.0098*** 0.0139*** 0.0070*** 0.0039*** Size (6.08) (7.11) (7.86) (8.30) (8.48) (8.43) (4.91) (5.77) -0.0002** -0.0002** -0.0001* -0.0002 -0.0002** -0.0001* -0.0001** -0.0001 PB (Price-to-book ratio) (-2.75) (-2.29) (-1.94) (-1.65) (-2.03) (-1.80) (-2.16) (-0.32)
CE
-
AC
-
27
ACCEPTED MANUSCRIPT -0.0224** (-2.02) 0.0137** (2.51) -0.0043** (-2.72) -0.0855** (-2.66) 0.0007*** (4.69) 0.0017*** (6.97) -0.0005** (-2.32) 0.0003*** (3.45) -0.0002** (-2.19) 0.8939
-0.0250** (-2.39) 0.0344*** (5.77) -0.0043** (-2.62) -0.0327 (-0.93) 0.0003** (2.16) 0.0017*** (7.06) -0.0007*** (-3.14) 0.0002** (2.15) -0.0001** (-2.51) 0.8480
-0.0246*** (-2.85) 0.0029 (0.41) -0.0049*** (-3.13) -0.0666* (-1.72) 0.0006*** (3.75) 0.0018*** (7.29) -0.0004** (-2.61) 0.0006*** (6.77) -0.0001** (-2.02) 0.8402
-0.0568*** -0.0556*** (-2.74) (-5.97) 0.0200*** 0.0115*** (2.89) (4.21) -0.0025 -0.0049 (-1.38) (-1.42) -0.1439*** -0.0159*** (-4.47) (-2.71) 0.0006*** 0.0003** (4.06) (2.31) 0.0019** 0.0015*** (7.75) (4.70) -0.0002** -0.0009*** (-2.23) (-2.94) 0.0005*** 0.0002** (4.70) (2.17) -0.0001** -0.0001** (-2.59) (-2.46) 0.9023 0.6221
0.1130 153 1.66
0.1136 153 1.71
0.1239 153 1.77
0.2214 153 1.69
0.16081 153 1.56
0.1040 153 1.64
0.1922 223 2.59
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.114
0.121
0.139
0.134
0.175
0.163
0.182
3891
3891
3891
3891
3891
3891
3891
30058
30058
30058
30058
30058
30058
30058
IP
CR
US
AN
T
-0.0261** -0.0264** (-2.32) (-2.35) 0.0264*** 0.0279*** (5.48) (5.33) -0.0055*** -0.0053*** (-3.59) (-3.44) -0.0445 -0.0862** (-1.47) (-2.69) 0.0025*** 0.0006*** (3.53) (4.06) 0.0031** 0.0018*** (2.23) (7.25) -0.0016** 0.0004 (-2.09) (1.68) 0.0037*** 0.0005*** (7.65) (4.62) -0.0020*** -0.0001** (-12.66) (-2.22) 0.8569 0.8809
M
-0.0376*** Ndts (Non-debt tax-shield) (-2.84) 0.0313*** Age (Firm-age) (5.71) -0.0037 DPO (Dividend pay-out) (-1.56) -0.1408*** OWS (Ownership structure) (-4.65) 0.0007*** Int (Interest rate) (4.49) 0.0019*** Inf (Inflation rate) (7.76) Gdpg (Economic growth -0.0013** rate) (-2.39) 0.0005*** BC (Banking Credit) (5.08) -0.0006** M C (M arket Capitalization) (-2.34) AR(2) [P-value] 0.8817 Difference Hansen Tests [P-value] 0.1060 Instruments 153 Variance Inflation Factor 1.55 Heteroscedasticity test (p val.) 0.00 Cross-dependency test (pval.) 0.142 Cross-sectional observation(N) 3891 Observ after estimation (N*T) 30058
Notes: a See Table 1 for the definition of variables and measurements. Asterisks indicate significance at 1% (***) and 5%
CE
PT
ED
(**). b T-statistics (in parenthesis) of the Two-step System-GMM model are based on Windmeijer-corrected standard errors. c 2nd order serial correlation in first difference is distributed as N (0, 1) under the null of no serial correlation in the residuals. d Difference-in-Hansen over identification test and null that instruments are valid. e TDBVit-2 , FA it-2, PRFit-2 , Sizeit-2 , Ndts it-2 , PBit-2 , Ageit-2 , DPOit-2 , INSTit-2 , ROLit-2 , REGQit-2 , GEit-2 , PSit-2 , VA it-2 , and CCit-2 are used as Instruments. f In all estimations, the number of ‘N’ cross -sectional observation (3,891) is greater than the number of instruments (153 & 223). g Industry dummies are included in all the estimations. The lists of the industries are in appendix II. Table 4 Direct effects of institutional quality on M arket-debt models
AC
System-GM M Two-step Estimation Results using M arket-debt and 3, 891 Listed Firms from 23 Developing Countries (Robustness Tests), 2006-2014 M odel 2a With Single M odel 2b M odel 2c M odel 2d M odel 2e M odel 2f M odel 2g M odel 2h INST Index With ROL With REGQ With GE With PS With VA With CC (Single Equation)
0.5252*** 0.5371*** 0.5418*** 0.5475*** 0.5330*** 0.5449*** 0.5262*** 0.4679*** TDM Vit-1 (Lag M arket-debt)(53.72) (55..71) (55.44) (56.98) (54.25) (56.14) (53.77) (33.78) INST (Single Institutional 0.0012*** Quality Index) (4.52) 0.0006** ROL (Rule of law) (2.27) REGQ (Regulatory quality) -
-
GE (Govt. Effectiveness)
-
-
-
-
0.0014*** (5.43) 0.0002** (2.04)
-
-
-
-
-
-
-
-
-
0.0012*** (4.14) 0.0002** (2.40) 0.0001* (1.87)
28
ACCEPTED MANUSCRIPT 0.0029*** (11.81) 0.0017*** (7.33) 0.0002** (2.01)
0.1554*** (8.69) -0.0019** (-2.10) 0.0021** (2.01)
0.1479*** 0.0562*** 0.1274*** (8.22) (3.77) (7.22) -0.0086*** -0.0108*** -0.0008** (-4.84) (-5.81) (-2.40) 0.0009*** 0.0078*** 0.0057*** (2.94) (5.11) (4.69)
0.13021*** (4.21) -0.0211*** (-3.72) 0.0014** (2.09)
-
-
-
-
VA (Voice & Account.)
-
-
-
-
CC (Control of Corruption) -
-
-
0.1140*** (6.89) -0.0035** (-2.05) 0.0003** (2.28)
0.1318*** (7.53) -0.0003** (-2.19) 0.0030*** (2.93)
0.1376*** (7.72) -0.0018** (-2.04) 0.0003** (2.30)
FA (Fixed Assets) PRF (Profits) Size
T
-
0.0023*** (12.63) 0.0003*** (3.14) 0.0005** (2.07)
PS (Political Stability)
IP
-0.0001*** -0.0001*** -0.0001*** -0.0001*** -0.0001** -0.0003*** -0.0004*** -0.0001*** (-5.39) (-5.22) (-3.92) (-3.89) (-2.44) (-5.36) (-6.84) (-5.38) -0.0903 -0.0239 -0.0399 -0.0639 -0.0116 -0.0691 -0.1154 -0.0542* Ndts (Non-debt tax-shield) (-0.67) (-0.15) (-0.27) (-0.38)) (-0.07) (-0.52) (-0.72) (-1.92) 0.0470*** 0.0345*** 0.0376*** 0.0400*** 0.0506*** 0.0151** 0.0550*** 0.0182*** Age (Firm-age) (10.52) (8.05) (8.19) (9.74) (10.44) (2.37) (10.35) (3.65) -0.0357*** -0.0359*** -0.0378*** -0.0331*** -0.0395*** -0.0392*** -0.0329*** -0.0190*** DPO (Dividend pay-out) (-12.43) (-13.10) (-13.29) (-12.59) (-13.88) (-13.38) (-11.45) (-2.94) -0.1947*** -0.0855*** -0.0512* -0.0820*** -0.0721** -0.1089*** -0.2402*** 0.0460*** OWS (Ownership structure) (-6.47) (-2.83) (1.87) (-3.05) (-2.38) (-3.05) (-6.90) (3.92) 0.0042*** 0.0045*** 0.0029*** 0.0032*** 0.0032*** 0.0029*** 0.0035*** 0.0017*** Int (Interest rate) (12.57) (14.30) (8.99) (10.56) (9.59) (10.87) (11.29) (6.09) 0.0065*** 0.0064*** 0.0058*** 0.0059*** 0.0060*** 0.0046*** 0.0064*** 0.0041*** Inf (Inflation rate) (13.41) (13.42) (11.82) (12.33) (12.49) (10.29) (12.93) (7.89) Gdpg (Economic growth -0.0038*** -0.0040*** -0.0029*** -0.0039*** -0.0040*** -0.0032*** -0.0033*** 0.0015*** rate) (-8.79) (-9.48) (-6.72) (-8.96) (-9.24) (-7.81) (-7.80) (3.26) 0.0011*** 0.0007*** 0.0011*** 0.0008*** 0.0013*** 0.0005*** 0.0009*** 0.0010*** BC (Banking Credit) (9.94) (5.88) (9.40) (7.75) (12.03) (6.98) (7.67) (7.27) -0.0008*** -0.0009*** -0.0007*** -0.0009*** -0.0008*** -0.0009*** -0.0008*** -0.0006*** M C (M arket Capitalization) (-18.01) (-19.75) (-16.07) (-20.41) (-17.95) (-20.68) (-18.11) (-12.26) 0.2018 0.1899 AR(2) [P-value] 0.1018 0.1022 0.1053 0.1117 0.1035 0.3015 Difference Hansen Tests [P-value] 0.1116 0.1299 0.2014 0.3610 0.2122 0.1549 0.1503 0.1041 153 223 Instruments 153 153 153 153 153 153 1.57 2.77 Variance Inflation Factor 1.54 1.69 1.75 1.64 1.68 1.43 Heteroscedasticity test (pval.) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00 Cross-dependency test (pval.) 0.159 0.173 0.181 0.195 0.194 0.152 0.177 0.183 Cross-sectional observation(N) 3891 3891 3891 3891 3891 3891 3891 3891 Observ. after estimation (N*T) 30058 30058 30058 30058 30058 30058 30058 30058
AC
CE
PT
ED
M
AN
US
CR
PB (Price-to-book ratio)
Notes: a See Table 1 for the definition of variables and measurements. Asterisks indicate significance at 1% (***) and 5%
(**). b T-statistics (in parenthesis) of the Two-step System-GMM model are based on Windmeijer-corrected standard errors. c 2nd order serial correlation in first difference is distributed as N (0, 1) under the null of no serial correlation in the residuals. d Difference-in-Hansen over identification test and null that instruments are valid. e TDMVit-2 , FA it-2, PRFit-2 , Sizeit-2 , Ndts it-2 , PBit-2 , Ageit-2 , DPOit-2 , INSTit-2 , ROLit-2 , REGQit-2 , GEit-2 , PSit-2 , VA it-2 , and CCit-2 are used as Instruments. f In all estimations, the number of ‘N’ cross -sectional observation (3,891) is greater than number of instruments (153 & 223). g Industry dummies are included in all the estimations. The lists of the industries are in appendix II.
4.2.
Regional Sample Results
29
ACCEPTED MANUSCRIPT Similarly, for the estimation that focuses on 2,741 listed firms from 7 Asian countries, the models passed the AR (2) tests, as indicated by the insignificant p-values suggesting the absence of second-order serial correlation. Overall, the validity of the instruments and additional instruments is confirmed, as indicated by the insignificant p-values of the difference in Hansen tests in all the models. Additionally, in all the estimations, the number of cross-sectional observations exceeds the number of instruments, which indicates that the estimations are valid.
T
The empirical results reveal that the single aggregated institutional quality index is statistically
IP
significant and positively related to both the book total debt ratio (see Table 5, Model 3a) and the
CR
market total debt ratio (see Table 6, Model 4a). Also, the six disaggregated measures of institutional quality are statistically significant and positively related to both the book total debt
US
ratio (see Table 5, Models 3b to 3g) and the market total debt ratio (see Table 6, Models 4b to 4g). Additional analysis include all of the six disaggregated measures of institutional quality
AN
together in a single model and the results are broadly similar. However, the coefficient of most of the institutional quality variables reduces. Moreover, the coefficient of the lagged debt (debt it-1 ),
M
firm-specific and other macroeconomic variables change (see Table 5, Model 3h and Table 6, Model 4h). The empirical results also show that Asian firms make adjustments to the target debt
ED
(especially for the book debt ratio) when there is a deviation from their target debt level, consistent with the dynamic version of trade-off theory. The speed of adjustments to the target
PT
debt level is calculated as 1-λ, where λ is the coefficient of the lagged debt variables. Moreover, firm-specific factors (i.e. fixed assets, profits, size, price-to-book ratio, dividend
CE
pay-out ratio, and ownership structure) and macroeconomic factors (i.e. interest rate, inflation rate, banking credit, and market capitalization) consistently affect the debt ratios in the Asian
AC
region, similar to previous findings in the literature. However, the effect of firm-age on debt ratios is mixed but we find more evidence that firm-age has a positive effect on the market debt ratio, consistent with some previous findings in the literature. Specifically, our results are consistent with Hovakimian (2001) and Sundaresan et al. (2015) findings that firm age is positively related to debt, suggesting that aging firms have more assets-in-pace which justify taking on more debt. Conversely, our results are inconsistent with King and Santor (2008) findings that firm age has negative but insignificant effects on the book–debt ratio. Our results are also inconsistent with Deangelo (2015) and Kieschnick and Moussawi (2018) findings that
30
ACCEPTED MANUSCRIPT firm age is negatively related to debt, indicating that older firms seem to value financial flexibility or unused debt capacity. Likewise, for the estimation that focuses on 1,091 listed firms from six Latin American and Eastern European countries, the models passed the AR (2) tests, indicating an absence of second-order serial correlation. Overall, we also confirm that the instruments are valid, as indicated by the insignificant p-values of the difference-in-Hansen tests in all the models.
T
Furthermore, all the model estimations are confirmed as valid because the number of cross-
IP
sectional observations is greater than the number of instruments. The empirical results indicate
CR
that the single aggregated institutional quality index is statistically significant and positively related to both the book total debt ratio (see Table 7, Model 5a) and the market total debt ratio
US
(see Table 8, Model 6a). Also, the six disaggregated measures of institutional quality are statistically significant and positively related to both the book total debt ratio (see Table 7,
AN
Models 5b to 5g) and the market total debt ratio (see Table 8, Models 6b to 6g). Furthermore, all the six disaggregated measures of institutional quality are included together in a single
M
model and the results are broadly similar. But the coefficient of most of the institutional quality variables change. Moreover, the coefficient of the lagged debt (debtit-1 ), firm-specific and other
ED
macroeconomic variables changes (see Table 7, Model 5h and Table 8, Model 6h). Additionally, firm-specific factors (i.e. fixed assets, profits, size, price-to-book ratio, and
PT
dividend pay-out ratio) and macroeconomic factors (i.e. annual growth rate in nominal gross domestic product) consistently affect the debt ratios in the Latin-American and Eastern
CE
European region, similar to previous findings in the literature. However, the effect of firm-age on debt ratios is mixed, but we find more evidence that firm-age has a negative effect on the
AC
market debt ratio, consistent with some previous findings in the literature. Our results are consistent with Deangelo (2015) and Kieschnick and Moussawi (2018) findings that firm age has a negative effect on debt, indicating that older firms seem to value financial flexibility or unused debt capacity. Conversely, our results are inconsistent with Hovakimian (2001) and Sundaresan et al. (2015) findings that firm age has a positive effect on debt, suggesting that aging firms have more assets-in-pace which justify taking on more debt. Similarly, ownership structure has a mixed effect on debt ratios, consistent with previous findings in the literature. Specifically, ownership structure is positively related to book debt ratio, but it is negatively related to market debt ratio. Wang et al. (2018) report negative effects 31
ACCEPTED MANUSCRIPT of ownership structure on debt, which is consistent with the view of the agency theory. Likewise, Harvey et al. (2004) find that higher managerial ownership levels provide benefits to controlling shareholders that are shared with other stakeholders, thereby, reducing the need for debt as an internal control mechanism. Conversely, managerial ownership is positively related to debt because managers signal growth opportunity by increasing debt, consistent with the
T
signaling theory.
0.1925*** (23.48)
0.2100*** (26.60)
0.2268 (27.99)
0.0066*** (3.33)
-
INST (Single Institutional 0.0252*** Quality Index) (7.35)
CR
M odel 3c M odel 3d With REGQ With GE
M odel 3e With PS
M odel 3f With VA
M odel 3g With CC
US
M odel 3a With Single M odel 3b INST Index With ROL
0.2545*** 0.2199*** 0.2534*** 0.2245*** (31.31) (29.08) (27.72) (25.79)
AN
TDBVit-1 (Lag Book-debt)
IP
Table 5 System-GM M Two-step Estimation Results using Book-debt and 2,187 Listed Firms from 7 Asian Countries, 2006-2014
-
-
0.6696*** (88.19)
-
0.0012*** ROL (Rule of law) (4.18) 0.0159*** 0.0007** REGQ (Regulatory quality) (5.77) (2.19) 0.0061** 0.0004* GE (Govt. Effectiveness) (2.01) (1.84) 0.0042** 0.0009*** PS (Political Stability) (2.17) (4.39) 0.0095** 0.0022*** VA (Voice & Account.) (2.29) (9.90) 0.0065*** 0.0001*** CC (Control of Corruption) (3.01) (3.09) 0.7538*** 0.7625*** 0.8389*** 0.4752*** 0.8374*** 0.7506*** 0.8232*** 0.1375*** FA (Fixed Assets) (9.30) (10.07) (11.91) (6.48) (12.40) (8.82) (10.21) (8.89) -0.0445*** -0.0095*** -0.0162*** -0.0905*** -0.0487*** -0.0928*** -0.0975*** -0.0075** PRF (Profits) (-3.94) (-5.49) (-5.85) (-5.45) (-7.33) (-6.14) (-6.00) (-2.28) 0.0909*** 0.0465*** 0.0085** 0.0409*** 0.0261*** 0.0434*** 0.0506*** 0.0026*** Size (8.72) (5.19) (2.09) (4.38) (3.25) (3.72) (5.37) (2.77) -0.3338*** -0.3237*** -0.3771*** -0.3177*** -0.3533*** -0.3428*** -0.3587*** -0.0025*** PB (Price-to-book ratio) (-9.35) (-5.66) (-5.25) (-5.87) (-5.41) (-8.15) (-10.15) (-5.88) 0.1111*** 0.0759*** 0.0867*** 0.0942*** 0.0839*** 0.0909*** 0.0319*** 0.0237*** Ndts (Non-debt tax-shield) (6.47) (6.76) (6.26) (7.19) (7.04) (7.65) (5.31) (4.10) -0.1228** -0.0473 0.1203** 0.0320 -0.0675* -0.0732 -0.0639 0.0029 Age (Firm-age) (-2.47) (-1.04) (2.54) (0.71) (-1.65) (-1.07) (-1.29) (0.19) -0.481*** -0.4526*** -0.2626*** -0.2516*** -0.3039*** -0.2247*** -0.4906*** 0.0157*** DPO (Dividend pay -out) (-20.25) (-18.43) (-11.05) (-10.43) (-14.49) (-8.55) (-18.75) (3.11) 0.9943*** 0.9569*** -0.2153 0.1768** 0.6033*** 0.7147* 0.9468*** 0.0442*** OWS (Ownership structure) (5.28) (15.08) (-0.84) (2.05) (2.93) (1.93) (4.11) (4.50) 0.0023** 0.0072** 0.0034** 0.0057** 0.0068** -0.0001 0.0039** 0.0001* Int (Interest rate) (2.67) (2.18) (2.05) (2.08) (2.24) (-0.01) (2.10) (1.82) 0.0065** 0.0049** 0.0109*** 0.0005* 0.0097*** 0.0044 0.0095** 0.0009** Inf (Inflation rate) (2.17) (2.35) (3.30) (1.79) (2.87) (1.54) (2.25) (2.40) Gdpg (Economic growth 0.0173** 0.0151** 0.0061** 0.0108*** 0.0072** 0.0039** 0.0102** 0.0008** rate) (2.64) (2.41) (2.08) (2.86) (2.29) (2.65) (2.57) (2.44) BC (Banking Credit) 0.0032**** 0.0013** 0.0042*** 0.0008** 0.0020** 0.0003** 0.0029*** 0.0001*
AC
CE
PT
ED
M
-
M odel 3h (Single Equation)
32
ACCEPTED MANUSCRIPT (3.47) -0.0008** (-2.67) 0.2897
(2.00) -0.0002** (-2.45) 0.2863
(2.08) -0.0007** (-2.13) 0.2864
(2.25) (2.83) -0.0006** -0.0006** (-2.41) (-2.18) 0.3024 0.2860
(1.87) 0.0001** (2.01) 0.1060
0.1364 153 3.18
0.1358 153 3.98
0.1149 153 3.28
0.2228 153 3.19
0.2118 153 3.14
0.1367 153 3.22
0.1008 223 3.39
0.000
0.000
0.000
0.000
0.000
0.000
0.00
0.139
0.233
0.221
0.181
0.299
0.195
0.206
2187
2187
2187
2187
2187
16998
16998
16998
16998
IP
T
(2.18) -0.0002** (-2.59) 0.2979
16998
2187
2187
16998
16998
CR
(2.81) -0.0009** M C (M arket Capitalization) (-2.16) AR(2) [P-value] 0.2841 Difference Hansen Tests [P-value] 0.3082 Instruments 153 Variance Inflation Factor 3.29 Heteroscedasticity test (p val.) 0.000 Cross-dependency test (pval.) 0.176 Cross-sectional observation(N) 2187 Observ after estimation (N*T) 16998
Notes: a See Table 1 for the definition of variables and measurements. Asterisks indicate significance at 1% (***), 5% (**)
AN
US
and 10% (*). b T-statistics (in parenthesis) of the Two-step System-GMM model are based on Windmeijer-corrected standard errors. c 2nd order serial correlation in first difference is distributed as N (0, 1) under the null of no serial correlation in the residuals. d Difference-in-Hansen over identification test and null that instruments are valid. e TDBVit-2 , FA it-2, PRFit-2 , Sizeit-2 , Ndts it-2 , PBit-2 , Ageit-2 , DPOit-2 , INSTit-2 , ROLit-2 , REGQit-2 , GEit-2 , PSit-2 , VA it-2 , and CCit-2 are used as Instruments. f In all estimations, the number of ‘N’ cross -sectional observation (2,187) is greater than the number of instruments (153 & 223). g Industry dummies are included in all the estimations. The lists of the industries are in appendix II.
M
Table 6 Direct effects of institutional quality on M arket-debt models System-GM M Two-step Estimation Results using M arket-debt and 2,187 Listed Firms from 7 Asian Countries (Robustness Tests), 2006-2014
ED
M odel 4a With Single M odel 4b M odel 4c M odel 4d INST Index With ROL With REGQ With GE
M odel 4e With PS
M odel 4f With VA
M odel 4g With CC
M odel 4h(Single Equation
0.5496*** 0.5587*** 0.5283*** 0.5418*** 0.4779*** (45.57) (48.90) (43.48) (45.76) (32.85)
INST (Single Institutional Quality Index)
-
PT
0.5519*** 0.5307*** 0.5617*** TDM Vit-1 (Lag M arket-debt) (47.24) (43.78) (47.86) 0.0009*** (2.84) 0.0004** (2.14)
0.0002** ROL (Rule of law) (2.08) 0.0029*** 0.0006** REGQ (Regulatory quality) (6.62) (2.03) 0.0004** 0.0003** GE (Govt. Effectiveness) (2.10) (2.63) 0.0012*** 0.0007** PS (Political Stability) (3.17) (2.05) 0.0014*** 0.0048*** VA (Voice & Account.) (5.12) (10.99) 0.0024*** 0.0010** CC (Control of Corruption) (8.93) (2.70) 0.0494** 0.1232*** 0.0712** 0.1311*** 0.0280** 0.0704** 0.0875*** 0.0277*** FA (Fixed Assets) (2.03) (3.97) (2.26) (4.13) (2.18) (2.33) (3.05) (2.93) -0.0067** -0.0124*** -0.0109*** -0.0096*** -0.0076*** -0.0002** -0.0064*** -0.0170*** PRF (Profits) (-5.95) (-10.25) (-9.40) (-7.65) (-6.77) (-2.22) (-5.63) (-3.21) 0.0914** 0.0034** 0.0026*** 0.0051*** 0.0040** 0.0012** 0.0023** 0.0144*** Size (2.23) (2.35) (2.70) (2.92) (3.24) (2.46) (2.15) (7.96) -0.0121*** -0.0131*** -0.0170*** -0.0134*** -0.0113*** -0.0137*** -0.0141*** -0.0054*** PB (Price-to-book ratio) (-6.36) (-6.52) (-7.76) (-6.59) (-6.21) (-6.94) (-6.96) (-6.26)
CE
-
-
-
AC
-
33
ACCEPTED MANUSCRIPT -0.1641** -0.0343 -0.3394** -0.0155* 0.3108 (-2.56) (-0.35) (-2.32) (-1.94) (0.75) 0.0535*** 0.0234*** 0.0562*** 0.0157*** 0.0037* (7.94) (3.91) (7.69) (3.06) (1.81) -0.0269*** -0.0263*** -0.0383*** -0.0273*** -0.0507*** (-5.95) (-6.16) (-8.25) (-6.39) (-5.45) 0.1893*** 0.1100*** 0.1319*** 0.0370 0.0315 (3.65) (2.88) (3.74) (1.21) (1.52) 0.0040*** 0.0049*** 0.0034*** 0.0045*** 0.0021*** (9.12) (10.65) (7.44) (9.96) (4.01) 0.0067*** 0.0069*** 0.0048*** 0.0077*** 0.0058*** (11.48) (12.14) (8.40) (12.30) (8.33) -0.0023** -0.0031*** -0.0022*** -0.0032*** 0.0018* (-2.74) (-3.68) (-2.60) (-3.92) (1.82) 0.0004** 0.0003* 0.0005** 0.0004*** 0.0001** (2.68) (1.93) (4.23) (3.50) (2.09) -0.0006*** -0.0006*** -0.0006*** -0.0006*** -0.0004*** (-8.23) (-9.01) (-7.92) (-9.60) (-4.98) 0.1141 0.1053 0.2018 0.1421 0.1214
0.1221 153 2.88
0.1405 153 3.97
0.1347 153 3.21
0.1099 153 3.99
0.1361 153 3.23
0.1212 153 3.07
0.1108 223 3.51
0.000
0.000
0.000
0.000
0.000
0.000
0.00
0.329
0.400
0.422
0.390
0.300
0.206
0.209
2187
2187
2187
2187
2187
2187
2187
16998
16998
16998
16998
16998
16998
AN
US
CR
IP
T
-0.1414** -0.2014* (-2.28) (-1.74) 0.0070*** 0.0390*** (5.45) (5.57) -0.0290*** -0.0205*** (-6.52) (-4.67) 0.1337*** 0.1897*** (3.07) (4.24) 0.0045*** 0.0045*** (9.91) (9.95) 0.0063*** 0.0068*** (10.27) (11.03) -0.0038*** -0.0009* (-4.42) (-1.81) 0.0002** 0.0002*** (2.40) (6.09) -0.0007*** -0.0006*** (-9.26) (-8.54) 0.1120 0.2239
M
-0.0458* (-1.77) 0.0090* Age (Firm-age) (1.71) -0.0278*** DPO (Dividend. pay -out) (-6.44) 0.0455 OWS (Ownership structure) (1.49) 0.0041*** Int (Interest rate) (9.11) 0.0062*** Inf (Inflation rate) (10.07) Gdpg (Economic growth -0.0029*** rate) (-3.34) 0.0001** BC (Banking Credit) (2.09) -0.0007*** M C (M arket Capitalization) (-10.00) AR(2) [P-value] 0.1039 Difference Hansen Tests [P-value] 0.1416 Instruments 153 Variance Inflation Factor 3.18 Heteroscedasticity Test (p val.) 0.000 Cross-dependency test (pval.) 0.327 Cross-sectional observation(N) 2187 Observ. after estimation (N*T) 16998 Ndts (Non-debt tax-shield)
16998
Notes: a See Table 1 for the definition of variables and measurements. Asterisks indicate significance at 1% (***), 5% (**)
AC
CE
PT
ED
and 10% (*). b T-statistics (in parenthesis) of the Two-step System-GMM model are based on Windmeijer-corrected standard errors. c 2nd order serial correlation in first difference is distributed as N (0, 1) under the null of no serial correlation in the residuals. d Difference-in-Hansen over identification test and null that instruments are valid. e TDMVit-2 , FA it-2, PRFit-2 , Sizeit-2 , Ndts it-2 , PBit-2 , Ageit-2 , DPOit-2 , INSTit-2 , ROLit-2 , REGQit-2 , GEit-2 , PSit-2 , VA it-2 , and CCit-2 are used as Instruments. f In all estimations, the number of cross -sectional observation (2,187) is greater than the number of instruments (153 & 223). g Industry dummies are included in all the estimations. The lists of the industries are in appendix II.
Table 7 Direct effects of institutional quality on Book-debt models System-GM M Two-step Estimation Results using Book-debt and 1,091 Listed Firms from 6 Latin American combined with Eastern European Countries, 2006-2014 M odel 5a M odel 5b M odel 5c M odel 5d With Single With ROL With REGQ With GE
M odel 5e M odel 5f With PS With VA
M odel 5g M odel 5h (Single
34
ACCEPTED MANUSCRIPT INST Index
TDBVit-1 (Lag Book-debt)
0.4708*** (50.38)
INST (Single Institutional 0.0033*** Quality Index) (9.01)
With CC
Equation)
0.4875*** 0.4739*** 0.4795*** 0.4732*** 0.4696*** 0.4850*** 0.7422*** (50.12) (49.79) (49.49) (52.57) (53.41) (49.16) (46.88)
-
-
0.0004** ROL (Rule of law) (2.42) 0.0034** REGQ (Regulatory quality) (2.15) 0.0002** GE (Govt. Effectiveness) (2.54) 0.0012*** 0.0002** PS (Political Stability) (6.66) (2.58) 0.0028*** 0.0010*** VA (Voice & Account.) (9.41) (3.24) 0.0012*** 0.0011*** CC (Control of Corruption) (5.18) (5.16) 0.1107*** 0.0870*** 0.1340*** 0.0805*** 0.1237*** 0.1173*** 0.1497*** 0.0178** FA (Fixed Assets) (7.53) (8.35) (8.04) (6.57) (10.30) (10.92) (8.07) (2.44) -0.4142*** -0.4281*** -0.4115*** -0.4171*** -0.4085*** -0.4208*** -0.4196*** -0.4440*** PRF (Profits) (-6.59) (-8.41) (-7.74) (-7.00) (-4.47) (-8.39) (-7.12) (-9.27) 0.0145*** 0.0085*** 0.0148*** 0.0167*** 0.0034* 0.0145*** 0.0107*** 0.0085*** Size (6.71) (4.01) (6.78) (6.70) (1.90) (6.82) (11.14) (8.69) -0.0001*** -0.0001*** -0.0001*** -0.0001*** -0.0001*** -0.0001*** -0.0001*** -0.000 PB (Price-to-book ratio) (-6.60) (-8.16) (-5.62) (-8.92) (-6.52) (-9.48) (-6.34) (-1.18) 0.0977*** 0.0966*** 0.0200*** 0.0723*** 0.0463*** 0.0849*** 0.0211*** 0.0136* Ndts (Non-debt tax-shield) (15.31) (14.00) (14.30) (15.76) (13.80) (15.81) (13.10) (1.99) -0.0117* -0.0103 0.0233*** -0.0008 -0.0036 0.0087 -0.0200*** -0.0010** Age (Firm-age) (-1.95) (-1.41) (2.86) (-0.11) (-0.62) (1.34) (-3.93) (-2.41) 0.0258*** 0.0322*** 0.0206*** 0.0279*** 0.0253*** 0.0236*** 0.0330*** 0.0059** DPO (Dividend pay -out) (9.27) (9.56) (9.18) (10.36) (7.64) (8.41) (8.96) (2.01) 0.1775** 0.1085** 0.2090** 0.2078** 0.0292** 0.0800* 0.1368* 0.0217* OWS (Ownership structure) (2.27) (2.49) (2.22) (2.32) (2.25) (1.96) (1.80) (1.93) -0.0001 0.0001 0.0001 -0.0011*** 0.0009*** 0.0001 0.0003 -0.0008*** Int (Interest rate) (-0.42) (0.35) (0.09) (-3.95) (3.13) (0.10) (0.83) (4.29) 0.0014 0.0022** -0.0010 0.0013 0.0029*** -0.0012 0.0029** 0.0012* Inf (Inflation rate) (1.53)) (2.11) (-1.45) (1.26) (3.15) (-1.31) (2.68) (1.80) Gdpgr (Economic growth 0.0015*** 0.0015*** 0.0015*** 0.0018*** 0.0012*** 0.0016*** 0.0014*** 0.0010*** rate) (4.87) (4.38) (4.67) (5.54) (4.12) (5.16) (4.07) (3.18) 0.0006**** 0.0004*** 0.0007*** 0.0004** 0.0006*** 0.0002 0.0001* 0.0001 BC (Banking Credit) (3.08) (2.30) (4.32) (2.16) (3.51) (1.16) (1.95)) (1.33)
T
-
CE
PT
ED
M
AN
US
CR
IP
0.0017*** (5.91) 0.0036*** (9.79) 0.0038*** (8.26)
AC
-0.0003*** -0.0005*** -0.0004*** -0.0004*** -0.0003*** -0.0005*** M C (M arket Capitalization) (-3.56) (-5.69) (-5.75) (-6.11) (-3.44) (-6.83) AR(2) [P-value] 0.2696 0.2706 0.2765 0.2727 0.2775 0.2678 Difference Hansen Tests [P-value] 0.1823 0.1759 0.1946 0.1850 0.1948 0.1805 Instruments 153 153 153 153 153 153 Variance Inflation Factor 2.37 2.39 2.48 2.71 1.97 1.93 Heteroscedasticity test (p-val.) 0.000 0.000 0.000 0.000 0.000 0.000 Cross-dependency test (p-val.) 0.321 0.253 0.214 0.263 0.251 0.219 Cross-sectional observation(N) 1091 1091 1091 1091 1091 1091 Observ after estimation (N*T) 8436 8436 8436 8436 8436 8436
-0.0005*** -0.0003*** (-5.46) (-6.15) 0.2748 0.3780 0.1934 153 1.89
0.1031 223 2.87
0.000
0.000
0.242
0.236
1091
1091
8436
8436
35
ACCEPTED MANUSCRIPT Notes: a See Table 1 for the definition of variables and measurements. Asterisks indicate significance at 1% (***), 5% (**)
and 10% (*). b T-statistics (in parenthesis) of the Two-step System-GMM model are based on Windmeijer-corrected standard errors. c 2nd order serial correlation in first difference is distributed as N (0, 1) under the null of no serial correlation in the residuals. d Difference-in-Hansen over identification test and null that instruments are valid. e TDBVit-2 , FA it-2, PRFit-2 , Sizeit-2 , Ndts it-2 , PBit-2 , Ageit-2 , DPOit-2 , INSTit-2 , ROLit-2 , REGQit-2 , GEit-2 , PSit-2 , VA it-2 , and CCit-2 are used as Instruments. f In all estimations, the number of ‘N’ cross -sectional observation (1,091) is greater than the number of instruments (153 & 223).
M odel 6c M odel 6d With REGQ With GE
M odel 6e With PS
0.4822*** 0.5009*** (37.46) (38.67)
0.5057*** (39.06)
0.4781*** (36.91)
0.4541*** 0.4621*** (33.57) (34.51)
IP
M odel 6a With Single M odel 6b INST Index With ROL
M odel 6f With VA
CR
US
TDM Vit-1 (Lag M arketdebt)
T
Table 8 Direct effects of institutional quality on M arket-debt models System-GM M Two-step Estimation Results using M arket-debt and 1,091 Listed Firms from 6 Latin American combined with Eastern European Countries (Robustness Tests), 2006-2014
M odel 6g With CC
M odel 6h (Single Equation)
0.5182*** (40.99)
0.3729*** (22.91)
0.0004* (1.85) 0.0007** (2.52) 0.0036*** (4.99) 0.0006** (2.15) 0.0022*** (3.81) 0.0014*** (3.76) 0.1410*** (4.89) -0.0044*** (-3.58) 0.0252*** (11.38) -0.0001*** (-17.35) -0.0458*** (-5.31) -0.001 (1.27) -0.0316*** (-4.96) -0.1464*** (-4.34) 0.0012*** (3.26) 0.004 (1.18) 0.0031*** (7.99) 0.0005** (2.04) -0.0016
-
-
0.0012*** (4.11)
-
GE (Govt. Effectiveness)
-
-
-
PS (Political Stability)
-
-
-
VA (Voice & Account.) CC (Control of Corruption)
-
-
-
-
0.0009*** (8.27) 0.0010*** (3.45)
0.0642*** (7.84) -0.0133** (-2.28) 0.0244*** (16.81)
0.0626*** (7.01) -0.0099* (-1.85) 0.0190*** (14.92)
0.0746*** (6.52) -0.0092** (-1.93) 0.0190*** (11.09)
0.0412*** (4.86) -0.0097* (-1.87) 0.0270*** (13.59)
0.0382*** (4.22) -0.0103** (-2.02) 0.0206*** (13.13)
0.0662*** (7.61) -0.0140** (-2.21) 0.0231*** (14.63)
0.0005** (2.36) 0.0674*** (7.47) -0.0121* (-1.89) 0.0205*** (14.48)
-0.0001 (-0.28) 0.4222*** (6.42) -0.0029 (-0.58) -0.0151*** (-9.72) -0.1165** (-2.18) -0.0003 (-1.11) 0.0054*** (5.14) 0.0039*** (10.28) 0.0008*** (5.02) -0.0025***
-0.0001*** (-8.91) 0.8057** (4.14) -0.0095* (-1.91) -0.0120*** (-6.76) -0.2383*** (-3.93) -0.0004 (-1.32) 0.0087*** (8.07) 0.0034*** (9.06) 0.0011*** (7.56) -0.0022***
-0.0001*** (-7.79) 0.7929*** (3.87) 0.0049 (0.80) -0.0176*** (-10.01) -0.4333*** (-6.77) 0.0002* (1.93) 0.0065*** (6.17) 0.0028*** (7.68) 0.0007*** (3.40) -0.0023***
-0.0001*** (-8.67) 0.3280*** (4.72) -0.0059 (-1.15) -0.0067*** (-3.90) -0.3643*** (-5.89) 0.0004* (1.90) 0.0045*** (4.27) 0.0025*** (6.72) 0.0005** (2.05) -0.0023***
-0.0001*** (-14.08) -0.3659* (-1.71) -0.0338** (-6.66) 0.0009 (0.56) -0.2471*** (-4.80) 0.0014*** (5.21) 0.0101*** (9.33) 0.0034*** (9.36) 0.0002 (1.50) -0.0024***
Size
-0.0001*** -0.0001*** (-8.54) (-9.43) 0.5212** 0.3483* Ndts (Non-debt tax-shield) (2.57) (1.81) -0.0145*** -0.0180*** Age (Firm-age) (-3.17) (-3.53) -0.0124** -0.0082*** DPO (Dividend pay -out) (-7.38) (-4.82) OWS (Ownership -0.3227*** -0.3736*** structure) (-5.46) (-5.73) 0.0000 0.0013*** Int (Interest rate) (0.32) (4.78) 0.0068 0.0034*** Inf (Inflation rate) (6.31) (3.08) Gdpg (Economic growth 0.0031*** 0.0030*** rate) (8.50) (8.08) 0.0005** 0.0001* BC (Banking Credit) (2.51) (1.99) M C (M arket -0.0022*** -0.0027***
AC
PB (Price-to-book ratio)
0.0026*** (6.53)
M
ED
PT
PRF (Profits)
CE
FA (Fixed Assets)
AN
INST (Single Institutional 0.0012*** Quality Index) (3.56) 0.0003* ROL (Rule of law) (1.99) REGQ (Regulatory quality) -
-
-
-
-
-
-
-
-
-
-
36
ACCEPTED MANUSCRIPT (-23.39) 0.1041
(-21.17) 0.1168
(-21.15) 0.1700
(-22.57) 0.1968
(-23.02) 0.1636
(-17.23) 0.1996
0.1549 153 2.63
0.1251 153 2.37
0.1411 153 2.62
0.1825 153 2.01
0.1563 153 2.11
0.1691 153 1.85
0.1842 223 2.83
0.000
0.000
0.000
0.000
0.000
0.000
0.00
0.205
0.149
0.216
0.227
0.285
0.279
0.237
1091
1091
1091
1091
1091
1091
1091
8436
8436
8436
8436
8436
T
(-23.72) 0.1137
8436
8436
IP
Capitalization) (-21.66) AR(2) [P-value] 0.2831 Difference Hansen Tests [P-value] 0.1338 Instruments 153 Variance Inflation Factor 2.19 Heteroscedasticity test (p-val.) 0.000 Cross-dependency test (p-val.) 0.303 Cross-sectional observation(N) 1091 Observ after estimation (N*T) 8436
Notes: a See Table 1 for the definition of variables and measurements. Asterisks indicate significance at 1% (***), 5% (**)
AN
US
CR
and 10% (*). b T-statistics (in parenthesis) of the Two-step System-GMM model are based on Windmeijer-corrected standard errors. c 2nd order serial correlation in first difference is distributed as N (0, 1) under the null of no serial correlation in the residuals. d Difference-in-Hansen over identification test and null that instruments are valid. e TDMVit-2 , FA it-2, PRFit-2 , Sizeit-2 , Ndts it-2 , PBit-2 , Ageit-2 , DPOit-2 , INSTit-2 , ROLit-2 , REGQit-2 , GEit-2 , PSit-2 , VA it-2 , and CCit-2 are used as Instruments. f In all estimations, the number of ‘N’ cross -sectional observation (1,091) is greater than the number of instruments (153 & 223). g Industry dummies are included in all the estimations. The lists of the industries are in appendix II.
Turning to the estimation that focuses on 613 listed firms from 10 African countries, the
M
results also reveal that the models passed the AR (2) tests, as indicated by the insignificant pvalues suggesting the absence of second order serial correlation. The validity of the instruments
ED
and the additional instruments is confirmed, as indicated by the insignificant p-values of the difference-in Hansen tests in all the models. Additionally, in all the estimations, the number of
PT
cross-sectional observation exceeds the number of instruments, indicating that the estimations are valid. However, the empirical results show that the single aggregated institutional quality
CE
index has insignificant effects on both the book total debt ratio (see Table 9, Model 7a) and the market total debt ratio (see Table 10, Model 8a). Also, the six disaggregated measures of
AC
institutional quality (except voice and accountability and political stability) have insignificant effects on both the book total debt ratio (see Table 9, Models 7b to 7g) and the market total debt ratio (see Table 10, Models 8b to 8g). Furthermore, all of the six disaggregated measures of institutional quality are included together in a single model and the results are broadly similar. But the regulatory quality becomes marginally significant in the book debt model and the coefficient of most of the institutional quality variables changes. Moreover, firm-specific factors (i.e. fixed assets, profits, size, price-to-book ratio, and firm age) and macroeconomic factors (i.e. banking credit, market capitalization, inflation, and annual growth rate in nominal gross domestic product) consistently affect the debt ratios in the 37
ACCEPTED MANUSCRIPT African sample, similar to previous findings in the literature. However, ownership structure has a mixed effect on debt ratios, consistent with past findings in the literature. Precisely, we find more evidence that ownership structure has a positive effect on book debt ratio, but it has mostly insignificant effect on market debt ratio. Our results are consistent with the signaling theory argument that managerial ownership is positively related to debt because managers signal growth opportunity by increasing debt. Conversely, our results are inconsistent with
T
Anderson and Reeb (2003) and King and Santor’s (2008) findings that insider (managerial)
IP
ownership has an insignificant effects on debt. Our results are also inconsistent with Harvey et
CR
al. (2004) findings that higher managerial ownership levels provide benefits to controlling shareholders that are shared with other stakeholders, thereby, reducing the need for debt as an
US
internal control mechanism.
Asian countries region results and the Latin-American and Eastern European countries
AN
(except most results from African countries region) region results reject the null hypothesis (H0 ) that institutional quality has no direct effects on listed firms’ debt ratios in developing countries. Thus, our alternative hypothesis that institutional quality has positive direct effects
M
on listed firms’ debt ratios is supported. The results suggest that as these developing countries
ED
institutional quality improves; lenders are more willing to grant credit (e.g. debt capital) to firms. Thus, holding the firms’ assets and investment plans constant, firms in developing
PT
countries with strong institutions are more likely to increase debt because better institutional quality encourages lender to lend money and it lowers bankruptcy costs, resulting in firms
CE
using more debt to capitalize on tax-shield benefits of debt interest. Conversely, most of the African countries region results fail to reject the null hypothesis (H0 ) that institutional quality
AC
has no direct effects on listed firms’ debt ratios in developing countries. Most African countries are characterized by low institutional quality and poor creditors’ protections (Fosu, 2013). Moreover, Table 1b compares the level of institutional quality across region and it reveals that the African region appears to have lowest level of institutional quality over time. Thus, this may partly explain why institutional quality has insignificant effects on debt ratios for the African countries region. The overall results (except most results from the African countries region) are consistent with Fan et al. (2012) findings that institutional factors such as country’s legal system and tax explain some variation in debt ratios. Fan et al. (2012) also find that more debt is used in 38
ACCEPTED MANUSCRIPT countries where there is greater tax gain from leverage which is in accordance with our argument that firms operating in countries with strong institutional quality use more debt because better institutional quality encourages lender to lend money and it lowers bankruptcy costs, resulting in firms using more debt to capitalize on tax-shield benefits of debt interest. Our results are consistent with Oztekin and Flannery (2012) findings that legal and financial factors significantly affect speed of adjustments to target debt. Our results are also in agreement with
T
Jong et al. (2008) findings that country specific factors have direct effects on debt. Comparing
IP
African region results (Tables 9 and 10) with Asian region results (Tables 5 and 6), our
CR
empirical findings reveal that Asian firms adjust faster to target debt (book debt) when they deviate from their target debt level.
US
In addition to institutional quality being a determinant of capital structure, firm-specific factors (e.g., fixed assets, profits, size, price-to-book ratio, and dividend pay-out ratio)
AN
consistently explain the capital structure decisions of firms in both the regional subsamples and the full sample, with correct signs. Macroeconomic factors (e.g., bank credit-to-private sector
M
ratio, stock market capitalization ratio) have significant effects on both the book and market debt ratios. Specifically, bank credit to the private sector is positively related to debt ratios while the
ED
stock market capitalization is negatively related to debt ratios in both the regional subsamples and full sample. These results are consistent with previous researchers’ (e.g., Matemilola and
PT
Ahmad, 2015; Flannery and Hankins, 2013; Chakraborty, 2010; Frank and Goyal, 2009)
AC
CE
findings.
Table 9 Direct effects on institutional quality on Book-debt models System-GM M Two-step Estimation Results using Book-debt and 613 Listed Firms from 10 African Countries, 2006-2014
M odel 7a With Single M odel 7b INST Index With ROL
M odel 7c M odel 7d With REGQ With GE
M odel 7e With PS
M odel 7f With VA
M odel 7g With CC
M odel 7h (Single Equation
39
ACCEPTED MANUSCRIPT
0.5523*** TDBVit-1 (Lag Book-debt) (55.00) INST (Single Institutional 0.0003 Quality Index) (1.40)
0.5429*** (51.52)
0.5608*** 0.5481*** 0.5524*** (53.41) (53.20) (46.29)
0.5550*** (44.13)
0.5526*** 0.5579*** (50.15) (41.72)
-
-
-
-
-
-
-
-
-
-
ROL (Rule of law) REGQ (Regulatory quality)
-
-0.0001 (-1.49)
-
-
-0.0002 (-1.51)
GE (Govt. Effectiveness)
-
-
-
0.0001 (1.54)
PS (Political Stability)
-
-
-
-
-0.0008** (-3.07)
VA (Voice & Account.) CC (Control of Corruption)
-
-
-
-
-
-
-
-
-
-
0.0867*** (7.39) -0.1371*** (-11.47) 0.0033* (1.81)
0.0853*** (6.71) -0.1530*** (-13.56) 0.0011** (2.06)
-0.0003** (-6.38) -0.5432*** Ndts (Non-debt tax-shield) (-5.02) 0.0077** Age (Firm-age) (2.51)
-0.0003*** (-6.19) -0.6358** (-5.64) 0.0009** (2.08)
-0.0044** DPO (Dividend pay -out) (-2.40) OWS (Ownership 0.0597* structure) (1.75) 0.0002** Int (Interest rate) (2.35) 0.0022*** Inf (Inflation rate) (7.21) Gdpg (Economic growth -0.0004** rate) (-2.98) 0.0011**** BC (Banking Credit) (4.62) M C (M arket -0.0001** Capitalization) (-2.41) AR(2) [P-value] 0.9026 Difference Hansen Tests [P-value] 0.3090 Instruments 153 Variance Inflation Factor 2.03 Heteroscedasticity test (p val.) 0.000 Cross-dependency test (pval.) 0.199 Cross-sectional observation(N) 613 Observ after estimation (N*T) 4624
-0.0025 (-1.32) 0.0484** (2.37) 0.0001 (1.19) 0.0027*** (7.35) -0.0006** (-2.35) 0.0005** (2.42) -0.0001** (-2.16) 0.8822
-0.0037** (-2.06) 0.0421 (1.15) -0.0001 (-1.19) 0.0023*** (7.58) -0.0002** (-2.17) 0.0005** (2.22) -0.0001 (-0.25) 0.8844
-0.0043** (-2.37) 0.0144 (0.39) -0.0003** (-2.69)) 0.0023** (7.60) -0.0001** (-2.30) 0.0004** (2.38) -0.0001* (-1.81) 0.9023
-0-0038** (-2.02) 0.0626** (2.10) -0.0001 (0.74) 0.0020*** (6.52) -0.0009* (-1.82) 0.0001** (2.71) -0.0001** (-2.49) 0.9054
-0.0038** (-2.04) 0.0217 (0.64) 0.0001 (0.75) 0.0022*** (6.94) -0.0002** (-2.38) 0.0001** (2.56) -0.0001** (-2.12) 0.9061
0.4069 153 1.79
0.3340 153 2.28
0.3538 153 1.90
0.2244 153 1.78
0.000
0.000
0.000
0.194
0.184
613 4624
AC
CE
T
IP
0.0963*** 0.0209** (8.63) (2.60) -0.1357*** -0.3382*** (-10.84) (-16.63) 0.0048** 0.0099*** (2.66) (8.46)
-0.0002*** -0.0003*** -0.0003** (-5.76) (-5.91) (-5.58) -0.6841** -0.6203** -0.5948*** (-6.78) (-5.81) (-5.36) 0.0123*** 0.0083*** 0.0012 (4.53) (2.87) (0.37)
-0.0002*** (-4.97) -0.6089*** (-5.46) 0.0129*** (4.21)
-0.0002** (-5.56) -0.6687** (-6.32) 0.0150*** (4.85) -0.0037* (-1.99)
-0.0053*** (-23.29) -0.0582 (0.66) 0.0028* (1.93)
0.1939 153 2.19
0.3086 153 1.91
0.3409 223 2.19
0.000
0.000
0.000
0.000
0.203
0.215
0.226
0.133 613
0.145
613
613
613
613
4624
4624
4624
4624
US
CR
0.0780** (6.83) -0.1442*** (-12.20) 0.0036** (2.05)
AN
PB (Price-to-book ratio)
-
0.1119*** 0.1038*** 0.0830** (10.55) (9.22) (7.76) -0.1448*** -0.1368*** -0.1639*** (-11.41) (-11.09) (-13.09) 0.0042** 0.0023** 0.0030** (2.54) (2.34) (2.10)
M
Size
0.0009*** (3.46) -
ED
PRF (Profits)
-
0.0003 (1.60)
PT
FA (Fixed Assets)
-
0.0003 (1.98) 0.0004 (1.65) 0.0005 (1.43) -0.0016*** (-7.43) 0.0003** (2.34) 0.0004 (1.10)
-0.0186*** (-5.39) 0.0972** 0.0121** (2.65) (2.16) -0.0001 0.0003*** (-0.83) (3.82) 0.0019*** 0.0015*** (6.19) (6.66) -0.0001* 0.008** (-1.84) (2.39) 0.0006*** 0.0006*** (2.92) (3.92) -0.0002** -0.0001* (-2.39) (-1.89) 0.8996 0.3229
613 4264
4624
40
ACCEPTED MANUSCRIPT Notes: a See Table 1 for the definition of variables and measurements. Asterisks indicate significance at 1% (***), 5% (**)
and 10% (*). b T-statistics (in parenthesis) of the Two-step System-GMM model are based on Windmeijer-corrected standard errors. c 2nd order serial correlation in first difference is distributed as N (0, 1) under the null of no serial correlation in the residuals. d Difference-in-Hansen over identification test and null that instruments are valid. e TDBVit-2 , FA it-2, PRFit-2 , Sizeit-2 , Ndts it-2 , PBit-2 , Ageit-2 , DPOit-2 , INSTit-2 , ROLit-2 , REGQit-2 , GEit-2 , PSit-2 , VA it-2 , and CCit-2 are used as Instruments. f In all estimations, the number of ‘N’ cross -sectional observation (613) is greater than the number of instruments (153 & 223). g Industry dummies are included in all the estimations. The lists of th e industries are in appendix II.
0.5330*** (47.89)
M odel 8h (Single Equation
0.4825*** (32.97)
-
-0.0002 (-0.66)
-
0.0003 (1.11) 0.0004 (1.04) 0.0006 (1.56) -0.0017*** (-5.28) 0.0003* (1.91) 0.0002 (1.10)
-
-
0.0003 (1.62)
GE (Govt. Effectiveness) -
-
-
PS (Political Stability)
-
-
-
-
VA (Voice & Account.) CC (Control of Corruption)
-
-
-
-
-0.0009** (-2.59) 0.0007** (2.03)
-
-
-
-
-
ED
PT
-
-
-
-
-
-
-0.0004 (-0.07)
-
-
-
0.0003 (0.74)
0.0467** (2.50) -0.3026*** (-14.04) 0.0093*** (6.69)
-0.0015*** PB (Price-to-book ratio) (-19.32) Ndts (Non-debt tax-0.2164* shield) (-1.70) 0.0126** Age (Firm-age) (2.41)
-0.0015*** -0.0015*** -0.0015*** -0.0016*** (-16.35) (-17.43) (-17.05) (-16.12) -0.1492 -0.0995 -0.2653** -0.1540* (-1.20) (-0.90) (-2.14) (-1.88) 0.0007 0.0023** 0.0097* 0.0053** (0.14) (2.49) (1.95) (2.04)
-0.0022*** (-18.92) -0.0427*** (-3.93) 0.0157*** (3.05)
-0.0061*** DPO (Dividend pay -out) (-2.91) OWS (Ownership 0.0869 structure) (1.52) 0.0010*** Int (Interest rate) (3.60) 0.0042*** Inf (Inflation rate) (6.79) Gdpg (Economic growth -0.0059***
-0.0101*** -0.0122*** -0.0089** -0.0058* -0.0030 -0.0093*** -0.0043 (-3.05) (-3.98) (-2.58) (-1.81) (-0.93) (-2.93) (-0.93) -0.0244 -0.0323 0.1009* 0.0104 0.1498** 0.0475 0.0290** (-0.44) (-0.66) (1.75) (0.21) (2.57) (0.77) (2.14) 0.0009*** 0.0016*** 0.0010*** 0.0013*** 0.0011*** 0.0009*** 0.0003** (3.37) (5.13) (3.79) (4.29) (3.70) (3.42) (2.57) 0.0037*** 0.0049*** 0.0048*** 0.0043*** 0.0038*** 0.0045*** 0.0019*** (5.53) (8.28) (8.07) (6.78) (5.99) (7.53) (4.97) -0.0066*** -0.0060*** -0.0059*** -0.0069*** -0.0071*** -0.0059*** 0.0024***
PRF (Profits)
AC
Size
CE
0.1246*** 0.1316*** 0.1623*** 0.1202*** 0.1295*** 0.1516*** (6.14) (7.01) (9.50) (6.00) (6.76) (8.26) -0.1095*** -0.1269*** -0.1145*** -0.1371*** -0.1148*** -0.1209*** (-5.65) (-6.66) (-6.21) (-6.72) (-5.50) ( -6.17) 0.0006** 0.0015** 0.0015* 0.0002** 0.0041** 0.0010** (2.27) (2.16) (1.95) (2.08) (2.55) (2.38)
FA (Fixed Assets)
0.1460*** (8.28) -0.1172*** (-4.98) 0.0013** (2.48)
-
AN
-
M
INST (Single Institutional 0.0004 Quality Index) (0.87) ROL (Rule of law) REGQ (Regulatory quality)
M odel 8g With CC
0.5343*** 0.5264*** 0.5263*** 0.5316*** 0.5337*** 0.5298** (49.52) (45.31) (47.73) (47.23) (42.56) (49.45)
US
TDM Vit-1 (Lag M arketdebt)
M odel 8e M odel 8f With PS With VA
CR
M odel 8a With Single M odel 8b M odel 8c M odel 8d INST Index With ROL With REGQ With GE
IP
T
Table 10 Direct effects of institutional quality on M arket-debt models System-GM M Two-step Estimation Results using M arket-debt and 613 Listed Firms from 10 African Countries (Robustness test), 2006-2014
-0.0015*** -0.0015*** (-17.60) (-19.85) -0.2450** -0.2166* (-2.01) (-1.70) 0.0125** 0.0111** (2.63) (2.34)
41
ACCEPTED MANUSCRIPT (-6.78) (-6.94) 0.0024*** 0.0006* (4.78) (1.99) -0.0001*** -0.0001* (-2.19) (-1.90) 0.2782 0.2901
(-7.03) 0.0019** (2.01) -0.0003** (-2.11) 0.3650
(-7.80) 0.0006** (2.08) -0.0002** (-2.39) 0.3776
(-6.77) 0.0002** (2.43) -0.0001** (-2.55) 0.2781
(4.76) 0.0011*** (5.27) -0.0003*** (-7.01) 0.4089
0.2398 153 1.80 0.000 0.199
0.1235 153 1.63 0.000 0.192
0.2602 153 1.69 0.000 0.195
0.2254 153 1.88 0.000 0.201
0.2412 153 1.77 0.000 0.166
0.1844 153 2.16 0.000 0.123
0.1757 153 1.81 0.000 0.106
0.1362 223 2.78 0.000 0.141
613
613
613
613
613
613
613
613
4624
4624
4624
4624
4624
4624
4624
4624
T
(-6.83) 0.0003** (2.05) -0.0001** (-2.69) 0.2650
CR
BC (Banking Credit) M C (M arket Capitalization) AR(2) [P-value] Difference Hansen Tests [P-value] Instruments Variance Inflation Factor Heteroscedasticity test Cross-dependency test Cross-sectional observation(N) Observ after estimation (N*T)
(-6.89) 0.0005** (2.17) -0.0001** (-2.38) 0.3265
IP
rate)
Notes: a See Table 1 for the definition of variables and measurements. Asterisks indicate significance at 1% (***), 5% (**)
AN
US
and 10% (*). b T-statistics (in parenthesis) of the Two-steps System-GMM model are based on Windmeijercorrected standard errors. c 2nd order serial correlation in first difference is distributed as N (0, 1) under the null of no serial correlation in the residuals. d Difference Sargan over identification test and null that instruments are valid, but it can only run if the error is System-GMM-type error. e TDMVit-2 , FA it-2, PRFit-2 , Sizeit-2 , Ndts it-2 , PBit-2 , Ageit-2 , DPOit-2 , INSTit-2 , ROLit-2 , REGQit-2 , GEit-2 , PSit-2 , VA it-2 , and CCit-2 are used as Instruments. f In all estimations, the number of ‘N’ cross-sectional observation (613) is greater than the number of instruments (153 & 223). g Industry dummies are included in all the estimations. The lists of the industries are in appendix II.
M
4.3. Indirect effects of institutional quality on debt models (Robustness Check 1) The focus of this study is not on the indirect effects of institutional quality on debt. Instead,
ED
we explore these indirect effects by interacting institutional quality with firm-level determinants (e.g. fixed assets, size, profits) to determine their effects on firm debt. The results of these
PT
indirect effects are not reported to save space. For example, if institutional quality proxies are important, the interaction terms (i.e. fixed assets * institutional quality proxy; size* institutional
CE
quality; profit * institutional quality) should be significant. Moreover, if the interaction term coefficients are greater than zero (interaction term coefficients are less than zero) and if the firm-
AC
level factors positively (negatively) affect debt, the institutional quality proxies strengthen the effects of the firm-level factors on debt, suggesting that the firm and institutional quality factors complement each other. Conversely, if the interaction term coefficients are less than zero (interaction term coefficients are greater than zero) and if the firm-level factors positively (negatively) affect debt, the institutional quality proxies moderate the effects of the firm-level factors on debt, suggesting that the firm and institutional quality factors are substitutes. The empirical results for the full sample show that the interaction terms of the single aggregated institutional quality index, fixed assets and size are positive and statistically significant. Conversely, the interaction of the single aggregated institutional quality index and 42
ACCEPTED MANUSCRIPT profits are negative and statistically significant. The interaction effects results are similar using disaggregated measures of institutional quality. These results indicate that the institutional quality index and disaggregated measures of institutional quality strengthen the effects of the firm-level factors (e.g. fixed assets, size, and profits) on the book debt and market debt ratio, suggesting that the firm and the institutional quality factors are complements. Like the results of the direct effects, institutional quality retains it positive effects on book
T
debt ratio. Likewise, institutional quality has positive effects on market debt ratio. Thus, the
IP
direct effects and indirect effects models reveal that institutional quality has positive effects on
CR
both the book debt and market debt ratios. The results are consistent with findings of Jong et al. (2008), who stated that macroeconomic and institutional factors affect the roles of the firm-
US
specific determinants of a capital structure. The empirical results also show that firms make faster adjustments to their target debt, especially the book debt ratio, when there is a deviation
AN
from the target debt level; this is consistent with the dynamic version of trade-off theory. The empirical results for the Asian sample shows that the interaction term coefficients of the
M
single aggregated institutional quality index and fixed assets as well as size are positive and statistically significant. Conversely, the interaction term coefficients of the single aggregated
ED
institutional quality index and profits are negative and statistically significant. The interaction effects results are similar using disaggregated measures of institutional quality. These results
PT
indicate that institutional quality index and disaggregated measures of institutional quality strengthen the effects of the firm-level factors (fixed assets, size, profits) on book debt ratio and
CE
market debt ratio, suggesting that the firm and institutional quality factors are complements. Like the results of the direct effects, institutional quality retains it positive effects on book debt ratio.
AC
Likewise, institutional quality has positive effects on market debt ratio. Hence, the direct effects and indirect effects models reveal that institutional quality is positively related to both the book debt and market debt ratios. Similarly, the empirical results for the Latin American and Eastern European countries’ sample shows that the interaction term coefficients of the single aggregated institutional quality index, and fixed assets and size are positive and statistically significant. Conversely, the interaction of single aggregated institutional quality index and profits is negative and statistically significant.
The
interaction effects results are similar using disaggregated
measures of
institutional quality. These results indicate that institutional quality index and disaggregated 43
ACCEPTED MANUSCRIPT measures of institutional quality strengthen the effects of firm-level factors (fixed assets, size profits) on book debt ratio and market debt ratio, suggesting that the firm and institutional quality factors are complements. Like the results of the direct effects, institutional quality retains it positive effects on book debt ratio. Likewise, institutional quality has positive effects on market debt ratio. Thus, the direct effects and indirect effects models reveal that institutional quality has positive effects on both the book debt and market debt ratios.
T
The empirical results for the African sample show that only the interaction term coefficients
IP
of the single aggregated institutional quality index and fixed assets are positive and statistically
CR
significant. However, the interaction term coefficients of single aggregated institutional quality index, and size and profits are insignificant. Like the results of the direct effects models,
US
institutional quality effects on both the book debt and market debt ratios are inconsistent. Thus, the direct effects and indirect effects models reveal that institutional quality has partial effects on
AN
both the book debt and market debt ratios in the African region.
M
4.4 Direct effects of institutional quality on debt models in Post-crisis period (Robustness Checks 2)
ED
The primary objective of this paper is not to account for the possible effects of the financial crisis periods. However, we conduct an additional analysis and exclude the 2007 to 2009 crisis
PT
periods from the sample and re-estimate all the regressions, focusing on the post-crisis period (2010 to 2014). The results are broadly similar to the main results that include the crisis periods
CE
(2006-2014), except that the variable coefficients and the significance of some variables changes slightly. The empirical results for the full sample show that the single aggregated institutional
AC
quality index is statistically significant and positively related to the total debt ratio (Table 11, Model 9a). Additionally, six disaggregated measures of institutional quality are statistically significant and positively related to the total debt ratio (Table 11, Models 9b to 9g). For all regional sub-sample results (except the African region sample, Table 14, Model 12a to 12g), the single aggregated index and the disaggregated measures of institutional quality are significantly and positively related to debt for the Asian region (Tables 12, Model 10a to 10g) and the Latin American and Eastern European regions (Table 13, Models 11a to 11g). Furthermore, all the six disaggregated measures of institutional quality are included together in a single model and the results are broadly similar. But the magnitude of the coefficient of most of the institutional 44
ACCEPTED MANUSCRIPT quality variables change. Moreover, the magnitude of the coefficient of the lagged debt (debt it-1 ), firm-specific and other macroeconomic variables change (see Table 11, Model 9h for the full sample results and Table 12, Model 10h, Table 13, Model 11h, Table 14, Model 12h for the regional sub-sample results). Consequently, we explore the indirect effects by interacting institutional quality on some firm-level determinants (e.g. fixed assets, size, profits) to determine their effects on firm debt but
T
the results are not reported to save space. The empirical results for the full sample show that the
IP
interaction terms of the single aggregated institutional quality index, fixed assets and size are
CR
positive and statistically significant. Conversely, the interaction of the single aggregated institutional quality index and profits is negative and statistically significant. These results
US
indicate that the institutional quality index strengthens the effects of the firm-level factors (fixed assets, size, and profits) on debt, suggesting that the firm and the institutional quality factors are
AN
complements. Moreover, the interaction term coefficients of the six disaggregated measures of institutional quality, and fixed assets and size is statistically significant and positive. These
M
results also indicate that the disaggregated measures of institutional quality strengthen the effects of the firm-level factors on debt ratio. At the regional level, we also confirm the indirect effects
ED
of aggregated and disaggregated measures on institutional quality on debt for the Asian region and the Latin American and Eastern European region. But, for the African region, we find partial
PT
evidence that institutional quality has indirect effects on debt. Like the results of the direct effects, institutional quality retains it positive effects on debt ratio. Likewise, institutional quality
CE
has positive effects on total debt ratio. Hence, the direct effects and indirect effects models reveal that institutional quality is positively related to total debt ratios.The empirical results also show
AC
that firms adjustments to their target debt when there is a deviation from the target debt level, consistent with the dynamic version of trade-off theory. Previous researchers (e.g., Matemilola and Ahmad (2015), Flannery and Hankins (2013), Oztekin and Flannery (2012)) find evidence that firms adjust to their target debt level.
Table 11 Direct effects of institutional quality on Book-debt models (Post-crisis period) System-GM M Two-step Estimation Results using Book-debt and 3,891 Listed Firms from 23 Developing Countries, 2010-2014 M odel 9a With Single M odel 9b INST Index With ROL
M odel 9c M odel 9d With REGQ With GE
M odel 9e M odel 9f With PS With VA
M odel 9g With CC
M odel 9h (Single Equation)
45
ACCEPTED MANUSCRIPT
TDBVit-1 (Lag Book-debt)
0.7492*** 0.7407*** (26.89) (27.12)
INST (Single Institutional 0.0007** Quality Index) (2.05)
0.7450*** (26.98)
0.7476*** 0.7444*** (27.14) (27.33)
0.7361*** (25.76)
-
-
-
-
-
0.7479*** (32.88)
0.0001** ROL (Rule of law) (2.07) 0.0004* 0.0002* REGQ (Regulatory quality) (1.98) (1.75) 0.0002** 0.0007** GE (Govt. Effectiveness) (2.25) (2.45) 0.0006** 0.0006*** PS (Political Stability) (2.71) (3.75) 0.0004** 0.0004** VA (Voice & Account.) (2.29) (2.45) 0.0004** 0.0001* CC (Control of Corruption) (2.19) (1.98) 0.0155* 0.0125* 0.0038** 0.0010** 0.0091** 0.0063** 0.0433** 0.0145*** FA (Fixed Assets) (1.98) (1.78) (2.16) (2.04) (2.32) (2.23) (2.59) (3.63) -0.0422*** -0.0423*** -0.0412*** -0.0417*** -0.0421*** -0.0420*** -0.0418*** -0.4207*** PRF (Profits) (-13.46) (-13.54) (-13.38) (-14.29) (-13.33) (-12.98) (-14.64) (-15.18) 0.0004*** 0.0043*** 0.0073*** 0.0062*** 0.0059*** 0.0080*** 0.0072*** 0.0045*** Size (3.35) (3.00) (3.15) (3.88) (3.14) (2.74) (3.21) (5.00) -0.0001* -0.0001 -0.0001** -0.0001** -0.0001** -0.0003 -0.0001** 0.0001 PB (Price-to-book ratio) (-1.80) (-0.73) (-2.19) (-2.11) (-2.05) (-1.52) (-2.34) (1.13) 0.0240* 0.0630** 0.0772* 0.0921*** 0.0942** 0.0630** 0.0531** 0.0216*** Ndts (Non-debt tax-shield) (1.95) (2.13) (1.84) (2.72) (2.19) (2.41) (2.40) (2.92) 0.0169** 0.0144** 0.0018 0.0061 0.0076* -0.0006** -0.0007 0.0009 Age (Firm-age) (2.59) (2.22) (0.20) (1.01) (1.84) (-2.06) (-1.06) (0.26) 0.0049* 0.0055* 0.0068* 0.0058* 0.0064* 0.0044** 0.0043** 0.0078** DPO (Dividend pay -out) (1.89) (1.98) (1.90) (1.76) (1.72) (2.24) (2.28) (2.48) -0.0109 -0.0023 -0.0131* -0.0026** -0.0019** -0.0023 -0.0306** -0.0008 OWS (Ownership structure) (-0.59) (-0.31) (-1.82) (-2.12) (-2.35) (-047) (-2.37) (-0.23) 0.0003* 0.0001* 0.0001** 0.0001** 0.0001** -0.0004** 0.0004** 0.0004* Int (Interest rate) (1.86) (1.79) (2.01) (2.15) (2.12) (-2.13) (2.24) (1.88) 0.0002** 0.0002*** 0.0020** 0.0020*** 0.0017** 0.0021** 0.0022*** 0.0018*** Inf (Inflation rate) (3.42) (3.48) (3.05) (3.06) (2.58) (3.13) (3.36) (3.22) Gdpg (Economic growth -0.0016** -0.0016** 0.0016** -0.0018*** -0.0015** -0.0013** -0.0012* 0.0006** rate) (-2.66) (-2.52) (2.42) (-2.76) (-2.64) (-2.13) (-1.75) (2.19) 0.0002* 0.0003** 0.0004* 0.0003** 0.0004*** 0.0001** 0.0001** 0.0001** BC (Banking Credit) (1.79) (2.09) (1.82) (2.62) (3.04) (2.08) (2.13) (2.27) -0.0003*** -0.0003* -0.0003*** -0.0004*** -0.0003*** -0.0003*** -0.0003*** -0.0002*** M C (M arket Capitalization) (-4.05) (-1.85) (-4.07) (-4.42) (-3.68) (-4.42) (-3.41) (-3.06) 0.8895 0.8789 AR(2) [P-value] 0.8974 0.9053 0.8793 0.8854 0.8931 0.9249 Difference Hansen Tests [P-value] 0.2175 0.2937 0.1808 0.3015 0.2007 0.3194 0.2084 0.215 84 123 Instruments 84 84 84 84 84 84 2.18 2.46 Variance Inflation Factor 1.98 1.33 1.96 2.03 1.89 1.90 Heteroscedasticity test (p-val.) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Cross-dependency test (p-val.) 0.210 0.235 0.205 0.301 0.299 0.211 0.241 0.217 Cross-sectional observation(N) 3891 3891 3891 3891 3891 3891 3891 3891 Observ after estimation (N*T) 15308 15308 15308 15308 15308 15308 15308 15308
AC
CE
PT
ED
M
AN
US
CR
IP
T
0.0001** (2.16)
0.7452*** (26.80)
Notes: a See Table 1 for the definition of variables and measurements. Asterisks indicate significance at 1% (***), 5% (**)
and 10% (*). b T-statistics (in parenthesis) of the Two-step System-GMM model are based on Windmeijer-corrected
46
ACCEPTED MANUSCRIPT standard errors. c 2nd order serial correlation in first difference is distributed as N (0, 1) under the null of no serial correlation in the residuals. d Difference-in-Hansen over identification test and null that instruments are valid. e TDBVit-2 , FA it-2, PRFit-2 , Sizeit-2 , Ndts it-2 , PBit-2 , Ageit-2 , DPOit-2 , INSTit-2 , ROLit-2 , REGQit-2 , GEit-2 , PSit-2 , VA it-2 , and CCit-2 are used as Instruments. f In all estimations, the number of ‘N’ cross -sectional observation (3,891) is greater than the number of instruments (84 & 123). g Industry dummies are included in all the estimations. The lists of the industries are in appendix II.
M odel 10g With CC
10h (Single Equation)
0.1283*** (42.36)
0.1345*** (50.92)
CR
IP
M odel 10a With Single M odel 10b M odel 10c M odel 10d M odel 10e M odel 10f INST Index With ROL With REGQ With GE With PS With VA
T
Table 12 Direct effects of institutional quality on Book-debt (Post-crisis period) System-GM M Two-step Estimation Results using Book-debt and 2,187 Listed Firms from 7 Asian Countries, 2010-2014
0.1278*** 0.1298*** 0.1289*** (41.00) (40.11) (42.46)
INST (Single Institutional 0.0001** Quality Index) (2.14)
-
-
PS (Political Stability)
-
VA (Voice & Account.) CC (Control of Corruption)
-
-
-
0.0013*** (2.88) 0.0005** 0.0010** (2.25) (2.36) 0.0006** 0.0008** (2.62) (2.65) 0.0001** 0.0003* (2.16) (1.96) 0.0002** 0.0016*** (3.27) (3.29) 0.0009** 0.0009** (2.38) (2.05) 0.1007*** 0.1196*** 0.1066*** 0.0433*** 0.1876** 0.0523*** 0.0449*** (7.67) (9.12) (6.02) (2.71) (12.61) (3.99) (3.02) -0.0027** -0.0341** -0.0294*** -0.0177** -0.0045** -0.0047*** -0.0552* (-2.07) (-2.10) (-2.79) (-2.41) (-2.09) (-3.11) (-1.81) 0.0136*** 0.0055*** 0.0163*** 0.0067** 0.0132*** 0.0141*** 0.0082*** (4.11) (4.28) (4.18) (2.50) (3.84) (4.99) (4.43) -0.0185*** -0.0200*** -0.0196*** -0.0161*** -0.0190*** -0.0187*** -0.0195*** (-9.30) (-9.57) (-9.12) (-8.90) (-9.68) (-10.03 (-12.90) 0.0639** 0.0141** 0.1211* 0.1794** 0.1611** 0.0184** 0.2352** (2.16) (2.04) (1.99) (2.44) (2.41) (2.52) (250) 0.0563** 0.0321* 0.0590*** 0.0773*** 0.0765*** 0.0479*** 0.0106 (2.40) (1.80) (3.85) (3.26) (4.64) (3.10) (1.51) -0.0218*** -0.0127* -0.0134* -0.0192** -0.0199*** -0.0221*** -0.0276*** (-3.17) (-1.89) (-1.99) (-2.65) (-2.83) (-3.28) (-5.22) -0.5584*** -0.7018*** -0.7116*** -0.6800*** -0.6964*** -0.618*** -0.0561* (-6.02) (-7.01) (-7.00) (-6.22) (-6.55) (-6.34) (-1.95) 0.0010** 0.0012** 0.0010* 0.0007** -0.0013** 0.0011** 0.0001* (2.06) (2.09) (1.93) (2.38) (-2.28) (2.04) (1.90) 0.0003** 0.0001* 0.0001 0.0005** -0.0010 0.0001* 0.0023*** (2.30) (1.98) (0.12) (2.59) (-1.13) (1.83) (3.03) 0.0010* 0.0024** 0.0017** 0.0013** 0.0001 0.0011* 0.0032*** (1.93) (2.02) (2.41) (2.39) (1.02) (1.81) (3.59) 0.0003** 0.0005** 0.0003** 0.0001*** 0.0004** 0.0003*** 0.0007 (2.16) (2.12) (2.06) (3.35) (2.50) (3.17) (3.82) -0.0003*** -0.0004*** -0.0004*** -0.0003*** -0.0003*** -0.0002** 0.0005*** (-2.86) (-3.53) (-3.25) (-3.10) (-2.73) (-2.02) (4.21) 0.2658 0.2346 0.2508 0.2711 0.2728 0.2610 0.2755
AN
GE (Govt. Effectiveness)
-
M
-
AC
CE
0.1246** FA (Fixed Assets) (8.82) -0.0183** PRF (Profits) (-2.45) 0.0161*** Size (5.89) -0.0182*** PB (Price-to-book ratio) (-8.92) 0.0398** Ndts (Non-debt tax-shield) (2.10) 0.0367** Age (Firm-age) (2.54) -0.0159** DPO (Dividend pay -out) (-2.37) OWS (Ownership -0.6843*** structure) (-7.21) 0.0011** Int (Interest rate) (2.19) 0.0002** Inf (Inflation rate) (2.21) Gdpg (Economic growth 0.0012 rate) (1.18) 0.0004** BC (Banking Credit) (2.44) M C (M arket -0.0003** Capitalization) (-2.62) AR(2) [P-value] 0.2639
ED
-
-
PT
ROL (Rule of law) REGQ (Regulatory quality)
0.0002** (2.39)
US
0.1267*** 0.1282*** 0.1266 TDBVit-1 (Lag Book-debt) (42.08) (40.34) (42.05)
47
ACCEPTED MANUSCRIPT 0.1202 84 3.91
0.1350 84 3.98
0.1832 84 3.49
0.1842 84 3.89
0.1469 84 3.18
0.1920 84 3.75
0.1388 84 3.56
0.2741 123 3.69
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.310
0.256
0.266
0.225
0.237
0.243
0.208
0.231
2187
2187
2187
2187
2187
2187
2187
2187
8649
8649
8649
8649
8649
8649
8649
8649
T
Difference Hansen Tests [P-value] Instruments Variance Inflation Factor Heteroscedasticity test (p-val.) Cross-dependency test (p-val.) Cross-sectional observation(N) Observ after estimation (N*T)
Notes: a See Table 1 for the definition of variables and measurements. Asterisks indicate significance at 1% (***), 5% (**)
US
CR
IP
and 10% (*). b T-statistics (in parenthesis) of the Two-step System-GMM model are based on Windmeijer-corrected standard errors. c 2nd order serial correlation in first difference is distributed as N (0, 1) under the null of no serial correlation in the residuals. d Difference-in-Hansen over identification test and null that instruments are valid. e TDBVit-2 , FA it-2, PRFit-2 , Sizeit-2 , Ndts it-2 , PBit-2 , Ageit-2 , DPOit-2 , INSTit-2 , ROLit-2 , REGQit-2 , GEit-2 , PSit-2 , VA it-2 , and CCit-2 are used as Instruments. f In all estimations, the number of ‘N’ cross -sectional observation (2,187) is greater than the number of instruments (84 & 123). g Industry dummies are included in all the estimations. The lists of the industries are in appendix II.
AN
Table 13 Direct effects of institutional quality on Book-debt models System-GM M Two-step Estimation Results using Book-debt and 1,091 Listed Firms from 6 Latin American combined with Eastern European Countries, 2010-2014
INST (Single Institutional 0.0003*** Quality Index) (4.11)
0.5222*** (26.92)
0.5136*** (27.97)
PT
0.5179*** TDBVit-1 (Lag Book-debt) (25.50)
ED
M
M odel 11a With Single M odel 11b M odel 11c M odel 11d INST Index With ROL With REGQ With GE
-
M odel 11g M odel 11h (Single With CC Equation)
0.5141*** (20.38)
0.5131*** 0.5135*** (26.29) (27.56)
0.5198*** (24.89)
-
-
-
-
0.5317*** (30.71)
0.0011** ROL (Rule of law) (2.15) REGQ (Regulatory 0.0032*** 0.0001** quality) (4.33) (2.04) 0.0037*** 0.0055*** GE (Govt. Effectiveness) (3.47) (4.09) 0.0016*** 0.0009** PS (Political Stability) (5.20) (2.03) 0.0028*** 0.0017** VA (Voice & Account.) (4.52) (2.45) CC (Control of 0.0004** 0.0016** Corruption) (2.33) (2.87) 0.01367*** 0.0186** 0.0307** 0.0168** 0.0911*** 0.0442** 0.0505** 0.0860** FA (Fixed Assets) (2.82) (2.52) (2.43) (2.51) (2.75) (2.47) (2.01) (2.34) -0.4737*** -0.4916*** -0.4830*** -0.4896*** -0.4825*** -0.4915*** -0.4820*** -0.4867*** PRF (Profits) (-22.62) (-23.97) (-24.99) (-25.23) (-23.80) (-24.72) (-22.49) (-25.87) 0.0209*** 0.0030** 0.0174*** 0.0181** 0.0125*** 0.0184*** 0.0062** 0.02247*** Size (3.94) (2.60) (3.31) (2.38) (3.17) (3.82) (2.43) (6.46) -0.0001*** -0.0001*** -0.0001*** -0.0001*** -0.0001*** -0.0001*** -0.0001*** 0.0001*** PB (Price-to-book ratio) (-5.90) (-6.27) (-6.16) (-5.04) (-3.44) (-5.47) (-4.50) (6.69) 0.0645*** 0.0756*** 0.0598*** 0.0602*** 0.0523*** 0.0719*** 0.0444*** 0.0377*** Ndts (Non-debt tax-shield) (6.42) (6.66) (6.52) (6.41) (4.90) (7.21) (5.12) (7.20) -0.0201** -0.0403*** Age (Firm-age) -0.0433*** 0.0009 -0.0187* -0.0135* -0.0175* -0.0146*
AC
CE
0.0018*** (2.94)
M odel 11e M odel 11f With PS With VA
48
(0.04) 0.0327*** (2.71) 0.1152* (1.72) 0.0011* (1.71) 0.0055*** (3.54) 0.0035*** (3.48) 0.0010** (2.06)
(-2.07) 0.0338** (2.54) -0.0386 (-0.84) 0.0001* (1.80) 0.0051*** (2.84) 0.0021** (2.29) 0.0003** (2.59)
(-6.93) 0.0514*** (6.30) 0.2552* (1.90) -0.0015** (-2.40) 0.0021 (1.32) 0.0038*** (3.29) 0.0017*** (4.80)
M C (M arket -0.0002** Capitalization) (-2.35) AR(2) [P-value] 0.3016 Difference Hansen Tests [P-value] 0.6177 Instruments 84 Variance Inflation Factor 2.83 Heteroscedasticity test (p-val.) 0.000 Cross-dependency test (p-val.) 0.233 Cross-sectional observation(N) 1091 Observ after estimation (N*T) 4303
-0.0005*** -0.0004** (- -0.0003*** -0.0003** -0.0004** (-2.80) 2.38) (-2.74) (-2.58) (-2.01) 0.2809 0.2798 0.2720 0.2987 0.2821
-0.0004** (-2.03) 0.2896
-0.0003* (-1.95) 0.2912
0.3010 84 2.49
0.3268 84 2.50
0.6034 123 2.74
0.000
0.000
0.000
0.000
0.325
0.293
0.129
0.117
1091
1091
1091
1091
1091
4303
4303
4303
4303
4303
0.3159 84 2.47
0.6364 84 3.74
0.000
0.000
0.000
0.292
0.327
0.318
1091
1091
4303
4303
0.3676 84 2.21
(-1.89) 0.0205* (1.87) -0.0318** (-2.21) 0.0003* (1.93) -0.0013** (-2.17) 0.0024** (2.26) 0.0001 (0.12)
M
AN
0.2044 84 3.56
(-1.88) 0.0104* (1.97) -0.2238* (-1.91) 0.0003** (2.58) 0.0042** (2.48) 0.0021** (2.13) 0.0005* (1.95)
CR
(-1.74) 0.0252** (2.21) 0.0603** (2.40) -0.0016*** (-2.94) 0.0039** (2.45) 0.0035*** (3.36) 0.0009* (1.76)
T
(-3.24) 0.0326** DPO (Dividend pay -out) (2.67) OWS (Ownership -0.0413 structure) (-0.21) -0.0009 Int (Interest rate) (-1.58) 0.0003* Inf (Inflation rate) (1.76) Gdpgr (Economic growth 0.0025** rate) (2.10) 0.0007** BC (Banking Credit) (2.14)
US
(-1.95) 0.0325*** (3.09) 0.1607** (2.12) 0.0009* (1.86) -0.0029* (-1.96) 0.0014 (1.45) 0.0007*** (2.94)
IP
ACCEPTED MANUSCRIPT
Notes: a See Table 1 for the definition of variables and measurements. Asterisks indicate significance at 1% (***) and 5%
PT
ED
(**). b T-statistics (in parenthesis) of the Two-step System-GMM model are based on Windmeijer-corrected standard errors. c 2nd order serial correlation in first difference is distributed as N (0, 1) under the null of no serial correlation in the residuals. d Difference-in-Hansen over identification test and null that instruments are valid. e TDBVit-2 , FA it-2, PRFit-2 , Sizeit-2 , Ndts it-2 , PBit-2 , Ageit-2 , DPOit-2 , INSTit-2 , ROLit-2 , REGQit-2 , GEit-2 , PSit-2 , VA it-2 , and CCit-2 are used as Instruments ( 84 & 123). f In all estimations, the number of ‘N’ cross -sectional observation (1,091) is greater than the number of instruments.
CE
Table 14 Direct effects on institutional quality on Book-debt models System-GM M Two-step Estimation Results using Book-debt and 613 Listed Firms from 10 African Countries, 2010-2014
M odel 12c M odel 12d With REGQ With GE
M odel 12e With PS
M odel 12f With VA
M odel 12g With CC
M odel 12h (Single Equation)
0.4862*** (19.08)
0.48001*** (18.23)
0.4939*** (18.90)
0.4876*** (19.12)
0.4782*** (19.46)
0.4844*** (18.30)
0.4940*** (19.00)
0.4648*** (23.07)
-
-
-
-
-
-
0.0006 (1.16)
-
-
-
-
-
-
-0.0011 (-1.05)
-
-
-
GE (Govt. Effectiveness) -
-
-
0.0006 (0.69)
-
-
PS (Political Stability)
-
-
-
-0.0003 (-1.09)
-
-
0.0004 (1.19) 0.0004 (0.48) 0.0011 (1.24) -0.0009* (-1.95)
TDBVit-1 (Lag Bookdebt)
AC
M odel 12a With Single M odel 12b INST Index With ROL
INST (Single Institutional 0.0009 Quality Index) (1.48) ROL (Rule of law) REGQ (Regulatory quality)
-
49
ACCEPTED MANUSCRIPT
0.1049*** (2.99) -0.6708*** (-16.94) 0.0107** (2.08) -0.0069*** (-22.42) -0.6618*** (-3.09) -0.0029 (-0.33) -0.0030* (-1.85) 0.2628** (2.61) 0.0012*** (3.08) 0.0047*** (3.48) -0.0090 (-3.65) 0.0001* (1.91) -0.0001** (-2.05) 0.5917
0.0015 (1.50) 0.0917** 0.0894** 0.0940*** 0.0662* 0.0748** (2.67) (2.48) (2.72) (1.71) (2.08) -0.6976*** -0.6828*** -0.6947*** -0.6722*** -0.6946*** (-17.01) (-16.88) (-17.17) (-15.85) (-17.06) 0.0083** 0.0107* 0.0103** 0.0142** 0.0090** (2.61) (1.82) (2.06) (2.68) (2.55) -0.0068*** -0.0068*** -0.0070*** -0.0069*** -0.0071*** (-20.95) (-21.46) (-22.84) (-21.67) (-22.94) -0.7653*** -0.7323*** -0.6823*** -0.6465*** -0.6157** (-3.29) (-3.22) (-2.93) (-2.91) (-2.67) 0.0135* 0.0033 0.0007 0.0064 0.0111* (1.73) (0.38) (0.28) (0.76) (1.82) -0.0071 -0.0061* -0.0084** -0.0064** -0.0083* (-1.46) (-1.76) (-2.38) (-2.35) (-1.91) 0.2077** 0.1504** 0.2681** 0.2453** 0,2119** (2.08) (2.39) (2.62) (2.48) (2.06) 0.0012*** -0.0011*** 0.0008* 0.0011*** -0.0011*** (3.30) (-2.94) (1.98) (3.13) (-3.01) 0.0048*** 0.0043*** 0.0041** 0.0049*** 0.0038*** (3.64) (3.34) (3.13) (3.70) (3.08) -0.0082** -0.0087*** -0.0043** -0.0086** -0.0064*** (-3.45) (-3.77) (-2.59) (-3.67) (-2.79) 0.0001* 0.0001** 0.0020** 0.0009*** 0.0009** (1.83) (2.05) (2.10) (2.80) (2.66) -0.0001** -0.0002** -0.0002* -0.0001* -0.0001** (-2.09) (-2.11) (-1.94) (-1.99) (-2.31) 0.5903 0.5802 0.5849 0.6394 0.5902
0.0006*** (3.08) 0.0002 (0.40) 0.0629** (2.39) -0.5865*** (-17.86) 0.0044* (1.94) -0.0067*** (-25.23) -0.6592*** (-3.56) 0.0046 (0.77) -0.0054** (-2.09) 0.2232*** (3.10) 0.0007** (2.37) 0.0016* (1.88) 0.0026** (2.21) 0.0003* (1.91) -0.0001 (1.10) 0.6515
0.4668 84 1.68
0.2628 84 2.82
0.3385 84 2.01
0.2996 84 2.69
0.2535 84 2.55
0.3496 84 2.33
0.4091 123 2.58
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.0017** (2.18)
-
AN
US
CR
IP
T
-
0.130
0.157
0.207
0.183
0.219
0.242
0.198
613
613
613
613
613
613
613
2356
2356
2356
2356
2356
2356
CE
0.0734** FA (Fixed Assets) (2.02) -0.6644*** PRF (Profits) (-16.56) 0.0082** Size (2.46) -0.0071*** PB (Price-to-book ratio) (-24.06) Ndts (Non-debt tax-0.5930** shield) (-2.69) 0.002 Age (Firm-age) (0.31) -0.0099* DPO (Dividend pay -out) (-1.99) OWS (Ownership 0.1903* structure) (1.86) 0.0010*** Int (Interest rate) (2.88) 0.0044** Inf (Inflation rate) (3.53) Gdpg (Economic growth -0.0069*** rate) (-3.23) 0.0021** BC (Banking Credit) (2.45) M C (M arket -0.0001** Capitalization) (-2.01) AR(2) [P-value] 0.6115 Difference Hansen Tests [P-value] 0.3119 Instruments 84 Variance Inflation Factor 2.71 Heteroscedasticity test (p-val.) 0.000 Cross-dependency test (p-val.) 0.231 Cross-sectional observation(N) 613 Observ after estimation (N*T) 2356
-
M
-
ED
-
PT
VA (Voice & Account.) CC (Control of Corruption)
2356
Notes: a See Table 1 for the definition of variables and measurements. Asterisks indicate significance at 1% (***), 5% (**)
AC
and 10% (*). b T-statistics (in parenthesis) of the Two-step System-GMM model are based on Windmeijer-corrected standard errors. c 2nd order serial correlation in first difference is distributed as N (0, 1) under the null of no serial correlation in the residuals. d Difference-in-Hansen over identification test and null that instruments are valid. e TDBVit-2 , FA it-2, PRFit-2 , Sizeit-2 , Ndts it-2 , PBit-2 , Ageit-2 , DPOit-2 , INSTit-2 , ROLit-2 , REGQit-2 , GEit-2 , PSit-2 , VA it-2 , and CCit-2 are used as Instruments. f In all estimations, the number of ‘N’ cross -sectional observation (613) is greater than the number of instruments (84 & 123). g Industry dummies are included in all the estimations. The lists of the industries are in appendix II.
4.5 Direct effects of legal enforcement on debt models (Robustness Check 3) The indices measuring institutional quality in prior studies proxy for the existence of the laws and regulations. However, enforcement of laws appears more important than existence of 50
ACCEPTED MANUSCRIPT the laws in developing countries. Weak enforcement is a general problem in most developing countries (Pistor, Raiser, and Gelfer, 2000), and it affects firms seeking external financing. A weak enforcement environment makes it more difficult for firms to make commitments to contractual agreement. Conversely, a strong enforcement environment makes it easier for firms to make commitments to contractual agreement. Consequently, this paper conducts additional analysis to investigate the effects of legal enforcement on capital structure of firms’ in
T
developing countries.
IP
Due to data limitation on legal enforcement proxy (data is available for one year), we are
CR
able to run cross-sectional regression, and corrected for heteroskedasticity in the ordinary least squares estimation using robust standard error. The paper uses 9 years average (averaging
US
2006 to 2014 periods) for all the variables (except legal enforcement variable) to obtain a single year. Moreover, the variance inflation factor indicates absence of multicollinearity
AN
problem. The paper reports new findings that legal enforcement has positive effects on capital structure of firms in developing countries. Specifically, legal enforcement variable is
M
positively related to book value measure of capital structure (see Table 15, model 13a that excludes country effects and model 13b that includes country effects). Likewise, legal
ED
enforcement is positively related to market value measure of capital structure (see Table 15, model 14a that excludes country effects and model 14b that includes country effects). The
PT
results suggest that strong legal enforcement encourages lender to lend money and it lowers bankruptcy costs, resulting in firms using more debt to take advantage of debt interest tax-
CE
shield benefits.
Our results are consistent with Bhattacharya and Daouk (2002) empirical findings that
AC
actions taken against insider trading, rather than the mere presence of insider trading laws, help explain the development of securities markets. Likewise, the results are consistent with La Porta, Lopez-deSilanes, and Shleifer (2006) who note that in area of securities regulation, private enforcement of the law seems highly effective for capital market development. Moreover Gibbons (2002) find that the level of enforcement is more important than the quality of laws in explaining the turnover of CEOs.
Table 15 Effects of legal enforcement on Book-debt and Market-debt ratios Ordinary least squares regression estimation results using 3891 listed firms from 23 developing countries
51
ACCEPTED MANUSCRIPT
Int (Interest rate) Inf (Inflation rate) Gdpgr (Economic growth rate) BC (Banking Credit) MC (Market Capitalization) Age (firm-age) DPO (dividend pay-out ratio)
PT
OWS (Ownership structure) Country dummy Adjusted R2 F-Test Variance Inflation Factor Cross-sectional observation (N) a
T
IP
Ndts (Non-debt tax-shield)
0.0003** (2.32) 0.1363*** (7.19) -0.3119*** (-9.81) 0.0155*** (9.16) -0.0001** (-2.09) -0.2681** (-2.39) 0.0019** (2.46) 0.0151*** (7.46) 0.0012* (1.96) 0.0005** (2.08) -0.0007*** (-4.96) 0.0067** (2.43) -0.0230*** (-8.88) -0.0032** (-2.28) 0.1188 33.72 2.58 3891
CR
PB (Price-to-book ratio)
0.0001** (2.02) 0.0731*** (3.29) -0.3210*** (-13.58) 0.0055** (2.98) -0.0001*** (-3.96) -0.4967** (-2.80) 0.0017* (1.93) 0.0047** (3.05) 0.0029** (2.33) 0.0001* (1.85) -0.0002** (-2.00) 0.0010 (1.19) -0.0091** (-3.44) -0.0038** (-2.61) Yes 0.1364 40.05 2.62 3891
US
Size
0.0002** (2.16) 0.1329** (8.09) -0.2992*** (-10.86) 0.0097*** (6.64) -0.0001** (-2.40) -0.5467*** (-3.99) 0.0020*** (2.96) 0.0088*** (5.04) 0.0051*** (2.71) 0.0003** (2.36) -0.0003*** (-2.99) 0.0039* (1.95) -0.0128*** (-5.70) -0.0050** (-2.51) 0.1258 35.92 2.61 3891
AN
PRF (Profits)
Model 14a Model 14b (Market Debt Model) Market Debt Model
M
FA (Fixed Assets)
Model 13b (Book Debt Model
ED
ENFOR (Legal Enforcement)
Model 13a (Book Debt Model)
0.0002** (2.14) 0.0538** (4.27) -0.3221** (7.39) 0.0039*** (4.13) -0.0001** (-2.08) -0.2264** (-2.11) 0.0013** (2.69) 0.0047* (2.83) 0.0013** (2.51) 0.0002* (1.99) -0.0006** (-2.62) -0.0068** (-2.47) -0.0121** (-5.09) -0.0037*** (-3.79) Yes 0.1296 38.39 2.60 3891
AC
CE
Legal Enforcement: measure the level of judicial independence and bribery, the quality of legal framework, the protection of private property and the effectiveness of both the parliament and police. The index is scaled to range from 0 to 10. b See Table 1 for the definition of other variables and measurements. c Debt i = β1 + β2 LENFORi + β3 FA i + β 4 Profit i + β 5 Sizei + β 6 PBi + β 7 Ndts i + β 8 INTi + β 9 INFi + β 10 MCi + β 11 BCi + β12 Gdpg i + β 13 DPOi + β 14 Agei + β15 DPOi + i . d Asterisks indicate significance at 1% (***), 5% (**), and 10% (*). e T-statistics (in parenthesis) of Notes:
OLS 9 years average are based on robust standard errors. c Industry dummies are included in all the estimations. The lists of the industries are in appendix II. Source of Legal Enforcement data: World Bank (2004).
4.6 Controlling for the country effects on debt models (Robustness check 4) Although the paper controls for several macroeconomic factors such as interest rate, inflation rate, economic growth, bank credits, and market capitalization (proxy for stock market size) that have been identified as important determinants of firms’ debt, there is possibility that some left over cross-county factors are not captured by our models. Therefore, as an additional robustness 52
ACCEPTED MANUSCRIPT check, the paper includes country effects (using the dummy variable approach) in our baseline panel regressions in order to control for the left over cross-country differences. The results are broadly similar in the full sample and regional sub-samples, but the coefficient of some of the institutional quality variables changes. Specifically, the single-index measure of institutional quality continue to positively affect firms’ debt. Likewise, the disaggregated measures of institutional quality continue to affect firms’ debt; but the coefficients of some variables are
T
different compared to the models that exclude the cross-country effects. Moreover, the
IP
coefficient of the firm specific and macroeconomic variables are different compared to the
CR
models that exclude the cross-country effects. These results indicate that the left over crosscountry factors have some impact on firms’ capital structure. Therefore future researchers should
US
control for the left over cross country factors when conducting capital structure research. Recently, Deleze and Korkeamaki (2018) test if new issuers on the European corporate
AN
bond markets experience changes in their interest rate sensitivity when they issue new bonds. They added country fixed effects into their model specifications to control for the cross-country
M
effects, and the results reveal that stock returns are less sensitive to interest rate changes for firms that enter the publicly traded bond market. Overall, the coefficient of some variables change in
ED
Deleze and Korkeamaki’s (2018) results, which are also similar to our findings. Similarly, in an earlier study, Jong et al. (2008) also control for cross-country differences via country dummies in
PT
their debt model, and their results reveal that there is an indirect impact because country specific
CE
factors influence the role of traditional firm-specific determinant of debt. Table 16 Direct effects of institutional quality on Book-debt models System-GM M Two-step Estimation Results using Book-debt and 3,891 Listed Firms from 23 Developing Countries, 2006-2014 M odel 15g M odel 15h With CC (Single Equation)
0.5791*** (36.88)
AC
M odel 15a With Single M odel 15b M odel 15c M odel 15d M odel 15e M odel 15f INST Index With ROL With REGQ With GE With PS With VA
0.6072*** 0.6077*** (42.34) (39.44)
0.6077*** 0.5973*** (47.37) (46.18)
0.5848*** (40.12)
0.6049*** 0.6381*** (51.48) (98.70)
-
-
-
-
-
-
-
REGQ (Regulatory quality) -
0.0013*** (3.18) 0.0003** (2.49)
-
-
GE (Govt. Effectiveness)
-
-
-
0.0014** (2.51)
-
PS (Political Stability)
-
-
-
-
0.0002** (2.58)
TDBVit-1 (Lag Book-debt)
INST (Single Institutional 0.0014** Quality Index) (2.80) ROL (Rule of law)
-
-
0.0009** (2.62) 0.0002** (2.49) 0.0001** (2.28) 0.0003** (2.12)
53
ACCEPTED MANUSCRIPT 0.0008* (1.97) 0.0011*** 0.0008* CC (Control of Corruption) (2.59) (3.59) 0.0018** 0.0025** 0.0471** 0.0577** 0.0026** 0.0292** 0.0478** 0.0790*** FA (Fixed Assets) (2.05) (2.06) (2.18) (2.58) (2.07) (2.22) (2.29) (4.54) -0.4447*** -0.4461*** -0.4419*** -0.4441*** -0.4453*** -0.4406*** -0.4451*** -0.4461*** PRF (Profits) (-8.44) (-7.25) (-7.56) (-7.43) (-8.82) (-7.02) (-8.22) (-10.88) 0.0125** 0.0107** 0.0119*** 0.0047** 0.0124** 0.0067** 0.0142** 0.0055** Size (2.18) (2.59) (2.76) (2.16) (2.29) (2.53) (2.32) (2.56) -0.0001* -0.0001** -0.0001 -0.0001 -0.0001** -0.0001* -0.0001** -0.0001 PB (Price-to-book ratio) (-1.79) (-2.10) (-0.94) (-1.42) (-2.25) (-1.83) (-2.44) (-0.54) -0.0572** -0.0592** -0.0260** -0.0608* -0.0575* -0.0643** -0.0308** -0.0478*** Ndts (Non-debt tax-shield) (-2.28) (-2.48) (-2.08) (-1.90) (-1.97) (-2.10) (-2.19) (-3.66) 0.0342** 0.0255* 0.0287** 0.0266* 0.0124** 0.0051 0.0210* 0.0097** Age (Firm-age) (2.08) (1.82) (2.59) (1.94) (2.17) (0.32) (1.98) (2.45) -0.0032 -0.0054*** -0.0006* -0.0014** -0.0044** -0.0029** -0.0019 -0.0079 DPO (Dividend pay-out) (-0.28) (-2.86) (-1.95) (-2.12) (-2.42) (-2.25) (-0.18) (-1.24) -0.1048** -0.0498 -0.0184** -0.2020** -0.0631*** -0.0120 -0.0692** -0.0710*** OWS (Ownership structure) (-2.23) (-0.80) (-2.29) (-2.61) (-3.02) (-0.16) (-2.38) (-14.09) 0.0002** 0.0002** 0.0002** 0.0002** 0.0003** 0.0002** 0.0002** 0.0001* Int (Interest rate) (2.39) (2.03) (2.02) (2.41) (2.19) (2.03) (2.11) (1.80) 0.0007* 0.0008** 0.0008** 0.0009** 0.0011*** 0.0006** 0.0011*** 0.0009** Inf (Inflation rate) (1.94) (2.01) (2.06) (2.37) (2.81) (2.60) (2.71) (2.49) Gdpg (Economic growth -0.0014*** -0.0012*** 0.0012*** -0.0017*** -0.0011*** -0.0014*** -0.0010*** -0.0006* rate) (-4.09) (-3.69) (3.24) (-4.50) (-3.29) (-3.94) (-2.87) (-1.93) 0.0005*** 0.0006** 0.0004** 0.0004* 0.0003* 0.0006** 0.0002** 0.0002** BC (Banking Credit) (5.08) (2.28) (2.35) (1.95) (1.89) (2.10) (2.14) (2.01) -0.0002*** -0.0001*** -0.0002*** -0.0002*** -0.0001*** -0.0001** -0.0002*** -0.0001*** M C (M arket Capitalization) (-3.64) (-3.53) (-4.00) (-4.04) (-3.47) (-3.66) (-3.90) (-3.27) Yes Yes Country Dummies Yes Yes Yes Yes Yes Yes 0.2239 0.2217 AR(2) [P-value] 0.2250 0.2243 0.2232 0.2233 0.2247 0.2245 Difference Hansen Tests [P-value] 0.1050 0.1143 0.1229 0.1225 0.1104 0.1510 0.1145 0.1701 153 223 Instruments 153 153 153 153 153 153 1.64 2.59 Variance Inflation Factor 1.55 1.66 1.71 1.77 1.69 1.56 Heteroscedasticity test (p val.) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Cross-dependency test (pval.) 0.142 0.114 0.121 0.139 0.134 0.175 0.163 0.117 Cross-sectional observation(N) 3891 3891 3891 3891 3891 3891 3891 3891 Observ after estimation (N*T) 30058 30058 30058 30058 30058 30058 30058 30058 -
-
-
-
0.0012** (2.45)
-
-
CE
PT
ED
M
AN
US
CR
IP
T
VA (Voice & Account.)
AC
Notes: a See Table 1 for the definition of variables and measurements. Asterisks indicate significance at 1% (***) and 5%
(**). b T-statistics (in parenthesis) of the Two-step System-GMM model are based on Windmeijer-corrected standard errors. c 2nd order serial correlation in first difference is distributed as N (0, 1) under the null of no serial correlation in the residuals. d Difference-in-Hansen over identification test and null that instruments are valid. e TDBVit-2 , FA it-2, PRFit-2 , Sizeit-2 , Ndts it-2 , PBit-2 , Ageit-2 , DPOit-2 , INSTit-2 , ROLit-2 , REGQit-2 , GEit-2 , PSit-2 , VA it-2 , and CCit-2 are used as Instruments. f In all estimations, the number of ‘N’ cross -sectional observation (3,891) is greater than the number of instruments (153 & 223). g Industry dummies are included in all the estimations. The lists of the industries are in appendix II. Table 17 System-GM M Two-step Estimation Results using Book-debt and 2,187 Listed Firms from 7 Asian Countries, 2006-2014 M odel 16a With Single M odel 16b M odel 16c M odel 16d M odel 16e M odel 16f M odel 16g INST Index With ROL With REGQ With GE With PS With VA With CC
M odel 16h (Single Equation)
54
ACCEPTED MANUSCRIPT
TDBVit-1 (Lag Book-debt)
0.6273*** (70.44)
INST (Single Institutional 0.0003*** Quality Index) (3.68)
0.6482*** (82.02)
0.6431 (87.53)
0.6472*** 0.6403*** 0.6474*** 0.6457*** (94.30) (29.08) (92.72) (92.03)
0.0010*** (2.88)
-
-
-
-
0.0008** ROL (Rule of law) (2.14) 0.0004** 0.0002** REGQ (Regulatory quality) (2.36) (2.79) 0.0008** 0.0002* GE (Govt. Effectiveness) (2.12) (1.81) 0.0003** 0.0005** PS (Political Stability) (2.13) (2.34) 0.0006** 0.0008** VA (Voice & Account.) (2.28) (2.26) 0.0009*** 0.0012*** CC (Control of Corruption) (3.08) (4.77) 0.1215*** 0.1483*** 0.1457*** 0.1604*** 0.1453*** 0.1757*** 0.1487*** 0.1522*** FA (Fixed Assets) (4.18) (5.51) (5.63) (6.55) (5.24) (6.91) (5.90) (6.91) -0.0066** -0.0067** -0.0039** -0.0003** -0.0035** -0.0034** -0.0021** -0.0031** PRF (Profits) (-2.55) (-2.62) (-2.38) (-2.08) (-2.35) (-2.34) (-2.51) (-2.02) 0.0282*** 0.0318*** 0.0285*** 0.0259*** 0.0306*** 0.0246*** 0.0304*** 0.0317*** Size (5.12) (5.66) (5.69) (5.36) (6.12) (4.48) (6.19) (7.69) -0.0027*** -0.0033*** -0.0027*** -0.0027*** -0.0028*** -0.031*** -0.0036*** -0.0035*** PB (Price-to-book ratio) (-5.69) (-6.90) (-5.76) (-5.63 (-5.97) (-6.66) (-7.35) (-7.62) 0.1466*** 0.0315*** 0.0361*** 0.0485*** 0.0818** 0.0512*** 0.0952*** 0.0224*** Ndts (Non-debt tax-shield) (3.45) (4.26) (3.50) (3.70) (2.46) (4.00) (3.16) (3.30) -0.0077* -0.0007 0.0101** 0.0092 -0.0113 -0.0003 -0.0036 0.0076 Age (Firm-age) (-1.85) (-0.07) (2.22) (1.34) (-1.28) (-0.03) (-0.51) (1.40) -0.0250*** -0.0573* -0.0230*** -0.0200** -0.0258*** -0.0225*** -0.0261*** 0.0296*** DPO (Dividend pay-out) (-2.92) (-1.85) (-2.84) (-2.63) (-3.02) (-2.77) (-3.21) (4.77) 0.0087** 0.0573* -0.0279 0.0923*** 0.0235** 0.0713** 0.0445** 0.0442*** OWS (Ownership structure) (2.28) (1.85) (-1.00) (3.30) (2.18) (2.51) (2.54) (4.50) 0.0005* 0.0005** 0.0005** 0.0007** 0.0005** -0.0004 0.0006* 0.0004* Int (Interest rate) (2.10) (2.43) (2.35) (2.07) (2.24) (-1.29) (1.92) (1.80) 0.0008* 0.0008** 0.0009* 0.0005 0.0010* 0.0011** 0.0011** 0.0013** Inf (Inflation rate) (1.84) (2.18) (1.96) (1.55) (1.94) (2.06) (2.15) (2.40) Gdpg (Economic growth 0.0007** 0.0002* 0.0001** 0.0010** 0.0004* 0.0005 0.0002** 0.0003** rate) (2.04) (1.91) (2.15) (2.44) (1.97) (1.57) (2.04) (2.09) 0.0008** 0.0010*** 0.0009*** 0.0009** 0.0007*** 0.0006** 0.0014*** 0.0010*** BC (Banking Credit) (2.37) (3.25) (2.83) (3.23) (2.73) (2.41) (4.46) (3.10) -0.0001** -0.0001* -0.0001* -0.0001** -0.0001* -0.0001* -0.0002* 0.0001* M C (M arket Capitalization) (-2.09) (-1.95) (-1.97) (-2.02) (-1.76) (-1.80) (-1.96) (1.90) Yes Yes Country Dummies Yes Yes Yes Yes Yes Yes 0.1086 0.1101 AR(2) [P-value] 0.1154 0.1147 0.1107 0.1082 0.1113 0.1097 Difference Hansen Tests [P-value] 0.1634 0.1853 0.1102 0.1086 0.1269 0.1129 0.1533 0.1678 153 223 Instruments 153 153 153 153 153 153 3.22 3.39 Variance Inflation Factor 3.29 3.18 3.98 3.28 3.19 3.14 Heteroscedasticity test (pval.) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00 Cross-dependency test (pval.) 0.176 0.139 0.233 0.221 0.181 0.299 0.195 0.166 Cross-sectional observation(N) 2187 2187 2187 2187 2187 2187 2187 2187 Observ after estimation (N*T) 16998 16998 16998 16998 16998 16998 16998 16998
AC
CE
PT
ED
M
AN
US
CR
IP
T
-
0.6431*** (94.10)
Notes: a See Table 1 for the definition of variables and measurements. Asterisks indicate significance at 1% (***), 5% (**)
and 10% (*). b T-statistics (in parenthesis) of the Two-step System-GMM model are based on Windmeijer-corrected
55
ACCEPTED MANUSCRIPT standard errors. c 2nd order serial correlation in first difference is distributed as N (0, 1) under the null of no serial correlation in the residuals. d Difference-in-Hansen over identification test and null that instruments are valid. e TDBVit-2 , FA it-2, PRFit-2 , Sizeit-2 , Ndts it-2 , PBit-2 , Ageit-2 , DPOit-2 , INSTit-2 , ROLit-2 , REGQit-2 , GEit-2 , PSit-2 , VA it-2 , and CCit-2 are used as Instruments. f In all estimations, the number of ‘N’ cross -sectional observation (2,187) is greater than the number of instruments (153 & 223). g Industry dummies are included in all the estimations. The lists of the industries are in appendix II.
IP
T
Table 18 Direct effects of institutional quality on Book-debt models System-GM M Two-step Estimation Results using Book-debt and 1,091 Listed Firms from 6 Latin American combined with Eastern European Countries, 2006-2014
0.7540*** (29.04)
INST (Single Institutional 0.0012** Quality Index) (2.48)
0.7474*** 0.7482*** 0.7410*** 0.7540*** 0.7625*** 0.7507*** 0.7700*** (28.08) (27.46) (27.93) (28.57) (53.41) (29.93) (46.88)
-
-
-
-
0.0002** ROL (Rule of law) (2.30) 0.0012* 0.0005** REGQ (Regulatory quality) (1.90) (2.08) 0.0025*** 0.0006** GE (Govt. Effectiveness) (2.90) (2.05) 0.0002** 0.0002** PS (Political Stability) (2.53) (2.42) 0.0020*** 0.0016*** VA (Voice & Account.) (2.86) (3.21) 0.0003** 0.0003*** CC (Control of Corruption) (2.33) (3.23) 0.0275** 0.0867*** 0.1337*** 0.0183** 0.0268** 0.0407** 0.0195* 0.0282** FA (Fixed Assets) (2.17) (6.32) (7.08) (2.40) (2.20) (2.19) (1.89) (2.14) -0.4438*** -0.4236*** -0.4432*** -0.4442*** -0.4437*** -0.4442*** -0.4439*** -0.4440*** PRF (Profits) (-7.33) (-8.12) (-7.00) (-8.50) (-7.14) (-9.82) (-9.93) (-8.60) 0.0064** 0.0096** 0.0102*** 0.0101** 0.0089** 0.0102** 0.0069*** 0.0045** Size (2.59) (2.58) (2.71) (2.57) (2.47) (2.68) (3.15) (2.19) -0.0001*** -0.0001*** -0.0001* -0.0001** -0.0001** -0.0001*** -0.0001*** -0.000 PB (Price-to-book ratio) (-4.58) (-4.15) (-1.79) (-2.48) (-2.66) (-3.59) (-3.94) (-1.52) 0.0365** 0.0244** 0.0679** 0.0495** 0.0375* 0.0483** 0.0102** 0.0136* Ndts (Non-debt tax-shield) (2.59) (2.20) (2.32) (2.40) (1.77) (2.28) (2.41) (1.99) -0.0184*** -0.0215*** 0.0185*** -0.0175*** -0.0174*** 0.0200*** -0.0124*** -0.0037** Age (Firm-age) (-4.41) (-4.77) (3.86) (-3.88) (-3.86) (4.79) (-3.15) (-2.37) 0.0107* 0.0005 0.0048* 0.0040* 0.0024 0.0051* 0.0036** 0.0049* DPO (Dividend pay -out) (1.95) (0.08) (1.81) (1.96) (0.43) (1.87) (2.57) (1.89) 0.0436** 0.0113** 0.0443** 0.0479** 0.0526** 0.0242* 0.0499* 0.0162* OWS (Ownership structure) (2.16) (2.37) (2.43) (2.16) (2.13) (1.82) (1.81) (1.97) -0.0001 0.0002 0.0006 -0.0005** 0.0002** 0.0004 0.0003 -0.0002** Int (Interest rate) (-0.41) (0.57) (1.61) (-2.35) (2.44) (0.99) (1.12) (2.12) 0.0012 0.0009* -0.0004 0.0003 0.0009** -0.0003 0.0008* 0.0006 Inf (Inflation rate) (1.53)) (1.92) (-0.38) (0.35) (2.59) (-0.29) (1.85) (0.79) Gdpgr (Economic growth 0.0012*** 0.0008** 0.0009** 0.0014*** 0.0009** 0.0011*** 0.0008** 0.0009*** rate) (3.31) (2.16) (2.42) (3.48) (2.50) (3.08) (2.13) (2.86) 0.0005** 0.0002** 0.0005* 0.0002* 0.0002** 0.0008*** 0.0001** 0.0001 BC (Banking Credit) (2.11) (2.13) (1.72) (1.80) (2.16) (3.16) (2.12) (1.27) M C (M arket Capitalization) -0.0005*** -0.0006*** -0.0005*** -0.0005*** -0.0006***
AN
-
AC
CE
PT
ED
M
0.0001** (2.91)
US
TDBVit-1 (Lag Book-debt)
CR
M odel 17a With Single M odel 17b M odel 17c M odel 17d M odel 17e M odel 17f M odel 17g M odel 17h (Single INST Index With ROL With REGQ With GE With PS With VA With CC Equation)
56
(-6.20)
(-6.35)
(-5.42)
(-6.98) Yes 0.9622
-0.0005*** (-6.90) Yes 0.8433
Yes 0.9152
Yes 0.9450
Yes 0.9835
0.1749 153 2.37
0.1490 153 2.39
0.1578 153 2.48
0.1298 153 2.71
0.1858 153 1.97
0.1459 153 1.93
0.1216 153 1.89
0.1122 223 2.87
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.321
0.253
0.214
0.263
0.251
0.219
0.242
0.216
1091
1091
1091
1091
1091
1091
1091
8436
8436
8436
8436
8436
8436
1091
IP
-0.0005*** (-5.73) (-5.63) Yes Yes 0.9748 0.9275
8436
8436
CR
Country dummies AR(2) [P-value] Difference Hansen Tests [P-value] Instruments Variance Inflation Factor Heteroscedasticity test (p-val.) Cross-dependency test (p-val.) Cross-sectional observation(N) Observ after estimation (N*T)
-0.0006*** (-7.22) Yes 0.9430
T
ACCEPTED MANUSCRIPT
Notes: a See Table 1 for the definition of variables and measurements. Asterisks indicate significance at 1% (***), 5% (**)
M
AN
US
and 10% (*). b T-statistics (in parenthesis) of the Two-step System-GMM model are based on Windmeijer-corrected standard errors. c 2nd order serial correlation in first difference is distributed as N (0, 1) under the null of no serial correlation in the residuals. d Difference-in-Hansen over identification test and null that instruments are valid. e TDBVit-2 , FA it-2, PRFit-2 , Sizeit-2 , Ndts it-2 , PBit-2 , Ageit-2 , DPOit-2 , INSTit-2 , ROLit-2 , REGQit-2 , GEit-2 , PSit-2 , VA it-2 , and CCit-2 are used as Instruments. f In all estimations, the number of ‘N’ cross -sectional observation (1,091) is greater than the number of instruments (153 & 223).
Table 19 Direct effects on institutional quality on Book-debt models
ED
System-GM M Two-step Estimation Results using Book-debt and 613 Listed Firms from 10 African Countries, 2006-2014
M odel 18f With VA
M odel 18gg M odel 18h With CC (Single Equation
0.4227*** (19.82)
0.4651*** 0.4523*** 0.4519*** (21.26) (19.65) (21.18)
0.4565*** (18.22)
0.4558*** 0.4987*** (19.76) (33.98)
-
-
-
-
-
-
-0.0001 (-0.42)
-
-
-
-
-
-0.0001 (-0.08)
-
-
GE (Govt. Effectiveness)
-
-
-
0.0001 (0.59)
-
PS (Political Stability)
-
-
-
-
-0.0001** (-2.19)
VA (Voice & Account.) CC (Control of Corruption)
-
-
-
-
-
0.0002** (2.23)
-
-
-
-
-
-
CE
0.4583*** TDBVit-1 (Lag Book-debt) (19.06)
PT
M odel 18a With Single M odel 18b M odel 18c M odel 18d M odel 18e INST Index With ROL With REGQ With GE With PS
ROL (Rule of law) REGQ (Regulatory quality)
FA (Fixed Assets) PRF (Profits)
AC
INST (Single Institutional 0.0003 Quality Index) (0.79)
0.0002 (1.52)
0.0002 (1.01) 0.0004 (1.04) 0.0001 (1.63) -0.0013*** (-4.49) 0.0003** (2.06) 0.0002 (0.92)
0.0572** 0.0353** 0.0672*** 0.0591** 0.0536** 0.0648** 0.0592** 0.0031** (2.33) (2.46) (2.78) (2.40) (2.20) (2.68) (2.46) (2.21) -0.4594*** -0.4582*** -0.4778*** -0.4471*** -0.4796*** -0.4487*** -0.4670*** -0.3562***
57
ACCEPTED MANUSCRIPT (-14.66) 0.0064** (2.43)
(-16.20) 0.0046** (2.02)
-0.0061*** -0.0059*** -0.0062*** (-6.91) (-7.88) (-9.60) -0.2992* -0.2617* -0.2630*** (-1.88) (-1.97) (-2.59) 0.0039** 0.0050** 0.0056 (2.51) (2.56) (0.79)
-0.0064*** (-10.34) -0.1164** (-2.10) 0.0049* (1.79)
-0.0063*** (-11.54) -0.1685** (-2.20) 0.0015* (1.80)
-0.0053*** (-12.01) -0.0340 (-0.44) 0.0086** (2.13)
-0.0291*** (-3.29) 0.0501** (2.34) 0.0002 (1.42) 0.0011*** (2.55) -0.0013** (-2.56) 0.0013*** (3.57) -0.0001** (-2.20) Yes 0.7394
-0.0275*** (-3.24) 0.0001** (2.09) -0.0001 (-1.03) 0.0015*** (3.45) -0.0010* (-1.97) 0.0008** (2.55) -0.0001 (-0.15) Yes 0.6613
-0.0029** (-2.68) 0.0509** (2.31) -0.0003** (-2.35)) 0.0016** (3.96) -0.0011** (-2.05) 0.0007** (2.15) -0.0001 (-0.81) Yes 0.6723
-0-0271*** (-3.15) 0.0441** (2.51) -0.0001 (0.89) 0.0013*** (3.25) -0.0011 (-1.49) 0.0010*** (3.04) -0.0001** (-2.17) Yes 0.6851
-0.0325*** (-3.49) 0.0595*** (2.93) 0.0001 (0.14) 0.0013*** (2.86) -0.0015** (-2.72) 0.0008** (2.50) -0.0001* (-1.85) Yes 0.6518
-0.0332*** (-4.03) 0.0490* (1.96) -0.0003** (-2.27) 0.0019*** (4.53) -0.0014** (-2.59) 0.0007** (2.04) -0.0001** (-2.11) Yes 0.6780
-0.0303*** (-5.82) 0.0570** (3.88) 0.0009*** (6.63) 0.0017*** (6.97) 0.0001** (2.25) 0.0002** (2.11) -0.0001** (-2.28) Yes 0.7064
0.2828 153 1.79
0.3673 153 2.28
0.4524 153 1.90
0.3881 153 1.78
0.2045 153 2.19
0.4138 153 1.91
0.3652 223 2.19
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.184
0.203
0.215
0.226
0.133
0.115
613
613
613
613
613
613
4624
4624
4624
4264
4264
613
ED
0.194
4624
4624
IP
-0.0306*** DPO (Dividend pay -out) (-3.63) OWS (Ownership 0.0471** structure) (2.04) 0.0002** Int (Interest rate) (2.07) 0.0013*** Inf (Inflation rate) (3.29) Gdpg (Economic growth -0.0010** rate) (-2.19) 0.0009*** BC (Banking Credit) (2.73) M C (M arket -0.0001** Capitalization) (-2.02) Country dummies Yes AR(2) [P-value] 0.6846 Difference Hansen Tests [P-value] 0.3798 Instruments 153 Variance Inflation Factor 2.03 Heteroscedasticity test (p val.) 0.000 Cross-dependency test (pval.) 0.199 Cross-sectional observation(N) 613 Observ after estimation (N*T) 4624
T
(-14.35) 0.0062** (2.50)
CR
-0.0062*** (-7.60) -0.3428** (-2.08) 0.0023** (2.32)
PB (Price-to-book ratio)
(-15.26) 0.0015** (2.39)
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-0.0060** (-7.80) -0.2168** Ndts (Non-debt tax-shield) (-2.38) 0.0085* Age (Firm-age) (1.92)
Size
(-14.58) 0.0089** (2.10)
AN
(-14.84) 0.0009** (2.21)
M
(-15.00) 0.0043** (2.05)
PT
(-14.47) 0.0016* (1.85)
Notes: a See Table 1 for the definition of variables and measurements. Asterisks indicate significance at 1% (***), 5% (**)
AC
CE
and 10% (*). b T-statistics (in parenthesis) of the Two-step System-GMM model are based on Windmeijer-corrected standard errors. c 2nd order serial correlation in first difference is distributed as N (0, 1) under the null of no serial correlation in the residuals. d Difference-in-Hansen over identification test and null that instruments are valid. e TDBVit-2 , FA it-2, PRFit-2 , Sizeit-2 , Ndts it-2 , PBit-2 , Ageit-2 , DPOit-2 , INSTit-2 , ROLit-2 , REGQit-2 , GEit-2 , PSit-2 , VA it-2 , and CCit-2 are used as Instruments. f In all estimations, the number of ‘N’ cross -sectional observation (613) is greater than the number of instruments (153 & 223). g Industry dummies are included in all the estimations. The lists of th e industries are in appendix II.
5.
Conclusion The limited studies available have documented evidence indicating that a subset of
institutional factors affects the speed of adjustment to the target capital structure (debt ratios). Our paper adds to the growing literature on capital structure-institution relationships using an
58
ACCEPTED MANUSCRIPT aggregated (single-institutional quality index) measure of institutional quality and disaggregated measure of institutional quality. Moreover, our paper divides the overall samples into various regional panels, namely, firms from Asian countries, firms from African countries, and firms from Latin American and Eastern European countries. New findings emerge from the regional sample separation. Among the six components of institutional quality, only political stability (negative sign) and voice and accountability (positive sign) are significantly related to debt ratios
T
in the African sample. Political stability measures perceptions of the likelihood that a
IP
government will be destabilized by violent means. The likelihood that a government will be
effects of political stability on debt ratios.
CR
destabilized by violence is high in most African countries, which may explain the negative However, voice and accountability have a positive
US
effect on debt ratios in the African sample. Voice and accountability measures perceptions of the likelihood that a country’s citizen are able to participate in selecting a government. Awareness of
AN
democracy is increasing in several African countries, which could explain the positive effect of voice and accountability on debt ratios.
M
Conversely, the six components of institutional quality have positive effects on debt ratios in the Latin American and Eastern European sample. Over the years, Latin-American and
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Eastern European countries have gone through reforms in terms of laws and institutions that promote transparency, accountability, political stability, and encourage citizens’ participation in
PT
selecting government. These law and institution reforms may partly explain why these six components of institutional quality have positive effects on debt ratios in Latin-American and
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Eastern European countries.
This paper investigates the effects of institutional quality on firms’ capital structure in
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developing countries. Our main findings can be summarized as follows. For the full sample of 3,891 firms from 23 developing countries, this paper finds that an aggregated (single-index) measure of institutional quality has a statistically significant and positive effect on both the book and market debt ratios. Likewise, the disaggregated measure of institutional quality has a statistically significant and positive effect on both the book and market debt ratios. These results support our argument that holding firms’ assets and investment plans constant, firms in developing countries with strong institutions appear to increase debt because better institutional quality encourages lenders to lend money and it lowers bankruptcy costs, resulting in firms using more debt to capitalize on tax-shield benefits of debt interest. Also, traditional capital structure 59
ACCEPTED MANUSCRIPT determinants (e.g., size, fixed assets, profits, price-to-book ratio) consistently explain the capital structure decisions of listed firms in developing countries, with correct signs. The empirical results also show that firms in developing countries make adjustment to their target debt level. Additional analysis reveals positive effects of legal enforcement on both the book debt and market debt measures of capital structure. At the regional level (for the 2,187 listed firms from Asian countries and the 1,091 listed
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firms from Latin American and Eastern European countries), the aggregated and disaggregated
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measures of institutional quality have a statistically significant and positive effect on both the
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book and market debt ratios. Conversely, for the 613 listed firms from African countries, this paper finds more evidence that institutional quality has an insignificant effect on both the book
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and market debt ratios. This result suggests that institutional quality appears to have little effect on debt ratios among African countries. Additionally, we find more evidence that institutional
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quality has indirect effects (via interacting institutional quality and some firm-specific factors) on debt in both the Asian region and Latin American and Eastern European region. The main
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results remain unchanged after re-estimating the models using post-financial crisis samples. In addition to institutional quality being a determinant of capital structure, firm-specific
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factors (e.g., fixed assets, profits, and price-to-book ratio) consistently explain the capital structure decisions of firms in both the regional subsamples and the full sample, with correct
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signs. As macroeconomic factors, bank credit to the private sector is positively related to debt ratios while stock market capitalization is negatively related to debt ratios in both the regional
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subsamples and full sample. We also provide evidence that Asian firms make faster adjustments to their target debt level than firms in the other two regions. The firms’ adjustment
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behavior is consistent with the dynamic version of trade-off theory. The main results remain unchanged after re-estimating the models using post-financial crisis samples. These results have several policy implications. Firstly, the results suggest that strong institutions that protect creditors’ rights improve loan availability and encourage lenders to provide debt capital. Secondly, the results suggest that debt financing decisions are not made in a vacuum and appear to depend on institutional environments. Therefore, policymakers should continue to strengthen the quality of institutions. Improving the quality of institutions would remove barriers to debt financing and prevent ineffective utilization of financial resources in developing countries. Third, as most African countries are characterized by weak institutional 60
ACCEPTED MANUSCRIPT quality, policymakers in this region should undertake institutional reforms that aim to improve the quality of institutions to ensure that firms have easy access to debt capital. The paper has some limitations. Our focus is on one aspect of debt structure (i.e. capital structure). Debt structure also includes debt maturity, the choices between public and private debt, and debt covenant which we do not cover in this paper. Moreover, firms make both the capital structure and other debt structure decisions simultaneously. Therefore, our results should
problem using
internal instruments,
it
does
not
completely eliminate
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endogeneity
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be interpreted with caution. Although, our paper uses the two-step system GMM to mitigate the the
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endogeneity problem caused by unobserved heterogeneity. Additionally, like previous studies, our paper assumes a causal relationship between capital structure and institutional quality. But
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the relationship between capital structure and institutional quality may only be association. Thus, readers are advised to interpret our results with caution.
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Future research can explore the indirect effects of institutional quality on stock returns and other firm performance measures using listed firms from more developing countries, as the data
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become available. Future research may use both the balanced panel dataset and unbalanced
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panel dataset in a single study to conduct capital structure research and compare the results.
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Appendix I Number of Firms and Developing Countries (with available data)
Malaysia Pakistan Philippines
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India
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Asian Countries
Number of Firms 795 728 93 103
Bangladesh
10
Srilanka
139
Indonesia
319
Total
2187
61
ACCEPTED MANUSCRIPT Number of Firms
Ghana
17
Kenya
38
Nigeria
40
Tunisia
32
Zimbabwe
6
Mauritius
29
Morocco
58
Egypt
88
Jordan
115
South Africa
190
Total
613
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98
PT
Mexico Chile
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Brazil
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Peru
Turkey
US
M
AN
Eastern European Countries
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Latin America combined with
Poland
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African Countries
144 188 77 339 245
Total
1091
Overall Total
(2187 + 613 + 1091) = 3891
Appendix II Industries Included
62
ACCEPTED MANUSCRIPT Agriculture, Forestry, and Fishing Construction Manufacturing Transportation and Communications
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Services
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Retail Trade
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Wholesale Trade
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Note: Wholesale Trade are excluded to avoid dummy variable trap.
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We investigate the effects of institutional quality on firms’ capital structure. We use both aggregated and disaggregated measure of institutional quality. Institutional quality has a positive effect on capital structure for full sample. Institutional quality has different effect on capital structure for regional samples. Legal enforcement has positive effects of on capital structure.
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Highlights
66
Bolaji Tunde Matemilola, A.N. Bany-Ariffin, W.N.W. AzmanSaini, Annuar Md Nassir PII: DOI: Reference:
S1566-0141(16)30145-5 https://doi.org/10.1016/j.ememar.2019.04.003 EMEMAR 613
To appear in:
Emerging Markets Review
Received date: Revised date: Accepted date:
27 November 2016 28 March 2019 23 April 2019
Please cite this article as: B.T. Matemilola, A.N. Bany-Ariffin, W.N.W. Azman-Saini, et al., Impact of institutional quality on the capital structure of firms in developing countries, Emerging Markets Review, https://doi.org/10.1016/j.ememar.2019.04.003
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Impact of institutional quality on the capital structure of firms in developing countries Bolaji Tunde Matemilolaa, Bany-Ariffin A.N b,* , Azman-Saini W.N.Wc, and Annuar Md Nassird abd Department of Accounting and Finance, Universiti Putra Malaysia, 43400 Serdang, Malaysia c Department of Economics, Universiti Putra Malaysia, 43400 Serdang, Malaysia
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Abstract
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This paper investigates the effects of institutional quality on firms’ capital structure for a panel of
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3,891 firms from 23 developing countries. Our main findings for the full panel show institutional quality has a significantly positive effect on firms’ capital structure. At the regional level, based
US
on the panel data for 2,187 firms from Asian countries and 1,091 firms from Latin American and Eastern European countries, institutional quality has a significantly positive effect on capital
AN
structure. However, for the 613 firms from African countries, institutional quality is mostly insignificant. Additional analysis reveals positive effects of legal enforcement on capital
M
structure.
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CE
PT
JEL classification– G32, G37
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Keywords: Capital structure, debt, institutional factors, international evidence, System GMM
* Corresponding author. Email address: [email protected] (Bany-Ariffin A.N)
1
ACCEPTED MANUSCRIPT Impact of institutional quality on the capital structure of firms in developing countries Abstract This paper investigates the effects of institutional quality on firms’ capital structure for a panel of 3,891 firms from 23 developing countries. Our main findings for the full panel show institutional
T
quality has a significantly positive effect on firms’ capital structure. At the regional level, based
IP
on the panel data for 2,187 firms from Asian countries and 1,091 firms from Latin American and
CR
Eastern European countries, institutional quality has a significantly positive effect on capital structure. However, for the 613 firms from African countries, institutional quality is mostly insignificant. Additional analysis reveals positive effects of legal enforcement on capital
US
structure.
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Keywords: Capital structure, debt, institutional factors, international evidence, System GMM
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JEL classification– G32, G37
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I. Introduction
A general consensus holds that low institutional quality is a major constraint that hinders
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some firms in developing countries from easily obtaining the external capital that is vital for funding profitable investments that enhance returns. Low institutional quality in developing
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countries seem to reduce loan availability because lenders are unwilling to provide credit to firms for fear of inadequate protections (Qian and Strahan, 2007). Prior studies (e.g., Awartani et al. (2016) , Oztekin and Flannery (2012), Fan et al. (2012), De Jong et al. (2008), Demirguc-
AC
Kunt and Maksimovic (1999) in areas of finance have recognized the importance of institutional quality in international samples covering firms from different countries. Specifically, Awartani et al. (2016) investigate debt maturity structure and its firm and institutional determinants using a large sample of listed firms in the MENA region. The authors find that better institutional quality lead to the usage of more long-term debt and limited use of long-term debt by MENA firms. Oztekin and Flannery (2012) compare firms’ capital structure adjustments across countries and investigate if institutional differences explain the variance in estimated speeds of adjustment in 37 countries (25 are developed countries) using a partial 2
ACCEPTED MANUSCRIPT adjustment model. The authors find that institutional features influence adjustment speeds. Fan et al. (2012) examine how the institutional environment affects capital structure and debt maturity choices of firms in 39 countries (25 are from developed countries). Fan et al.’s (2012) findings reveal that a country’s legal and tax system, corruption, and preferences of creditors explain a significant portion of the variation in debt maturity and debt ratios. This study builds on this line of research in three important ways. First, this paper
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innovates using aggregated (single-index) as the main measure of institutional quality because
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it is a broader measure of overall institutional quality, and the disaggregated measure of
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institutional quality assembled by Kaufmann et al. (2009) to investigate the effects of institutional quality on the capital structure of firms in developing countries, within the
US
framework of Myers’ (1984) trade-off theory. As developing countries’ institutional quality and legal enforcement improve, lenders are more willing to grant credit to firms. Thus, holding
AN
firms’ assets and investment plans constant, firms in developing countries with strong institutions are more likely to increase debt because better institutional quality encourages
M
lenders to lend money and it lowers bankruptcy costs, resulting in firms using more debt to take advantage of tax-shield benefits of debt interest to increase returns.
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Secondly, our paper accounts for regional factor effects on capital structure by segregating the data into three regions – Asian countries’ listed firms, African countries’ listed firms, and
PT
Latin American and Eastern European countries’ listed firms. Although, regional sample segregation has some limitation, it enables us to compare the likely effects of institutional factors
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on firms’ capital structure at the regional level. Third, the indices in prior studies used to measure institutional quality proxy for the
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existence of the laws and regulations. However, enforcement of laws is key to creating strong institutions and an effective business environment. Weak enforcement is a general problem in most developing countries, and it affects firms seeking external financing. A weak enforcement environment makes is more difficult for firms to make commitments to contractual agreement. As legal enforcement seems important in developing countries, this paper conduct additional analysis to investigate the effects of legal enforcement on capital structure of firms’ in developing countries. The paper reports new findings that legal enforcement has positive effects on capital structure of firms in developing countries. Specifically, legal enforcement variable is positively related to book value measure of capital structure. Likewise, legal enforcement is 3
ACCEPTED MANUSCRIPT positively related to market value measure of capital structure. Fourth, more recent and adequately large firm-level and country-level datasets of developing countries are used to enhance the robustness of our conclusion. Specifically, the full sample consisted of annual firmlevel and country-level data of 3,891 listed firms from 23 developing countries covering the 2006 to 2014 time period. Our main results can be summarized as follows. In the case of the full sample of 3,891
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listed firms from 23 developing countries, this paper finds that a single aggregated index
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measure of institutional quality is significantly and positively related to both the book and
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market value measures of capital structure (debt ratios). This positive relationship between institutional quality and debt ratios remains unchanged using six disaggregated measures of
voice and accountability, and control of corruption.
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institutional quality: rule of law, regulatory quality, governance effectiveness, political stability,
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Similar results are found using 2,187 listed firms from 7 Asian countries and the 1,091 listed firms from 6 Latin American and Eastern European countries. The single aggregated
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index measure of institutional quality is significantly and positively related to both the book and market value measures of capital structure (debt ratios). The positive relationship between
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institutional quality and debt ratios remains unchanged using the six disaggregated measures of institutional quality. These results support our argument that holding firms’ assets and
PT
investment plans constant, firms in developing countries with strong institutions appear to increase debt because better institutional quality encourages lenders to lend money, resulting in
CE
firms using more debt to take advantage of tax-shield benefits of debt interest. Conversely, the results are different using the 613 listed firms from 10 African countries.
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The results reveal that a single aggregated index measure of institutional quality is not significantly and positively related to either the book or market value measures of capital structure (debt ratios). The results remain insignificant in four disaggregated measures of institutional quality (i.e., rule of law, regulatory quality, governance effectiveness, and control of corruption). Specifically, among the six components of institutional quality, only political stability (negative sign) and voice and accountability (positive sign) are significantly related to debt ratios in the African sample. Political stability measures perceptions of the likelihood that a government will be destabilized by violent means. The likelihood that a government will be destabilized by violence is high in most African countries, which may explain the negative 4
ACCEPTED MANUSCRIPT effects of political stability on debt ratios. Unstable environment discourages creditors to provide debt capital to firms, which reduces their debt ratios. However, voice and accountability have a positive effect on debt ratios in the African sample. Voice and accountability measures perceptions of the likelihood or extent to which a country’s citizen are able to participate in selecting a government. Awareness of the need for more citizens to participate in selecting government and elect credible leaders (democracy) is
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increasing in several African countries, which could explain the positive effect of voice and
IP
accountability on debt ratios. As the awareness to embrace democracy increases, investors
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appear more confident to provide debt capital to firms, which increases their debt ratios. Moreover, variations in quality of law are central to understanding why firms raise more capital
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in some countries than in others. Rule of law, regulatory quality, and control of corruption are subcomponents of institutional quality that influences availability of debt capital. Firms that
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operate in countries where there are a strong rule of law, regulatory quality, and control of corruption have easy access to debt capital compared with countries that are weak in these
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subcomponents of institutional quality. Most African countries have a weak rule of law and corruption control as well as poor regulatory quality, which may explain why these
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subcomponents of institutional quality have insignificant effects on debt ratios in the African sample.
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Contrariwise, the six components of institutional quality (e.g., rule of law, regulatory quality, control of corruption, governance effectiveness, political stability, and voice and
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accountability) have positive effects on debt ratios in the Latin-American and Eastern European sample. Over the years, Latin-American countries have gone through reforms in terms of laws citizens’
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and institutions that promote transparency, accountability, political stability, and encourage participation
in selecting government.
Most countries in Latin America have
incorporated basic democratic principles of the rule of law into their constitutions, but more compliance to the rule of law is needed (Zurbriggen, 2014). Likewise, over the years, there are improvements in institution reform in Eastern European countries that promote rule of law, regulatory quality, government quality, and control of corruption (Quality of Government Institute, 2017). These law and institution reforms may partly explain why these six components of institutional quality have positive effects on debt ratios in Latin-American and Eastern
5
ACCEPTED MANUSCRIPT European countries. Improvement in these six components of institutional quality encourages investors to provide debt capital to firms, which increase their debt ratios. Additionally, we find more evidence of indirect effects of institutional quality (via interaction of institutional quality and some firm-specific factors) on debt in both Asian region and Latin American and Eastern European region. Moreover, our results reveal that some firmspecific factors (e.g., size, fixed assets, profits, and price-to-book ratio) and macroeconomic
T
factors such as banking credit (ratio of the domestic credit provided by the banking sector to
IP
gross domestic product) and market capitalization (ratio of stock market capitalization of listed
CR
firms to gross domestic product) consistently affect the debt ratio with correct signs similar to previous findings in the literature. Specifically, size and fixed assets are positively related to
US
both the book and market value measures of capital structure (debt ratios) while profits and growth opportunity are negatively related to both the book and market value measures of
AN
capital structure (debt ratios) in all the results (both the full sample results and regional subsample results).
M
The rest of the paper is organized as follows: Section 2 reviews the relevant literature. Section 3 describes the data and methodology. Section 4 discusses the results, and Section 5
Literature Review
PT
2.
ED
concludes the paper.
This section explains the theoretical framework to establish the relationship between
CE
institutional quality and debt ratios and reviews the most relevant empirical literature. Moreover, this section briefly explains relevant capital structure theories, but the study’s main
AC
focus is on the trade-off theory of capital structure. Our paper does not focus on broad determinants of capital structure. 2.1.
Theoretical Framework Low institutional quality in developing countries has made it difficult for firms to raise
the external debt capital (Agca et al., 2013) needed for growth. Institutions and laws are believed to shape financial contracts with respect to banks. Strong institutions and laws that protect creditors’ rights improve loan availability and encourage lenders to provide debt capital to firms (Qian and Strahan, 2007). Likewise, well-developed legal institutions are important for
6
ACCEPTED MANUSCRIPT firm growth, and firms that operate in countries with strong institutions can obtain external capital and grow faster (Demirguc-Kunt and Maksimovic, 1999). However, countries with weak institutions in terms of legal rules, low investor protections, and low quality of law enforcement are likely to be characterized by narrower capital markets (La Porta et al., 1997), which limits the capital available to firms to fund profitable investments that increase shareholders’ returns. Similarly, weak institutions, legal inefficiencies, weak
T
protection of property rights, and high risk of expropriation are identified as the main factors
IP
limiting the growth of firms’ capital (Papaionannou, 2009). Weak institutions distort lenders’
CR
ability to channel resources to fund profitable investments efficiently (Law et al., 2014). A subset of institutional quality is property rights. Institutional quality significantly
US
increases firm value because of its impacts on both firm-level investment and financing decisions, which confirms the importance of property rights protections (Berkowitz et al.,
AN
2015). In an earlier study, Beck et al. (2005) also show that a subset of institutional quality, property rights protection, enhances firm value.
M
According to the supply side theory which is largely embraced in the law and finance literature, strong creditor protection which is a subset of institutional quality, induces lenders to
ED
provide credit on more favorable terms (La Porta et al., 1997), resulting in firms using more debt. As developing countries’ institutional quality improves, lenders are more willing to grant
PT
credit (e.g., debt capital) to firms. Moreover, the trade-off theory maintains that firms’ decision to use more debt is the outcome of the trade-off between the costs (e.g. bankruptcy costs) and
CE
the benefits (interest tax-shield) of debt (Myers, 1984). Based on the trade-off theory, more debt in institutional settings, with higher tax-shield and lower bankruptcy costs, would be
AC
expected. Thus, holding firms’ assets and investment plans constant, firms in developing countries with strong institutions and legal enforcement are more likely to increase debt in their capital structure because better institutional quality encourages lenders to lend money and it lowers bankruptcy costs, resulting in firms using more debt to take advantage of tax-shield benefits of debt interest. Capital structure research is usually conducted within the framework of trade-off theory, agency theory and pecking order theory. The trade-off theory argues for the existence of an optimal capital structure that balances the interest tax-shield benefits of debt against the bankruptcy costs of debt (Frank and Goyal, 2009). Similarly, agency theory argues for the 7
ACCEPTED MANUSCRIPT existence of an optimal capital structure that minimizes total agency costs (Jensen, 1986). Conversely, the pecking order theory argues that there is no clearly defined optimal capital structure, rather due to information asymmetry costs, firms follow the hierarchy of financing, first using retained earnings, followed by debt, and then equity issues (Frank and Goyal, 2009). This current paper investigates the effects of institutional quality on capital structure within the framework of Myers (1984) trade-off theory. This paper argues that better institutional quality
T
and legal enforcement encourages lender to lend money and it lowers bankruptcy costs,
2.2.
CR
IP
resulting in firms using more debt to take advantage of debt interest tax-shield benefits. Empirical Review
Turning to empirical studies, research to date on capital structure within the Myers (1984)
US
tradeoff theory framework has given inadequate attention to the importance of institutional factors (at aggregate level) on capital structure. On the importance of institutional quality,
AN
researchers (e.g. Belkhir et al., 2016; Fan et al., 2012; Oztekin and Flannery, 2012; Jong et al., 2008) provide argument supporting the need to focus on institutional factors as a determinant of
M
firms’ capital structure. Although several studies (e.g. Demirguc-Kunt and Maksimovic, 1999
ED
and Bancel and Mittoo, 2004) have investigated the effects of macroeconomic factors on capital structure, they focus on limited aspects of institutions measures. This study extends this line of research using aggregated (single index) and disaggregated measures of institutional
PT
quality to thoroughly disentangle the effects of institutio nal quality on capital structure. Belkhir et al. (2016) examine firm-level and country-level determinants of firm capital
CE
structure decisions in Middle East and North Africa (MENA) region. Using 444 listed firms from 10 countries over the 2003 to 2011 period, they find that MENA firms have target debt
AC
towards which they adjust over time. Moreover, they examine the indirect effects of institutional quality (institutional quality * size and fixed assets) on debt but find that the interaction of institutional quality and fixed assets is significantly and negatively related to debt. Conversely, the interaction of institutional quality (except regulatory effectiveness * size) and size has insignificantly effects on debt. Belkhir et al. (2016) research focuses on MENA region where forces of demand and supply may not fully determine the prices of financial assets (due to regulatory authorities’ intervention) in some countries (e.g. Middle Eastern countries). Conversely, we focus on large sample of listed firms (3891) from 23 developing
8
ACCEPTED MANUSCRIPT countries where forces of demand and supply better determine the prices of financial assets. Moreover, we account for regional factor effects on capital structure by segregating the data into three regions – Asian region (with 2187 listed firms from 7 countries), African region (with 613 listed firms from 10 countries), and Latin American and Eastern European region (with 2187 listed firms from 6 countries), and report new findings that the effects of institutional quality on debt ratios is different among the regions. Only political stability and
T
voice and accountability affects debt ratios in the African region, while all the six components
IP
and single index measures of institutional quality affects debt ratios in the Asian and Latin
CR
American and Eastern European regions.
Fan et al. (2012) examine the effects of institutional environment (e.g. legal system, tax
US
treatment of debt and equity, and importance of regulation of financial institutions) on the capital structure and debt maturity of firms in 39 countries (25 developed and 14 developing countries).
AN
The authors’ findings reveal that a country’s legal and tax system, corruption, and preferences of capital suppliers explain the substantial variation in debt and debt maturity ratios. Fan et al.
M
(2012) also find that more debt is used in countries where there is greater tax gain from leverage, which is in accordance with our argument that firms operating in countries with strong
ED
institutional quality use more debt in order to maximize the tax-shield benefits of debt. Oztekin and Flannery (2012) compare firms’ capital structure adjustments across 37 countries (25
PT
developed and 12 developing countries); they investigate whether differences in disaggregated measure of institutional characteristics (i.e. legal origin, shareholder rights, creditor rights, financial emphasis, efficiency) explain the variation in estimated
CE
corporate transparency,
adjustment speeds. Applying the system generalized method of moments, they find that legal and
AC
financial factors significantly affect firms’ adjustment speeds. Oztekin and Flannery (2012) conclude that their results support the theory that better institutional quality reduces transaction costs, which is linked to firms’ adjustment of their debt. Our work is different from Fan et al (2012) and Oztekin and Flannery (2012) in significant ways. Firstly, we innovate using an aggregate index as the main measure of institutional quality to investigate the relationship between debt ratios and institutional quality using large sample of 3,891 listed firms from 23 developing countries, and report new findings that the aggregate index measure of institutional quality is positively related debt ratios in developing countries. Aggregate or single index is a comprehensive measure, and it eliminates multicollinearity 9
ACCEPTED MANUSCRIPT problem associated with the use of disaggregated measures in same model specification. This aggregate index approach has been used to investigate the relationship between economic growth and institutional quality. Secondly, we account for regional factor effects on capital structure by segregating the data into three regions. Our results reveal that only two sub-components of institutional quality affect debt ratios in the African region. Specifically, political stability negatively affects debt ratios while voice and accountability positively affects debt ratios in the
T
African region. Conversely, the six components of institutional quality (e.g. rule of law,
IP
regulatory quality, control of corruption, governance effectiveness, political stability, and voice
CR
and accountability) have positive effects on debt ratios in the Latin-American and Eastern European region and the Asian region.
quality,
governance effectiveness,
US
Furthermore, our disaggregated measures of institutional quality (rule of law, regulatory political stability,
voice and
accountability,
control of
AN
corruption) is different from disaggregated measures used by Oztekin and Flannery (2012). Moreover, we also conduct additional analysis and explore the indirect effects of institutional
M
quality (via combining some firm-specific factors and institutional quality variables) on capital structure. Additional analysis investigates the effects of legal enforcement on capital structure of
ED
firms, and we report new findings that legal enforcement has positive effects on both the book debt and market debt measures of capital structure.
PT
Focusing on the indirect effects of institutional and macroeconomic factors, Jong et al. (2008) use ordinary least squares method to analyze the importance of country-specific factors
CE
in the capital structure choices of firms from 42 developed and developing countries. They acknowledge that country-specific factors have direct effects on capital structure, but their
AC
analysis focuses mainly on the indirect effects of macroeconomic and institutional factors on the firm-specific determinants of capital structure. The findings by Jong et al. (2008) indicate that macroeconomic and institutional factors affect the roles of firm-specific determinants of capital structure. They conclude that it is necessary for future research to take into account the impact of macroeconomics and institutional factors in the analysis of firm capital structure. Motivated by the possibility that capital structure decisions may be influenced differently by institutional factors, Bancel and Mittoo (2004) use a literature survey approach to examine the roles of legal institutions in explaining the capital structure decisions of firms from 16 countries. More specifically, they use a literature survey approach to investigate whether capital 10
ACCEPTED MANUSCRIPT structure decisions are determined largely by the legal institutions of the home country or are the result of a complex interaction of several institutions in a country. Bancel and Mittoo (2004) findings show that firms’ capital structure decisions are influenced positively by their institutional environment. Moreover, their results suggest that firms determine their optimal capital structures by trading off benefits and costs of debt financing. Rather than a survey approach, this study uses empirical approach and investigates the effects of institutional quality
T
(using aggregated and disaggregated measures of institutional quality) on capital structure
IP
within the Myers (1984) tradeoff theory that postulate that firms balance the benefits and costs
CR
of debt to determine their optimal capital structure. Moreover, we separate the data into regions and report new findings that the effects of aggregated and disaggregated measures of
US
institutional quality are different across regions.
In an early study, Demirguc-Kunt and Maksimovic (1999) compare capital structure of firms
AN
from 11 developing and 19 developed countries. They find that institutional differences between developed and developing countries explain large fraction of the variation in firms’ capital
M
structure. Institutional factors also affect the capital structure of large and small firms differently. Likewise, Booth et al. (2001) investigate if capital structure theory is applicable to developing
ED
countries with different level of stock market and banking sector development. They conclude that capital structure in developing countries appear to be affected by similar factors (firm-
PT
specific and macroeconomic factors) identified in developed countries. Unlike Booth et al. (2001) and Demirguc-Kunt and Maksimovic (1999), we segregate the data into 3 regions and
CE
report new findings that only two components of institutional quality, namely, political stability (negative effect) and voice and accountability (positive effect) affect debt ratios in the African
AC
region while the six components of institutional quality have positive effects on debt ratios in the Latin American and Eastern European region and Asian region. Furthermore, this paper investigates the effects of institutional quality on firms’ capital structure in developing countries using large annual firm-level and country-level data of 3,891 listed firms from 23 developing countries covering the 2006 to 2014 time period. Moreover, in an additional analysis, this paper investigates the effects of legal enforcement on capital structure of firms’ in developing countries and report new findings that legal enforcement is positively related to both the book and market value measures of capital structure.
11
ACCEPTED MANUSCRIPT 3.
Data and Method
This section describes the data, justification for each variable, and hypothesis development. It then explains the method employed in this study. Moreover, the study’s main focus is on the trade-off theory of capital structure. 3.1.
Data and Descriptive Statistics
T
The full sample data consist of 3,891 listed firms from 23 developing countries. The paper
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defines developing countries based on their income level following World Bank classification.
CR
For the regional sample, the number of firms is 2,187 listed firms from 7 Asian countries, 1,091 listed firms from 6 Latin American and Eastern European countries, and 613 listed firms from
US
10 African countries. The years covered are 2006 to 2014. The data start from 2006 and end in 2014 due to data availability for capital structure (debt) determinants. In a robustness analysis,
AN
this paper focuses on 2010 to 2014 (periods after the crisis) periods to remove the impact of the financial crisis years on debt and institutional quality relationship. The financial crisis years
M
may have some influence on capital structure decisions (Adomako, 2014). Debt and capital structure are used interchangeably following conventional practice in the literature. Institutional
ED
quality is our main independent variable and it is obtained from the World Governance Indicators (WGI). Other macroeconomic data such as interest rate, inflation rate, bank credit to
PT
the private sector, market capitalization, and gross domestic product growth rate are obtained from the World Development Indicators (World Bank database) and are unbalanced panel data.
CE
The other firm-specific data were extracted from Datastream databases and are also unbalanced panel data. As part of the data-sampling process, financial firms are excluded
AC
because their financial statement differs significantly from that of non-financial listed firms. Furthermore, the paper excludes regulated firms (e.g. real estate investment trusts) because their debt ratio is usually higher than in other non-financial firms (Rajan and Zingales, 1995). The final full sample comprises 33,971 firm-year observations. The paper applies the winsorization technique as in Lemmon et al. (2008) to mitigate the effects of extreme values of some data on the estimated parameters. All the firm-level data used as control variables (e.g., fixed assets, profits, size, price-to-book ratio, non-debt tax-shield) are the traditional firm-level determinants of capital structure with clear predicted signs based on trade-off theory.
12
ACCEPTED MANUSCRIPT Moreover, the paper controls for firm age, ownership structure, dividend pay-out ratio, and other macroeconomic determinants of capital structure.
Unit of M easurement
M ean
Std dev.
M ini.
M axi.
the ratio of short-term debt plus long-term debt to total assets (property, plant and equipment). the ratio of book value of total debt to market value of equity plus book value to total debt. Aggregate Institutions Index [(Rol + Regq + GE + PS + VA + CC)/6]
0.2741
1.2588
0.0000
1.0000
0.3467
T
Table 1a Descriptive statistics for 3, 891 listed firms from 23 developing countries, 2006-2014
0.0000
1.0000
59.1932
14.7141
5.3734
84.8161
53.7313
15.2705
0.9390
89.4737
55.6094
16.4995
1.4563
93.3014
59.8978
16.7616
2.8708
87.3786
32.4812
22.1718
0.4717
83.9623
49.6918
17.7269
7.1090
84.3602
49.7276
17.5084
3.3175
91.3876
Int
Rule of Law: reflects perceptions of the extent to which agents have confidence and abide by society rules (ranges from 0 to 100) Regulatory Quality: reflects perceptions of the ability of the government to formulate & implement sound policies (ranges from 0 to 100) Governance Effectiveness: reflects perceptions of the quality of public services and the degree of its independence from political pressure (from 0 to 100) Political S tability: reflects perceptions of the likelihood that the government will be destabilize by violent means (ranges from 0 to 100) Voice & Accountability: reflects perceptions of the extent to which a country’s citizen are able to participate in selecting government (ranges from 0 to 100) Control of Corruption: reflects perceptions of the extent to which public power is exercised for gain (ranges from 0 to 100). Interest rate: annual interest rate
4.6320
7.3936
-42.3102
41.3454
Inf
Inflation: annual inflation rate. Growth in consumer price index
5.8909
3.7363
-0.6782
26.2398
Gdpg
Annual growth in nominal gross domestic product (in percentage)
5.1081
2.7396
-17.6690
14.0460
BC
Banking Credit: ratio of the domestic credit provided by the banking sector to gross domestic products (in percentages) Market Capitalization: ratio of stock market capitalization of listed firms to gross domestic products (in percentage) the ratio of property, plant and equipment to book value of total assets
47.6357
40.1663
0.0010
123.8840
80.6793
54.0112
7.8273
387.8241
0.3542
0.2440
0.0001
1.8240
The ratio of earnings before interest, tax and depreciation to book value of total assets the log of total assets
0.0510
1.9451
-2.8818
24.0402
14.6175
3.1427
1.2011
31.4936
PB
the ratio of book value of debt plus market value of equity to book value of total assets
2.6314
5.7226
3.9100
43.0000
Ndts
Ndts is the ratio of depreciation to total assets
0.0259
0.0424
0.0000
5.4915
Age
Firm-age: natural log of (one plus firm-age)
3.2736
0.9604
0.0000
5.5174
DPO
Dividend pay-out: natural log of (one plus percentage of dividend pay -
0.4447
0.7455
0.0000
0.8151
0.5427
0.4988
0.0000
1.0000
Tdmv Inst
CR
Rol Regq
US
GE
PS
AN
VA
ED
M
CC
PT
MC FA
CE
PRF
AC
Size
0.2878
IP
Tdbv
out) OWS
Ownership structure: dummy variable equal to 1 if managers own more than 5 percent shares and zero otherwise a
Notes: N*T Total Observations (33971) is for listed firms from 23 developing countries. Number of firms for each country and the 23 Countries covered are included in appendix 1. b Descriptive statistics for the regional panel (Asian countries’ listed firms, African countries’ listed firms, and Latin America combined with Eastern European countries’ listed firms) are not reported to save space. c They are available upon request.
13
ACCEPTED MANUSCRIPT Table 1b M ean value of institutional quality each year across regions, 2006-2014 2007
2008
2009
2010
2011
2012
2013
2014
Inst (African Region)
44.25
41.46
40.27
42.79
43.19
41.82
38.51
39.52
39.74
Inst (Asian Region)
57.56
50.43
51.90
58.91
50.42
45.71
45.66
46.91
49.63
Inst (Latin America & Eastern Europe Region
58.37
59.01
60.47
62.04
63.14
63.15
63.00
62.05
61.74
IP
CR
Note: Inst is the aggregate institutions index [Rol + Regq + GE + PS + VA + CC)/6]
T
2006
US
Table 1a above shows the descriptive statistics. The single aggregate institutional quality index (Inst) has a minimum value of 5.3734 and a maximum value of 84.8161. As expected, the mean value of the single aggregate institutional quality index falls within the minimum and maximum
AN
values. Moreover, market capitalization has the highest standard deviation value suggesting that it is the most volatile variable. Conversely, non-debt tax-shield has the lowest standard deviation
M
value suggesting that it is the least volatile. Moreover, Table 1b shows that the aggregate
ED
institutional quality index each year is lower for the African region compare to the other two regions (Asian, Latin America & Eastern European regions). We conduct panel unit root test to
3.2.
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confirm if the variables are stationary1 .
Variables Justification and Hypotheses Development
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The dependent variable is debt ratios and the paper uses two measures of capital structure (debt ratios). The paper uses book total debt ratio as main dependent variable because it is not
AC
affected by stock price changes compared to the market total debt ratios. Ratio of total debt-tototal assets is the primary and the most utilized measure of capital structure (Frank and Goyal, 2009; Bany-Ariffin, 2010). Previous studies mostly use either book total debt ratio or market total debt ratio as proxy for capital structure. But this paper uses book total debt ratio as main proxy of capital structure and market total debt ratio as a robustness tests. Specifically, capital structure (debt ratio) is measured as the ratio of book value of total debt to book value of total 1
The paper adopts the Levin-Lin-Chu [LLC] (2002), the Im-Pesaran-Shin [IPS] (2003), the Augmented Dickey and Fuller (1979), and PP-Fisher Chi-square (Phillips and Perron, 1988). Based on the results of the stationary test of each variable, it is clear that the variables have stationary characteristics because the null of the unit root are rejected.
14
ACCEPTED MANUSCRIPT assets (TDBV), and the ratio of book value of total debt to market value of equity plus book value to total debt (TDMV). The main independent variable is institutional quality (aggregate institutional quality which is a single index). This institutional quality data set is based on information gathered through cross-country surveys and expert polls. Kaufmann et al. (2009) apply unobserved components model which allow them to measure institutional quality using six indicators (rule law,
regulatory
quality,
governance
effectiveness,
political
stability,
T
of
voice
and
IP
accountability, and control of corruption) for many countries. As a measure of institutional
CR
quality, this paper averages the six indicators to obtain a single broader index (aggregate institutional quality index) as in Langbein and Knack (2010). Langbein and Knack (2010) note
US
that World Governance Indicators (WGI) measure the same underlying governance or institutional quality concept; although, the six indicators are meant to capture different concept.
AN
They also argue that the six institutional quality indicators are highly correlated. Researchers (e.g. Fan et al., 2012; Oztekin and Flannery, 2012; Jong et al., 2008) provide argument supporting the need to focus on institutional factors as a determinant of firms’ capital
M
structure. Fan et al. (2012) findings reveal that institutional factors such as country’s legal
ED
system and control of corruption explain substantial variation in debt ratios. Oztekin and Flannery (2012) find that legal and financial factors significantly affect firms’ adjustment
PT
speeds. They conclude that their results support the theory that better institutional quality reduces transaction costs that is linked to firms’ adjusting their debt. Bancel and Mittoo (2004)
CE
survey findings show that firms’ capital structure decisions are influenced by their institutional environment. Moreover, their survey results suggest that firms determine their optimal capital
AC
structure (debt) by trading off benefits and costs of debt financing. This paper argues that as developing countries institutional quality improves; lenders are more willing to grant credit (e.g. debt capital) to firms. Moreover, the trade-off theory maintains that firms’ decision to use more debt is the outcome of the trade-off between the costs (e.g. bankruptcy costs) and the benefits (interest tax-shield) of debt (Myers, 1984). Thus, holding the firms’ assets and investment plans constant, firms in developing countries with strong institutions are likely to increase debt because better institutional quality encourages lenders to lend money and it lowers bankruptcy costs, resulting in firms using more debt to capitalize on tax-shield benefits of debt interest. Therefore, our alternative hypothesis (HAlternate) 15
ACCEPTED MANUSCRIPT is that institutional quality has positive direct effects on listed firms’ debt ratios in developing countries. The null hypothesis (H0 ) is that institutional quality has no direct effects on listed firms’ debt ratios in developing countries. A rejection of the null hypothesis would support our alternative hypothesis. Moreover, it is possible that the effects of institutional quality on firms’ debt ratios are different across regions of developing countries. Therefore, this paper divides the samples into
T
three regions. The division of the samples into regions enables us to compare the effects of
IP
institutional quality on firms’ debt ratios across regions of developing countries. These
CR
divisions also enable us to focus on countries having relatively homogenous macroeconomic factors (Narayan et al., 2011). Additionally, the paper controls for variables that are established
US
in the trade-off theory of capital structure. The control variables are fixed assets, profits, size, price-to-book ratio, and non-debt tax shield. Fixed assets (FA) are the ratio of property, plant
AN
and equipment to book value of total assets. Based on the trade-off theory, fixed assets are positively related to debt because fixed assets can be used as collateral to obtain debt capital
M
(Frank and Goyal, 2009). Fixed assets are positively related to debt in several empirical studies (e.g. Flannery and Hankins, 2013; Matemilola et al., 2013; Oztekin and Flannery, 2012; Frank
ED
and Goyal, 2009). This paper expects fixed assets to be positively related to debt because fixed assets serve as collateral to obtained more debt capital.
PT
Profits are the ratio of earnings before interest, tax and depreciation to book value of total assets. According to the trade-off theory, profits are positively related to debt because
CE
profitable firms are able to repay principal amount borrowed plus interest, resulting in usage of more debt (Matemilola et al., 2013; Rajan and Zingales, 1995). Conversely, according to the
AC
pecking order theory, profits are negatively related to debt. Due to information asymmetry costs, firms prefer internal finance (profits) to debt. Thus, firms that generate more profits use less debt (Flannery and Hankins, 2013; Lemmon et al., 2008). Based on the trade-off theory, this paper expects profits to be positively related to debt because profitable firms can easily repay principal amount borrowed plus interest. Size is log of total assets as in Oztekin and Flannery (2012). The trade-off theory predicts that size is positively related to debt because bigger firm are more stable and less likely to go bankrupt (Frank and Goyal, 2009). Available evidence on the effects of size on debt give mixed results. Frank and Goyal (2009), Gormley and Matsa (2013), Matemilola et al. (2013), and 16
ACCEPTED MANUSCRIPT Oino and Ukaegbu (2015) find a positive relationship between debt ratios and size. Conversely, some authors find a negative relationship between debt ratios and size (Hanousek and Shamshur, 2011; Lemmon et al., 2008; Chakraborty, 2010). This paper expects size to be positively related to debt because bigger firms are more stable and less likely to go bankrupt. Price-to-book ratio (PB) is the ratio of book value of debt plus market value of equity to book value of total assets. From the perspective of the trade-off theory, growth firms should use
T
less debt because growth opportunity is intangible assets without collateral value if firms face
IP
bankruptcy (Myers, 1984). From the perspective of pecking order theory, growth firms that
CR
need finance should issue security with less information asymmetry costs (Frank and Goyal, 2009). Flannery and Hankins (2013) find growth opportunity is positively related to debt.
US
Conversely, Gormley and Matsa (2013) and Lemmon et al. (2008) report a negative relationship between debt and growth opportunity. Based on the trade-off theory, this paper
AN
expects price-to-book ratio to have a negative relationship with debt because it captures growth opportunity which is an intangible asset without collateral value.
M
Non-debt tax shield (Ndts) is the ratio of depreciation to total assets. Although non-debt tax shield does not consist of debt related costs, it acts as substitutes for tax shields and should
ED
be negatively related to debt (Oztekin and Flannery, 2012; De-Angelo and Masulis, 1980). Oztekin and Flannery (2012) and De-Angelo and Masulis (1980) find negative relationship
PT
between non-debt tax shield and debt. Conversely, Chakraborty (2010) and Oino and Ukaegbu (2015) report a positive relationship between debt and non-debt tax shield. In accordance with
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the trade-off theory, this paper expects non-debt tax shield to be negatively related to debt because alternative tax-shield sources reduce attractiveness of debt.
AC
Additional control variables such as ownership structure, dividend payout ratio, and firm age are included in the models to substantiate the validity of our findings. Following prior literature, ownership structure is measured as a dummy variable equal to 1 if managers own more than 5 percent shares and zero otherwise. Little is known about the effects of ownership structure on debt (Driffield et al., 2007). Limited available studies predict positive, negative, and insignificant effects of ownership structure on debt. For example, Anderson and Reeb (2003) and King and Santor’s (2008) findings reveal that insider (managerial) ownership has insignificant effects on debt. Harvey et al. (2004) find that higher managerial ownership levels provide benefits to controlling shareholders that are shared with other stakeholders, thereby, 17
ACCEPTED MANUSCRIPT reducing the need for debt as an internal control mechanism. Harvey et al. (2004) interpretation is consistent with the view of the agency theory. Likewise, Wang et al. (2018) report negative effects of ownership structure on debt, which is consistent with the view of the agency theory. Conversely, based on the signaling theory, managerial ownership is positively related to debt because managers signal growth opportunity by increasing debt. Consistent with the signaling theory, Kim and Sorensen (1986) document positive effects of managerial ownership on debt,
T
suggesting that managers use debt to signal growth opportunity. In an early study, Stulz (1988)
IP
argues that controlling shareholders should increase debt because it increases their voting
CR
control for a given level of equity investment; further, it reduces the risk of hostile takeover. This study expects ownership structure to be positively related to debt because managers signal
US
growth opportunity by increasing debt.
In accordance with previous literature, dividend payout is measured as natural log of (one
AN
plus percentage of profits paid out as dividends). The existing capital structure theories have ambiguous prediction on the relationship between dividend pay-out ratio and debt (Frank and
M
Goyal, 2009). However, Frank and Goyal (2009) findings indicate that firms that pay dividends have less debt. Conversely, Cooper and Lambertides (2018) results reveal that large dividend
ED
increases are followed by a significant increase in debt, which suggest that management increases dividend to use up excess debt capacity. Similarly, Bhaduri (2002) suggests that a
PT
dividend-paying firm signals its quality to outsiders; further, it can avoid an information premium and likely gain access to external sources of capital. As dividend payments indicate a
CE
signal of strong financial health and therefore higher debt-issuing capacity, this study expects dividend payout ratio to be positively related to debt.
AC
Firm age is the time between the initial creation of a firm and the present time, measured as natural log of (one plus firm age). The empirical evidence on the effect of firm age on debt is mixed. Firm age has negative but insignificant effects on the book–debt ratio (King & Santor, 2008), for example. In addition, firm age is negatively related to debt, indicating that older firms seem to value financial flexibility or unused debt capacity (DeAngelo, 2015). Consistent with this reasoning, Kieschnick and Moussawi (2018) document that firm age is negatively related to debt. Conversely, firm age is positively related to debt, according to Hovakimian et al. (2001) and Sundaresan et al. (2015), suggesting that aging firms have more assets-in-pace
18
ACCEPTED MANUSCRIPT which justify taking on more debt. This study expects a negative effect of firm age on debt because older firms appear to value financial flexibility. Macroeconomic factors have been argued to affect firms’ capital structure decisions (e.g. Oztekin and Flannery, 2012; Fan et al., 2012; Jong et al., 2008; Bancel and Mittoo, 2004; Booth et al., 2001; Demirguc-Kunt and Maksimovic, 1999). As macroeconomic factors such as interest rate, inflation rate, GDP growth rate, bank credits, stock market capitalization changes,
T
the firm would adjust their capital structure accordingly in response to favorable or unfavorable
IP
changes in macroeconomic factors. For instance, higher interest rate increases the costs of debt
CR
financing and discourages the firms to use more debt. Inflation rate is usually considered as a proxy for a government’s ability to manage the economy (Demirgu¨c¸-Kunt and Maksimovic,
provide debt capital, resulting in lower debt usage.
US
1999). As inflation rate increases, risk of lending to firms increases and lenders are unwilling to
AN
The firms’ capital structure decisions may be affected by the economic growth rate. As economic growth is believed to be correlated with firm growth which is a proxy for firm’s
M
investment opportunity set and its financing needs (Demirgu¨c¸-Kunt and Maksimovic, 1999). As the economic growth rate increases, it signals good economic prospects encouraging firms
ED
to use more debt (Cheng and Shiu, 2007). Moreover, the stock markets and banking sector developments affect capital structure of the firms. The financing patterns (either through the
PT
capital market or banks) fit the governance system in the sense that those to whom the governance system gives most power to influence the policies of firms would also be the main
CE
providers of capital (Antoniou et al., 2008; Fan et al., 2012). As the stock markets develop, transaction costs reduce, and firms are likely to raise capital through the stock markets lowering
AC
debt usage (Fan et al., 2012). Conversely, as banking sector develops, costs of borrowing reduce, and firms are likely to raise debt through the banks which increase debt usage (Booth et al., 2001). 3.3.
Econometric Model Prior studies (e.g. Oztekin, 2015; Flannery and Hankins, 2013; Oztekin and Flannery,
2012) have concluded that adjustment costs are nontrivial and that firm-fixed effects are important to capture unobserved firm-specific heterogeneity. The paper follows Oztekin and Flannery et al. (2012) and applies the standard partial adjustment model to capture the dynamic 19
ACCEPTED MANUSCRIPT adjustment toward the target capital structure. Rather than estimate a static panel model based on contemporaneous debt ratios, we estimate a dynamic panel model that produce an estimate of the unobserved target debt as well as the adjustment speed to the target debt, that is: Debtij ,t Debtij ,t 1 ( Debt *ij ,t Debtij ,t 1 ) ij ,t
(1)
Where λ is the average speed of adjustment (SOA) to the target capital structure each
T
period for all the sample firms, Debt*ij,t is the target debt level while Debtij,t and Debtij,t-1 are the
IP
current and lagged 1 period debt ratios, respectively. The paper uses two measures of debt
CR
(book total debt ratio and market total debt ratio). The model assumes that firm has a target debt level and adjust if there is a deviation from the target debt level. Full adjustment occurs
US
when λ =1 while λ =0 means there is no adjustment. In the partial adjustment model, the actual adjustment of debt should be between 0 and 1. The target debt or capital structure is unobservable, so, we proxy it with the fitted values from a regression of observed debt on a set
AN
of firms’ specific determinants and macroeconomic determinants of the target capital structure (Flannery and Hankins, 2013; Oztekin and Flannery, 2012) shown in equation 2.
M
Debtij ,t X ij ,t i t ij ,t
(2)
ED
Where Xijt represents the firm specific and macroeconomic determinants of capital structure, ηi and αt are firm specific effects and year fixed effects, respectively. After we substitute the target
PT
debt or target capital structure from Equation (2) into the partial adjustment model in Equation (1) and rearranging the terms, the estimation in a single equation becomes:
CE
Debtij ,t (1 ) Debtij ,t 1 X ij ,t i t ij ,t
AC
Debtij ,t (1 ) Debtij ,t 1 ( 1 2 INST jt 3 FAijt 4 PRFij ,t 5 Sizeij ,t 6 PBij ,t 7 Ndtsij ,t
(3a) (3b)
Agei j ,t DPOi j ,t OWSi j ,t 8 INT jt 9 INFjt 10MC jt 11BC jt 12Gdpg jt ) i t ij ,t
Where: Debtij,t Debtij,t-1 ß1 INSTjt FAij,t
= debt for the i firm in country j and t time (using book debt and market debt ratios as proxy for capital structure) = lagged 1 period debt ratios for the i firm in country j and t time = the constant = institutional quality for the j country and t time (single institutional quality index is the main measure, and disaggregated measures of institutional quality are robust check) = fixed assets for the i firm in country j and t time 20
ACCEPTED MANUSCRIPT
CR
IP
T
= profits for the i firm in country j and t time = size for the i firm in country j and t time = price-to-book ratio for the i firm in country j and t time = non-debt tax shield for the i firm in country j and t time = firm-age for the i firm in country j and t time = dividend pay-out ratio for the i firm in country j and t time = ownership structure for the i firm in country j and t time = interest rate for the j country and t time = inflation rate for the j country and t time = stock market capitalization for the j country and t time = bank credit to private sector for the j country and t time = gross domestic product growth rate for the j country and t time = the unobservable firm-specific effects = the year fixed effects = speed of adjustment to target debt level = the residual term
US
PRFij,t Sizeij,t PBij,t Ndtsij,t Ageij,t DPO ij,t OWSij,t INTjt INFjt MCjt BCjt Gdpgjt ηi αt 1-λ µijt
AN
Subscript 'i' ‘j’ and‘t’ represents a firm, country and time period, respectively. The model is estimated with two-step system generalized method of moments (GMM) because debt displays persistence behaviour (Lemmon et al., 2008). This suggests that previous
M
year debt affect current year debt. Moreover, the paper uses two-step system generalized
ED
method of moments because there is possibility of reverse causality between debt and the explanatory variables. Application of traditional ordinary least squares methods to estimate
PT
parameters in a dynamic model that include firm-specific effects and lagged debt variable would produce biased coefficients (Flannery and Hankins, 2013). Therefore, this paper applies
CE
the two-step system generalized method of moments because it is recognized as one of the best methods to estimate parameters of the target debt in the presence of firm-specific effects and
AC
lagged debt variable (Flannery and Hankins, 2013). Two-steps system generalized method of moments correct for endogenous and reverse causality problems between variables using an efficient instrumental variable technique (Blundell and Bond, 1998). Blundell and Bond (1998) two-step system generalized method of moments combine level-equation and differenceequation, and it better addresses reverse causality or endogenous problem using efficient instrumental variable technique. The two-step system generalized method of moments combine the difference generalized method of moments’ conditions in equation (4) and additional moment condition in equation (5) to produce unbiased estimators. E [∆µij,t * Debtt-k ] = E [∆µij,t * Xt-k ] = 0 k > 1.
(4) 21
ACCEPTED MANUSCRIPT Where: Debtt-k is the higher order lags of debt (dependent variable) and Xt-k is the higher order lags of independent variables used as internal instrument. In words, the correlation between the difference error-term and lagged debt variable used as internal instruments as well as lagged independent variables used as internal instruments equals zero. The firm-specific factors and institutional quality variable are treated as endogenous variables and the two-steps system generalized method of moments internal instruments are used to resolve the endogenous
T
problem. The lagged levels of the dependent variable (debt) used as instruments in the difference
IP
generalized method of moments become weak instrument if it is persistent (Blundell and Bond,
CR
1998). Thus, the two-steps system generalized method of moments adds additional moment conditions:
US
E [∆Debtij,t * ηi] = 0
(5)
AN
In words, the correlation between the difference instruments (∆Debtij,t ), and unobservable firmspecific effects (ηi) in the level equation equals zero. In all estimations, the paper uses two-step
M
estimates because this method uses the first-step errors to construct heteroskedasticity-consistent
4.
ED
standard errors or optimal weighting matrices (Blundell and Bond, 1998)).
Empirical Results
PT
This section presents correlation results for the full sample and panel regressions (for the full sample and three regional subsamples, separately) that estimate the effects of aggregated
CE
(single-index) and disaggregated measures of institutional quality on capital structure (book debt and market debt ratios), controlling for firm-level and macroeconomic factors. The book
AC
total debt ratio is our main proxy for capital structure and market total debt ratio is used to check the robustness of our findings to alternative measures of capital structure. Additionally, an aggregated (single-index) measure of institutional quality is the main focus; the six disaggregated measures of institutional quality are used as robustness checks. Our estimation is based on the two-step system of generalized method of moments (GMM) that accounts for the possibility that the error terms are heteroskedastic and serially correlated across both firm-level and country-level observations. 4.1.
Full Sample Results
22
ACCEPTED MANUSCRIPT Tables 2a and 2b contain the correlation results. The correlation results reveal that the degree of association between most of the variables is weak because the correlation coefficients are generally lower among the independent variables and statistically significant. Thus, there is little risk of multi-collinearity among the independent variables. However, the correlation analysis confirms that the six institutional quality indicators are highly correlated (see Table 2a). Therefore, our main results average the six indicators to obtain a single index (aggregated Table 2a Correlations results for 3,891 listed firms from 23 developing countries, 2006-2014 Rol
Regq
GE
PS
1.00 0.14a -0.01c -0.01b -0.03a -0.02b -0.02b 0.02b -0.02b 0.01b 0.02b 0.01b 0.03b -0.02b
1.00 -0.07a -0.02a -0.13a -0.05a -0.10a 0.06a -0.09a 0.02a 0.11a 0.06a 0.13a -0.05a
1.00 0.84a 0.93a 0.87a 0.89a 0.52a 0.94a -0.04a -0.46 a -0.20 a 0.38 a 0.34 a
1.00 0.70a 0.79a 0.64a 0.34a 0.82a -0.09a -0.40a -0.08 a 0.37a 0.40a
1.00 0.84a 0.85a 0.34a 0.90a -0.05a -0.50a -0.28a 0.37 a 0.31a
1.00 0.72a 0.21a 0.83a -0.14a -0.44a -0.12a 0.39 a 0.40 a
1.00 0.38a 0.80a -0.07a -0.41a -0.29a 0.38a 0.27a
AN
M
VA
CC
CR
Inst
Int
Inf
Gdpg
BC
MC
1.00 0.17a -0.13a -0.04a -0.04a
1.00 0.23a -0.51a -0.34a
1.00 -0.33a -0.09a
1.00 -0.34a
1.00
US
Tdmv
1.00 0.36a 0.24a -0.08a -0.11a -0.24a 0.09a
1.00 0.07a -0.49a -0.24a 0.31a 0.34a
ED
Tdbv Tdmv Inst Rol Regq GE PS VA CC Int Inf Gdpg BC MC
Tdbv
IP
T
institutional quality index), as in Langbein and Knack (2010).
AC
CE
PT
Notes: See Table 1 for the definition of variables and measurements.
23
ACCEPTED MANUSCRIPT
Table 2b Correlations results for 3,891 listed firms from 23 developing countries, 2006-2014 Tdbv
Tdmv
Inst
Rol
Regq
GE
PS
VA
CC
FA
PRF
Size
PB
Ndts
Age
DPO
OWS
T P
Tdbv
1.00
Tdmv
0.14a
1.00
Inst
-0.01c
-0.07a
1.00
Rol
-0.01b
-0.02a
0.84a
1.00
Regq
-0.03a
-0.13a
0.93a
0.70a
1.00
GE
-0.02b
-0.05a
0.87a
0.79a
0.84a
1.00
PS
-0.02b
-0.10a
0.89a
0.64a
0.85a
0.72a
1.00
VA
0.02b
0.06a
0.52a
0.34a
0.34a
0.21a
0.38a
1.00
M
CC
-0.02b
-0.09a
0.94a
0.82a
0.90a
0.83a
0.80a
0.36a
1.00
FA
0.02b
0.10a
-0.08a
-0.07a
-0.06a
-0.06a
-0.07a
-0.06a
0.06a
1.00
PRF
0.06b
0.02a
-0.02 a
-0.02a
-0.02a
-0.03a
-0.02a
-0.01a
-0.01a
0.05a
1.00
Size
0.01b
0.11a
-0.22 a
-0.31 a
-0.22a
-0.24a
-0.23a
0.16a
-0.31a
0.06a
0.03a
1.00
PB
0.03b
0.12a
0.10 a
0.20a
0.17 a
0.04a
0.05a
-0.01a
0.03a
-0.02a
0.07a
0.05a
1.00
Ndts
-0.04b
-0.08a
0.12 a
0.18a
0.04a
0.03 a
0.08a
0.02a
0.09a
0.24a
-0.04a
0.03a
-0.07a
1.00
Age
0.01b
0.03a
-0.04a
-0.04a
-0.04a
-0.05a
-0.09a
0.06a
-0.03a
0.03a
0.00
0.18a
-0.02a
0.02a
1.00
DPO
-0.02b
-0.14a
-0.03a
-0.02a
-0.02a
-0.00
-0.03a
-0.05a
-0.00
0.00
0.01b
0.17a
0.00
0.00
0.13a
1.00
OWS
0.01b
0.06a
-0.07a
-0.03a
-0.12a
0.15a
-0.07a
-0.18a
-0.10a
0.04a
0.01b
0.13a
-0.02a
0.02a
0.05a
0.09a
I R
C S
E C
C A
A
D E
T P
U N
1.00
24
ACCEPTED MANUSCRIPT Notes: See Table 1 for the definition of variables and measurements. The correlation results reported is for the full sample, and ‘Inst’ is the single index measure of institutional quality. Correlation results for the regional subsamples (Asian countries’ listed firms, African countries’ listed firms, and Latin American and Eastern European countries’ listed firms) and disaggregated measures of institutional quality are not reported to save space. They are available upon request. a and b indicate correlation coefficient is significant at 1 and 5 per cent, respectivel
T P
I R
C S
U N
A
D E
M
T P
E C
C A
25
ACCEPTED MANUSCRIPT
Tables 3 and 4 report the main effects of a single aggregated institutional quality index and six disaggregated institutional quality measures for the full sample of 3,891 listed firms from 23 developing countries. The diagnostic checks on the two-steps system generalized method of moments reveal the following. The models passed the AR (2) tests, as indicated by the
T
insignificant p-values showing the absence of second-order serial correlation. Overall, the
IP
validity of the instruments and the additional instruments is confirmed, as indicated by the
CR
insignificant p-values of the difference in Hansen tests in the models. Moreover, in all estimations, the number of ‘N’ cross-sectional observations is greater than the number of
US
instruments, supporting the validity of the estimations. In the empirical results, the book total debt ratio is our main proxy for capital structure and the market total debt ratio is used to check
AN
the robustness of our findings to alternative measures of capital structure. The lagged dependent variable is statistically significant at the 1% level in all the models,
M
which supports the relevance of the dynamic model for capital structure research. Intuitively, the results suggest that if firms deviate from their target debt they make adjustments. The
ED
empirical results for the full sample show that the single aggregated institutional quality index is statistically significant and positively related to the book total debt ratio (see Table 3, Model
PT
1a). As a robustness test, the single aggregated institutional quality index is statistically significant and positively related to the market total debt ratio (see Table 4, Model 2a).
CE
Additionally, the six disaggregated measures of institutional quality are statistically significant and positively related to both the book total debt ratio (see Table 3, Models 1b to 1g) and the
AC
market total debt ratio (see Table 4, Models 2b to 2g). Furthermore, all of the six disaggregated measures of institutional quality are included together in a single model2 , and the results are broadly similar. But the coefficient of most of the institutional quality variables reduces. Moreover, the coefficient of the lagged debt (debtit-1 ), firm-specific and other macroeconomic variables changes (see Table 3, Model 1h and Table 4, Model 2h). The dynamic panel model specification and two-step system generalized method of moment enable us to model the dynamic behavior of firms’ capital structure. Specifically, the empirical results also show that 2
The Stata software automatically removes the aggregated (single index) measure of institutional quality from the single model during estimation because of multicollinearity. 26
ACCEPTED MANUSCRIPT firms make some adjustments to their target debt when there is a deviation from the target debt level, consistent with the dynamic version of trade-off theory. The speed of adjustment to the target debt level is calculated as 1-λ, where λ is the coefficient of the lagged debt variables. Previous researchers (e.g., Matemilola and Ahmad, 2015), Flannery and Hankins (2013), Oztekin and Flannery (2012)) find evidence that firms adjust to their target debt level. Our empirical results also show that firms make faster adjustments to their target debt, especially
T
the book debt ratio, when there is a deviation from the target debt level; this is consistent with
IP
the dynamic version of trade-off theory.
CR
Additionally, firm-specific factors (i.e. fixed assets, profits, size, firm age, and ownership structure) and macroeconomic factors (e.g. interest rate, inflation rate, banking credit, market
US
capitalization, and annual growth rate in nominal gross domestic product) consistently affect both the book and market debt ratios in the full sample, similar to previous findings in the
AN
literature.
M
Table 3 Direct effects of institutional quality on Book-debt models
System-GM M Two-step Estimation Results using Book-debt and 3,891 Listed Firms from 23 Developing Countries, 2006-2014
0.2797*** (67.63)
INST (Single Institutional 0.0014*** Quality Index) (3.68)
M odel 1e With PS
M odel 1f With VA
M odel 1g With CC
M odel 1h (Single Equation)
0.2859*** 0.2789*** (67.56) (67.91)
0.2759*** 0.2888*** (70.95) (61.93)
0.2907*** (66.14)
0.2759*** 0.666*** (70.17) (96.22)
0.0003** (2.27)
-
-
-
PT
TDBVit-1 (Lag Book-debt)
ED
M odel 1a With Single M odel 1b M odel 1c M odel 1d INST Index With ROL With REGQ With GE
0.0001** ROL (Rule of law) (2.02) 0.0014** 0.0007*** REGQ (Regulatory quality) (2.65) (2.81) 0.0010** 0.0007*** GE (Govt. Effectiveness) (2.51) (3.09) 0.0011*** 0.0005*** PS (Political Stability) (6.54) (3.80) 0.0021*** 0.0004*** VA (Voice & Account.) (2.92) (2.87) 0.0010*** 0.0003* CC (Control of Corruption) (6.10) (1.89) 0.0793*** 0.0719*** 0.0717*** 0.0686*** 0.0960*** 0.0587*** 0.0987*** 0.1072*** FA (Fixed Assets) (5.60) (5.16) (5.19) (4.91) (6.90) (4.35) (6.72) (9.05) -0.4930*** -0.0506*** -0.4922*** -0.4845*** -0.5129*** -0.5156*** -0.4839*** -0.4458*** PRF (Profits) (-9.89) (-9.56) (-10.11) (-6.51) (-5.13) (-8.57) (-6.65) (-16.11) 0.0070*** 0.0081*** 0.0084*** 0.0111*** 0.0098*** 0.0139*** 0.0070*** 0.0039*** Size (6.08) (7.11) (7.86) (8.30) (8.48) (8.43) (4.91) (5.77) -0.0002** -0.0002** -0.0001* -0.0002 -0.0002** -0.0001* -0.0001** -0.0001 PB (Price-to-book ratio) (-2.75) (-2.29) (-1.94) (-1.65) (-2.03) (-1.80) (-2.16) (-0.32)
CE
-
AC
-
27
ACCEPTED MANUSCRIPT -0.0224** (-2.02) 0.0137** (2.51) -0.0043** (-2.72) -0.0855** (-2.66) 0.0007*** (4.69) 0.0017*** (6.97) -0.0005** (-2.32) 0.0003*** (3.45) -0.0002** (-2.19) 0.8939
-0.0250** (-2.39) 0.0344*** (5.77) -0.0043** (-2.62) -0.0327 (-0.93) 0.0003** (2.16) 0.0017*** (7.06) -0.0007*** (-3.14) 0.0002** (2.15) -0.0001** (-2.51) 0.8480
-0.0246*** (-2.85) 0.0029 (0.41) -0.0049*** (-3.13) -0.0666* (-1.72) 0.0006*** (3.75) 0.0018*** (7.29) -0.0004** (-2.61) 0.0006*** (6.77) -0.0001** (-2.02) 0.8402
-0.0568*** -0.0556*** (-2.74) (-5.97) 0.0200*** 0.0115*** (2.89) (4.21) -0.0025 -0.0049 (-1.38) (-1.42) -0.1439*** -0.0159*** (-4.47) (-2.71) 0.0006*** 0.0003** (4.06) (2.31) 0.0019** 0.0015*** (7.75) (4.70) -0.0002** -0.0009*** (-2.23) (-2.94) 0.0005*** 0.0002** (4.70) (2.17) -0.0001** -0.0001** (-2.59) (-2.46) 0.9023 0.6221
0.1130 153 1.66
0.1136 153 1.71
0.1239 153 1.77
0.2214 153 1.69
0.16081 153 1.56
0.1040 153 1.64
0.1922 223 2.59
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.114
0.121
0.139
0.134
0.175
0.163
0.182
3891
3891
3891
3891
3891
3891
3891
30058
30058
30058
30058
30058
30058
30058
IP
CR
US
AN
T
-0.0261** -0.0264** (-2.32) (-2.35) 0.0264*** 0.0279*** (5.48) (5.33) -0.0055*** -0.0053*** (-3.59) (-3.44) -0.0445 -0.0862** (-1.47) (-2.69) 0.0025*** 0.0006*** (3.53) (4.06) 0.0031** 0.0018*** (2.23) (7.25) -0.0016** 0.0004 (-2.09) (1.68) 0.0037*** 0.0005*** (7.65) (4.62) -0.0020*** -0.0001** (-12.66) (-2.22) 0.8569 0.8809
M
-0.0376*** Ndts (Non-debt tax-shield) (-2.84) 0.0313*** Age (Firm-age) (5.71) -0.0037 DPO (Dividend pay-out) (-1.56) -0.1408*** OWS (Ownership structure) (-4.65) 0.0007*** Int (Interest rate) (4.49) 0.0019*** Inf (Inflation rate) (7.76) Gdpg (Economic growth -0.0013** rate) (-2.39) 0.0005*** BC (Banking Credit) (5.08) -0.0006** M C (M arket Capitalization) (-2.34) AR(2) [P-value] 0.8817 Difference Hansen Tests [P-value] 0.1060 Instruments 153 Variance Inflation Factor 1.55 Heteroscedasticity test (p val.) 0.00 Cross-dependency test (pval.) 0.142 Cross-sectional observation(N) 3891 Observ after estimation (N*T) 30058
Notes: a See Table 1 for the definition of variables and measurements. Asterisks indicate significance at 1% (***) and 5%
CE
PT
ED
(**). b T-statistics (in parenthesis) of the Two-step System-GMM model are based on Windmeijer-corrected standard errors. c 2nd order serial correlation in first difference is distributed as N (0, 1) under the null of no serial correlation in the residuals. d Difference-in-Hansen over identification test and null that instruments are valid. e TDBVit-2 , FA it-2, PRFit-2 , Sizeit-2 , Ndts it-2 , PBit-2 , Ageit-2 , DPOit-2 , INSTit-2 , ROLit-2 , REGQit-2 , GEit-2 , PSit-2 , VA it-2 , and CCit-2 are used as Instruments. f In all estimations, the number of ‘N’ cross -sectional observation (3,891) is greater than the number of instruments (153 & 223). g Industry dummies are included in all the estimations. The lists of the industries are in appendix II. Table 4 Direct effects of institutional quality on M arket-debt models
AC
System-GM M Two-step Estimation Results using M arket-debt and 3, 891 Listed Firms from 23 Developing Countries (Robustness Tests), 2006-2014 M odel 2a With Single M odel 2b M odel 2c M odel 2d M odel 2e M odel 2f M odel 2g M odel 2h INST Index With ROL With REGQ With GE With PS With VA With CC (Single Equation)
0.5252*** 0.5371*** 0.5418*** 0.5475*** 0.5330*** 0.5449*** 0.5262*** 0.4679*** TDM Vit-1 (Lag M arket-debt)(53.72) (55..71) (55.44) (56.98) (54.25) (56.14) (53.77) (33.78) INST (Single Institutional 0.0012*** Quality Index) (4.52) 0.0006** ROL (Rule of law) (2.27) REGQ (Regulatory quality) -
-
GE (Govt. Effectiveness)
-
-
-
-
0.0014*** (5.43) 0.0002** (2.04)
-
-
-
-
-
-
-
-
-
0.0012*** (4.14) 0.0002** (2.40) 0.0001* (1.87)
28
ACCEPTED MANUSCRIPT 0.0029*** (11.81) 0.0017*** (7.33) 0.0002** (2.01)
0.1554*** (8.69) -0.0019** (-2.10) 0.0021** (2.01)
0.1479*** 0.0562*** 0.1274*** (8.22) (3.77) (7.22) -0.0086*** -0.0108*** -0.0008** (-4.84) (-5.81) (-2.40) 0.0009*** 0.0078*** 0.0057*** (2.94) (5.11) (4.69)
0.13021*** (4.21) -0.0211*** (-3.72) 0.0014** (2.09)
-
-
-
-
VA (Voice & Account.)
-
-
-
-
CC (Control of Corruption) -
-
-
0.1140*** (6.89) -0.0035** (-2.05) 0.0003** (2.28)
0.1318*** (7.53) -0.0003** (-2.19) 0.0030*** (2.93)
0.1376*** (7.72) -0.0018** (-2.04) 0.0003** (2.30)
FA (Fixed Assets) PRF (Profits) Size
T
-
0.0023*** (12.63) 0.0003*** (3.14) 0.0005** (2.07)
PS (Political Stability)
IP
-0.0001*** -0.0001*** -0.0001*** -0.0001*** -0.0001** -0.0003*** -0.0004*** -0.0001*** (-5.39) (-5.22) (-3.92) (-3.89) (-2.44) (-5.36) (-6.84) (-5.38) -0.0903 -0.0239 -0.0399 -0.0639 -0.0116 -0.0691 -0.1154 -0.0542* Ndts (Non-debt tax-shield) (-0.67) (-0.15) (-0.27) (-0.38)) (-0.07) (-0.52) (-0.72) (-1.92) 0.0470*** 0.0345*** 0.0376*** 0.0400*** 0.0506*** 0.0151** 0.0550*** 0.0182*** Age (Firm-age) (10.52) (8.05) (8.19) (9.74) (10.44) (2.37) (10.35) (3.65) -0.0357*** -0.0359*** -0.0378*** -0.0331*** -0.0395*** -0.0392*** -0.0329*** -0.0190*** DPO (Dividend pay-out) (-12.43) (-13.10) (-13.29) (-12.59) (-13.88) (-13.38) (-11.45) (-2.94) -0.1947*** -0.0855*** -0.0512* -0.0820*** -0.0721** -0.1089*** -0.2402*** 0.0460*** OWS (Ownership structure) (-6.47) (-2.83) (1.87) (-3.05) (-2.38) (-3.05) (-6.90) (3.92) 0.0042*** 0.0045*** 0.0029*** 0.0032*** 0.0032*** 0.0029*** 0.0035*** 0.0017*** Int (Interest rate) (12.57) (14.30) (8.99) (10.56) (9.59) (10.87) (11.29) (6.09) 0.0065*** 0.0064*** 0.0058*** 0.0059*** 0.0060*** 0.0046*** 0.0064*** 0.0041*** Inf (Inflation rate) (13.41) (13.42) (11.82) (12.33) (12.49) (10.29) (12.93) (7.89) Gdpg (Economic growth -0.0038*** -0.0040*** -0.0029*** -0.0039*** -0.0040*** -0.0032*** -0.0033*** 0.0015*** rate) (-8.79) (-9.48) (-6.72) (-8.96) (-9.24) (-7.81) (-7.80) (3.26) 0.0011*** 0.0007*** 0.0011*** 0.0008*** 0.0013*** 0.0005*** 0.0009*** 0.0010*** BC (Banking Credit) (9.94) (5.88) (9.40) (7.75) (12.03) (6.98) (7.67) (7.27) -0.0008*** -0.0009*** -0.0007*** -0.0009*** -0.0008*** -0.0009*** -0.0008*** -0.0006*** M C (M arket Capitalization) (-18.01) (-19.75) (-16.07) (-20.41) (-17.95) (-20.68) (-18.11) (-12.26) 0.2018 0.1899 AR(2) [P-value] 0.1018 0.1022 0.1053 0.1117 0.1035 0.3015 Difference Hansen Tests [P-value] 0.1116 0.1299 0.2014 0.3610 0.2122 0.1549 0.1503 0.1041 153 223 Instruments 153 153 153 153 153 153 1.57 2.77 Variance Inflation Factor 1.54 1.69 1.75 1.64 1.68 1.43 Heteroscedasticity test (pval.) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00 Cross-dependency test (pval.) 0.159 0.173 0.181 0.195 0.194 0.152 0.177 0.183 Cross-sectional observation(N) 3891 3891 3891 3891 3891 3891 3891 3891 Observ. after estimation (N*T) 30058 30058 30058 30058 30058 30058 30058 30058
AC
CE
PT
ED
M
AN
US
CR
PB (Price-to-book ratio)
Notes: a See Table 1 for the definition of variables and measurements. Asterisks indicate significance at 1% (***) and 5%
(**). b T-statistics (in parenthesis) of the Two-step System-GMM model are based on Windmeijer-corrected standard errors. c 2nd order serial correlation in first difference is distributed as N (0, 1) under the null of no serial correlation in the residuals. d Difference-in-Hansen over identification test and null that instruments are valid. e TDMVit-2 , FA it-2, PRFit-2 , Sizeit-2 , Ndts it-2 , PBit-2 , Ageit-2 , DPOit-2 , INSTit-2 , ROLit-2 , REGQit-2 , GEit-2 , PSit-2 , VA it-2 , and CCit-2 are used as Instruments. f In all estimations, the number of ‘N’ cross -sectional observation (3,891) is greater than number of instruments (153 & 223). g Industry dummies are included in all the estimations. The lists of the industries are in appendix II.
4.2.
Regional Sample Results
29
ACCEPTED MANUSCRIPT Similarly, for the estimation that focuses on 2,741 listed firms from 7 Asian countries, the models passed the AR (2) tests, as indicated by the insignificant p-values suggesting the absence of second-order serial correlation. Overall, the validity of the instruments and additional instruments is confirmed, as indicated by the insignificant p-values of the difference in Hansen tests in all the models. Additionally, in all the estimations, the number of cross-sectional observations exceeds the number of instruments, which indicates that the estimations are valid.
T
The empirical results reveal that the single aggregated institutional quality index is statistically
IP
significant and positively related to both the book total debt ratio (see Table 5, Model 3a) and the
CR
market total debt ratio (see Table 6, Model 4a). Also, the six disaggregated measures of institutional quality are statistically significant and positively related to both the book total debt
US
ratio (see Table 5, Models 3b to 3g) and the market total debt ratio (see Table 6, Models 4b to 4g). Additional analysis include all of the six disaggregated measures of institutional quality
AN
together in a single model and the results are broadly similar. However, the coefficient of most of the institutional quality variables reduces. Moreover, the coefficient of the lagged debt (debt it-1 ),
M
firm-specific and other macroeconomic variables change (see Table 5, Model 3h and Table 6, Model 4h). The empirical results also show that Asian firms make adjustments to the target debt
ED
(especially for the book debt ratio) when there is a deviation from their target debt level, consistent with the dynamic version of trade-off theory. The speed of adjustments to the target
PT
debt level is calculated as 1-λ, where λ is the coefficient of the lagged debt variables. Moreover, firm-specific factors (i.e. fixed assets, profits, size, price-to-book ratio, dividend
CE
pay-out ratio, and ownership structure) and macroeconomic factors (i.e. interest rate, inflation rate, banking credit, and market capitalization) consistently affect the debt ratios in the Asian
AC
region, similar to previous findings in the literature. However, the effect of firm-age on debt ratios is mixed but we find more evidence that firm-age has a positive effect on the market debt ratio, consistent with some previous findings in the literature. Specifically, our results are consistent with Hovakimian (2001) and Sundaresan et al. (2015) findings that firm age is positively related to debt, suggesting that aging firms have more assets-in-pace which justify taking on more debt. Conversely, our results are inconsistent with King and Santor (2008) findings that firm age has negative but insignificant effects on the book–debt ratio. Our results are also inconsistent with Deangelo (2015) and Kieschnick and Moussawi (2018) findings that
30
ACCEPTED MANUSCRIPT firm age is negatively related to debt, indicating that older firms seem to value financial flexibility or unused debt capacity. Likewise, for the estimation that focuses on 1,091 listed firms from six Latin American and Eastern European countries, the models passed the AR (2) tests, indicating an absence of second-order serial correlation. Overall, we also confirm that the instruments are valid, as indicated by the insignificant p-values of the difference-in-Hansen tests in all the models.
T
Furthermore, all the model estimations are confirmed as valid because the number of cross-
IP
sectional observations is greater than the number of instruments. The empirical results indicate
CR
that the single aggregated institutional quality index is statistically significant and positively related to both the book total debt ratio (see Table 7, Model 5a) and the market total debt ratio
US
(see Table 8, Model 6a). Also, the six disaggregated measures of institutional quality are statistically significant and positively related to both the book total debt ratio (see Table 7,
AN
Models 5b to 5g) and the market total debt ratio (see Table 8, Models 6b to 6g). Furthermore, all the six disaggregated measures of institutional quality are included together in a single
M
model and the results are broadly similar. But the coefficient of most of the institutional quality variables change. Moreover, the coefficient of the lagged debt (debtit-1 ), firm-specific and other
ED
macroeconomic variables changes (see Table 7, Model 5h and Table 8, Model 6h). Additionally, firm-specific factors (i.e. fixed assets, profits, size, price-to-book ratio, and
PT
dividend pay-out ratio) and macroeconomic factors (i.e. annual growth rate in nominal gross domestic product) consistently affect the debt ratios in the Latin-American and Eastern
CE
European region, similar to previous findings in the literature. However, the effect of firm-age on debt ratios is mixed, but we find more evidence that firm-age has a negative effect on the
AC
market debt ratio, consistent with some previous findings in the literature. Our results are consistent with Deangelo (2015) and Kieschnick and Moussawi (2018) findings that firm age has a negative effect on debt, indicating that older firms seem to value financial flexibility or unused debt capacity. Conversely, our results are inconsistent with Hovakimian (2001) and Sundaresan et al. (2015) findings that firm age has a positive effect on debt, suggesting that aging firms have more assets-in-pace which justify taking on more debt. Similarly, ownership structure has a mixed effect on debt ratios, consistent with previous findings in the literature. Specifically, ownership structure is positively related to book debt ratio, but it is negatively related to market debt ratio. Wang et al. (2018) report negative effects 31
ACCEPTED MANUSCRIPT of ownership structure on debt, which is consistent with the view of the agency theory. Likewise, Harvey et al. (2004) find that higher managerial ownership levels provide benefits to controlling shareholders that are shared with other stakeholders, thereby, reducing the need for debt as an internal control mechanism. Conversely, managerial ownership is positively related to debt because managers signal growth opportunity by increasing debt, consistent with the
T
signaling theory.
0.1925*** (23.48)
0.2100*** (26.60)
0.2268 (27.99)
0.0066*** (3.33)
-
INST (Single Institutional 0.0252*** Quality Index) (7.35)
CR
M odel 3c M odel 3d With REGQ With GE
M odel 3e With PS
M odel 3f With VA
M odel 3g With CC
US
M odel 3a With Single M odel 3b INST Index With ROL
0.2545*** 0.2199*** 0.2534*** 0.2245*** (31.31) (29.08) (27.72) (25.79)
AN
TDBVit-1 (Lag Book-debt)
IP
Table 5 System-GM M Two-step Estimation Results using Book-debt and 2,187 Listed Firms from 7 Asian Countries, 2006-2014
-
-
0.6696*** (88.19)
-
0.0012*** ROL (Rule of law) (4.18) 0.0159*** 0.0007** REGQ (Regulatory quality) (5.77) (2.19) 0.0061** 0.0004* GE (Govt. Effectiveness) (2.01) (1.84) 0.0042** 0.0009*** PS (Political Stability) (2.17) (4.39) 0.0095** 0.0022*** VA (Voice & Account.) (2.29) (9.90) 0.0065*** 0.0001*** CC (Control of Corruption) (3.01) (3.09) 0.7538*** 0.7625*** 0.8389*** 0.4752*** 0.8374*** 0.7506*** 0.8232*** 0.1375*** FA (Fixed Assets) (9.30) (10.07) (11.91) (6.48) (12.40) (8.82) (10.21) (8.89) -0.0445*** -0.0095*** -0.0162*** -0.0905*** -0.0487*** -0.0928*** -0.0975*** -0.0075** PRF (Profits) (-3.94) (-5.49) (-5.85) (-5.45) (-7.33) (-6.14) (-6.00) (-2.28) 0.0909*** 0.0465*** 0.0085** 0.0409*** 0.0261*** 0.0434*** 0.0506*** 0.0026*** Size (8.72) (5.19) (2.09) (4.38) (3.25) (3.72) (5.37) (2.77) -0.3338*** -0.3237*** -0.3771*** -0.3177*** -0.3533*** -0.3428*** -0.3587*** -0.0025*** PB (Price-to-book ratio) (-9.35) (-5.66) (-5.25) (-5.87) (-5.41) (-8.15) (-10.15) (-5.88) 0.1111*** 0.0759*** 0.0867*** 0.0942*** 0.0839*** 0.0909*** 0.0319*** 0.0237*** Ndts (Non-debt tax-shield) (6.47) (6.76) (6.26) (7.19) (7.04) (7.65) (5.31) (4.10) -0.1228** -0.0473 0.1203** 0.0320 -0.0675* -0.0732 -0.0639 0.0029 Age (Firm-age) (-2.47) (-1.04) (2.54) (0.71) (-1.65) (-1.07) (-1.29) (0.19) -0.481*** -0.4526*** -0.2626*** -0.2516*** -0.3039*** -0.2247*** -0.4906*** 0.0157*** DPO (Dividend pay -out) (-20.25) (-18.43) (-11.05) (-10.43) (-14.49) (-8.55) (-18.75) (3.11) 0.9943*** 0.9569*** -0.2153 0.1768** 0.6033*** 0.7147* 0.9468*** 0.0442*** OWS (Ownership structure) (5.28) (15.08) (-0.84) (2.05) (2.93) (1.93) (4.11) (4.50) 0.0023** 0.0072** 0.0034** 0.0057** 0.0068** -0.0001 0.0039** 0.0001* Int (Interest rate) (2.67) (2.18) (2.05) (2.08) (2.24) (-0.01) (2.10) (1.82) 0.0065** 0.0049** 0.0109*** 0.0005* 0.0097*** 0.0044 0.0095** 0.0009** Inf (Inflation rate) (2.17) (2.35) (3.30) (1.79) (2.87) (1.54) (2.25) (2.40) Gdpg (Economic growth 0.0173** 0.0151** 0.0061** 0.0108*** 0.0072** 0.0039** 0.0102** 0.0008** rate) (2.64) (2.41) (2.08) (2.86) (2.29) (2.65) (2.57) (2.44) BC (Banking Credit) 0.0032**** 0.0013** 0.0042*** 0.0008** 0.0020** 0.0003** 0.0029*** 0.0001*
AC
CE
PT
ED
M
-
M odel 3h (Single Equation)
32
ACCEPTED MANUSCRIPT (3.47) -0.0008** (-2.67) 0.2897
(2.00) -0.0002** (-2.45) 0.2863
(2.08) -0.0007** (-2.13) 0.2864
(2.25) (2.83) -0.0006** -0.0006** (-2.41) (-2.18) 0.3024 0.2860
(1.87) 0.0001** (2.01) 0.1060
0.1364 153 3.18
0.1358 153 3.98
0.1149 153 3.28
0.2228 153 3.19
0.2118 153 3.14
0.1367 153 3.22
0.1008 223 3.39
0.000
0.000
0.000
0.000
0.000
0.000
0.00
0.139
0.233
0.221
0.181
0.299
0.195
0.206
2187
2187
2187
2187
2187
16998
16998
16998
16998
IP
T
(2.18) -0.0002** (-2.59) 0.2979
16998
2187
2187
16998
16998
CR
(2.81) -0.0009** M C (M arket Capitalization) (-2.16) AR(2) [P-value] 0.2841 Difference Hansen Tests [P-value] 0.3082 Instruments 153 Variance Inflation Factor 3.29 Heteroscedasticity test (p val.) 0.000 Cross-dependency test (pval.) 0.176 Cross-sectional observation(N) 2187 Observ after estimation (N*T) 16998
Notes: a See Table 1 for the definition of variables and measurements. Asterisks indicate significance at 1% (***), 5% (**)
AN
US
and 10% (*). b T-statistics (in parenthesis) of the Two-step System-GMM model are based on Windmeijer-corrected standard errors. c 2nd order serial correlation in first difference is distributed as N (0, 1) under the null of no serial correlation in the residuals. d Difference-in-Hansen over identification test and null that instruments are valid. e TDBVit-2 , FA it-2, PRFit-2 , Sizeit-2 , Ndts it-2 , PBit-2 , Ageit-2 , DPOit-2 , INSTit-2 , ROLit-2 , REGQit-2 , GEit-2 , PSit-2 , VA it-2 , and CCit-2 are used as Instruments. f In all estimations, the number of ‘N’ cross -sectional observation (2,187) is greater than the number of instruments (153 & 223). g Industry dummies are included in all the estimations. The lists of the industries are in appendix II.
M
Table 6 Direct effects of institutional quality on M arket-debt models System-GM M Two-step Estimation Results using M arket-debt and 2,187 Listed Firms from 7 Asian Countries (Robustness Tests), 2006-2014
ED
M odel 4a With Single M odel 4b M odel 4c M odel 4d INST Index With ROL With REGQ With GE
M odel 4e With PS
M odel 4f With VA
M odel 4g With CC
M odel 4h(Single Equation
0.5496*** 0.5587*** 0.5283*** 0.5418*** 0.4779*** (45.57) (48.90) (43.48) (45.76) (32.85)
INST (Single Institutional Quality Index)
-
PT
0.5519*** 0.5307*** 0.5617*** TDM Vit-1 (Lag M arket-debt) (47.24) (43.78) (47.86) 0.0009*** (2.84) 0.0004** (2.14)
0.0002** ROL (Rule of law) (2.08) 0.0029*** 0.0006** REGQ (Regulatory quality) (6.62) (2.03) 0.0004** 0.0003** GE (Govt. Effectiveness) (2.10) (2.63) 0.0012*** 0.0007** PS (Political Stability) (3.17) (2.05) 0.0014*** 0.0048*** VA (Voice & Account.) (5.12) (10.99) 0.0024*** 0.0010** CC (Control of Corruption) (8.93) (2.70) 0.0494** 0.1232*** 0.0712** 0.1311*** 0.0280** 0.0704** 0.0875*** 0.0277*** FA (Fixed Assets) (2.03) (3.97) (2.26) (4.13) (2.18) (2.33) (3.05) (2.93) -0.0067** -0.0124*** -0.0109*** -0.0096*** -0.0076*** -0.0002** -0.0064*** -0.0170*** PRF (Profits) (-5.95) (-10.25) (-9.40) (-7.65) (-6.77) (-2.22) (-5.63) (-3.21) 0.0914** 0.0034** 0.0026*** 0.0051*** 0.0040** 0.0012** 0.0023** 0.0144*** Size (2.23) (2.35) (2.70) (2.92) (3.24) (2.46) (2.15) (7.96) -0.0121*** -0.0131*** -0.0170*** -0.0134*** -0.0113*** -0.0137*** -0.0141*** -0.0054*** PB (Price-to-book ratio) (-6.36) (-6.52) (-7.76) (-6.59) (-6.21) (-6.94) (-6.96) (-6.26)
CE
-
-
-
AC
-
33
ACCEPTED MANUSCRIPT -0.1641** -0.0343 -0.3394** -0.0155* 0.3108 (-2.56) (-0.35) (-2.32) (-1.94) (0.75) 0.0535*** 0.0234*** 0.0562*** 0.0157*** 0.0037* (7.94) (3.91) (7.69) (3.06) (1.81) -0.0269*** -0.0263*** -0.0383*** -0.0273*** -0.0507*** (-5.95) (-6.16) (-8.25) (-6.39) (-5.45) 0.1893*** 0.1100*** 0.1319*** 0.0370 0.0315 (3.65) (2.88) (3.74) (1.21) (1.52) 0.0040*** 0.0049*** 0.0034*** 0.0045*** 0.0021*** (9.12) (10.65) (7.44) (9.96) (4.01) 0.0067*** 0.0069*** 0.0048*** 0.0077*** 0.0058*** (11.48) (12.14) (8.40) (12.30) (8.33) -0.0023** -0.0031*** -0.0022*** -0.0032*** 0.0018* (-2.74) (-3.68) (-2.60) (-3.92) (1.82) 0.0004** 0.0003* 0.0005** 0.0004*** 0.0001** (2.68) (1.93) (4.23) (3.50) (2.09) -0.0006*** -0.0006*** -0.0006*** -0.0006*** -0.0004*** (-8.23) (-9.01) (-7.92) (-9.60) (-4.98) 0.1141 0.1053 0.2018 0.1421 0.1214
0.1221 153 2.88
0.1405 153 3.97
0.1347 153 3.21
0.1099 153 3.99
0.1361 153 3.23
0.1212 153 3.07
0.1108 223 3.51
0.000
0.000
0.000
0.000
0.000
0.000
0.00
0.329
0.400
0.422
0.390
0.300
0.206
0.209
2187
2187
2187
2187
2187
2187
2187
16998
16998
16998
16998
16998
16998
AN
US
CR
IP
T
-0.1414** -0.2014* (-2.28) (-1.74) 0.0070*** 0.0390*** (5.45) (5.57) -0.0290*** -0.0205*** (-6.52) (-4.67) 0.1337*** 0.1897*** (3.07) (4.24) 0.0045*** 0.0045*** (9.91) (9.95) 0.0063*** 0.0068*** (10.27) (11.03) -0.0038*** -0.0009* (-4.42) (-1.81) 0.0002** 0.0002*** (2.40) (6.09) -0.0007*** -0.0006*** (-9.26) (-8.54) 0.1120 0.2239
M
-0.0458* (-1.77) 0.0090* Age (Firm-age) (1.71) -0.0278*** DPO (Dividend. pay -out) (-6.44) 0.0455 OWS (Ownership structure) (1.49) 0.0041*** Int (Interest rate) (9.11) 0.0062*** Inf (Inflation rate) (10.07) Gdpg (Economic growth -0.0029*** rate) (-3.34) 0.0001** BC (Banking Credit) (2.09) -0.0007*** M C (M arket Capitalization) (-10.00) AR(2) [P-value] 0.1039 Difference Hansen Tests [P-value] 0.1416 Instruments 153 Variance Inflation Factor 3.18 Heteroscedasticity Test (p val.) 0.000 Cross-dependency test (pval.) 0.327 Cross-sectional observation(N) 2187 Observ. after estimation (N*T) 16998 Ndts (Non-debt tax-shield)
16998
Notes: a See Table 1 for the definition of variables and measurements. Asterisks indicate significance at 1% (***), 5% (**)
AC
CE
PT
ED
and 10% (*). b T-statistics (in parenthesis) of the Two-step System-GMM model are based on Windmeijer-corrected standard errors. c 2nd order serial correlation in first difference is distributed as N (0, 1) under the null of no serial correlation in the residuals. d Difference-in-Hansen over identification test and null that instruments are valid. e TDMVit-2 , FA it-2, PRFit-2 , Sizeit-2 , Ndts it-2 , PBit-2 , Ageit-2 , DPOit-2 , INSTit-2 , ROLit-2 , REGQit-2 , GEit-2 , PSit-2 , VA it-2 , and CCit-2 are used as Instruments. f In all estimations, the number of cross -sectional observation (2,187) is greater than the number of instruments (153 & 223). g Industry dummies are included in all the estimations. The lists of the industries are in appendix II.
Table 7 Direct effects of institutional quality on Book-debt models System-GM M Two-step Estimation Results using Book-debt and 1,091 Listed Firms from 6 Latin American combined with Eastern European Countries, 2006-2014 M odel 5a M odel 5b M odel 5c M odel 5d With Single With ROL With REGQ With GE
M odel 5e M odel 5f With PS With VA
M odel 5g M odel 5h (Single
34
ACCEPTED MANUSCRIPT INST Index
TDBVit-1 (Lag Book-debt)
0.4708*** (50.38)
INST (Single Institutional 0.0033*** Quality Index) (9.01)
With CC
Equation)
0.4875*** 0.4739*** 0.4795*** 0.4732*** 0.4696*** 0.4850*** 0.7422*** (50.12) (49.79) (49.49) (52.57) (53.41) (49.16) (46.88)
-
-
0.0004** ROL (Rule of law) (2.42) 0.0034** REGQ (Regulatory quality) (2.15) 0.0002** GE (Govt. Effectiveness) (2.54) 0.0012*** 0.0002** PS (Political Stability) (6.66) (2.58) 0.0028*** 0.0010*** VA (Voice & Account.) (9.41) (3.24) 0.0012*** 0.0011*** CC (Control of Corruption) (5.18) (5.16) 0.1107*** 0.0870*** 0.1340*** 0.0805*** 0.1237*** 0.1173*** 0.1497*** 0.0178** FA (Fixed Assets) (7.53) (8.35) (8.04) (6.57) (10.30) (10.92) (8.07) (2.44) -0.4142*** -0.4281*** -0.4115*** -0.4171*** -0.4085*** -0.4208*** -0.4196*** -0.4440*** PRF (Profits) (-6.59) (-8.41) (-7.74) (-7.00) (-4.47) (-8.39) (-7.12) (-9.27) 0.0145*** 0.0085*** 0.0148*** 0.0167*** 0.0034* 0.0145*** 0.0107*** 0.0085*** Size (6.71) (4.01) (6.78) (6.70) (1.90) (6.82) (11.14) (8.69) -0.0001*** -0.0001*** -0.0001*** -0.0001*** -0.0001*** -0.0001*** -0.0001*** -0.000 PB (Price-to-book ratio) (-6.60) (-8.16) (-5.62) (-8.92) (-6.52) (-9.48) (-6.34) (-1.18) 0.0977*** 0.0966*** 0.0200*** 0.0723*** 0.0463*** 0.0849*** 0.0211*** 0.0136* Ndts (Non-debt tax-shield) (15.31) (14.00) (14.30) (15.76) (13.80) (15.81) (13.10) (1.99) -0.0117* -0.0103 0.0233*** -0.0008 -0.0036 0.0087 -0.0200*** -0.0010** Age (Firm-age) (-1.95) (-1.41) (2.86) (-0.11) (-0.62) (1.34) (-3.93) (-2.41) 0.0258*** 0.0322*** 0.0206*** 0.0279*** 0.0253*** 0.0236*** 0.0330*** 0.0059** DPO (Dividend pay -out) (9.27) (9.56) (9.18) (10.36) (7.64) (8.41) (8.96) (2.01) 0.1775** 0.1085** 0.2090** 0.2078** 0.0292** 0.0800* 0.1368* 0.0217* OWS (Ownership structure) (2.27) (2.49) (2.22) (2.32) (2.25) (1.96) (1.80) (1.93) -0.0001 0.0001 0.0001 -0.0011*** 0.0009*** 0.0001 0.0003 -0.0008*** Int (Interest rate) (-0.42) (0.35) (0.09) (-3.95) (3.13) (0.10) (0.83) (4.29) 0.0014 0.0022** -0.0010 0.0013 0.0029*** -0.0012 0.0029** 0.0012* Inf (Inflation rate) (1.53)) (2.11) (-1.45) (1.26) (3.15) (-1.31) (2.68) (1.80) Gdpgr (Economic growth 0.0015*** 0.0015*** 0.0015*** 0.0018*** 0.0012*** 0.0016*** 0.0014*** 0.0010*** rate) (4.87) (4.38) (4.67) (5.54) (4.12) (5.16) (4.07) (3.18) 0.0006**** 0.0004*** 0.0007*** 0.0004** 0.0006*** 0.0002 0.0001* 0.0001 BC (Banking Credit) (3.08) (2.30) (4.32) (2.16) (3.51) (1.16) (1.95)) (1.33)
T
-
CE
PT
ED
M
AN
US
CR
IP
0.0017*** (5.91) 0.0036*** (9.79) 0.0038*** (8.26)
AC
-0.0003*** -0.0005*** -0.0004*** -0.0004*** -0.0003*** -0.0005*** M C (M arket Capitalization) (-3.56) (-5.69) (-5.75) (-6.11) (-3.44) (-6.83) AR(2) [P-value] 0.2696 0.2706 0.2765 0.2727 0.2775 0.2678 Difference Hansen Tests [P-value] 0.1823 0.1759 0.1946 0.1850 0.1948 0.1805 Instruments 153 153 153 153 153 153 Variance Inflation Factor 2.37 2.39 2.48 2.71 1.97 1.93 Heteroscedasticity test (p-val.) 0.000 0.000 0.000 0.000 0.000 0.000 Cross-dependency test (p-val.) 0.321 0.253 0.214 0.263 0.251 0.219 Cross-sectional observation(N) 1091 1091 1091 1091 1091 1091 Observ after estimation (N*T) 8436 8436 8436 8436 8436 8436
-0.0005*** -0.0003*** (-5.46) (-6.15) 0.2748 0.3780 0.1934 153 1.89
0.1031 223 2.87
0.000
0.000
0.242
0.236
1091
1091
8436
8436
35
ACCEPTED MANUSCRIPT Notes: a See Table 1 for the definition of variables and measurements. Asterisks indicate significance at 1% (***), 5% (**)
and 10% (*). b T-statistics (in parenthesis) of the Two-step System-GMM model are based on Windmeijer-corrected standard errors. c 2nd order serial correlation in first difference is distributed as N (0, 1) under the null of no serial correlation in the residuals. d Difference-in-Hansen over identification test and null that instruments are valid. e TDBVit-2 , FA it-2, PRFit-2 , Sizeit-2 , Ndts it-2 , PBit-2 , Ageit-2 , DPOit-2 , INSTit-2 , ROLit-2 , REGQit-2 , GEit-2 , PSit-2 , VA it-2 , and CCit-2 are used as Instruments. f In all estimations, the number of ‘N’ cross -sectional observation (1,091) is greater than the number of instruments (153 & 223).
M odel 6c M odel 6d With REGQ With GE
M odel 6e With PS
0.4822*** 0.5009*** (37.46) (38.67)
0.5057*** (39.06)
0.4781*** (36.91)
0.4541*** 0.4621*** (33.57) (34.51)
IP
M odel 6a With Single M odel 6b INST Index With ROL
M odel 6f With VA
CR
US
TDM Vit-1 (Lag M arketdebt)
T
Table 8 Direct effects of institutional quality on M arket-debt models System-GM M Two-step Estimation Results using M arket-debt and 1,091 Listed Firms from 6 Latin American combined with Eastern European Countries (Robustness Tests), 2006-2014
M odel 6g With CC
M odel 6h (Single Equation)
0.5182*** (40.99)
0.3729*** (22.91)
0.0004* (1.85) 0.0007** (2.52) 0.0036*** (4.99) 0.0006** (2.15) 0.0022*** (3.81) 0.0014*** (3.76) 0.1410*** (4.89) -0.0044*** (-3.58) 0.0252*** (11.38) -0.0001*** (-17.35) -0.0458*** (-5.31) -0.001 (1.27) -0.0316*** (-4.96) -0.1464*** (-4.34) 0.0012*** (3.26) 0.004 (1.18) 0.0031*** (7.99) 0.0005** (2.04) -0.0016
-
-
0.0012*** (4.11)
-
GE (Govt. Effectiveness)
-
-
-
PS (Political Stability)
-
-
-
VA (Voice & Account.) CC (Control of Corruption)
-
-
-
-
0.0009*** (8.27) 0.0010*** (3.45)
0.0642*** (7.84) -0.0133** (-2.28) 0.0244*** (16.81)
0.0626*** (7.01) -0.0099* (-1.85) 0.0190*** (14.92)
0.0746*** (6.52) -0.0092** (-1.93) 0.0190*** (11.09)
0.0412*** (4.86) -0.0097* (-1.87) 0.0270*** (13.59)
0.0382*** (4.22) -0.0103** (-2.02) 0.0206*** (13.13)
0.0662*** (7.61) -0.0140** (-2.21) 0.0231*** (14.63)
0.0005** (2.36) 0.0674*** (7.47) -0.0121* (-1.89) 0.0205*** (14.48)
-0.0001 (-0.28) 0.4222*** (6.42) -0.0029 (-0.58) -0.0151*** (-9.72) -0.1165** (-2.18) -0.0003 (-1.11) 0.0054*** (5.14) 0.0039*** (10.28) 0.0008*** (5.02) -0.0025***
-0.0001*** (-8.91) 0.8057** (4.14) -0.0095* (-1.91) -0.0120*** (-6.76) -0.2383*** (-3.93) -0.0004 (-1.32) 0.0087*** (8.07) 0.0034*** (9.06) 0.0011*** (7.56) -0.0022***
-0.0001*** (-7.79) 0.7929*** (3.87) 0.0049 (0.80) -0.0176*** (-10.01) -0.4333*** (-6.77) 0.0002* (1.93) 0.0065*** (6.17) 0.0028*** (7.68) 0.0007*** (3.40) -0.0023***
-0.0001*** (-8.67) 0.3280*** (4.72) -0.0059 (-1.15) -0.0067*** (-3.90) -0.3643*** (-5.89) 0.0004* (1.90) 0.0045*** (4.27) 0.0025*** (6.72) 0.0005** (2.05) -0.0023***
-0.0001*** (-14.08) -0.3659* (-1.71) -0.0338** (-6.66) 0.0009 (0.56) -0.2471*** (-4.80) 0.0014*** (5.21) 0.0101*** (9.33) 0.0034*** (9.36) 0.0002 (1.50) -0.0024***
Size
-0.0001*** -0.0001*** (-8.54) (-9.43) 0.5212** 0.3483* Ndts (Non-debt tax-shield) (2.57) (1.81) -0.0145*** -0.0180*** Age (Firm-age) (-3.17) (-3.53) -0.0124** -0.0082*** DPO (Dividend pay -out) (-7.38) (-4.82) OWS (Ownership -0.3227*** -0.3736*** structure) (-5.46) (-5.73) 0.0000 0.0013*** Int (Interest rate) (0.32) (4.78) 0.0068 0.0034*** Inf (Inflation rate) (6.31) (3.08) Gdpg (Economic growth 0.0031*** 0.0030*** rate) (8.50) (8.08) 0.0005** 0.0001* BC (Banking Credit) (2.51) (1.99) M C (M arket -0.0022*** -0.0027***
AC
PB (Price-to-book ratio)
0.0026*** (6.53)
M
ED
PT
PRF (Profits)
CE
FA (Fixed Assets)
AN
INST (Single Institutional 0.0012*** Quality Index) (3.56) 0.0003* ROL (Rule of law) (1.99) REGQ (Regulatory quality) -
-
-
-
-
-
-
-
-
-
-
36
ACCEPTED MANUSCRIPT (-23.39) 0.1041
(-21.17) 0.1168
(-21.15) 0.1700
(-22.57) 0.1968
(-23.02) 0.1636
(-17.23) 0.1996
0.1549 153 2.63
0.1251 153 2.37
0.1411 153 2.62
0.1825 153 2.01
0.1563 153 2.11
0.1691 153 1.85
0.1842 223 2.83
0.000
0.000
0.000
0.000
0.000
0.000
0.00
0.205
0.149
0.216
0.227
0.285
0.279
0.237
1091
1091
1091
1091
1091
1091
1091
8436
8436
8436
8436
8436
T
(-23.72) 0.1137
8436
8436
IP
Capitalization) (-21.66) AR(2) [P-value] 0.2831 Difference Hansen Tests [P-value] 0.1338 Instruments 153 Variance Inflation Factor 2.19 Heteroscedasticity test (p-val.) 0.000 Cross-dependency test (p-val.) 0.303 Cross-sectional observation(N) 1091 Observ after estimation (N*T) 8436
Notes: a See Table 1 for the definition of variables and measurements. Asterisks indicate significance at 1% (***), 5% (**)
AN
US
CR
and 10% (*). b T-statistics (in parenthesis) of the Two-step System-GMM model are based on Windmeijer-corrected standard errors. c 2nd order serial correlation in first difference is distributed as N (0, 1) under the null of no serial correlation in the residuals. d Difference-in-Hansen over identification test and null that instruments are valid. e TDMVit-2 , FA it-2, PRFit-2 , Sizeit-2 , Ndts it-2 , PBit-2 , Ageit-2 , DPOit-2 , INSTit-2 , ROLit-2 , REGQit-2 , GEit-2 , PSit-2 , VA it-2 , and CCit-2 are used as Instruments. f In all estimations, the number of ‘N’ cross -sectional observation (1,091) is greater than the number of instruments (153 & 223). g Industry dummies are included in all the estimations. The lists of the industries are in appendix II.
Turning to the estimation that focuses on 613 listed firms from 10 African countries, the
M
results also reveal that the models passed the AR (2) tests, as indicated by the insignificant pvalues suggesting the absence of second order serial correlation. The validity of the instruments
ED
and the additional instruments is confirmed, as indicated by the insignificant p-values of the difference-in Hansen tests in all the models. Additionally, in all the estimations, the number of
PT
cross-sectional observation exceeds the number of instruments, indicating that the estimations are valid. However, the empirical results show that the single aggregated institutional quality
CE
index has insignificant effects on both the book total debt ratio (see Table 9, Model 7a) and the market total debt ratio (see Table 10, Model 8a). Also, the six disaggregated measures of
AC
institutional quality (except voice and accountability and political stability) have insignificant effects on both the book total debt ratio (see Table 9, Models 7b to 7g) and the market total debt ratio (see Table 10, Models 8b to 8g). Furthermore, all of the six disaggregated measures of institutional quality are included together in a single model and the results are broadly similar. But the regulatory quality becomes marginally significant in the book debt model and the coefficient of most of the institutional quality variables changes. Moreover, firm-specific factors (i.e. fixed assets, profits, size, price-to-book ratio, and firm age) and macroeconomic factors (i.e. banking credit, market capitalization, inflation, and annual growth rate in nominal gross domestic product) consistently affect the debt ratios in the 37
ACCEPTED MANUSCRIPT African sample, similar to previous findings in the literature. However, ownership structure has a mixed effect on debt ratios, consistent with past findings in the literature. Precisely, we find more evidence that ownership structure has a positive effect on book debt ratio, but it has mostly insignificant effect on market debt ratio. Our results are consistent with the signaling theory argument that managerial ownership is positively related to debt because managers signal growth opportunity by increasing debt. Conversely, our results are inconsistent with
T
Anderson and Reeb (2003) and King and Santor’s (2008) findings that insider (managerial)
IP
ownership has an insignificant effects on debt. Our results are also inconsistent with Harvey et
CR
al. (2004) findings that higher managerial ownership levels provide benefits to controlling shareholders that are shared with other stakeholders, thereby, reducing the need for debt as an
US
internal control mechanism.
Asian countries region results and the Latin-American and Eastern European countries
AN
(except most results from African countries region) region results reject the null hypothesis (H0 ) that institutional quality has no direct effects on listed firms’ debt ratios in developing countries. Thus, our alternative hypothesis that institutional quality has positive direct effects
M
on listed firms’ debt ratios is supported. The results suggest that as these developing countries
ED
institutional quality improves; lenders are more willing to grant credit (e.g. debt capital) to firms. Thus, holding the firms’ assets and investment plans constant, firms in developing
PT
countries with strong institutions are more likely to increase debt because better institutional quality encourages lender to lend money and it lowers bankruptcy costs, resulting in firms
CE
using more debt to capitalize on tax-shield benefits of debt interest. Conversely, most of the African countries region results fail to reject the null hypothesis (H0 ) that institutional quality
AC
has no direct effects on listed firms’ debt ratios in developing countries. Most African countries are characterized by low institutional quality and poor creditors’ protections (Fosu, 2013). Moreover, Table 1b compares the level of institutional quality across region and it reveals that the African region appears to have lowest level of institutional quality over time. Thus, this may partly explain why institutional quality has insignificant effects on debt ratios for the African countries region. The overall results (except most results from the African countries region) are consistent with Fan et al. (2012) findings that institutional factors such as country’s legal system and tax explain some variation in debt ratios. Fan et al. (2012) also find that more debt is used in 38
ACCEPTED MANUSCRIPT countries where there is greater tax gain from leverage which is in accordance with our argument that firms operating in countries with strong institutional quality use more debt because better institutional quality encourages lender to lend money and it lowers bankruptcy costs, resulting in firms using more debt to capitalize on tax-shield benefits of debt interest. Our results are consistent with Oztekin and Flannery (2012) findings that legal and financial factors significantly affect speed of adjustments to target debt. Our results are also in agreement with
T
Jong et al. (2008) findings that country specific factors have direct effects on debt. Comparing
IP
African region results (Tables 9 and 10) with Asian region results (Tables 5 and 6), our
CR
empirical findings reveal that Asian firms adjust faster to target debt (book debt) when they deviate from their target debt level.
US
In addition to institutional quality being a determinant of capital structure, firm-specific factors (e.g., fixed assets, profits, size, price-to-book ratio, and dividend pay-out ratio)
AN
consistently explain the capital structure decisions of firms in both the regional subsamples and the full sample, with correct signs. Macroeconomic factors (e.g., bank credit-to-private sector
M
ratio, stock market capitalization ratio) have significant effects on both the book and market debt ratios. Specifically, bank credit to the private sector is positively related to debt ratios while the
ED
stock market capitalization is negatively related to debt ratios in both the regional subsamples and full sample. These results are consistent with previous researchers’ (e.g., Matemilola and
PT
Ahmad, 2015; Flannery and Hankins, 2013; Chakraborty, 2010; Frank and Goyal, 2009)
AC
CE
findings.
Table 9 Direct effects on institutional quality on Book-debt models System-GM M Two-step Estimation Results using Book-debt and 613 Listed Firms from 10 African Countries, 2006-2014
M odel 7a With Single M odel 7b INST Index With ROL
M odel 7c M odel 7d With REGQ With GE
M odel 7e With PS
M odel 7f With VA
M odel 7g With CC
M odel 7h (Single Equation
39
ACCEPTED MANUSCRIPT
0.5523*** TDBVit-1 (Lag Book-debt) (55.00) INST (Single Institutional 0.0003 Quality Index) (1.40)
0.5429*** (51.52)
0.5608*** 0.5481*** 0.5524*** (53.41) (53.20) (46.29)
0.5550*** (44.13)
0.5526*** 0.5579*** (50.15) (41.72)
-
-
-
-
-
-
-
-
-
-
ROL (Rule of law) REGQ (Regulatory quality)
-
-0.0001 (-1.49)
-
-
-0.0002 (-1.51)
GE (Govt. Effectiveness)
-
-
-
0.0001 (1.54)
PS (Political Stability)
-
-
-
-
-0.0008** (-3.07)
VA (Voice & Account.) CC (Control of Corruption)
-
-
-
-
-
-
-
-
-
-
0.0867*** (7.39) -0.1371*** (-11.47) 0.0033* (1.81)
0.0853*** (6.71) -0.1530*** (-13.56) 0.0011** (2.06)
-0.0003** (-6.38) -0.5432*** Ndts (Non-debt tax-shield) (-5.02) 0.0077** Age (Firm-age) (2.51)
-0.0003*** (-6.19) -0.6358** (-5.64) 0.0009** (2.08)
-0.0044** DPO (Dividend pay -out) (-2.40) OWS (Ownership 0.0597* structure) (1.75) 0.0002** Int (Interest rate) (2.35) 0.0022*** Inf (Inflation rate) (7.21) Gdpg (Economic growth -0.0004** rate) (-2.98) 0.0011**** BC (Banking Credit) (4.62) M C (M arket -0.0001** Capitalization) (-2.41) AR(2) [P-value] 0.9026 Difference Hansen Tests [P-value] 0.3090 Instruments 153 Variance Inflation Factor 2.03 Heteroscedasticity test (p val.) 0.000 Cross-dependency test (pval.) 0.199 Cross-sectional observation(N) 613 Observ after estimation (N*T) 4624
-0.0025 (-1.32) 0.0484** (2.37) 0.0001 (1.19) 0.0027*** (7.35) -0.0006** (-2.35) 0.0005** (2.42) -0.0001** (-2.16) 0.8822
-0.0037** (-2.06) 0.0421 (1.15) -0.0001 (-1.19) 0.0023*** (7.58) -0.0002** (-2.17) 0.0005** (2.22) -0.0001 (-0.25) 0.8844
-0.0043** (-2.37) 0.0144 (0.39) -0.0003** (-2.69)) 0.0023** (7.60) -0.0001** (-2.30) 0.0004** (2.38) -0.0001* (-1.81) 0.9023
-0-0038** (-2.02) 0.0626** (2.10) -0.0001 (0.74) 0.0020*** (6.52) -0.0009* (-1.82) 0.0001** (2.71) -0.0001** (-2.49) 0.9054
-0.0038** (-2.04) 0.0217 (0.64) 0.0001 (0.75) 0.0022*** (6.94) -0.0002** (-2.38) 0.0001** (2.56) -0.0001** (-2.12) 0.9061
0.4069 153 1.79
0.3340 153 2.28
0.3538 153 1.90
0.2244 153 1.78
0.000
0.000
0.000
0.194
0.184
613 4624
AC
CE
T
IP
0.0963*** 0.0209** (8.63) (2.60) -0.1357*** -0.3382*** (-10.84) (-16.63) 0.0048** 0.0099*** (2.66) (8.46)
-0.0002*** -0.0003*** -0.0003** (-5.76) (-5.91) (-5.58) -0.6841** -0.6203** -0.5948*** (-6.78) (-5.81) (-5.36) 0.0123*** 0.0083*** 0.0012 (4.53) (2.87) (0.37)
-0.0002*** (-4.97) -0.6089*** (-5.46) 0.0129*** (4.21)
-0.0002** (-5.56) -0.6687** (-6.32) 0.0150*** (4.85) -0.0037* (-1.99)
-0.0053*** (-23.29) -0.0582 (0.66) 0.0028* (1.93)
0.1939 153 2.19
0.3086 153 1.91
0.3409 223 2.19
0.000
0.000
0.000
0.000
0.203
0.215
0.226
0.133 613
0.145
613
613
613
613
4624
4624
4624
4624
US
CR
0.0780** (6.83) -0.1442*** (-12.20) 0.0036** (2.05)
AN
PB (Price-to-book ratio)
-
0.1119*** 0.1038*** 0.0830** (10.55) (9.22) (7.76) -0.1448*** -0.1368*** -0.1639*** (-11.41) (-11.09) (-13.09) 0.0042** 0.0023** 0.0030** (2.54) (2.34) (2.10)
M
Size
0.0009*** (3.46) -
ED
PRF (Profits)
-
0.0003 (1.60)
PT
FA (Fixed Assets)
-
0.0003 (1.98) 0.0004 (1.65) 0.0005 (1.43) -0.0016*** (-7.43) 0.0003** (2.34) 0.0004 (1.10)
-0.0186*** (-5.39) 0.0972** 0.0121** (2.65) (2.16) -0.0001 0.0003*** (-0.83) (3.82) 0.0019*** 0.0015*** (6.19) (6.66) -0.0001* 0.008** (-1.84) (2.39) 0.0006*** 0.0006*** (2.92) (3.92) -0.0002** -0.0001* (-2.39) (-1.89) 0.8996 0.3229
613 4264
4624
40
ACCEPTED MANUSCRIPT Notes: a See Table 1 for the definition of variables and measurements. Asterisks indicate significance at 1% (***), 5% (**)
and 10% (*). b T-statistics (in parenthesis) of the Two-step System-GMM model are based on Windmeijer-corrected standard errors. c 2nd order serial correlation in first difference is distributed as N (0, 1) under the null of no serial correlation in the residuals. d Difference-in-Hansen over identification test and null that instruments are valid. e TDBVit-2 , FA it-2, PRFit-2 , Sizeit-2 , Ndts it-2 , PBit-2 , Ageit-2 , DPOit-2 , INSTit-2 , ROLit-2 , REGQit-2 , GEit-2 , PSit-2 , VA it-2 , and CCit-2 are used as Instruments. f In all estimations, the number of ‘N’ cross -sectional observation (613) is greater than the number of instruments (153 & 223). g Industry dummies are included in all the estimations. The lists of th e industries are in appendix II.
0.5330*** (47.89)
M odel 8h (Single Equation
0.4825*** (32.97)
-
-0.0002 (-0.66)
-
0.0003 (1.11) 0.0004 (1.04) 0.0006 (1.56) -0.0017*** (-5.28) 0.0003* (1.91) 0.0002 (1.10)
-
-
0.0003 (1.62)
GE (Govt. Effectiveness) -
-
-
PS (Political Stability)
-
-
-
-
VA (Voice & Account.) CC (Control of Corruption)
-
-
-
-
-0.0009** (-2.59) 0.0007** (2.03)
-
-
-
-
-
ED
PT
-
-
-
-
-
-
-0.0004 (-0.07)
-
-
-
0.0003 (0.74)
0.0467** (2.50) -0.3026*** (-14.04) 0.0093*** (6.69)
-0.0015*** PB (Price-to-book ratio) (-19.32) Ndts (Non-debt tax-0.2164* shield) (-1.70) 0.0126** Age (Firm-age) (2.41)
-0.0015*** -0.0015*** -0.0015*** -0.0016*** (-16.35) (-17.43) (-17.05) (-16.12) -0.1492 -0.0995 -0.2653** -0.1540* (-1.20) (-0.90) (-2.14) (-1.88) 0.0007 0.0023** 0.0097* 0.0053** (0.14) (2.49) (1.95) (2.04)
-0.0022*** (-18.92) -0.0427*** (-3.93) 0.0157*** (3.05)
-0.0061*** DPO (Dividend pay -out) (-2.91) OWS (Ownership 0.0869 structure) (1.52) 0.0010*** Int (Interest rate) (3.60) 0.0042*** Inf (Inflation rate) (6.79) Gdpg (Economic growth -0.0059***
-0.0101*** -0.0122*** -0.0089** -0.0058* -0.0030 -0.0093*** -0.0043 (-3.05) (-3.98) (-2.58) (-1.81) (-0.93) (-2.93) (-0.93) -0.0244 -0.0323 0.1009* 0.0104 0.1498** 0.0475 0.0290** (-0.44) (-0.66) (1.75) (0.21) (2.57) (0.77) (2.14) 0.0009*** 0.0016*** 0.0010*** 0.0013*** 0.0011*** 0.0009*** 0.0003** (3.37) (5.13) (3.79) (4.29) (3.70) (3.42) (2.57) 0.0037*** 0.0049*** 0.0048*** 0.0043*** 0.0038*** 0.0045*** 0.0019*** (5.53) (8.28) (8.07) (6.78) (5.99) (7.53) (4.97) -0.0066*** -0.0060*** -0.0059*** -0.0069*** -0.0071*** -0.0059*** 0.0024***
PRF (Profits)
AC
Size
CE
0.1246*** 0.1316*** 0.1623*** 0.1202*** 0.1295*** 0.1516*** (6.14) (7.01) (9.50) (6.00) (6.76) (8.26) -0.1095*** -0.1269*** -0.1145*** -0.1371*** -0.1148*** -0.1209*** (-5.65) (-6.66) (-6.21) (-6.72) (-5.50) ( -6.17) 0.0006** 0.0015** 0.0015* 0.0002** 0.0041** 0.0010** (2.27) (2.16) (1.95) (2.08) (2.55) (2.38)
FA (Fixed Assets)
0.1460*** (8.28) -0.1172*** (-4.98) 0.0013** (2.48)
-
AN
-
M
INST (Single Institutional 0.0004 Quality Index) (0.87) ROL (Rule of law) REGQ (Regulatory quality)
M odel 8g With CC
0.5343*** 0.5264*** 0.5263*** 0.5316*** 0.5337*** 0.5298** (49.52) (45.31) (47.73) (47.23) (42.56) (49.45)
US
TDM Vit-1 (Lag M arketdebt)
M odel 8e M odel 8f With PS With VA
CR
M odel 8a With Single M odel 8b M odel 8c M odel 8d INST Index With ROL With REGQ With GE
IP
T
Table 10 Direct effects of institutional quality on M arket-debt models System-GM M Two-step Estimation Results using M arket-debt and 613 Listed Firms from 10 African Countries (Robustness test), 2006-2014
-0.0015*** -0.0015*** (-17.60) (-19.85) -0.2450** -0.2166* (-2.01) (-1.70) 0.0125** 0.0111** (2.63) (2.34)
41
ACCEPTED MANUSCRIPT (-6.78) (-6.94) 0.0024*** 0.0006* (4.78) (1.99) -0.0001*** -0.0001* (-2.19) (-1.90) 0.2782 0.2901
(-7.03) 0.0019** (2.01) -0.0003** (-2.11) 0.3650
(-7.80) 0.0006** (2.08) -0.0002** (-2.39) 0.3776
(-6.77) 0.0002** (2.43) -0.0001** (-2.55) 0.2781
(4.76) 0.0011*** (5.27) -0.0003*** (-7.01) 0.4089
0.2398 153 1.80 0.000 0.199
0.1235 153 1.63 0.000 0.192
0.2602 153 1.69 0.000 0.195
0.2254 153 1.88 0.000 0.201
0.2412 153 1.77 0.000 0.166
0.1844 153 2.16 0.000 0.123
0.1757 153 1.81 0.000 0.106
0.1362 223 2.78 0.000 0.141
613
613
613
613
613
613
613
613
4624
4624
4624
4624
4624
4624
4624
4624
T
(-6.83) 0.0003** (2.05) -0.0001** (-2.69) 0.2650
CR
BC (Banking Credit) M C (M arket Capitalization) AR(2) [P-value] Difference Hansen Tests [P-value] Instruments Variance Inflation Factor Heteroscedasticity test Cross-dependency test Cross-sectional observation(N) Observ after estimation (N*T)
(-6.89) 0.0005** (2.17) -0.0001** (-2.38) 0.3265
IP
rate)
Notes: a See Table 1 for the definition of variables and measurements. Asterisks indicate significance at 1% (***), 5% (**)
AN
US
and 10% (*). b T-statistics (in parenthesis) of the Two-steps System-GMM model are based on Windmeijercorrected standard errors. c 2nd order serial correlation in first difference is distributed as N (0, 1) under the null of no serial correlation in the residuals. d Difference Sargan over identification test and null that instruments are valid, but it can only run if the error is System-GMM-type error. e TDMVit-2 , FA it-2, PRFit-2 , Sizeit-2 , Ndts it-2 , PBit-2 , Ageit-2 , DPOit-2 , INSTit-2 , ROLit-2 , REGQit-2 , GEit-2 , PSit-2 , VA it-2 , and CCit-2 are used as Instruments. f In all estimations, the number of ‘N’ cross-sectional observation (613) is greater than the number of instruments (153 & 223). g Industry dummies are included in all the estimations. The lists of the industries are in appendix II.
M
4.3. Indirect effects of institutional quality on debt models (Robustness Check 1) The focus of this study is not on the indirect effects of institutional quality on debt. Instead,
ED
we explore these indirect effects by interacting institutional quality with firm-level determinants (e.g. fixed assets, size, profits) to determine their effects on firm debt. The results of these
PT
indirect effects are not reported to save space. For example, if institutional quality proxies are important, the interaction terms (i.e. fixed assets * institutional quality proxy; size* institutional
CE
quality; profit * institutional quality) should be significant. Moreover, if the interaction term coefficients are greater than zero (interaction term coefficients are less than zero) and if the firm-
AC
level factors positively (negatively) affect debt, the institutional quality proxies strengthen the effects of the firm-level factors on debt, suggesting that the firm and institutional quality factors complement each other. Conversely, if the interaction term coefficients are less than zero (interaction term coefficients are greater than zero) and if the firm-level factors positively (negatively) affect debt, the institutional quality proxies moderate the effects of the firm-level factors on debt, suggesting that the firm and institutional quality factors are substitutes. The empirical results for the full sample show that the interaction terms of the single aggregated institutional quality index, fixed assets and size are positive and statistically significant. Conversely, the interaction of the single aggregated institutional quality index and 42
ACCEPTED MANUSCRIPT profits are negative and statistically significant. The interaction effects results are similar using disaggregated measures of institutional quality. These results indicate that the institutional quality index and disaggregated measures of institutional quality strengthen the effects of the firm-level factors (e.g. fixed assets, size, and profits) on the book debt and market debt ratio, suggesting that the firm and the institutional quality factors are complements. Like the results of the direct effects, institutional quality retains it positive effects on book
T
debt ratio. Likewise, institutional quality has positive effects on market debt ratio. Thus, the
IP
direct effects and indirect effects models reveal that institutional quality has positive effects on
CR
both the book debt and market debt ratios. The results are consistent with findings of Jong et al. (2008), who stated that macroeconomic and institutional factors affect the roles of the firm-
US
specific determinants of a capital structure. The empirical results also show that firms make faster adjustments to their target debt, especially the book debt ratio, when there is a deviation
AN
from the target debt level; this is consistent with the dynamic version of trade-off theory. The empirical results for the Asian sample shows that the interaction term coefficients of the
M
single aggregated institutional quality index and fixed assets as well as size are positive and statistically significant. Conversely, the interaction term coefficients of the single aggregated
ED
institutional quality index and profits are negative and statistically significant. The interaction effects results are similar using disaggregated measures of institutional quality. These results
PT
indicate that institutional quality index and disaggregated measures of institutional quality strengthen the effects of the firm-level factors (fixed assets, size, profits) on book debt ratio and
CE
market debt ratio, suggesting that the firm and institutional quality factors are complements. Like the results of the direct effects, institutional quality retains it positive effects on book debt ratio.
AC
Likewise, institutional quality has positive effects on market debt ratio. Hence, the direct effects and indirect effects models reveal that institutional quality is positively related to both the book debt and market debt ratios. Similarly, the empirical results for the Latin American and Eastern European countries’ sample shows that the interaction term coefficients of the single aggregated institutional quality index, and fixed assets and size are positive and statistically significant. Conversely, the interaction of single aggregated institutional quality index and profits is negative and statistically significant.
The
interaction effects results are similar using disaggregated
measures of
institutional quality. These results indicate that institutional quality index and disaggregated 43
ACCEPTED MANUSCRIPT measures of institutional quality strengthen the effects of firm-level factors (fixed assets, size profits) on book debt ratio and market debt ratio, suggesting that the firm and institutional quality factors are complements. Like the results of the direct effects, institutional quality retains it positive effects on book debt ratio. Likewise, institutional quality has positive effects on market debt ratio. Thus, the direct effects and indirect effects models reveal that institutional quality has positive effects on both the book debt and market debt ratios.
T
The empirical results for the African sample show that only the interaction term coefficients
IP
of the single aggregated institutional quality index and fixed assets are positive and statistically
CR
significant. However, the interaction term coefficients of single aggregated institutional quality index, and size and profits are insignificant. Like the results of the direct effects models,
US
institutional quality effects on both the book debt and market debt ratios are inconsistent. Thus, the direct effects and indirect effects models reveal that institutional quality has partial effects on
AN
both the book debt and market debt ratios in the African region.
M
4.4 Direct effects of institutional quality on debt models in Post-crisis period (Robustness Checks 2)
ED
The primary objective of this paper is not to account for the possible effects of the financial crisis periods. However, we conduct an additional analysis and exclude the 2007 to 2009 crisis
PT
periods from the sample and re-estimate all the regressions, focusing on the post-crisis period (2010 to 2014). The results are broadly similar to the main results that include the crisis periods
CE
(2006-2014), except that the variable coefficients and the significance of some variables changes slightly. The empirical results for the full sample show that the single aggregated institutional
AC
quality index is statistically significant and positively related to the total debt ratio (Table 11, Model 9a). Additionally, six disaggregated measures of institutional quality are statistically significant and positively related to the total debt ratio (Table 11, Models 9b to 9g). For all regional sub-sample results (except the African region sample, Table 14, Model 12a to 12g), the single aggregated index and the disaggregated measures of institutional quality are significantly and positively related to debt for the Asian region (Tables 12, Model 10a to 10g) and the Latin American and Eastern European regions (Table 13, Models 11a to 11g). Furthermore, all the six disaggregated measures of institutional quality are included together in a single model and the results are broadly similar. But the magnitude of the coefficient of most of the institutional 44
ACCEPTED MANUSCRIPT quality variables change. Moreover, the magnitude of the coefficient of the lagged debt (debt it-1 ), firm-specific and other macroeconomic variables change (see Table 11, Model 9h for the full sample results and Table 12, Model 10h, Table 13, Model 11h, Table 14, Model 12h for the regional sub-sample results). Consequently, we explore the indirect effects by interacting institutional quality on some firm-level determinants (e.g. fixed assets, size, profits) to determine their effects on firm debt but
T
the results are not reported to save space. The empirical results for the full sample show that the
IP
interaction terms of the single aggregated institutional quality index, fixed assets and size are
CR
positive and statistically significant. Conversely, the interaction of the single aggregated institutional quality index and profits is negative and statistically significant. These results
US
indicate that the institutional quality index strengthens the effects of the firm-level factors (fixed assets, size, and profits) on debt, suggesting that the firm and the institutional quality factors are
AN
complements. Moreover, the interaction term coefficients of the six disaggregated measures of institutional quality, and fixed assets and size is statistically significant and positive. These
M
results also indicate that the disaggregated measures of institutional quality strengthen the effects of the firm-level factors on debt ratio. At the regional level, we also confirm the indirect effects
ED
of aggregated and disaggregated measures on institutional quality on debt for the Asian region and the Latin American and Eastern European region. But, for the African region, we find partial
PT
evidence that institutional quality has indirect effects on debt. Like the results of the direct effects, institutional quality retains it positive effects on debt ratio. Likewise, institutional quality
CE
has positive effects on total debt ratio. Hence, the direct effects and indirect effects models reveal that institutional quality is positively related to total debt ratios.The empirical results also show
AC
that firms adjustments to their target debt when there is a deviation from the target debt level, consistent with the dynamic version of trade-off theory. Previous researchers (e.g., Matemilola and Ahmad (2015), Flannery and Hankins (2013), Oztekin and Flannery (2012)) find evidence that firms adjust to their target debt level.
Table 11 Direct effects of institutional quality on Book-debt models (Post-crisis period) System-GM M Two-step Estimation Results using Book-debt and 3,891 Listed Firms from 23 Developing Countries, 2010-2014 M odel 9a With Single M odel 9b INST Index With ROL
M odel 9c M odel 9d With REGQ With GE
M odel 9e M odel 9f With PS With VA
M odel 9g With CC
M odel 9h (Single Equation)
45
ACCEPTED MANUSCRIPT
TDBVit-1 (Lag Book-debt)
0.7492*** 0.7407*** (26.89) (27.12)
INST (Single Institutional 0.0007** Quality Index) (2.05)
0.7450*** (26.98)
0.7476*** 0.7444*** (27.14) (27.33)
0.7361*** (25.76)
-
-
-
-
-
0.7479*** (32.88)
0.0001** ROL (Rule of law) (2.07) 0.0004* 0.0002* REGQ (Regulatory quality) (1.98) (1.75) 0.0002** 0.0007** GE (Govt. Effectiveness) (2.25) (2.45) 0.0006** 0.0006*** PS (Political Stability) (2.71) (3.75) 0.0004** 0.0004** VA (Voice & Account.) (2.29) (2.45) 0.0004** 0.0001* CC (Control of Corruption) (2.19) (1.98) 0.0155* 0.0125* 0.0038** 0.0010** 0.0091** 0.0063** 0.0433** 0.0145*** FA (Fixed Assets) (1.98) (1.78) (2.16) (2.04) (2.32) (2.23) (2.59) (3.63) -0.0422*** -0.0423*** -0.0412*** -0.0417*** -0.0421*** -0.0420*** -0.0418*** -0.4207*** PRF (Profits) (-13.46) (-13.54) (-13.38) (-14.29) (-13.33) (-12.98) (-14.64) (-15.18) 0.0004*** 0.0043*** 0.0073*** 0.0062*** 0.0059*** 0.0080*** 0.0072*** 0.0045*** Size (3.35) (3.00) (3.15) (3.88) (3.14) (2.74) (3.21) (5.00) -0.0001* -0.0001 -0.0001** -0.0001** -0.0001** -0.0003 -0.0001** 0.0001 PB (Price-to-book ratio) (-1.80) (-0.73) (-2.19) (-2.11) (-2.05) (-1.52) (-2.34) (1.13) 0.0240* 0.0630** 0.0772* 0.0921*** 0.0942** 0.0630** 0.0531** 0.0216*** Ndts (Non-debt tax-shield) (1.95) (2.13) (1.84) (2.72) (2.19) (2.41) (2.40) (2.92) 0.0169** 0.0144** 0.0018 0.0061 0.0076* -0.0006** -0.0007 0.0009 Age (Firm-age) (2.59) (2.22) (0.20) (1.01) (1.84) (-2.06) (-1.06) (0.26) 0.0049* 0.0055* 0.0068* 0.0058* 0.0064* 0.0044** 0.0043** 0.0078** DPO (Dividend pay -out) (1.89) (1.98) (1.90) (1.76) (1.72) (2.24) (2.28) (2.48) -0.0109 -0.0023 -0.0131* -0.0026** -0.0019** -0.0023 -0.0306** -0.0008 OWS (Ownership structure) (-0.59) (-0.31) (-1.82) (-2.12) (-2.35) (-047) (-2.37) (-0.23) 0.0003* 0.0001* 0.0001** 0.0001** 0.0001** -0.0004** 0.0004** 0.0004* Int (Interest rate) (1.86) (1.79) (2.01) (2.15) (2.12) (-2.13) (2.24) (1.88) 0.0002** 0.0002*** 0.0020** 0.0020*** 0.0017** 0.0021** 0.0022*** 0.0018*** Inf (Inflation rate) (3.42) (3.48) (3.05) (3.06) (2.58) (3.13) (3.36) (3.22) Gdpg (Economic growth -0.0016** -0.0016** 0.0016** -0.0018*** -0.0015** -0.0013** -0.0012* 0.0006** rate) (-2.66) (-2.52) (2.42) (-2.76) (-2.64) (-2.13) (-1.75) (2.19) 0.0002* 0.0003** 0.0004* 0.0003** 0.0004*** 0.0001** 0.0001** 0.0001** BC (Banking Credit) (1.79) (2.09) (1.82) (2.62) (3.04) (2.08) (2.13) (2.27) -0.0003*** -0.0003* -0.0003*** -0.0004*** -0.0003*** -0.0003*** -0.0003*** -0.0002*** M C (M arket Capitalization) (-4.05) (-1.85) (-4.07) (-4.42) (-3.68) (-4.42) (-3.41) (-3.06) 0.8895 0.8789 AR(2) [P-value] 0.8974 0.9053 0.8793 0.8854 0.8931 0.9249 Difference Hansen Tests [P-value] 0.2175 0.2937 0.1808 0.3015 0.2007 0.3194 0.2084 0.215 84 123 Instruments 84 84 84 84 84 84 2.18 2.46 Variance Inflation Factor 1.98 1.33 1.96 2.03 1.89 1.90 Heteroscedasticity test (p-val.) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Cross-dependency test (p-val.) 0.210 0.235 0.205 0.301 0.299 0.211 0.241 0.217 Cross-sectional observation(N) 3891 3891 3891 3891 3891 3891 3891 3891 Observ after estimation (N*T) 15308 15308 15308 15308 15308 15308 15308 15308
AC
CE
PT
ED
M
AN
US
CR
IP
T
0.0001** (2.16)
0.7452*** (26.80)
Notes: a See Table 1 for the definition of variables and measurements. Asterisks indicate significance at 1% (***), 5% (**)
and 10% (*). b T-statistics (in parenthesis) of the Two-step System-GMM model are based on Windmeijer-corrected
46
ACCEPTED MANUSCRIPT standard errors. c 2nd order serial correlation in first difference is distributed as N (0, 1) under the null of no serial correlation in the residuals. d Difference-in-Hansen over identification test and null that instruments are valid. e TDBVit-2 , FA it-2, PRFit-2 , Sizeit-2 , Ndts it-2 , PBit-2 , Ageit-2 , DPOit-2 , INSTit-2 , ROLit-2 , REGQit-2 , GEit-2 , PSit-2 , VA it-2 , and CCit-2 are used as Instruments. f In all estimations, the number of ‘N’ cross -sectional observation (3,891) is greater than the number of instruments (84 & 123). g Industry dummies are included in all the estimations. The lists of the industries are in appendix II.
M odel 10g With CC
10h (Single Equation)
0.1283*** (42.36)
0.1345*** (50.92)
CR
IP
M odel 10a With Single M odel 10b M odel 10c M odel 10d M odel 10e M odel 10f INST Index With ROL With REGQ With GE With PS With VA
T
Table 12 Direct effects of institutional quality on Book-debt (Post-crisis period) System-GM M Two-step Estimation Results using Book-debt and 2,187 Listed Firms from 7 Asian Countries, 2010-2014
0.1278*** 0.1298*** 0.1289*** (41.00) (40.11) (42.46)
INST (Single Institutional 0.0001** Quality Index) (2.14)
-
-
PS (Political Stability)
-
VA (Voice & Account.) CC (Control of Corruption)
-
-
-
0.0013*** (2.88) 0.0005** 0.0010** (2.25) (2.36) 0.0006** 0.0008** (2.62) (2.65) 0.0001** 0.0003* (2.16) (1.96) 0.0002** 0.0016*** (3.27) (3.29) 0.0009** 0.0009** (2.38) (2.05) 0.1007*** 0.1196*** 0.1066*** 0.0433*** 0.1876** 0.0523*** 0.0449*** (7.67) (9.12) (6.02) (2.71) (12.61) (3.99) (3.02) -0.0027** -0.0341** -0.0294*** -0.0177** -0.0045** -0.0047*** -0.0552* (-2.07) (-2.10) (-2.79) (-2.41) (-2.09) (-3.11) (-1.81) 0.0136*** 0.0055*** 0.0163*** 0.0067** 0.0132*** 0.0141*** 0.0082*** (4.11) (4.28) (4.18) (2.50) (3.84) (4.99) (4.43) -0.0185*** -0.0200*** -0.0196*** -0.0161*** -0.0190*** -0.0187*** -0.0195*** (-9.30) (-9.57) (-9.12) (-8.90) (-9.68) (-10.03 (-12.90) 0.0639** 0.0141** 0.1211* 0.1794** 0.1611** 0.0184** 0.2352** (2.16) (2.04) (1.99) (2.44) (2.41) (2.52) (250) 0.0563** 0.0321* 0.0590*** 0.0773*** 0.0765*** 0.0479*** 0.0106 (2.40) (1.80) (3.85) (3.26) (4.64) (3.10) (1.51) -0.0218*** -0.0127* -0.0134* -0.0192** -0.0199*** -0.0221*** -0.0276*** (-3.17) (-1.89) (-1.99) (-2.65) (-2.83) (-3.28) (-5.22) -0.5584*** -0.7018*** -0.7116*** -0.6800*** -0.6964*** -0.618*** -0.0561* (-6.02) (-7.01) (-7.00) (-6.22) (-6.55) (-6.34) (-1.95) 0.0010** 0.0012** 0.0010* 0.0007** -0.0013** 0.0011** 0.0001* (2.06) (2.09) (1.93) (2.38) (-2.28) (2.04) (1.90) 0.0003** 0.0001* 0.0001 0.0005** -0.0010 0.0001* 0.0023*** (2.30) (1.98) (0.12) (2.59) (-1.13) (1.83) (3.03) 0.0010* 0.0024** 0.0017** 0.0013** 0.0001 0.0011* 0.0032*** (1.93) (2.02) (2.41) (2.39) (1.02) (1.81) (3.59) 0.0003** 0.0005** 0.0003** 0.0001*** 0.0004** 0.0003*** 0.0007 (2.16) (2.12) (2.06) (3.35) (2.50) (3.17) (3.82) -0.0003*** -0.0004*** -0.0004*** -0.0003*** -0.0003*** -0.0002** 0.0005*** (-2.86) (-3.53) (-3.25) (-3.10) (-2.73) (-2.02) (4.21) 0.2658 0.2346 0.2508 0.2711 0.2728 0.2610 0.2755
AN
GE (Govt. Effectiveness)
-
M
-
AC
CE
0.1246** FA (Fixed Assets) (8.82) -0.0183** PRF (Profits) (-2.45) 0.0161*** Size (5.89) -0.0182*** PB (Price-to-book ratio) (-8.92) 0.0398** Ndts (Non-debt tax-shield) (2.10) 0.0367** Age (Firm-age) (2.54) -0.0159** DPO (Dividend pay -out) (-2.37) OWS (Ownership -0.6843*** structure) (-7.21) 0.0011** Int (Interest rate) (2.19) 0.0002** Inf (Inflation rate) (2.21) Gdpg (Economic growth 0.0012 rate) (1.18) 0.0004** BC (Banking Credit) (2.44) M C (M arket -0.0003** Capitalization) (-2.62) AR(2) [P-value] 0.2639
ED
-
-
PT
ROL (Rule of law) REGQ (Regulatory quality)
0.0002** (2.39)
US
0.1267*** 0.1282*** 0.1266 TDBVit-1 (Lag Book-debt) (42.08) (40.34) (42.05)
47
ACCEPTED MANUSCRIPT 0.1202 84 3.91
0.1350 84 3.98
0.1832 84 3.49
0.1842 84 3.89
0.1469 84 3.18
0.1920 84 3.75
0.1388 84 3.56
0.2741 123 3.69
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.310
0.256
0.266
0.225
0.237
0.243
0.208
0.231
2187
2187
2187
2187
2187
2187
2187
2187
8649
8649
8649
8649
8649
8649
8649
8649
T
Difference Hansen Tests [P-value] Instruments Variance Inflation Factor Heteroscedasticity test (p-val.) Cross-dependency test (p-val.) Cross-sectional observation(N) Observ after estimation (N*T)
Notes: a See Table 1 for the definition of variables and measurements. Asterisks indicate significance at 1% (***), 5% (**)
US
CR
IP
and 10% (*). b T-statistics (in parenthesis) of the Two-step System-GMM model are based on Windmeijer-corrected standard errors. c 2nd order serial correlation in first difference is distributed as N (0, 1) under the null of no serial correlation in the residuals. d Difference-in-Hansen over identification test and null that instruments are valid. e TDBVit-2 , FA it-2, PRFit-2 , Sizeit-2 , Ndts it-2 , PBit-2 , Ageit-2 , DPOit-2 , INSTit-2 , ROLit-2 , REGQit-2 , GEit-2 , PSit-2 , VA it-2 , and CCit-2 are used as Instruments. f In all estimations, the number of ‘N’ cross -sectional observation (2,187) is greater than the number of instruments (84 & 123). g Industry dummies are included in all the estimations. The lists of the industries are in appendix II.
AN
Table 13 Direct effects of institutional quality on Book-debt models System-GM M Two-step Estimation Results using Book-debt and 1,091 Listed Firms from 6 Latin American combined with Eastern European Countries, 2010-2014
INST (Single Institutional 0.0003*** Quality Index) (4.11)
0.5222*** (26.92)
0.5136*** (27.97)
PT
0.5179*** TDBVit-1 (Lag Book-debt) (25.50)
ED
M
M odel 11a With Single M odel 11b M odel 11c M odel 11d INST Index With ROL With REGQ With GE
-
M odel 11g M odel 11h (Single With CC Equation)
0.5141*** (20.38)
0.5131*** 0.5135*** (26.29) (27.56)
0.5198*** (24.89)
-
-
-
-
0.5317*** (30.71)
0.0011** ROL (Rule of law) (2.15) REGQ (Regulatory 0.0032*** 0.0001** quality) (4.33) (2.04) 0.0037*** 0.0055*** GE (Govt. Effectiveness) (3.47) (4.09) 0.0016*** 0.0009** PS (Political Stability) (5.20) (2.03) 0.0028*** 0.0017** VA (Voice & Account.) (4.52) (2.45) CC (Control of 0.0004** 0.0016** Corruption) (2.33) (2.87) 0.01367*** 0.0186** 0.0307** 0.0168** 0.0911*** 0.0442** 0.0505** 0.0860** FA (Fixed Assets) (2.82) (2.52) (2.43) (2.51) (2.75) (2.47) (2.01) (2.34) -0.4737*** -0.4916*** -0.4830*** -0.4896*** -0.4825*** -0.4915*** -0.4820*** -0.4867*** PRF (Profits) (-22.62) (-23.97) (-24.99) (-25.23) (-23.80) (-24.72) (-22.49) (-25.87) 0.0209*** 0.0030** 0.0174*** 0.0181** 0.0125*** 0.0184*** 0.0062** 0.02247*** Size (3.94) (2.60) (3.31) (2.38) (3.17) (3.82) (2.43) (6.46) -0.0001*** -0.0001*** -0.0001*** -0.0001*** -0.0001*** -0.0001*** -0.0001*** 0.0001*** PB (Price-to-book ratio) (-5.90) (-6.27) (-6.16) (-5.04) (-3.44) (-5.47) (-4.50) (6.69) 0.0645*** 0.0756*** 0.0598*** 0.0602*** 0.0523*** 0.0719*** 0.0444*** 0.0377*** Ndts (Non-debt tax-shield) (6.42) (6.66) (6.52) (6.41) (4.90) (7.21) (5.12) (7.20) -0.0201** -0.0403*** Age (Firm-age) -0.0433*** 0.0009 -0.0187* -0.0135* -0.0175* -0.0146*
AC
CE
0.0018*** (2.94)
M odel 11e M odel 11f With PS With VA
48
(0.04) 0.0327*** (2.71) 0.1152* (1.72) 0.0011* (1.71) 0.0055*** (3.54) 0.0035*** (3.48) 0.0010** (2.06)
(-2.07) 0.0338** (2.54) -0.0386 (-0.84) 0.0001* (1.80) 0.0051*** (2.84) 0.0021** (2.29) 0.0003** (2.59)
(-6.93) 0.0514*** (6.30) 0.2552* (1.90) -0.0015** (-2.40) 0.0021 (1.32) 0.0038*** (3.29) 0.0017*** (4.80)
M C (M arket -0.0002** Capitalization) (-2.35) AR(2) [P-value] 0.3016 Difference Hansen Tests [P-value] 0.6177 Instruments 84 Variance Inflation Factor 2.83 Heteroscedasticity test (p-val.) 0.000 Cross-dependency test (p-val.) 0.233 Cross-sectional observation(N) 1091 Observ after estimation (N*T) 4303
-0.0005*** -0.0004** (- -0.0003*** -0.0003** -0.0004** (-2.80) 2.38) (-2.74) (-2.58) (-2.01) 0.2809 0.2798 0.2720 0.2987 0.2821
-0.0004** (-2.03) 0.2896
-0.0003* (-1.95) 0.2912
0.3010 84 2.49
0.3268 84 2.50
0.6034 123 2.74
0.000
0.000
0.000
0.000
0.325
0.293
0.129
0.117
1091
1091
1091
1091
1091
4303
4303
4303
4303
4303
0.3159 84 2.47
0.6364 84 3.74
0.000
0.000
0.000
0.292
0.327
0.318
1091
1091
4303
4303
0.3676 84 2.21
(-1.89) 0.0205* (1.87) -0.0318** (-2.21) 0.0003* (1.93) -0.0013** (-2.17) 0.0024** (2.26) 0.0001 (0.12)
M
AN
0.2044 84 3.56
(-1.88) 0.0104* (1.97) -0.2238* (-1.91) 0.0003** (2.58) 0.0042** (2.48) 0.0021** (2.13) 0.0005* (1.95)
CR
(-1.74) 0.0252** (2.21) 0.0603** (2.40) -0.0016*** (-2.94) 0.0039** (2.45) 0.0035*** (3.36) 0.0009* (1.76)
T
(-3.24) 0.0326** DPO (Dividend pay -out) (2.67) OWS (Ownership -0.0413 structure) (-0.21) -0.0009 Int (Interest rate) (-1.58) 0.0003* Inf (Inflation rate) (1.76) Gdpgr (Economic growth 0.0025** rate) (2.10) 0.0007** BC (Banking Credit) (2.14)
US
(-1.95) 0.0325*** (3.09) 0.1607** (2.12) 0.0009* (1.86) -0.0029* (-1.96) 0.0014 (1.45) 0.0007*** (2.94)
IP
ACCEPTED MANUSCRIPT
Notes: a See Table 1 for the definition of variables and measurements. Asterisks indicate significance at 1% (***) and 5%
PT
ED
(**). b T-statistics (in parenthesis) of the Two-step System-GMM model are based on Windmeijer-corrected standard errors. c 2nd order serial correlation in first difference is distributed as N (0, 1) under the null of no serial correlation in the residuals. d Difference-in-Hansen over identification test and null that instruments are valid. e TDBVit-2 , FA it-2, PRFit-2 , Sizeit-2 , Ndts it-2 , PBit-2 , Ageit-2 , DPOit-2 , INSTit-2 , ROLit-2 , REGQit-2 , GEit-2 , PSit-2 , VA it-2 , and CCit-2 are used as Instruments ( 84 & 123). f In all estimations, the number of ‘N’ cross -sectional observation (1,091) is greater than the number of instruments.
CE
Table 14 Direct effects on institutional quality on Book-debt models System-GM M Two-step Estimation Results using Book-debt and 613 Listed Firms from 10 African Countries, 2010-2014
M odel 12c M odel 12d With REGQ With GE
M odel 12e With PS
M odel 12f With VA
M odel 12g With CC
M odel 12h (Single Equation)
0.4862*** (19.08)
0.48001*** (18.23)
0.4939*** (18.90)
0.4876*** (19.12)
0.4782*** (19.46)
0.4844*** (18.30)
0.4940*** (19.00)
0.4648*** (23.07)
-
-
-
-
-
-
0.0006 (1.16)
-
-
-
-
-
-
-0.0011 (-1.05)
-
-
-
GE (Govt. Effectiveness) -
-
-
0.0006 (0.69)
-
-
PS (Political Stability)
-
-
-
-0.0003 (-1.09)
-
-
0.0004 (1.19) 0.0004 (0.48) 0.0011 (1.24) -0.0009* (-1.95)
TDBVit-1 (Lag Bookdebt)
AC
M odel 12a With Single M odel 12b INST Index With ROL
INST (Single Institutional 0.0009 Quality Index) (1.48) ROL (Rule of law) REGQ (Regulatory quality)
-
49
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0.1049*** (2.99) -0.6708*** (-16.94) 0.0107** (2.08) -0.0069*** (-22.42) -0.6618*** (-3.09) -0.0029 (-0.33) -0.0030* (-1.85) 0.2628** (2.61) 0.0012*** (3.08) 0.0047*** (3.48) -0.0090 (-3.65) 0.0001* (1.91) -0.0001** (-2.05) 0.5917
0.0015 (1.50) 0.0917** 0.0894** 0.0940*** 0.0662* 0.0748** (2.67) (2.48) (2.72) (1.71) (2.08) -0.6976*** -0.6828*** -0.6947*** -0.6722*** -0.6946*** (-17.01) (-16.88) (-17.17) (-15.85) (-17.06) 0.0083** 0.0107* 0.0103** 0.0142** 0.0090** (2.61) (1.82) (2.06) (2.68) (2.55) -0.0068*** -0.0068*** -0.0070*** -0.0069*** -0.0071*** (-20.95) (-21.46) (-22.84) (-21.67) (-22.94) -0.7653*** -0.7323*** -0.6823*** -0.6465*** -0.6157** (-3.29) (-3.22) (-2.93) (-2.91) (-2.67) 0.0135* 0.0033 0.0007 0.0064 0.0111* (1.73) (0.38) (0.28) (0.76) (1.82) -0.0071 -0.0061* -0.0084** -0.0064** -0.0083* (-1.46) (-1.76) (-2.38) (-2.35) (-1.91) 0.2077** 0.1504** 0.2681** 0.2453** 0,2119** (2.08) (2.39) (2.62) (2.48) (2.06) 0.0012*** -0.0011*** 0.0008* 0.0011*** -0.0011*** (3.30) (-2.94) (1.98) (3.13) (-3.01) 0.0048*** 0.0043*** 0.0041** 0.0049*** 0.0038*** (3.64) (3.34) (3.13) (3.70) (3.08) -0.0082** -0.0087*** -0.0043** -0.0086** -0.0064*** (-3.45) (-3.77) (-2.59) (-3.67) (-2.79) 0.0001* 0.0001** 0.0020** 0.0009*** 0.0009** (1.83) (2.05) (2.10) (2.80) (2.66) -0.0001** -0.0002** -0.0002* -0.0001* -0.0001** (-2.09) (-2.11) (-1.94) (-1.99) (-2.31) 0.5903 0.5802 0.5849 0.6394 0.5902
0.0006*** (3.08) 0.0002 (0.40) 0.0629** (2.39) -0.5865*** (-17.86) 0.0044* (1.94) -0.0067*** (-25.23) -0.6592*** (-3.56) 0.0046 (0.77) -0.0054** (-2.09) 0.2232*** (3.10) 0.0007** (2.37) 0.0016* (1.88) 0.0026** (2.21) 0.0003* (1.91) -0.0001 (1.10) 0.6515
0.4668 84 1.68
0.2628 84 2.82
0.3385 84 2.01
0.2996 84 2.69
0.2535 84 2.55
0.3496 84 2.33
0.4091 123 2.58
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.0017** (2.18)
-
AN
US
CR
IP
T
-
0.130
0.157
0.207
0.183
0.219
0.242
0.198
613
613
613
613
613
613
613
2356
2356
2356
2356
2356
2356
CE
0.0734** FA (Fixed Assets) (2.02) -0.6644*** PRF (Profits) (-16.56) 0.0082** Size (2.46) -0.0071*** PB (Price-to-book ratio) (-24.06) Ndts (Non-debt tax-0.5930** shield) (-2.69) 0.002 Age (Firm-age) (0.31) -0.0099* DPO (Dividend pay -out) (-1.99) OWS (Ownership 0.1903* structure) (1.86) 0.0010*** Int (Interest rate) (2.88) 0.0044** Inf (Inflation rate) (3.53) Gdpg (Economic growth -0.0069*** rate) (-3.23) 0.0021** BC (Banking Credit) (2.45) M C (M arket -0.0001** Capitalization) (-2.01) AR(2) [P-value] 0.6115 Difference Hansen Tests [P-value] 0.3119 Instruments 84 Variance Inflation Factor 2.71 Heteroscedasticity test (p-val.) 0.000 Cross-dependency test (p-val.) 0.231 Cross-sectional observation(N) 613 Observ after estimation (N*T) 2356
-
M
-
ED
-
PT
VA (Voice & Account.) CC (Control of Corruption)
2356
Notes: a See Table 1 for the definition of variables and measurements. Asterisks indicate significance at 1% (***), 5% (**)
AC
and 10% (*). b T-statistics (in parenthesis) of the Two-step System-GMM model are based on Windmeijer-corrected standard errors. c 2nd order serial correlation in first difference is distributed as N (0, 1) under the null of no serial correlation in the residuals. d Difference-in-Hansen over identification test and null that instruments are valid. e TDBVit-2 , FA it-2, PRFit-2 , Sizeit-2 , Ndts it-2 , PBit-2 , Ageit-2 , DPOit-2 , INSTit-2 , ROLit-2 , REGQit-2 , GEit-2 , PSit-2 , VA it-2 , and CCit-2 are used as Instruments. f In all estimations, the number of ‘N’ cross -sectional observation (613) is greater than the number of instruments (84 & 123). g Industry dummies are included in all the estimations. The lists of the industries are in appendix II.
4.5 Direct effects of legal enforcement on debt models (Robustness Check 3) The indices measuring institutional quality in prior studies proxy for the existence of the laws and regulations. However, enforcement of laws appears more important than existence of 50
ACCEPTED MANUSCRIPT the laws in developing countries. Weak enforcement is a general problem in most developing countries (Pistor, Raiser, and Gelfer, 2000), and it affects firms seeking external financing. A weak enforcement environment makes it more difficult for firms to make commitments to contractual agreement. Conversely, a strong enforcement environment makes it easier for firms to make commitments to contractual agreement. Consequently, this paper conducts additional analysis to investigate the effects of legal enforcement on capital structure of firms’ in
T
developing countries.
IP
Due to data limitation on legal enforcement proxy (data is available for one year), we are
CR
able to run cross-sectional regression, and corrected for heteroskedasticity in the ordinary least squares estimation using robust standard error. The paper uses 9 years average (averaging
US
2006 to 2014 periods) for all the variables (except legal enforcement variable) to obtain a single year. Moreover, the variance inflation factor indicates absence of multicollinearity
AN
problem. The paper reports new findings that legal enforcement has positive effects on capital structure of firms in developing countries. Specifically, legal enforcement variable is
M
positively related to book value measure of capital structure (see Table 15, model 13a that excludes country effects and model 13b that includes country effects). Likewise, legal
ED
enforcement is positively related to market value measure of capital structure (see Table 15, model 14a that excludes country effects and model 14b that includes country effects). The
PT
results suggest that strong legal enforcement encourages lender to lend money and it lowers bankruptcy costs, resulting in firms using more debt to take advantage of debt interest tax-
CE
shield benefits.
Our results are consistent with Bhattacharya and Daouk (2002) empirical findings that
AC
actions taken against insider trading, rather than the mere presence of insider trading laws, help explain the development of securities markets. Likewise, the results are consistent with La Porta, Lopez-deSilanes, and Shleifer (2006) who note that in area of securities regulation, private enforcement of the law seems highly effective for capital market development. Moreover Gibbons (2002) find that the level of enforcement is more important than the quality of laws in explaining the turnover of CEOs.
Table 15 Effects of legal enforcement on Book-debt and Market-debt ratios Ordinary least squares regression estimation results using 3891 listed firms from 23 developing countries
51
ACCEPTED MANUSCRIPT
Int (Interest rate) Inf (Inflation rate) Gdpgr (Economic growth rate) BC (Banking Credit) MC (Market Capitalization) Age (firm-age) DPO (dividend pay-out ratio)
PT
OWS (Ownership structure) Country dummy Adjusted R2 F-Test Variance Inflation Factor Cross-sectional observation (N) a
T
IP
Ndts (Non-debt tax-shield)
0.0003** (2.32) 0.1363*** (7.19) -0.3119*** (-9.81) 0.0155*** (9.16) -0.0001** (-2.09) -0.2681** (-2.39) 0.0019** (2.46) 0.0151*** (7.46) 0.0012* (1.96) 0.0005** (2.08) -0.0007*** (-4.96) 0.0067** (2.43) -0.0230*** (-8.88) -0.0032** (-2.28) 0.1188 33.72 2.58 3891
CR
PB (Price-to-book ratio)
0.0001** (2.02) 0.0731*** (3.29) -0.3210*** (-13.58) 0.0055** (2.98) -0.0001*** (-3.96) -0.4967** (-2.80) 0.0017* (1.93) 0.0047** (3.05) 0.0029** (2.33) 0.0001* (1.85) -0.0002** (-2.00) 0.0010 (1.19) -0.0091** (-3.44) -0.0038** (-2.61) Yes 0.1364 40.05 2.62 3891
US
Size
0.0002** (2.16) 0.1329** (8.09) -0.2992*** (-10.86) 0.0097*** (6.64) -0.0001** (-2.40) -0.5467*** (-3.99) 0.0020*** (2.96) 0.0088*** (5.04) 0.0051*** (2.71) 0.0003** (2.36) -0.0003*** (-2.99) 0.0039* (1.95) -0.0128*** (-5.70) -0.0050** (-2.51) 0.1258 35.92 2.61 3891
AN
PRF (Profits)
Model 14a Model 14b (Market Debt Model) Market Debt Model
M
FA (Fixed Assets)
Model 13b (Book Debt Model
ED
ENFOR (Legal Enforcement)
Model 13a (Book Debt Model)
0.0002** (2.14) 0.0538** (4.27) -0.3221** (7.39) 0.0039*** (4.13) -0.0001** (-2.08) -0.2264** (-2.11) 0.0013** (2.69) 0.0047* (2.83) 0.0013** (2.51) 0.0002* (1.99) -0.0006** (-2.62) -0.0068** (-2.47) -0.0121** (-5.09) -0.0037*** (-3.79) Yes 0.1296 38.39 2.60 3891
AC
CE
Legal Enforcement: measure the level of judicial independence and bribery, the quality of legal framework, the protection of private property and the effectiveness of both the parliament and police. The index is scaled to range from 0 to 10. b See Table 1 for the definition of other variables and measurements. c Debt i = β1 + β2 LENFORi + β3 FA i + β 4 Profit i + β 5 Sizei + β 6 PBi + β 7 Ndts i + β 8 INTi + β 9 INFi + β 10 MCi + β 11 BCi + β12 Gdpg i + β 13 DPOi + β 14 Agei + β15 DPOi + i . d Asterisks indicate significance at 1% (***), 5% (**), and 10% (*). e T-statistics (in parenthesis) of Notes:
OLS 9 years average are based on robust standard errors. c Industry dummies are included in all the estimations. The lists of the industries are in appendix II. Source of Legal Enforcement data: World Bank (2004).
4.6 Controlling for the country effects on debt models (Robustness check 4) Although the paper controls for several macroeconomic factors such as interest rate, inflation rate, economic growth, bank credits, and market capitalization (proxy for stock market size) that have been identified as important determinants of firms’ debt, there is possibility that some left over cross-county factors are not captured by our models. Therefore, as an additional robustness 52
ACCEPTED MANUSCRIPT check, the paper includes country effects (using the dummy variable approach) in our baseline panel regressions in order to control for the left over cross-country differences. The results are broadly similar in the full sample and regional sub-samples, but the coefficient of some of the institutional quality variables changes. Specifically, the single-index measure of institutional quality continue to positively affect firms’ debt. Likewise, the disaggregated measures of institutional quality continue to affect firms’ debt; but the coefficients of some variables are
T
different compared to the models that exclude the cross-country effects. Moreover, the
IP
coefficient of the firm specific and macroeconomic variables are different compared to the
CR
models that exclude the cross-country effects. These results indicate that the left over crosscountry factors have some impact on firms’ capital structure. Therefore future researchers should
US
control for the left over cross country factors when conducting capital structure research. Recently, Deleze and Korkeamaki (2018) test if new issuers on the European corporate
AN
bond markets experience changes in their interest rate sensitivity when they issue new bonds. They added country fixed effects into their model specifications to control for the cross-country
M
effects, and the results reveal that stock returns are less sensitive to interest rate changes for firms that enter the publicly traded bond market. Overall, the coefficient of some variables change in
ED
Deleze and Korkeamaki’s (2018) results, which are also similar to our findings. Similarly, in an earlier study, Jong et al. (2008) also control for cross-country differences via country dummies in
PT
their debt model, and their results reveal that there is an indirect impact because country specific
CE
factors influence the role of traditional firm-specific determinant of debt. Table 16 Direct effects of institutional quality on Book-debt models System-GM M Two-step Estimation Results using Book-debt and 3,891 Listed Firms from 23 Developing Countries, 2006-2014 M odel 15g M odel 15h With CC (Single Equation)
0.5791*** (36.88)
AC
M odel 15a With Single M odel 15b M odel 15c M odel 15d M odel 15e M odel 15f INST Index With ROL With REGQ With GE With PS With VA
0.6072*** 0.6077*** (42.34) (39.44)
0.6077*** 0.5973*** (47.37) (46.18)
0.5848*** (40.12)
0.6049*** 0.6381*** (51.48) (98.70)
-
-
-
-
-
-
-
REGQ (Regulatory quality) -
0.0013*** (3.18) 0.0003** (2.49)
-
-
GE (Govt. Effectiveness)
-
-
-
0.0014** (2.51)
-
PS (Political Stability)
-
-
-
-
0.0002** (2.58)
TDBVit-1 (Lag Book-debt)
INST (Single Institutional 0.0014** Quality Index) (2.80) ROL (Rule of law)
-
-
0.0009** (2.62) 0.0002** (2.49) 0.0001** (2.28) 0.0003** (2.12)
53
ACCEPTED MANUSCRIPT 0.0008* (1.97) 0.0011*** 0.0008* CC (Control of Corruption) (2.59) (3.59) 0.0018** 0.0025** 0.0471** 0.0577** 0.0026** 0.0292** 0.0478** 0.0790*** FA (Fixed Assets) (2.05) (2.06) (2.18) (2.58) (2.07) (2.22) (2.29) (4.54) -0.4447*** -0.4461*** -0.4419*** -0.4441*** -0.4453*** -0.4406*** -0.4451*** -0.4461*** PRF (Profits) (-8.44) (-7.25) (-7.56) (-7.43) (-8.82) (-7.02) (-8.22) (-10.88) 0.0125** 0.0107** 0.0119*** 0.0047** 0.0124** 0.0067** 0.0142** 0.0055** Size (2.18) (2.59) (2.76) (2.16) (2.29) (2.53) (2.32) (2.56) -0.0001* -0.0001** -0.0001 -0.0001 -0.0001** -0.0001* -0.0001** -0.0001 PB (Price-to-book ratio) (-1.79) (-2.10) (-0.94) (-1.42) (-2.25) (-1.83) (-2.44) (-0.54) -0.0572** -0.0592** -0.0260** -0.0608* -0.0575* -0.0643** -0.0308** -0.0478*** Ndts (Non-debt tax-shield) (-2.28) (-2.48) (-2.08) (-1.90) (-1.97) (-2.10) (-2.19) (-3.66) 0.0342** 0.0255* 0.0287** 0.0266* 0.0124** 0.0051 0.0210* 0.0097** Age (Firm-age) (2.08) (1.82) (2.59) (1.94) (2.17) (0.32) (1.98) (2.45) -0.0032 -0.0054*** -0.0006* -0.0014** -0.0044** -0.0029** -0.0019 -0.0079 DPO (Dividend pay-out) (-0.28) (-2.86) (-1.95) (-2.12) (-2.42) (-2.25) (-0.18) (-1.24) -0.1048** -0.0498 -0.0184** -0.2020** -0.0631*** -0.0120 -0.0692** -0.0710*** OWS (Ownership structure) (-2.23) (-0.80) (-2.29) (-2.61) (-3.02) (-0.16) (-2.38) (-14.09) 0.0002** 0.0002** 0.0002** 0.0002** 0.0003** 0.0002** 0.0002** 0.0001* Int (Interest rate) (2.39) (2.03) (2.02) (2.41) (2.19) (2.03) (2.11) (1.80) 0.0007* 0.0008** 0.0008** 0.0009** 0.0011*** 0.0006** 0.0011*** 0.0009** Inf (Inflation rate) (1.94) (2.01) (2.06) (2.37) (2.81) (2.60) (2.71) (2.49) Gdpg (Economic growth -0.0014*** -0.0012*** 0.0012*** -0.0017*** -0.0011*** -0.0014*** -0.0010*** -0.0006* rate) (-4.09) (-3.69) (3.24) (-4.50) (-3.29) (-3.94) (-2.87) (-1.93) 0.0005*** 0.0006** 0.0004** 0.0004* 0.0003* 0.0006** 0.0002** 0.0002** BC (Banking Credit) (5.08) (2.28) (2.35) (1.95) (1.89) (2.10) (2.14) (2.01) -0.0002*** -0.0001*** -0.0002*** -0.0002*** -0.0001*** -0.0001** -0.0002*** -0.0001*** M C (M arket Capitalization) (-3.64) (-3.53) (-4.00) (-4.04) (-3.47) (-3.66) (-3.90) (-3.27) Yes Yes Country Dummies Yes Yes Yes Yes Yes Yes 0.2239 0.2217 AR(2) [P-value] 0.2250 0.2243 0.2232 0.2233 0.2247 0.2245 Difference Hansen Tests [P-value] 0.1050 0.1143 0.1229 0.1225 0.1104 0.1510 0.1145 0.1701 153 223 Instruments 153 153 153 153 153 153 1.64 2.59 Variance Inflation Factor 1.55 1.66 1.71 1.77 1.69 1.56 Heteroscedasticity test (p val.) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Cross-dependency test (pval.) 0.142 0.114 0.121 0.139 0.134 0.175 0.163 0.117 Cross-sectional observation(N) 3891 3891 3891 3891 3891 3891 3891 3891 Observ after estimation (N*T) 30058 30058 30058 30058 30058 30058 30058 30058 -
-
-
-
0.0012** (2.45)
-
-
CE
PT
ED
M
AN
US
CR
IP
T
VA (Voice & Account.)
AC
Notes: a See Table 1 for the definition of variables and measurements. Asterisks indicate significance at 1% (***) and 5%
(**). b T-statistics (in parenthesis) of the Two-step System-GMM model are based on Windmeijer-corrected standard errors. c 2nd order serial correlation in first difference is distributed as N (0, 1) under the null of no serial correlation in the residuals. d Difference-in-Hansen over identification test and null that instruments are valid. e TDBVit-2 , FA it-2, PRFit-2 , Sizeit-2 , Ndts it-2 , PBit-2 , Ageit-2 , DPOit-2 , INSTit-2 , ROLit-2 , REGQit-2 , GEit-2 , PSit-2 , VA it-2 , and CCit-2 are used as Instruments. f In all estimations, the number of ‘N’ cross -sectional observation (3,891) is greater than the number of instruments (153 & 223). g Industry dummies are included in all the estimations. The lists of the industries are in appendix II. Table 17 System-GM M Two-step Estimation Results using Book-debt and 2,187 Listed Firms from 7 Asian Countries, 2006-2014 M odel 16a With Single M odel 16b M odel 16c M odel 16d M odel 16e M odel 16f M odel 16g INST Index With ROL With REGQ With GE With PS With VA With CC
M odel 16h (Single Equation)
54
ACCEPTED MANUSCRIPT
TDBVit-1 (Lag Book-debt)
0.6273*** (70.44)
INST (Single Institutional 0.0003*** Quality Index) (3.68)
0.6482*** (82.02)
0.6431 (87.53)
0.6472*** 0.6403*** 0.6474*** 0.6457*** (94.30) (29.08) (92.72) (92.03)
0.0010*** (2.88)
-
-
-
-
0.0008** ROL (Rule of law) (2.14) 0.0004** 0.0002** REGQ (Regulatory quality) (2.36) (2.79) 0.0008** 0.0002* GE (Govt. Effectiveness) (2.12) (1.81) 0.0003** 0.0005** PS (Political Stability) (2.13) (2.34) 0.0006** 0.0008** VA (Voice & Account.) (2.28) (2.26) 0.0009*** 0.0012*** CC (Control of Corruption) (3.08) (4.77) 0.1215*** 0.1483*** 0.1457*** 0.1604*** 0.1453*** 0.1757*** 0.1487*** 0.1522*** FA (Fixed Assets) (4.18) (5.51) (5.63) (6.55) (5.24) (6.91) (5.90) (6.91) -0.0066** -0.0067** -0.0039** -0.0003** -0.0035** -0.0034** -0.0021** -0.0031** PRF (Profits) (-2.55) (-2.62) (-2.38) (-2.08) (-2.35) (-2.34) (-2.51) (-2.02) 0.0282*** 0.0318*** 0.0285*** 0.0259*** 0.0306*** 0.0246*** 0.0304*** 0.0317*** Size (5.12) (5.66) (5.69) (5.36) (6.12) (4.48) (6.19) (7.69) -0.0027*** -0.0033*** -0.0027*** -0.0027*** -0.0028*** -0.031*** -0.0036*** -0.0035*** PB (Price-to-book ratio) (-5.69) (-6.90) (-5.76) (-5.63 (-5.97) (-6.66) (-7.35) (-7.62) 0.1466*** 0.0315*** 0.0361*** 0.0485*** 0.0818** 0.0512*** 0.0952*** 0.0224*** Ndts (Non-debt tax-shield) (3.45) (4.26) (3.50) (3.70) (2.46) (4.00) (3.16) (3.30) -0.0077* -0.0007 0.0101** 0.0092 -0.0113 -0.0003 -0.0036 0.0076 Age (Firm-age) (-1.85) (-0.07) (2.22) (1.34) (-1.28) (-0.03) (-0.51) (1.40) -0.0250*** -0.0573* -0.0230*** -0.0200** -0.0258*** -0.0225*** -0.0261*** 0.0296*** DPO (Dividend pay-out) (-2.92) (-1.85) (-2.84) (-2.63) (-3.02) (-2.77) (-3.21) (4.77) 0.0087** 0.0573* -0.0279 0.0923*** 0.0235** 0.0713** 0.0445** 0.0442*** OWS (Ownership structure) (2.28) (1.85) (-1.00) (3.30) (2.18) (2.51) (2.54) (4.50) 0.0005* 0.0005** 0.0005** 0.0007** 0.0005** -0.0004 0.0006* 0.0004* Int (Interest rate) (2.10) (2.43) (2.35) (2.07) (2.24) (-1.29) (1.92) (1.80) 0.0008* 0.0008** 0.0009* 0.0005 0.0010* 0.0011** 0.0011** 0.0013** Inf (Inflation rate) (1.84) (2.18) (1.96) (1.55) (1.94) (2.06) (2.15) (2.40) Gdpg (Economic growth 0.0007** 0.0002* 0.0001** 0.0010** 0.0004* 0.0005 0.0002** 0.0003** rate) (2.04) (1.91) (2.15) (2.44) (1.97) (1.57) (2.04) (2.09) 0.0008** 0.0010*** 0.0009*** 0.0009** 0.0007*** 0.0006** 0.0014*** 0.0010*** BC (Banking Credit) (2.37) (3.25) (2.83) (3.23) (2.73) (2.41) (4.46) (3.10) -0.0001** -0.0001* -0.0001* -0.0001** -0.0001* -0.0001* -0.0002* 0.0001* M C (M arket Capitalization) (-2.09) (-1.95) (-1.97) (-2.02) (-1.76) (-1.80) (-1.96) (1.90) Yes Yes Country Dummies Yes Yes Yes Yes Yes Yes 0.1086 0.1101 AR(2) [P-value] 0.1154 0.1147 0.1107 0.1082 0.1113 0.1097 Difference Hansen Tests [P-value] 0.1634 0.1853 0.1102 0.1086 0.1269 0.1129 0.1533 0.1678 153 223 Instruments 153 153 153 153 153 153 3.22 3.39 Variance Inflation Factor 3.29 3.18 3.98 3.28 3.19 3.14 Heteroscedasticity test (pval.) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00 Cross-dependency test (pval.) 0.176 0.139 0.233 0.221 0.181 0.299 0.195 0.166 Cross-sectional observation(N) 2187 2187 2187 2187 2187 2187 2187 2187 Observ after estimation (N*T) 16998 16998 16998 16998 16998 16998 16998 16998
AC
CE
PT
ED
M
AN
US
CR
IP
T
-
0.6431*** (94.10)
Notes: a See Table 1 for the definition of variables and measurements. Asterisks indicate significance at 1% (***), 5% (**)
and 10% (*). b T-statistics (in parenthesis) of the Two-step System-GMM model are based on Windmeijer-corrected
55
ACCEPTED MANUSCRIPT standard errors. c 2nd order serial correlation in first difference is distributed as N (0, 1) under the null of no serial correlation in the residuals. d Difference-in-Hansen over identification test and null that instruments are valid. e TDBVit-2 , FA it-2, PRFit-2 , Sizeit-2 , Ndts it-2 , PBit-2 , Ageit-2 , DPOit-2 , INSTit-2 , ROLit-2 , REGQit-2 , GEit-2 , PSit-2 , VA it-2 , and CCit-2 are used as Instruments. f In all estimations, the number of ‘N’ cross -sectional observation (2,187) is greater than the number of instruments (153 & 223). g Industry dummies are included in all the estimations. The lists of the industries are in appendix II.
IP
T
Table 18 Direct effects of institutional quality on Book-debt models System-GM M Two-step Estimation Results using Book-debt and 1,091 Listed Firms from 6 Latin American combined with Eastern European Countries, 2006-2014
0.7540*** (29.04)
INST (Single Institutional 0.0012** Quality Index) (2.48)
0.7474*** 0.7482*** 0.7410*** 0.7540*** 0.7625*** 0.7507*** 0.7700*** (28.08) (27.46) (27.93) (28.57) (53.41) (29.93) (46.88)
-
-
-
-
0.0002** ROL (Rule of law) (2.30) 0.0012* 0.0005** REGQ (Regulatory quality) (1.90) (2.08) 0.0025*** 0.0006** GE (Govt. Effectiveness) (2.90) (2.05) 0.0002** 0.0002** PS (Political Stability) (2.53) (2.42) 0.0020*** 0.0016*** VA (Voice & Account.) (2.86) (3.21) 0.0003** 0.0003*** CC (Control of Corruption) (2.33) (3.23) 0.0275** 0.0867*** 0.1337*** 0.0183** 0.0268** 0.0407** 0.0195* 0.0282** FA (Fixed Assets) (2.17) (6.32) (7.08) (2.40) (2.20) (2.19) (1.89) (2.14) -0.4438*** -0.4236*** -0.4432*** -0.4442*** -0.4437*** -0.4442*** -0.4439*** -0.4440*** PRF (Profits) (-7.33) (-8.12) (-7.00) (-8.50) (-7.14) (-9.82) (-9.93) (-8.60) 0.0064** 0.0096** 0.0102*** 0.0101** 0.0089** 0.0102** 0.0069*** 0.0045** Size (2.59) (2.58) (2.71) (2.57) (2.47) (2.68) (3.15) (2.19) -0.0001*** -0.0001*** -0.0001* -0.0001** -0.0001** -0.0001*** -0.0001*** -0.000 PB (Price-to-book ratio) (-4.58) (-4.15) (-1.79) (-2.48) (-2.66) (-3.59) (-3.94) (-1.52) 0.0365** 0.0244** 0.0679** 0.0495** 0.0375* 0.0483** 0.0102** 0.0136* Ndts (Non-debt tax-shield) (2.59) (2.20) (2.32) (2.40) (1.77) (2.28) (2.41) (1.99) -0.0184*** -0.0215*** 0.0185*** -0.0175*** -0.0174*** 0.0200*** -0.0124*** -0.0037** Age (Firm-age) (-4.41) (-4.77) (3.86) (-3.88) (-3.86) (4.79) (-3.15) (-2.37) 0.0107* 0.0005 0.0048* 0.0040* 0.0024 0.0051* 0.0036** 0.0049* DPO (Dividend pay -out) (1.95) (0.08) (1.81) (1.96) (0.43) (1.87) (2.57) (1.89) 0.0436** 0.0113** 0.0443** 0.0479** 0.0526** 0.0242* 0.0499* 0.0162* OWS (Ownership structure) (2.16) (2.37) (2.43) (2.16) (2.13) (1.82) (1.81) (1.97) -0.0001 0.0002 0.0006 -0.0005** 0.0002** 0.0004 0.0003 -0.0002** Int (Interest rate) (-0.41) (0.57) (1.61) (-2.35) (2.44) (0.99) (1.12) (2.12) 0.0012 0.0009* -0.0004 0.0003 0.0009** -0.0003 0.0008* 0.0006 Inf (Inflation rate) (1.53)) (1.92) (-0.38) (0.35) (2.59) (-0.29) (1.85) (0.79) Gdpgr (Economic growth 0.0012*** 0.0008** 0.0009** 0.0014*** 0.0009** 0.0011*** 0.0008** 0.0009*** rate) (3.31) (2.16) (2.42) (3.48) (2.50) (3.08) (2.13) (2.86) 0.0005** 0.0002** 0.0005* 0.0002* 0.0002** 0.0008*** 0.0001** 0.0001 BC (Banking Credit) (2.11) (2.13) (1.72) (1.80) (2.16) (3.16) (2.12) (1.27) M C (M arket Capitalization) -0.0005*** -0.0006*** -0.0005*** -0.0005*** -0.0006***
AN
-
AC
CE
PT
ED
M
0.0001** (2.91)
US
TDBVit-1 (Lag Book-debt)
CR
M odel 17a With Single M odel 17b M odel 17c M odel 17d M odel 17e M odel 17f M odel 17g M odel 17h (Single INST Index With ROL With REGQ With GE With PS With VA With CC Equation)
56
(-6.20)
(-6.35)
(-5.42)
(-6.98) Yes 0.9622
-0.0005*** (-6.90) Yes 0.8433
Yes 0.9152
Yes 0.9450
Yes 0.9835
0.1749 153 2.37
0.1490 153 2.39
0.1578 153 2.48
0.1298 153 2.71
0.1858 153 1.97
0.1459 153 1.93
0.1216 153 1.89
0.1122 223 2.87
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.321
0.253
0.214
0.263
0.251
0.219
0.242
0.216
1091
1091
1091
1091
1091
1091
1091
8436
8436
8436
8436
8436
8436
1091
IP
-0.0005*** (-5.73) (-5.63) Yes Yes 0.9748 0.9275
8436
8436
CR
Country dummies AR(2) [P-value] Difference Hansen Tests [P-value] Instruments Variance Inflation Factor Heteroscedasticity test (p-val.) Cross-dependency test (p-val.) Cross-sectional observation(N) Observ after estimation (N*T)
-0.0006*** (-7.22) Yes 0.9430
T
ACCEPTED MANUSCRIPT
Notes: a See Table 1 for the definition of variables and measurements. Asterisks indicate significance at 1% (***), 5% (**)
M
AN
US
and 10% (*). b T-statistics (in parenthesis) of the Two-step System-GMM model are based on Windmeijer-corrected standard errors. c 2nd order serial correlation in first difference is distributed as N (0, 1) under the null of no serial correlation in the residuals. d Difference-in-Hansen over identification test and null that instruments are valid. e TDBVit-2 , FA it-2, PRFit-2 , Sizeit-2 , Ndts it-2 , PBit-2 , Ageit-2 , DPOit-2 , INSTit-2 , ROLit-2 , REGQit-2 , GEit-2 , PSit-2 , VA it-2 , and CCit-2 are used as Instruments. f In all estimations, the number of ‘N’ cross -sectional observation (1,091) is greater than the number of instruments (153 & 223).
Table 19 Direct effects on institutional quality on Book-debt models
ED
System-GM M Two-step Estimation Results using Book-debt and 613 Listed Firms from 10 African Countries, 2006-2014
M odel 18f With VA
M odel 18gg M odel 18h With CC (Single Equation
0.4227*** (19.82)
0.4651*** 0.4523*** 0.4519*** (21.26) (19.65) (21.18)
0.4565*** (18.22)
0.4558*** 0.4987*** (19.76) (33.98)
-
-
-
-
-
-
-0.0001 (-0.42)
-
-
-
-
-
-0.0001 (-0.08)
-
-
GE (Govt. Effectiveness)
-
-
-
0.0001 (0.59)
-
PS (Political Stability)
-
-
-
-
-0.0001** (-2.19)
VA (Voice & Account.) CC (Control of Corruption)
-
-
-
-
-
0.0002** (2.23)
-
-
-
-
-
-
CE
0.4583*** TDBVit-1 (Lag Book-debt) (19.06)
PT
M odel 18a With Single M odel 18b M odel 18c M odel 18d M odel 18e INST Index With ROL With REGQ With GE With PS
ROL (Rule of law) REGQ (Regulatory quality)
FA (Fixed Assets) PRF (Profits)
AC
INST (Single Institutional 0.0003 Quality Index) (0.79)
0.0002 (1.52)
0.0002 (1.01) 0.0004 (1.04) 0.0001 (1.63) -0.0013*** (-4.49) 0.0003** (2.06) 0.0002 (0.92)
0.0572** 0.0353** 0.0672*** 0.0591** 0.0536** 0.0648** 0.0592** 0.0031** (2.33) (2.46) (2.78) (2.40) (2.20) (2.68) (2.46) (2.21) -0.4594*** -0.4582*** -0.4778*** -0.4471*** -0.4796*** -0.4487*** -0.4670*** -0.3562***
57
ACCEPTED MANUSCRIPT (-14.66) 0.0064** (2.43)
(-16.20) 0.0046** (2.02)
-0.0061*** -0.0059*** -0.0062*** (-6.91) (-7.88) (-9.60) -0.2992* -0.2617* -0.2630*** (-1.88) (-1.97) (-2.59) 0.0039** 0.0050** 0.0056 (2.51) (2.56) (0.79)
-0.0064*** (-10.34) -0.1164** (-2.10) 0.0049* (1.79)
-0.0063*** (-11.54) -0.1685** (-2.20) 0.0015* (1.80)
-0.0053*** (-12.01) -0.0340 (-0.44) 0.0086** (2.13)
-0.0291*** (-3.29) 0.0501** (2.34) 0.0002 (1.42) 0.0011*** (2.55) -0.0013** (-2.56) 0.0013*** (3.57) -0.0001** (-2.20) Yes 0.7394
-0.0275*** (-3.24) 0.0001** (2.09) -0.0001 (-1.03) 0.0015*** (3.45) -0.0010* (-1.97) 0.0008** (2.55) -0.0001 (-0.15) Yes 0.6613
-0.0029** (-2.68) 0.0509** (2.31) -0.0003** (-2.35)) 0.0016** (3.96) -0.0011** (-2.05) 0.0007** (2.15) -0.0001 (-0.81) Yes 0.6723
-0-0271*** (-3.15) 0.0441** (2.51) -0.0001 (0.89) 0.0013*** (3.25) -0.0011 (-1.49) 0.0010*** (3.04) -0.0001** (-2.17) Yes 0.6851
-0.0325*** (-3.49) 0.0595*** (2.93) 0.0001 (0.14) 0.0013*** (2.86) -0.0015** (-2.72) 0.0008** (2.50) -0.0001* (-1.85) Yes 0.6518
-0.0332*** (-4.03) 0.0490* (1.96) -0.0003** (-2.27) 0.0019*** (4.53) -0.0014** (-2.59) 0.0007** (2.04) -0.0001** (-2.11) Yes 0.6780
-0.0303*** (-5.82) 0.0570** (3.88) 0.0009*** (6.63) 0.0017*** (6.97) 0.0001** (2.25) 0.0002** (2.11) -0.0001** (-2.28) Yes 0.7064
0.2828 153 1.79
0.3673 153 2.28
0.4524 153 1.90
0.3881 153 1.78
0.2045 153 2.19
0.4138 153 1.91
0.3652 223 2.19
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.184
0.203
0.215
0.226
0.133
0.115
613
613
613
613
613
613
4624
4624
4624
4264
4264
613
ED
0.194
4624
4624
IP
-0.0306*** DPO (Dividend pay -out) (-3.63) OWS (Ownership 0.0471** structure) (2.04) 0.0002** Int (Interest rate) (2.07) 0.0013*** Inf (Inflation rate) (3.29) Gdpg (Economic growth -0.0010** rate) (-2.19) 0.0009*** BC (Banking Credit) (2.73) M C (M arket -0.0001** Capitalization) (-2.02) Country dummies Yes AR(2) [P-value] 0.6846 Difference Hansen Tests [P-value] 0.3798 Instruments 153 Variance Inflation Factor 2.03 Heteroscedasticity test (p val.) 0.000 Cross-dependency test (pval.) 0.199 Cross-sectional observation(N) 613 Observ after estimation (N*T) 4624
T
(-14.35) 0.0062** (2.50)
CR
-0.0062*** (-7.60) -0.3428** (-2.08) 0.0023** (2.32)
PB (Price-to-book ratio)
(-15.26) 0.0015** (2.39)
US
-0.0060** (-7.80) -0.2168** Ndts (Non-debt tax-shield) (-2.38) 0.0085* Age (Firm-age) (1.92)
Size
(-14.58) 0.0089** (2.10)
AN
(-14.84) 0.0009** (2.21)
M
(-15.00) 0.0043** (2.05)
PT
(-14.47) 0.0016* (1.85)
Notes: a See Table 1 for the definition of variables and measurements. Asterisks indicate significance at 1% (***), 5% (**)
AC
CE
and 10% (*). b T-statistics (in parenthesis) of the Two-step System-GMM model are based on Windmeijer-corrected standard errors. c 2nd order serial correlation in first difference is distributed as N (0, 1) under the null of no serial correlation in the residuals. d Difference-in-Hansen over identification test and null that instruments are valid. e TDBVit-2 , FA it-2, PRFit-2 , Sizeit-2 , Ndts it-2 , PBit-2 , Ageit-2 , DPOit-2 , INSTit-2 , ROLit-2 , REGQit-2 , GEit-2 , PSit-2 , VA it-2 , and CCit-2 are used as Instruments. f In all estimations, the number of ‘N’ cross -sectional observation (613) is greater than the number of instruments (153 & 223). g Industry dummies are included in all the estimations. The lists of th e industries are in appendix II.
5.
Conclusion The limited studies available have documented evidence indicating that a subset of
institutional factors affects the speed of adjustment to the target capital structure (debt ratios). Our paper adds to the growing literature on capital structure-institution relationships using an
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ACCEPTED MANUSCRIPT aggregated (single-institutional quality index) measure of institutional quality and disaggregated measure of institutional quality. Moreover, our paper divides the overall samples into various regional panels, namely, firms from Asian countries, firms from African countries, and firms from Latin American and Eastern European countries. New findings emerge from the regional sample separation. Among the six components of institutional quality, only political stability (negative sign) and voice and accountability (positive sign) are significantly related to debt ratios
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in the African sample. Political stability measures perceptions of the likelihood that a
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government will be destabilized by violent means. The likelihood that a government will be
effects of political stability on debt ratios.
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destabilized by violence is high in most African countries, which may explain the negative However, voice and accountability have a positive
US
effect on debt ratios in the African sample. Voice and accountability measures perceptions of the likelihood that a country’s citizen are able to participate in selecting a government. Awareness of
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democracy is increasing in several African countries, which could explain the positive effect of voice and accountability on debt ratios.
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Conversely, the six components of institutional quality have positive effects on debt ratios in the Latin American and Eastern European sample. Over the years, Latin-American and
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Eastern European countries have gone through reforms in terms of laws and institutions that promote transparency, accountability, political stability, and encourage citizens’ participation in
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selecting government. These law and institution reforms may partly explain why these six components of institutional quality have positive effects on debt ratios in Latin-American and
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Eastern European countries.
This paper investigates the effects of institutional quality on firms’ capital structure in
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developing countries. Our main findings can be summarized as follows. For the full sample of 3,891 firms from 23 developing countries, this paper finds that an aggregated (single-index) measure of institutional quality has a statistically significant and positive effect on both the book and market debt ratios. Likewise, the disaggregated measure of institutional quality has a statistically significant and positive effect on both the book and market debt ratios. These results support our argument that holding firms’ assets and investment plans constant, firms in developing countries with strong institutions appear to increase debt because better institutional quality encourages lenders to lend money and it lowers bankruptcy costs, resulting in firms using more debt to capitalize on tax-shield benefits of debt interest. Also, traditional capital structure 59
ACCEPTED MANUSCRIPT determinants (e.g., size, fixed assets, profits, price-to-book ratio) consistently explain the capital structure decisions of listed firms in developing countries, with correct signs. The empirical results also show that firms in developing countries make adjustment to their target debt level. Additional analysis reveals positive effects of legal enforcement on both the book debt and market debt measures of capital structure. At the regional level (for the 2,187 listed firms from Asian countries and the 1,091 listed
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firms from Latin American and Eastern European countries), the aggregated and disaggregated
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measures of institutional quality have a statistically significant and positive effect on both the
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book and market debt ratios. Conversely, for the 613 listed firms from African countries, this paper finds more evidence that institutional quality has an insignificant effect on both the book
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and market debt ratios. This result suggests that institutional quality appears to have little effect on debt ratios among African countries. Additionally, we find more evidence that institutional
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quality has indirect effects (via interacting institutional quality and some firm-specific factors) on debt in both the Asian region and Latin American and Eastern European region. The main
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results remain unchanged after re-estimating the models using post-financial crisis samples. In addition to institutional quality being a determinant of capital structure, firm-specific
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factors (e.g., fixed assets, profits, and price-to-book ratio) consistently explain the capital structure decisions of firms in both the regional subsamples and the full sample, with correct
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signs. As macroeconomic factors, bank credit to the private sector is positively related to debt ratios while stock market capitalization is negatively related to debt ratios in both the regional
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subsamples and full sample. We also provide evidence that Asian firms make faster adjustments to their target debt level than firms in the other two regions. The firms’ adjustment
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behavior is consistent with the dynamic version of trade-off theory. The main results remain unchanged after re-estimating the models using post-financial crisis samples. These results have several policy implications. Firstly, the results suggest that strong institutions that protect creditors’ rights improve loan availability and encourage lenders to provide debt capital. Secondly, the results suggest that debt financing decisions are not made in a vacuum and appear to depend on institutional environments. Therefore, policymakers should continue to strengthen the quality of institutions. Improving the quality of institutions would remove barriers to debt financing and prevent ineffective utilization of financial resources in developing countries. Third, as most African countries are characterized by weak institutional 60
ACCEPTED MANUSCRIPT quality, policymakers in this region should undertake institutional reforms that aim to improve the quality of institutions to ensure that firms have easy access to debt capital. The paper has some limitations. Our focus is on one aspect of debt structure (i.e. capital structure). Debt structure also includes debt maturity, the choices between public and private debt, and debt covenant which we do not cover in this paper. Moreover, firms make both the capital structure and other debt structure decisions simultaneously. Therefore, our results should
problem using
internal instruments,
it
does
not
completely eliminate
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endogeneity
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be interpreted with caution. Although, our paper uses the two-step system GMM to mitigate the the
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endogeneity problem caused by unobserved heterogeneity. Additionally, like previous studies, our paper assumes a causal relationship between capital structure and institutional quality. But
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the relationship between capital structure and institutional quality may only be association. Thus, readers are advised to interpret our results with caution.
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Future research can explore the indirect effects of institutional quality on stock returns and other firm performance measures using listed firms from more developing countries, as the data
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become available. Future research may use both the balanced panel dataset and unbalanced
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panel dataset in a single study to conduct capital structure research and compare the results.
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Appendix I Number of Firms and Developing Countries (with available data)
Malaysia Pakistan Philippines
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India
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Asian Countries
Number of Firms 795 728 93 103
Bangladesh
10
Srilanka
139
Indonesia
319
Total
2187
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ACCEPTED MANUSCRIPT Number of Firms
Ghana
17
Kenya
38
Nigeria
40
Tunisia
32
Zimbabwe
6
Mauritius
29
Morocco
58
Egypt
88
Jordan
115
South Africa
190
Total
613
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98
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Mexico Chile
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Brazil
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Peru
Turkey
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Eastern European Countries
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Latin America combined with
Poland
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African Countries
144 188 77 339 245
Total
1091
Overall Total
(2187 + 613 + 1091) = 3891
Appendix II Industries Included
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ACCEPTED MANUSCRIPT Agriculture, Forestry, and Fishing Construction Manufacturing Transportation and Communications
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Services
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Retail Trade
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Wholesale Trade
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Note: Wholesale Trade are excluded to avoid dummy variable trap.
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We investigate the effects of institutional quality on firms’ capital structure. We use both aggregated and disaggregated measure of institutional quality. Institutional quality has a positive effect on capital structure for full sample. Institutional quality has different effect on capital structure for regional samples. Legal enforcement has positive effects of on capital structure.
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Highlights
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