The interaction of quantity and quality of finance: Did it make industries more resilient to the recent global financial crisis?

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International Review of Economics and Finance 64 (2019) 493–512

Contents lists available at ScienceDirect

International Review of Economics and Finance journal homepage: www.elsevier.com/locate/iref

The interaction of quantity and quality of ﬁnance: Did it make industries more resilient to the recent global ﬁnancial crisis? Ali Mirzaei a, *, Robert Grosse b a b

School of Business Administration, American University of Sharjah, Sharjah, 26666, United Arab Emirates Thunderbird School of Global Management, Arizona State University, Phoenix, AZ, USA

A R T I C L E I N F O

A B S T R A C T

JEL classiﬁcation: G01 G15 E44 O4

While the literature on ﬁnancial development generally shows a strong correlation between quantity of ﬁnance and economic development, the issue of ﬁnancial crises and the effect of quality of ﬁnance have not been taken adequately into account. The 2008-9 global ﬁnancial crisis provides a useful context in which to explore this relationship. We argue that, for the purposes of mitigating the adverse effects of ﬁnancial crises on the real sector, the quantity of ﬁnance (measured as domestic credit) should be backed by quality of ﬁnance (measured as bank efﬁciency, integrity in bank lending, and bank private monitoring). Using a sample of 28 industries from 63 countries, we ﬁnd support for our main argument. Speciﬁcally, we ﬁnd that industries that are more dependent on external ﬁnance were disproportionately more resilient during the recent crisis. This was especially true if they were located in countries where high ﬁnancial quantity during the pre-crisis period was accompanied by a better ﬁnancial quality. These results suggest that paying attention to the quality of ﬁnance may assist in mitigating the adverse real impact of ﬁnancial crises, and that there is indeed such a thing as an excessive quantity of ﬁnance.

Keywords: Financial crises Quality ﬁnance Financial dependence Industry performance

1. Introduction In recent years, a number of authors have explored the relationship between ﬁnancial development and economic development, usually concluding that the former supports the latter (Levine, 1997; Rajan & Zingales, 1998; Valickova, Tomas, & Horvath, 2015). Even so, some authors have found that ﬁnancial sector growth, particularly of banking assets, can exceed an optimal level, which then leads to slower economic development (Aizenman, Yothin, & Park, 2015; Cournede & Denk, 2015; Swamy & Dharani, 2019). Yet others have found that sequencing among ﬁnancing sources is important; countries beneﬁt more from an initial expansion of bank ﬁnancing, whereas later along the development path, they beneﬁt more from an expansion of (stock) market ﬁnancing (Demirguc-Kunt, Feyen, & Levine, 2013; Zhuang et al., 2009). Among the various reasons why ﬁnancial development may not stimulate economic growth, researchers have highlighted the role of ﬁnancial quality and the impact of ﬁnancial crises (See for example, Hasan, Koetter, & Wedow, 2009; Hasan, Kobeissi, Wang, & Zhou, 2017; Lucchetti, Papi, & Zazzaro, 2001). Our study contributes to and extends this stream of research, focusing on the impact of ﬁnancial quality on the relationship between the quantity of ﬁnance and economic growth in the context of the recent ﬁnancial crisis. The global ﬁnancial crisis affected most countries around the world (Berkmen, Gelos, Rennhack, & Walsh, 2012; Claessens, Dell’Ariccia, Igan, & Laeven, 2010; Giannone, Lenza, & Reichlin, 2011; Lane & Milesi-Ferretti, 2010), and the adverse effect on industry

* Corresponding author. E-mail addresses: [email protected] (A. Mirzaei), [email protected] (R. Grosse). https://doi.org/10.1016/j.iref.2019.08.010 Received 18 April 2017; Received in revised form 31 August 2019; Accepted 31 August 2019 Available online 4 September 2019 1059-0560/© 2019 Elsevier Inc. All rights reserved.

A. Mirzaei, R. Grosse

International Review of Economics and Finance 64 (2019) 493–512

performance has been documented by numerous studies (Klapper & Love, 2011; Laeven & Valencia, 2013; Moore & Mirzaei, 2016). Yet, not all countries, or even within countries, not all industries were affected to the same extent. Even a cursory examination of the data reveals that the average decline in industry growth rates (measured as the rate of growth of value added in manufacturing) during the crisis of 2008–09 (compared to pre-crisis 2006–07) is about 15%: from þ11.4% in pre-crisis to 3.4% during the crisis.1 There are, however, substantial differences in industry performance across countries, and within countries across industries. For example, while the industry growth rate declined in the UK by 30% during the crisis, it increased in Australia by 8%, and the overall difference across countries ranges from a decline of 59% in Latvia to an increase of 27% in Tanzania. Signiﬁcant variations also can be observed within countries. For example, in the UK, while the growth rate in the iron and steel sector (ISIC 371) decreased by 88%, the growth rate in beverages (ISIC 313) increased by 12% – and such variation is also evident in other countries. What then could explain such a severe impact of the crisis for some countries/industries and not others? Some researchers have highlighted the role of the real channel (for example, trade in products) and of the ﬁnancial channel (e.g. ﬁnancial interconnection and cross-border bank activity) through which the crisis was spread. While some countries were affected through the collapse of international trade, others were affected principally through rapid ﬁnancial spillovers (Claessens et al., 2010). The existing literature has attempted to distinguish between these channels by using proxies for trade and ﬁnancial integration (see for instance, Milesi-Ferretti & Lane, 2011; Rose & Spiegel, 2010 and 2011). However, a clean separation of these different channels was not accomplished, since the proxies tended to be highly correlated (Claessens, Tong, & Wei, 2012). Other studies focus on ﬁnancial liberalization and its impact on economic performance to explain the severity of the recent crisis.2 Many countries around the world have substantially developed and liberalized their ﬁnancial sectors over the past two decades (Delis, 2012).3 Financial liberalization reduces moral hazard and adverse selection problems that constitute ﬁnancial obstacles for ﬁrms. Well-developed and deeper ﬁnancial markets are hence thought to beneﬁt ﬁrms (Kroszner, Laeven, & Klingebiel, 2007). The channels through which ﬁnancial development contributes to economic growth are the credit channel (Rajan & Zingales, 1998), by ﬁnancing external-ﬁnance-dependent industries, and the capital reallocation channel (Wurgler, 2000), by investing funds in growing industries and withdrawing funds from declining industries. These two channels play positive roles, as long as the economy is in a normal phase. However, when a ﬁnancial crisis occurs, both channels may deteriorate and thus intensify the adverse real effect of the crisis (Fernandez, Gonz alez, & Su arez, 2013a). Hence, in relation to industry performance, the global crisis was expected to affect growth most adversely in those countries/industries that were most reliant on credit expansion (greater quantity of ﬁnance) during the pre-crisis period.4 We add to the above debate by exploring the question of how some countries and some industries were more able to cope with the ﬁnancial crisis than others. Using data for 28 industries in 64 countries, we ﬁnd that the positive interaction between the quantity and quality of ﬁnance indeed assists industries to be more resilient to ﬁnancial crises. Speciﬁcally, we ﬁnd that industries that are more dependent on external ﬁnance were relatively more resilient during the crisis, if located in countries where a high quantity of ﬁnance during the pre-crisis period was accompanied by better ﬁnance quality. These results suggest that paying attention to the quality of ﬁnance may assist in mitigating the adverse real impact of ﬁnancial crises, and that there is such a thing as an excessive quantity of ﬁnance (beyond the optimal level). Economically, our estimation coefﬁcients suggest that a one-standard deviation increase in the quality of ﬁnance enhances the growth rate of ﬁnancially dependent industries by approximately 3–4%, depending on the proxy for quality of ﬁnance. Our results are also broadly consistent with recent studies which found that initial ﬁnancial conditions help to explain the severity of the crisis, and with our assertion that ﬁnancial quantity, if accompanied by ﬁnancial quality, is associated with a greater ability to withstand the ﬁnancial crisis. Our paper draws on and develops three strands of literature. First, the paper is closely related to some recent studies that have attempted to explain cross-country differences in the impact of the global ﬁnancial crisis. They usually examine whether initial conditions can explain the differential impact of the crisis across countries. In two related papers, Rose and Spiegel (2010, 2011) ﬁnd that initial conditions generally do not help in explaining the poor performance of some countries during the crisis. Still, they ﬁnd that countries with higher incomes, looser credit market regulation, and current account deﬁcits were likely to be affected more severely in terms of economic performance. Claessens et al. (2010) ﬁnd that variables such as increased ﬁnancial integration and dependence on wholesale funding correlate positively with the intensity of the crisis across countries. Using output growth for 102 countries, Giannone et al. (2011) ﬁnd that the set of policies that favour liberalization in credit markets is positively associated with country vulnerability to the recent crisis. They further ﬁnd that higher interest margins and overhead costs (low quality of ﬁnance) in the banking sector also relate positively to the degree of vulnerability of countries to the crisis. Lane and Milesi-Ferretti (2010) also show that a pre-crisis current account deﬁcit and a high rate of domestic credit expansion (high quantity of ﬁnance) are signiﬁcantly correlated with the decline in output during the crisis period, while in a later study (Lane & Milesi-Ferretti, 2012), they show that unusual current account deﬁcits during the years approaching the crisis were associated with

1 The ﬁgures mentioned in this introduction are derived from a sample of the 28 industries in 63 countries for which we have data on industry growth around the global ﬁnancial crisis (see Data Section). Thus, there is the caveat that these ﬁgures do not represent all countries in the world. Furthermore, industry growth here indicates only growth in real value added in manufacturing sectors. 2 Cetorelli and Goldberg (2011) also ﬁnd evidence that country resilience to the crisis was negatively associated with ﬁnancial market integration. 3 Abiad, Detragiache, and Tressel (2010) develop an index to measure the pace of ﬁnancial reforms in different types of economy. Moreover, Angkinand, Sawangngoenyuang, and Wihlborg (2010) ﬁnd an inverted U-shape relationship between ﬁnancial liberalization and the likelihood of a ﬁnancial crisis. 4 Using aggregate output growth indicators, Lane and Milesi-Ferretti (2010) ﬁnd that pre-crisis development in ﬁnance is indeed helpful in understanding the severity of the crisis.

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sharp current account reversals and expenditure reductions during the crisis. Berkmen et al. (2012) focus explaining cross-country differences in the growth impact. They ﬁnd that growth projections were revised downwards more severely in countries with more leveraged domestic ﬁnancial systems, more rapid credit growth, and more short-term debt.5 Finally, Caprio, D’Apice, Ferri, and Puopolo (2014) provide a cross-country analysis of the macro-ﬁnancial determinants of the recent global ﬁnancial crisis. They ﬁnd that the probability of suffering a greater impact of the crisis was larger for countries with a more competitive banking sector and fewer restrictions on bank activities. Second, our paper is similar, but complementary to a few studies that document the real effect of ﬁnancial crises, using industry-level data.6 Kroszner et al. (2007) examine the impact of banking crises on the growth of industries over the crisis episodes between 1980 and 2000. Using data for 38 countries that experienced a banking crisis, they ﬁnd that ﬁnancial crises had a disproportionately negative impact on sectors that rely more on external sources of ﬁnance. Dell’Ariccia et al. (2008) investigate the effects of banking crises on growth in industrial sectors and ﬁnd that during the crisis period sectors that are more dependent on external ﬁnance grew slower than sectors less dependent on external ﬁnance. Klapper and Love (2011) study the effects of the 2008 global ﬁnancial crisis on new ﬁrm registrations. Using data for 95 countries, they ﬁnd that more ﬁnancially developed countries experienced a greater decline in business entry during the crisis. Laeven and Valencia (2013) also study the real effects of disruption in the supply of credit. Using data for the recapitalization of banking sectors, as well as ﬁrm-level data for 50 countries during the crisis period 2008–2009, they ﬁnd that recapitalization policies enhanced the value-added growth of ﬁrms that are more dependent on external ﬁnance. Similarly, Mirzaei and Moore (2019) ﬁnd that industries that depend heavily on external ﬁnance grow faster in countries with more efﬁcient banking systems (i.e., higher quality of ﬁnance), both during normal conditions and to some degree, during ﬁnancial crises.7 Third, our paper is also closely related to several other studies that highlight the importance of ﬁnancial quality. Lucchetti et al. (2001) investigate the impact of bank efﬁciency on economic growth, pointing out that such efﬁciency reveals the allocative function, which is neglected if using only quantitative indicators of ﬁnancial development. Barth, Caprio, and Levine (2004) ﬁnd that ﬁnancial regulatory quality as deﬁned by rules on information disclosure and rules that allow private-sector forces to operate freely correlated with better banking performance and development. Hasan et al. (2009) ﬁnd that bank efﬁciency is associated with greater economic growth. They argue that banks promote growth through three channels of quantity-based variables (for example, credit), quality-based variables (for example, efﬁcient intermediaries), and the interaction of the two. Koetter and Wedow (2010) also ﬁnd that ﬁnancial quality (bank efﬁciency) affects growth positively. Hasan et al. (2017) ﬁnd that the quality rather than types of bank ﬁnancing matters for regional entrepreneurship activities in China. Regarding the quality of bank lending, Jayaratne and Strahan (1996) ﬁnd that a higher quality (but not quantity) of bank lending is associated with more rapid growth. These papers study the impact of the quality of ﬁnance on growth during economically normal conditions. Little is known, however, whether countries whose banking sector provides higher-quality ﬁnance in pre-crises are more resilient during crises. For example, one would expect efﬁcient banks (during the years approaching the recent ﬁnancial crisis) to sustain their lending more effectively during the crisis than inefﬁcient banks. Overall, our analysis makes two main contributions to the literature. First, in contrast to previous studies that commonly use aggregate GDP growth to test the resilience of a country’s real sector to the recent ﬁnancial crisis, we use industry-level data. For the economy as a whole, negative growth in one economic sector during the crisis could be offset by positive growth in another. In addition, industry-level data may also mitigate concerns about reverse causality from country macroeconomic features to the crisis, since it is highly unlikely that the banking crisis is driven in a systematic manner by an individual industry’s value-added growth prospects.8 Our second contribution is that we employ a range of interactions between the quantity and quality of ﬁnance that may mitigate the adverse real effect of the recent ﬁnancial crisis. Since a decline in industry performance is associated with the ﬁnancial crisis, and because the years approaching the crisis are characterised by increasing ﬁnancial liberalization (more “quantity of ﬁnance”) in many countries, we focus on the role of ﬁnancial quality that may help to explain the severity of the crisis. The remainder of the paper is organized as follows. Section 2 discusses the importance of ﬁnancial quality and presents our hypothesis. Section 3 describes the industry and ﬁnancial data, contains our empirical methodology, and presents some evidence showing how resilient countries/industries were to the recent global ﬁnancial crisis. In Section 4, we present the empirical results. Section 5 draws conclusions. 2. Financial quality and hypothesis Analyses of the relationship between ﬁnancial development and economic development focus largely on the quantity of ﬁnancial resources available in a country for measuring ﬁnancial development (Valickova et al., 2015). In fact, both the quantity of ﬁnancial resources and the quality of the ﬁnancial system play important roles in economic development (Cournede & Denk, 2015). An unbridled growth of ﬁnancing (credit expansion) provided, for example, by a central bank leads typically to bouts of hyperinﬂation rather than increased growth of the overall economy (as the cases of Argentina, Brazil and Peru demonstrated rather painfully in the 1980s, and the former Yugoslavia and

5 For the severity of the crisis on emerging economies see Berglof, Korniyenko, Plekhanov, and Zettelmeyer (2009), Blanchard, Das, and Faruqee (2010), International Monetary Fund (2010), and Didier, Hevia, and Schmukler (2012) and for the depths of the crisis and differences across countries see Masciandaro, Pansini, and Quintyn (2011). 6 See Mirzaei and Al-Khouri (2016) for country-speciﬁc studies. 7 See also Serwa (2010), Fernandez, Gonzalez, and Suarez (2013b), Mirzaei and Kutan (2016) and Moore and Mirzaei (2016). 8 We are aware of Claessens et al. (2012) and Duchin, Ozbas, and Sensoy (2010) who use ﬁrm-level data. However, these studies use data only on listed ﬁrms.

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Zimbabwe in the 1990s and early 2000s). Theoretically, both the availability of an adequate quantity of ﬁnancial resources, and an adequate management of those resources by ﬁrms and regulators (quality) are important to successful economic development. However, quality of ﬁnance may have a broad deﬁnition. In this paper, ﬁnancial quality refers to the more or less effective management of the ﬁnance providers (especially banks), the more or less effective regulation of the ﬁnance providers, and the degree of corruption in bank lending. Thus, in this paper, we refer to three different dimensions of ﬁnancial quality as bank efﬁciency (Hasan et al., 2009; Koetter & Wedow, 2010; Lucchetti et al., 2001), integrity of bank lending (Akins, Dou, & Ng, 2017; Barth, Lin, & Song, 2009; Beck, Demirguc-Kunt, & Levine, 2006), and quality of regulation measured as bank private monitoring (Barth, Caprio, & Levine, 2013). Greater bank efﬁciency is associated with better monitoring of allocated loans, better bank loan portfolio quality, and lower costs of capital, among others, and hence a higher quality of ﬁnance (Jayaratne & Strahan, 1996). Integrity in bank lending is deﬁned as the avoidance of private negotiations between the bank lending ofﬁcers and the borrowers (Barth et al., 2009), because corruption in bank lending represents a serious obstacle to ﬁrms or a reduction in the quality of loans accessible to associated ﬁrms. Quality of regulation (i.e. bank private monitoring) is identiﬁed as regulatory policies that lead to better performance of the ﬁnancial sector, including pro-competition rules and rules for accurate information disclosure (Barth et al., 2004).9 A well-functioning banking system most certainly matters for economic development, because bank lending is a crucial source of external ﬁnance for non-ﬁnancial ﬁrms. Thus, banking systems that operate efﬁciently, are better-regulated and are less corrupt in their lending processes, would facilitate the channelling and monitoring of scarce resources to the most productive investment projects, and thus enhance economic performance (Barth et al., 2009). Previous papers focusing on the quality of ﬁnance (Hasan et al., 2009; Koetter & Wedow, 2010; Lucchetti et al., 2001) have all used bank efﬁciency as a proxy for ﬁnancial quality. Related to the global ﬁnancial crisis, some argue that efﬁcient banks were more resilient to the crisis, and hence, the real sectors were affected less severely. Conceptually, consider two industries i and j; where Industry i is a ﬁnancially more dependent industry and Industry j is less dependent on ﬁnance. Now assume two countries A and B, that are both ﬁnancially developed (more quantity), but the quality of credit in Country B is greater than that in Country A. We expect that Industry i may not grow disproportionately faster than Industry j in country A, despite the fact that the country provides a high quantity of ﬁnance. On the other hand, we expect that Industry i will grow faster, compared to Industry j, in Country B, where a high quantity of ﬁnance is accompanied with quality ﬁnance. We further conjecture that this makes industries more resilient to cross-border ﬁnancial crises. Thus, we theorize that the quality of ﬁnance along with quantity of ﬁnance is fundamental to sustainable economic development, and to surviving ﬁnancial crises. This leads to our main research proposition and testable hypothesis: Hypothesis 1. Financial quantity, if accompanied by ﬁnancial quality, makes industries that depend on external ﬁnance more resilient to ﬁnancial crises. Financial quantity alone does not accomplish this goal, nor does ﬁnancial quality alone. 3. Data and models 3.1. Data Recall that our aim is to examine whether cross-country differences in the interaction between quantity and quality of ﬁnance can explain industry performance during the recent global ﬁnancial crisis. To pursue this, we collect the industry and ﬁnancial data as follows: 3.1.1. Industry data The industry data are from the UNIDO Industrial Statistics Database, which contains highly disaggregated yearly data on manufacturing sectors. We select 73 industries of mixed 3&4-digit codes. In order to use, for example, the industry ﬁnancial-dependence data of Rajan and Zingales (1998), we regroup these 73 industries of ISIC Rev. 3 data into 28 industries of ISIC Rev. 2 (Hoxha, 2013). We initially selected all 135 countries included in the UNIDO database. However, we then removed 71 countries for which data on our main industry performance variable (that is value added growth) are not available during the crisis period 2008–09. We further dropped the U.S. from our dataset, because it is the source of the crisis and also it is used for industry benchmarking. This left us with a sample of 28 industries in 63 countries. The time span of the data is 1998–2010, but our main analysis is based on industry performance during the crisis period 2008–09, as opposed to the pre-crisis period 2006–07. Our sample is diverse in terms of income groups and geographical areas - 25 countries labelled advanced, 25 emerging and 13 developing countries. Out of the total of 63, 30 countries are high-income, 28 are middle-income and 5 are low-income. Emerging and developing countries include 4 countries from East Asia and the Paciﬁc, 12 countries from Europe and Central Asia, 7 from Latin America, 2 from the Middle East and North Africa, 2 from South Asia and 6 from Sub-Saharan Africa10.

9

While it would be useful to have a measure of regulatory quality in terms of government rules, it is not clear what speciﬁc rules would imply better quality as opposed to just greater intervention in the market. The measure used here avoids this problem by looking at private-sector monitoring of the banks. 10 Note that such important emerging countries as Argentina, China, Czech Republic, Philippines, Thailand and Ukraine, and such important advanced countries as Greece, Ireland and Switzerland have been excluded from our sample, because no detailed industry data were available during the crisis period 2008–09 for these countries. 496

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The ﬁnal number of industries in our dataset varies by country. On average, each country has 23 industries with data available, and 30 countries have data on 25 or more industries. The available industries among the countries range from 28 (Spain, Finland, Kenya and Poland) to 12 (Australia). Furthermore, the availability of data among industries also differs. While data for food products (ISIC 311) are available for 63 countries, only 17 countries report data for miscellaneous petroleum and coal products (ISIC 354). On average, each industry is reported by 52 countries. 3.1.2. Financial data We obtain our ﬁnancial data from several sources: (i) the World Development Indicators database (the World Bank) is used to create a proxy for quantity of ﬁnance (namely domestic credit); (ii) the Financial Development and Structure dataset (the World Bank) is used to construct the ﬁrst proxy for the quality of ﬁnance (bank efﬁciency); (ii) the World Business Environment Survey (WBES) is used to create a proxy for another dimension of ﬁnancial quality, that is, the integrity of bank lending; and (iii) the World Bank surveys11 on bank regulation and supervision is used to generate the third proxy that is bank private monitoring. Bank private monitoring measures whether there are incentives/abilities for the private monitoring of ﬁrms, with higher values indicating more private monitoring, and hence higher quality of ﬁnance. Furthermore, the WBES is a survey conducted by the World Bank in 1999 of more than 10,000 ﬁrms from 81 countries. The main aim of the survey was to acquire managers’ views of factors that facilitate or curb ﬁrm performance and growth, including questions on bank lending corruption.12 Following Beck et al. (2006) and Barth et al. (2009), we establish the proxy of lending corruption based on the key question in the survey ‘‘Is the corruption of bank ofﬁcials an obstacle for the operation and growth of your business?’’ Answers vary between: 1—(no obstacle), 2—(a minor obstacle), 3—(a moderate obstacle), and 4—(a major obstacle). A higher value indicates more severe corruption in bank lending. Note that this is at the ﬁrm level. In order to construct a country-level lending corruption index, we take the average of all ﬁrms in each country. Furthermore, to use it as a proxy for the quality of ﬁnance, we use its inverse to measure integrity in bank lending. Accordingly, the higher the integrity of bank lending, the higher quality of ﬁnance. Note that unfortunately, the data on the integrity of bank lending is available only for 37 countries of our dataset. 3.2. Models Our main aim is to examine whether the initial, pre-crisis interactions of quantity- and quality-related ﬁnancial variables can explain between-country and within-country (i.e. across industries) differences in industry performance during the crisis. Thus, our empirical strategy can be formulated as follows: QL QN ΔPerformanceic ¼ ϕ0 þ ϕ1 :Shareic;Pre þ ϕ2 :Financial dep:i FinanceQN c; Pre Financec; Pre þ ϕ3 :Financial dep:i Financec; Pre QN QL þϕ4 :Financial dep:i FinanceQL c; Pre þ ϕ5 :Financec; Pre Financec; Pre þ ϕ6 :ðindustry dummiesÞi þ ϕ7 :ðcountry dummiesÞc

þεic

(1)

The dependent variable is ΔPerformanceic ¼ Performanceic;Crisis Performanceic;Pre where Performanceic;Crisis is performance of industry i in country c during the crisis period 2008–09 and Performanceic;Pre is industry performance during the pre-crisis period 2006–07. Δ Performanceic is thus the change in industry-level performance due to the crisis in industry i in country c between the crisis and pre-crisis periods. Industry performance is estimated using real value-added growth, and for robustness, using real output growth. Note that the choice of our dependent variables (change in industry performance) and our model speciﬁcation is similar to that of Kroszner et al. (2007) who examine the impact of ﬁnancial crises (excluding the recent global crisis) on industry growth. Shareic;Pre is the share of value added by industry i to total value added of all industries in country c, averaged over the pre-crisis period 2006–07. Financial dep: is a measure of an industry’s dependence on external ﬁnance, from Laeven and Valencia (2013), and based on a methodology developed originally by Rajan and Zingales (1998). It deﬁned as the portion of capital expenditures not ﬁnanced with cash ﬂows from operations, which are calculated for large publicly traded U.S. ﬁrms over 1980–2006 and used as a benchmark for industries in other countries.13 It represents the dependence of an industry on outside sources of funds (both equity and debt) that companies need for long-term investment, and therefore relates mostly to ﬁxed investment (Manova, Wei, & Zhang, 2015). QL FinanceQN c; Pre is a proxy for the country-speciﬁc quantity of ﬁnance, averaged over the pre-crisis period 2006–07, and Financec; Pre is a vector of country-speciﬁc quality of ﬁnance, (where applicable) averaged over the pre-crisis period 2006–07. Note that, following previous researchers (for example, Lane & Milesi-Ferretti, 2010), we aim to identify initial country conditions that help to explain industry performance slowdown during the crisis, and hence, we do not include any regressors that are based on the crisis period 2008–09 itself. Following previous literature (Rajan & Zingales, 1998), we also include two sets of industry and country dummies: ðindustry dummiesÞi refers to those which capture industry-speciﬁc factors affecting cross-industry growth, such as industrial R&D, and ðcountry dummiesÞc are those that capture time-invariant country-speciﬁc factors that might drive cross-country differences in growth, such as cultural and legal environments. The error term is clustered at the industry level. However, in robustness tests, we cluster at the

11 Surveys were conducted in 1999, 2003, 2007, and 2011. Note that we use the previously available survey data until a new survey becomes available. 12 Barry, Lepetit, and Strobel (2016) discuss the relevance of the WBES survey for studying corruption in bank lending. See also Barth et al. (2009). 13 We refer readers to Rajan and Zingales (1998) and Manova et al. (2015) for a detail discussion on why ﬁnancial dependence scores estimated using US data constitute a valid benchmark for industries in other countries.

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country and industry-country levels as well. Note also that as in previous studies (for instance, Kroszner et al., 2007), we do not include the levels of ﬁnancial dependence, quantity of ﬁnance and quality of ﬁnance (that are without interaction terms) in the equation, since they are already captured by industry and country dummies. For country-speciﬁc ﬁnancial variables we collect two sets of variables. The ﬁrst set of variables is related to the quantity of ﬁnance (FinanceQN c; Pre ), capturing development of the ﬁnancial sector. Following Rajan and Zingales (1998) and Hsu, Tian, and Xu (2014), we use “Domestic credit”14 as a proxy for the quantity of ﬁnance, which is deﬁned as domestic credit to the private sector as a percentage of GDP. We interpret higher levels of domestic credit as indicating a higher quantity of ﬁnance (or greater ﬁnancial development). Overall, we expect countries with more developed ﬁnancial sectors to have suffered more during the crisis. By examining a “megaset” of variables, Feldkircher (2014) attempted to explain the severity of the crisis in 2008–09. He ﬁnds credit growth as the main determinant of vulnerability. Demirguc-Kunt and Detragiache (1998) investigate the impact of ﬁnancial liberalization on banking crises in 53 countries over the period 1980–1995. They ﬁnd that banking crises are more pronounced in countries with liberalized ﬁnancial systems.15 The second set of variables includes proxies for the quality of ﬁnance (FinanceQL c; Pre ). Our core variables here are: (i) bank efﬁciency, which is calculated as the inverse of average bank overheads to total assets; (ii) the integrity of bank lending, which is calculated as the inverse of bank-lending corruption; and (iii) quality of bank regulation, which is measured as an index of bank private monitoring. The higher these ratios are, the higher is quality of ﬁnance. Furthermore, propagation of the crisis can depend not just on country characteristics, but also on industry features. Thus, for explaining within-country (across industries) differences, we also include financial dep:, which has the advantage of incorporating information about heterogeneity across industries within countries. Like countries, industries entered into the crisis with substantial differences in terms of their reliance on external ﬁnance. We examine whether or not such differences can explain the severity of the crisis within countries across industries. Kroszner et al. (2007), for instance, ﬁnd that sectors dependent more on external sources of ﬁnance suffered more during the crisis periods. See also Cowan and Raddatz (2013). One of the most important issues with the ﬁnance and growth nexus is the conventional problem of endogeneity. Industries with potential growth opportunity may demand more ﬁnance both in terms of quantity and quality, raising the concern of reverse causality. It might also be the case that both growth and ﬁnance are affected by a latent factor, implying omitted variable bias. We address this issue from several aspects. First, following previous studies (e.g., Hsu et al., 2014), we use the framework applied to industry-level data, initiated by Rajan and Zingales (1998). It takes account of the varying degrees of external ﬁnance dependence across industrial sectors using data from U.S.-listed ﬁrms. Researchers (e.g. Fernandez et al., 2013a) argue that it is unexpected that U.S. ﬁnancial dependence responds to output growth elsewhere. Thus, including a proxy for industry characteristics has the advantage of showing the channel through which ﬁnancial development (including both quantity and quality) enhances economic growth. Second, ﬁnancial development could be regarded as exogenous to ﬁrms (and hence industry-level) ﬁnancing decisions, as country-level ﬁnance is beyond the control of individual ﬁrms (Igan, Kutan, & Mirzaei, 2016). Third, we include a set of industry and country dummies that may mitigate omitted variable and endogeneity problems. Fourth, we propose that pre-crisis interaction of quantity and quality of ﬁnance affects positively industry performance during the crisis period. We believe that it is unlikely that changes in growth of industries during the crisis affected the quality (or quantity) of ﬁnance in the pre-crisis period. Fifth and ﬁnally, we control for observable characteristics that may affect industry performance, and then we use selections of these observable factors to determine the possibility that our estimates are being driven by unobserved heterogeneity across countries (See Section 4.2). That said, there may still be some residual endogeneity issues, as growth opportunities in some industries (for example, large politically-connected sectors might demand for better functioning of the ﬁnancial system). Given these limitations, we are cautious in drawing causal inferences from our results. Overall, the coefﬁcient of the interest in Eq. (1) is the coefﬁcient on the triple interaction term (ϕ2 ). If the quality of ﬁnance has implications for the quantity of ﬁnance –growth relationship during the crisis, then, a higher quality of ﬁnance will be associated with higher industry performance during the crisis, and hence the coefﬁcient of the interaction term of Financial dep:i FinanceQN c; Pre FinanceQL c; Pre will be positive. More speciﬁcally, if ϕ2 > 0 and statistically signiﬁcant, we conclude that industries which are more dependent on external ﬁnance located in ﬁnancially more developed countries (an increasing quantity of ﬁnance) grew disproportionately faster, if the country had a better quality of ﬁnance in the pre-crisis period. 3.3. Descriptive evidence As a preliminary way of exploring the data, in this section, we provide some graphic and descriptive evidence on how the crisis has affected industry performance across and within countries. We ﬁrst need to identify the crisis period. To do so, we look at the pattern of industry performance over the period 1999–2010. Fig. 1 shows that industry performance, measured by real value-added growth and real output growth, increased up to 2007 and declined sharply during the period 2008–09, and then started to recover in 2010.16 The same pattern is observed for both industry performance indicators.

14 15 16

Levine, Loayza, and Beck (2000) state that domestic credit is a superior measure of ﬁnancial development to other measures. See also Hagen (2013) and Giannone et al. (2011). Note that the last year of availability of industry data at the time of establishing dataset is 2010 as UNIDO releases the data with a several-year

lag. 498

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Fig. 1. Industry performance (growth in real value added or real output) in 28 industries and 63 countries over the period 1999–2010.

Table 1 provides descriptive statistics of all variables, used in this study, across the 63 sample countries. Table 2 reports the mean value of industry performance and ﬁnancial variables by country, while Table 3 reports the mean value of our industry-speciﬁc variables by industry. There are wide variations in ﬁnancial variables (quantity of ﬁnance, quality of ﬁnance and dependence on external ﬁnance) and of other variables across countries and across industries in years approaching the crisis. Moreover, it seems that the decline given by the negative difference in industry growth during the crisis period extends to most countries and industries. The adverse effect of the crisis is statistically signiﬁcant in 40 (out of 63) countries and 21 (out of 28) industries. The affected counties are evident right across the spectrum from developed to emerging to developing countries, and also from different income groups and geographical areas. The severity of the crisis is more pronounced in high-income and emerging Europe than in other regions. Fig. 2 ranks changes in industry performance during the crisis by country (2a) and by industry (2b) .According to Fig. 2a, some Eastern European countries (for example, Latvia, Slovakia and Bulgaria) appear as suffering the most from the crisis. Developing countries (for instance, Tanzania, Sri Lanka and Malawi), on the other hand, suffered much less. These ﬁndings are consistent with the literature (for example, Frankel & Saravelos, 2012) that reports the Baltic countries having suffered some of the largest drops in industrial production and GDP.17 Claessens et al. (2010) also report that Latvia is one of the countries that were severely affected by the crisis; its GDP declined by more than 20% over 2008–09. Surprisingly, we ﬁnd such an advanced country as Australia to be among top best-performer countries. This may be due to the fact that Australia is protected to some degree by its extensive economic relations with less-affected China (and perhaps India as well). Fig. 2b ranks changes in performance by industry (that is within-country variations). Industries such as other non-metallic mineral products (ISIC 369), non-ferrous metals (ISIC 372), and iron and steel (ISIC 371) are among the worst-performing industries during the crisis.18 These industries are all related to the hard-hit construction sector. Industries such as tobacco (ISIC 314) and food products (ISIC 311), on the other hand, appear among top-performer industries, possibly due to the fact that they entail non-cyclical products, rather than luxuries or construction products. We also observe that the worst performing industries are, on average, more dependent on external ﬁnance and produce durable goods, compared to the best-performing industries. Overall, our descriptive statistics yield wide dispersion across countries and within countries (across industries) of industry performance during the crisis. Indeed, while industry performance weakened for many countries/industries, there were countries/industries that actually increased their industry growth in spite of the crisis. These variations allow us to perform meaningful regression analyses in the following section.

17 Frankel and Saravelos (2012) also show that emerging European countries are among the worst-performing countries in terms of changes in GDP, Industrial production, local currency, and stock market performance. 18 The ILO (2009) reports that the hardest-hit sectors were manufacturing, construction, trade, transportation and mining.

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Table 1 Summary statistics of dependent and explanatory variables. This table reports summary statistics of all variables. See Appendix I for detail deﬁnition of variables. Note that the key dependent variable is change in industry performance (growth in real value added or real output) between pre-crisis (2006–07) and crisis period (2008–09). Explanatory variables are averaged (where applicable) for pre-crisis period (2006–2007). Variable Change in industry performance Value added Output Industry characteristics share Financial dep. Quantity of ﬁnance Domestic credit (%) Quality of ﬁnance Bank efﬁciency Integrity in bank lending Bank private monitoring Others Market cap. (%) FDI (%) Competition Foreign bank (%) Property right Corruption Law and order GDPPC (Log)

Obs.

Mean

S.D.

Min.

P25

Median

P75

Max.

1445 1456

0.15 0.17

0.39 0.34

2.32 2.54

0.31 0.3

0.15 0.16

0 0.03

2.36 1.84

1575 28

0.04 0.04

0.06 0.49

0 1.76

0.01 0.04

0.02 0.13

0.05 0.26

0.6 0.85

63

78.57

56.58

12.76

32.17

61.76

115.91

236.37

63 37 56

0.54 0.62 8.36

0.41 0.18 1.15

0.14 0.35 6

0.22 0.48 7.5

0.43 0.59 8.5

0.73 0.78 9

2.33 0.97 10.5

58 59 63 58 63 63 63 63

79.14 18.33 0.07 42 57.94 0.52 0.51 8.97

60.42 52.45 0.1 27.54 23.45 1.02 0.95 1.5

0.66 1.57 0.75 1 10 1.25 1.29 5.19

36.14 4.91 0.1 15 30 0.28 0.40 7.96

63.75 7.98 0.06 40.75 50 0.33 0.49 9.02

115.95 15.78 0.03 68.5 90 1.27 1.34 10.46

282.61 407.94 0.2 99 90 2.54 1.99 11.36

4. Regression results 4.1. Baseline results We present our basic results of cross-industry/country regressions of Eq. (1).19 We are interested in whether the quantity of ﬁnance, if interacted with quality of ﬁnance, can signiﬁcantly explain the cross-country/industry variations in (poor) industry performance during the crisis. We ﬁrst examine the effect of the ﬁrst dimension of quality ﬁnance that is bank efﬁciency. Table 4a reports the results. Columns 1, 2 and 3 report the results when we run regressions for the whole sample, only affected countries (see Fig. 2a), and only affected industries (see Fig. 2b), respectively. We expect that if quality of ﬁnance has any effect on the quantity of ﬁnance–growth relationship during the crisis, it must remain when excluding non-affected countries/industries. Note that we cluster the standard errors at the industry level. Columns 4 and 5 report the results when we cluster error terms at the country and industry-country levels, rather than the industry level, respectively. No matter what, we ﬁnd that ﬁnancially dependent industries were more resilient to the recent ﬁnancial crisis if located in countries where high-quantity ﬁnance was associated with high-quality ﬁnance during the years approaching the crisis. More importantly, when we exclude non-affected industries/countries, the effect of ﬁnancial quality on sectoral growth remains large and statistically signiﬁcant. This indicates that a higher quantity of ﬁnance is associated with a higher growth rate of ﬁnancially dependent industries, if backed by a better quality of ﬁnance. The results support our main proposition, that interactions of the quantity and quality of ﬁnance rendered ﬁnancially dependent industries less vulnerable to the ﬁnancial crisis. Our ﬁndings are consistent with experiences in individual countries. For instance, consider the two countries of Malaysia and South Africa. Both allocated a high quantity of ﬁnance to the private sector during the pre-crisis (103% of GDP and 165% of GDP, respectively). However, the change in industry growth from the pre-crisis to the crisis period is 1% less in Malaysia compared to South Africa. We attribute this to a high quality of ﬁnance (high bank efﬁciency) in Malaysia, compared to low-quality ﬁnance (low bank efﬁciency) in South Africa. Table 4b presents the regressions when quality of ﬁnance measure is integrity of bank lending. All regressions are analogous to the ones reported in Table 4a. Turning to this dimension of ﬁnancial quality, the integrity of bank lending, we again ﬁnd that externalﬁnance-dependent industries were less fragile to the recent crisis in countries where a high quantity of ﬁnance during the pre-crisis was accompanied with high quality of ﬁnance. These results continue to apply if we run regressions for sub-samples (Columns 2 and 3) and/or if we cluster error terms at the country and industry-country levels (Columns 4 and 5). Note again that our results are consistent with experiences in individual countries. For example, consider the two countries Malaysia and Turkey, where a high quantity of ﬁnance (especially domestic credit) is pronounced in both countries. We observe that Malaysia has a better position in terms of high-

19

In order to check robustness of previous studies, the simple modelling of industry performance based on quantity and quality of ﬁnance (without their interactions) is carried out. Here, we provide the separate impact of quantity and quality ﬁnance on industry growth for both pre- and during the recent global banking crisis. Consistent with previous studies, we ﬁnd that: (i) the quantity of ﬁnance improves growth in economically normal periods, but it has a negative effect during ﬁnancial crises, suggesting that the adverse impact of ﬁnancial crises is more severe in ﬁnancially developed countries (Klapper & Love, 2011); (ii) quality of ﬁnance (as measured by bank efﬁciency and bank lending integrity) enhances economic growth, especially during ﬁnancial crises (Hasan et al., 2009; Mirzaei & Moore, 2019). These results are available from authors upon request. 500

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Table 2 Summary statistics of variables by country. This table reports averages value of variables by country. See Appendix I for detail deﬁnition of variables. Note that the key dependent variable is change in industry performance (growth in real value added or real output) between pre-crisis (2006–07) and crisis period (2008–09). Explanatory variables are averaged (where applicable) for pre-crisis period (2006–2007). Country Code

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63

Albania Australia Austria Azerbaijan Belgium Brazil Bulgaria Canada Chile Colombia Cyprus Denmark Ecuador Estonia Ethiopia Finland France Georgia Germany Hungary India Indonesia Ireland Israel Italy Japan Jordan Kenya Korea Kuwait Kyrgyz Rep. Latvia Lithuania Luxembourg Macedonia Malaysia Malta Mauritius Mexico Moldova Mongolia Morocco Netherlands New Zealand Norway Oman Peru Poland Qatar Romania Russia Senegal Slovak Rep. Slovenia South Africa Spain Sri Lanka Sweden Tanzania Turkey UK Uruguay Vietnam

Change in industry performance

Share

Obs.

Value added

Obs.

Output

12 24 24 27 25 25 24 25 21 24 23 21 27 22 22 24 26 24 27 27 28 28 21 22 27 28 27 16 27 23 18 22 27 11 26 27 26 16 26 17 19 27 21 11 22 25 25 26 16 25 25 23 23 25 22 27 22 24 11 24 26 11 28

0.129 0.076 0.156 0.286 0.149 0.112 0.377 0.197 0.059 0.190 0.204 0.283 0.117 0.272 0.061 0.204 0.178 0.330 0.132 0.212 0.210 0.150 0.309 0.136 0.210 0.025 0.007 0.208 0.141 0.074 0.079 0.591 0.371 0.241 0.196 0.060 0.012 0.306 0.056 0.101 0.098 0.013 0.082 0.070 0.215 0.041 0.075 0.293 0.185 0.315 0.348 0.047 0.385 0.122 0.074 0.221 0.153 0.241 0.222 0.239 0.300 0.057 0.001

12 24 24 27 25 25 24 25 21 24 23 21 27 22 22 24 26 24 27 27 28 28 21 22 27 28 27 16 27 23 26 22 27 11 26 27 27 15 26 26 16 28 21 10 23 25 25 26 16 28 25 24 23 25 22 27 21 24 13 24 26 12 18

0.161 0.066 0.106 0.241 0.210 0.151 0.309 0.190 0.004 0.191 0.206 0.295 0.050 0.287 0.019 0.224 0.184 0.255 0.159 0.250 0.140 0.183 0.345 0.164 0.203 0.021 0.003 0.185 0.135 0.141 0.181 0.550 0.254 0.177 0.177 0.079 0.014 0.365 0.044 0.245 0.371 0.081 0.087 0.143 0.118 0.037 0.082 0.311 0.212 0.256 0.370 0.108 0.420 0.159 0.033 0.226 0.089 0.287 0.003 0.258 0.240 0.125 0.127

0.083 0.036 0.038 0.036 0.037 0.040 0.042 0.038 0.048 0.042 0.037 0.042 0.036 0.042 0.036 0.038 0.039 0.037 0.036 0.037 0.036 0.036 0.044 0.045 0.037 0.036 0.036 0.063 0.036 0.042 0.036 0.040 0.036 0.043 0.037 0.036 0.036 0.063 0.037 0.059 0.040 0.036 0.038 0.091 0.040 0.037 0.040 0.036 0.059 0.038 0.040 0.040 0.038 0.036 0.045 0.037 0.039 0.040 0.048 0.042 0.036 0.037 0.036

Quantity ﬁnance

Quality ﬁnance

Domestic credit (%)

Bank efﬁciency

Integrity in bank lending

Private monitoring

25.9 117.6 115.9 13.1 86.5 44.1 53.8 160.8 80.8 35.5 236.4 194.1 21.8 87.1 21.5 80.2 102.0 23.6 107.4 59.1 44.0 25.0 190.2 92.0 97.5 184.9 91.7 26.5 139.8 61.8 12.8 88.1 55.0 169.7 32.5 102.6 116.4 73.4 20.5 32.2 35.8 53.5 177.6 133.8 86.2 33.4 18.7 36.4 38.9 30.5 35.6 22.7 40.6 72.3 165.5 177.4 33.6 117.1 13.8 27.7 176.2 23.7 75.5

0.35 0.50 0.56 0.19 1.28 0.18 0.32 0.44 0.40 0.22 0.46 0.98 0.15 0.53 0.40 1.44 1.14 0.15 0.86 0.14 0.48 0.28 2.33 0.40 0.50 1.11 0.55 0.17 1.26 0.88 0.19 0.43 0.47 1.51 0.26 0.73 0.82 0.19 0.16 0.20 0.32 0.45 0.91 0.59 0.72 0.45 0.21 0.29 0.77 0.26 0.14 0.23 0.41 0.41 0.29 0.87 0.25 0.81 0.20 0.21 0.60 0.15 0.67

0.47 . . 0.35 . 0.78 0.48 0.94 0.83 0.63 . . 0.37 0.74 0.54 . 0.78 0.49 0.66 0.68 0.63 0.40 . . 0.86 . . 0.61 . . 0.37 . 0.46 . . 0.59 . . 0.49 0.48 . . . . . . 0.46 0.72 . 0.51 0.53 0.52 0.50 0.81 0.90 0.80 . 0.95 0.50 0.43 0.97 0.89 .

6 10 6 8 7 9 7.33 9 7.33 9.5 7.5 9.33 10 8 . 9 6.67 7 7.67 8.33 7 8.5 9.67 7.33 9 7 7.67 10.33 10.67 8.67 9 8.33 7.67 6.67 9.33 8.67 8 7.33 . 8 8.67 10 7.5 8 7.67 7.67 10 6 8 6.5 8 9.67 8.33 7 7 7.5 10 9.5 . . . . .

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Table 3 Summary statistics of variables by industry. This table reports averages value of variables by industry. See Appendix I for detail deﬁnition of variables. Note that the key dependent variable is change in industry performance (growth in real value added or real output) between pre-crisis (2006–07) and crisis period (2008–09). Explanatory variables are averaged (where applicable) for pre-crisis period (2006–2007). Industry ISIC 311 313 314 321 322 323 324 331 332 341 342 351 352 353 354 355 356 361 362 369 371 372 381 382 383 384 385 390

Food products Beverages Tobacco Textiles Wearing apparel, except footwear Leather and fur products Footwear, except rubber or plastic Wood products, except furniture Furniture and ﬁxtures, excel. metal Paper products Printing and publishing Industrial chemicals Other chemical product Petroleum reﬁneries Misc. petroleum and coal products Rubber products Plastic products Pottery, china, earthenware Glass and products Other non-metalic mineral products Iron and steel Non-ferrous metals Fabricated metal products Non-electrical machinery Electrical machinery Transport equipment Professional and scientiﬁc equipment Other manufacturing

Change in industry performance Obs.

Value added

Obs.

Output

63 56 33 61 58 55 47 62 61 57 52 57 45 32 17 57 54 32 56 48 58 49 55 61 59 60 52 48

0.021 0.083 0.065 0.112 0.123 0.157 0.073 0.181 0.106 0.061 0.185 0.216 0.079 0.109 0.165 0.184 0.124 0.104 0.210 0.395 0.286 0.343 0.116 0.130 0.111 0.281 0.188 0.156

63 56 35 60 57 56 48 63 62 58 54 58 45 34 17 57 54 32 56 48 59 48 54 61 59 61 52 49

0.002 0.076 0.002 0.131 0.141 0.195 0.102 0.148 0.182 0.098 0.169 0.136 0.063 0.136 0.105 0.205 0.186 0.168 0.155 0.340 0.213 0.431 0.196 0.194 0.250 0.305 0.122 0.183

share

Financial dep.

0.140 0.049 0.021 0.030 0.036 0.005 0.006 0.028 0.022 0.025 0.036 0.057 0.058 0.085 0.018 0.014 0.033 0.002 0.022 0.059 0.043 0.044 0.059 0.059 0.063 0.063 0.017 0.010

0.14 0.06 1.76 0.17 0.05 0.98 0.56 0.14 0.07 0.13 0.06 0.06 0.07 0.03 0.27 0.37 0.24 0.52 0.24 0.09 0.24 0.32 0.19 0.50 0.39 0.13 0.85 0.52

quality ﬁnance (that is, less lending corruption) than Turkey, so that the impact of the crisis is less severe. In fact, the change in industry growth is only 6% in Malaysia, compared to that of 24% in Turkey. Finally, considering another dimension of ﬁnancial quality, bank private monitoring, we again ﬁnd that industries that rely more on external ﬁnance were more resilient to the 2008 global ﬁnancial crisis in countries where a high quantity of ﬁnance during the pre-crisis was supported with high quality of ﬁnance (Table 4c). These results remain if we run regressions for subsamples and cluster error terms at the country and industry-country levels. Overall, our main ﬁndings from these baseline regressions support the ﬁndings of several papers that highlight the important role of ﬁnancial quality in stimulating economic growth (Koetter & Wedow, 2010; Lucchetti et al., 2001). This applies especially to Hasan et al. (2009) who emphasize the role of the interaction of the quality and quantity of ﬁnance. Jayaratne and Strahan (1996) also show that when the quality of bank loans rose in the U.S. – following intrastate branching restrictions – per capita GDP grew as well. Our paper further links the analysis to the recent global ﬁnancial crisis. How sizeable is the estimated impact of the quality of ﬁnance on the quantity – growth nexus? To answer this question, we consider Column 1 in Table 4a. The estimated coefﬁcient suggests that a one-standard deviation rise in bank efﬁciency (0.41) increased the growth of a ﬁnancially dependent industry (at the 75th percentile of its distribution) by about 3.21%, with quantity of ﬁnance set at its 75th percentile value (that is countries with high quantity of ﬁnance). To conﬁrm whether this ﬁgure is economically meaningful, we compare it to the average change in industry growth rate during the crisis. We notice that this effect of bank efﬁciency accounts for about 21 percent of the sample growth mean of 15.4%. This indeed suggest that while the level of ﬁnance has a destabilizing effect on ﬁnancially dependent industries during the recent crisis, quality of ﬁnance can mitigate this adverse impact. Similarly, when considering other dimensions of quality of ﬁnance (Column 1 in Tables 4b and 4c), the growth increases by 4.01% (2.84%) that accounts for nearly 26% (18%) of sample mean, for integrity in bank lending (bank private monitoring).

4.2. Robustness checks In this sub-section, we conduct a battery of robustness tests to ensure that our main ﬁndings are not driven by the choice of variables, or of the econometric speciﬁcations. While the inclusion of country ﬁxed effects enabled us to control for any country-level shocks directly affecting industry growth, it could be the case that some other ﬁnancial and real variables correlated with ﬁnancial development affect growth through the industry’s ﬁnancial needs. We examine the robustness of our ﬁndings by including the interaction terms between these ﬁnancial/real variables with the ﬁnancial dependence variable. Speciﬁcally, we use the following variables. First, we use a proxy for equity market that is 502

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International Review of Economics and Finance 64 (2019) 493–512

Fig. 2. Ranking change in industry performance (that is growth in real value added) from pre-crisis (2006–07) to crisis period (2008–09) by country (2a) and by industry (2b).

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Table 4 The impact of the interaction between quantity and quality ﬁnance on industry performance during the crisis. This table reports the results estimating. QL QN QL ΔPerformanceic ¼ ϕ0 þ ϕ1 :Shareic;Pre þ ϕ2 :Financial dep:i FinanceQN c; Pre Financec; Pre þ ϕ3 :Financial dep:i Financec; Pre þ ϕ4 :Financial dep:i Financec; Pre þ QL ϕ5 :FinanceQN c; Pre Financec; Pre þ ϕ6 :ðindustry dummiesÞi þ ϕ7 :ðcountry dummiesÞc þ εic

The dependent variable is ΔPerformanceic ¼ Performanceic;Crisis Performanceic;Pre where Performanceic;Crisis is performance (that is real value added growth) of industry i in country c during the crisis period 2008–09 and Performanceic;Pre is industry performance during the pre-crisis period 2006–07. ΔPerformanceic is thus change in industry performance in industry i in country c between the crisis and pre-crisis periods. Shareic;Pre is the share of value added of industry i to total value added of all industries in country c, averaged over the pre-crisis period 2006–07. Financial dep: is a measure of an industry dependence on external ﬁnance. FinanceQN c; Pre is a country-speciﬁc proxy for quantity of ﬁnance (i.e. domestic credit), averaged over the precrisis period 2006–07 and FinanceQL c; Pre is a vector of country-speciﬁc quality of ﬁnance (i.e. bank efﬁciency, integrity in bank lending, and bank private monitoring), averaged over the pre-crisis period 2006–07. See Appendix I for detail deﬁnition of variables. Columns 1, 2 and 3 report the results when we run regressions for the whole sample, only affected countries (see Fig. 2a), and only affected industries (see Fig. 2b), respectively. Columns 4 and 5 report the results when we cluster error terms at the country and industry-country levels, rather than the industry level, respectively. Regressions are estimated using OLS with industry and country dummies. The statistical inferences are based on robust standard errors (associated t-values reported in parentheses) clustered by industry level, unless otherwise speciﬁed. ***, **, and * denote statistical signiﬁcance at the 1%, 5%, and 10% levels, respectively. Our sample includes 28 industries with three-digit ISIC, Rev.2 for 63 countries. Sample size varies across regression speciﬁcations because not all variables are available for all industries or all countries. 4a: Bank efﬁciency Whole sample

Only affected countries

Only affected industries

Cluster at country level

Cluster at industry * country level

[1]

[2]

[3]

[4]

[5]

Share

0.6784*** (-3.09)

0.6739*** (-2.78)

0.6014* (-1.84)

0.6784*** (-3.21)

0.6784*** (-2.77)

Financial dep. FinanceQN FinanceQL

0.0026** (2.44)

0.0027*** (2.78)

0.0024* (1.86)

0.0026** (2.25)

0.0026* (1.80)

Financial dep. FinanceQN

0.0017** (-2.46) 0.2392** (-2.09) 0.0029 (1.12) 0.0842 (0.80)

0.0025*** (-3.96) 0.2511*** (-2.87) 0.0082*** (-6.61) 0.7890*** (5.89)

0.0016** (-2.36) 0.2216 (-1.46) 0.0045 (1.40) 0.0939 (-0.76)

0.0017** (-2.34) 0.2392 (-1.22) 0.0029*** (8.09) 0.0842 (1.63)

0.0017* (-1.80) 0.2392 (-1.26) 0.0029 (1.19) 0.0842 (0.80)

Y Y 63 28 1445 0.136

Y Y 41 28 946 0.138

Y Y 63 21 1164 0.124

Y Y 63 28 1445 0.136

Y Y 63 28 1445 0.136

Financial dep. Finance

QL

FinanceQN FinanceQL Constant

Industry FE Country FE # Countries # Industries Observations Adj. R2 4b: Integrity in bank lending

Whole sample

Only affected countries

Only affected industries

Cluster at country level

Cluster at industry * country level

[1]

[2]

[3]

[4]

[5]

Share

0.6218 (-1.45)

0.7492 (-1.30)

0.2553 (-0.45)

0.6218** (-2.27)

0.6218* (-1.91)

Financial dep. FinanceQN FinanceQL

0.0074*** (3.05)

0.0064* (1.99)

0.0101*** (3.68)

0.0074** (2.37)

0.0074** (2.04)

Financial dep. FinanceQN

0.0066*** (-3.78) 0.1808 (-0.61) 0.0175 (0.72) 0.1428 (-0.41)

0.0072*** (-2.93) 0.1587 (0.52) 0.0001 (-0.10) 0.1283 (-0.97)

0.0082*** (-4.20) 0.5222 (-1.38) 0.0239 (0.83) 0.3844 (-0.94)

0.0066** (-2.51) 0.1808 (-0.67) 0.0175*** (6.71) 0.1428 (-1.49)

0.0066** (-2.28) 0.1808 (-0.55) 0.0175 (0.73) 0.1428 (-0.42)

Y Y

Y Y

Y Y

Y Y

Y Y

Financial dep. FinanceQL FinanceQN FinanceQL Constant

Industry FE Country FE

(continued on next page)

504

A. Mirzaei, R. Grosse

International Review of Economics and Finance 64 (2019) 493–512

Table 4 (continued ) 4b: Integrity in bank lending

# Countries # Industries Observations Adj. R2

Whole sample

Only affected countries

Only affected industries

Cluster at country level

Cluster at industry * country level

[1]

[2]

[3]

[4]

[5]

37 28 864 0.153

25 28 561 0.134

37 21 687 0.168

37 28 864 0.153

37 28 864 0.153

4c: Bank private monitoring Whole sample

Only affected countries

Only affected industries

Cluster at industry

Cluster at country

[1]

[2]

[3]

[4]

[5]

Share

0.6467*** (-3.24)

0.6228** (-2.68)

0.5448* (-1.82)

0.6467*** (-2.97)

0.6467** (-2.53)

Financial dep. FinanceQN FinanceQL

0.0008** (2.43)

0.0011*** (2.96)

0.0012*** (2.93)

0.0008** (2.03)

0.0008** (2.19)

Financial dep. FinanceQN

0.0071** (-2.74) 0.0548* (-1.85) 0.0001 (-1.43) 0.2004* (1.73)

0.0102*** (-3.01) 0.0727 (-1.63) 0.0049*** (5.26) 5.4443*** (-5.12)

0.0099*** (-3.49) 0.0761* (-1.81) 0.0001 (-0.93) 0.0103 (-0.08)

0.0071* (-1.98) 0.0548 (-1.29) 0.0001*** (-6.33) 0.2004** (2.20)

0.0071** (-2.22) 0.0548 (-1.46) 0.0001 (-1.47) 0.2004* (1.68)

Y Y 56 28 1297 0.137

Y Y 51 28 872 0.144

Y Y 56 21 1048 0.128

Y Y 56 28 1297 0.137

Y Y 56 28 1297 0.137

Financial dep. Finance FinanceQN FinanceQL Constant

Industry FE Country FE # Countries # Industries Observations Adj. R2

QL

market capitalization of listed companies as percent of GDP (Market cap.). The equity market provides important services for economic growth, which complement bank services in this respect (Kim, Lin, & Chen, 2016). A well-functioning stock market facilitates investment in longer-run, higher-yield projects that enhance productivity growth (Levine & Zervos, 1998). Second, we utilize a proxy for foreign capital that is foreign direct investment (FDI). Firms routinely demand ﬁnance both from domestic and foreign sources. Countries can beneﬁt from foreign capital in the form of stronger growth (Igan et al., 2016). Third, we use a proxy for bank competition (Competition). Competitive banking system reduces the cost of lending and other bank services, which can result in a rise in demand for bank funds in order to support business and growth (Liu, Mirzaei, & Vandoros, 2014). Fourth, we include a proxy for bank foreign ownership (Foreign bank). Foreign banks provide additional loanable funds for domestic ﬁrms by, for example, overcoming informational and legal obstacles to lending, and hence, acting as catalysts for economic development (Bruno & Hauswald, 2014). Fifth, we include a proxy for the degree to which a country’s laws protect private property rights (Property right). Previous studies (e.g. Claessens & Laeven, 2003) ﬁnd that the degree to which property rights are enforced in a country matters for economic growth, as ﬁrms may utilizes resources better and thus grow faster. Finally, following Kroszner et al. (2007), we add a proxy for the country’s level of corruption (Corruption), an index of the law and order tradition of each country for the quality of legal institutions (Law and order), and a proxy for economic development that is natural logarithm of GDP per capita (GDPPC). Appendix I provides further detail and sources of these variables. We take the average of pre-crisis of these variables and then interact them with the proxy for external ﬁnancial dependence. We include the variables either one by one or simultaneously all into the model. Table 5a, b and c report the results for bank efﬁciency, integrity in bank lending, and bank private monitoring, respectively. We ﬁnd that our main ﬁndings remain intact and are not driven by omitted variables. That is, the interaction of quantity and quality of ﬁnance made industries more resilient to the recent global ﬁnancial crisis. While the above control variables provide a considerable amount of country-level information, they may not fully account for all appropriate factors, and thus the likelihood of some omitted variable bias persists. To mitigate such a concern, we use the Altonji, Todd, and Christopher’s (2005) approach to compute how much greater the inﬂuence of unobservable factors needs to be to completely explain away the positive relationship between the interaction of quality and quantity of ﬁnance and growth of ﬁnancially dependent industries. Following Nunn and Wantchekon (2011), we use the ratio of ϕF2 =ðϕR2 ϕF2 Þ where it compares the coefﬁcient without a

505

QL QN QL QL þϕ5 :FinanceQN ΔPerformanceic ¼ ϕ0 þ ϕ1 :Shareic;Pre þ ϕ2 :Financial dep:i FinanceQN c; Pre Financec; Pre þ ϕ3 :Financial dep:i Financec; Pre þ ϕ4 :Financial dep:i Financec; Pre c; Pre Financec; Pre þ ϕ6 :Financial dep:i Xc; pre þ ϕ7 :ðindustry dummiesÞi þ ϕ8 :ðcountry dummiesÞc þ εic The dependent variable is ΔPerformanceic ¼ Performanceic;Crisis Performanceic;Pre where Performanceic;Crisis is performance (that is real value added growth) of industry i in country c during the crisis period 2008–09 and Performanceic;Pre is industry performance during the pre-crisis period 2006–07. ΔPerformanceic is thus change in industry performance in industry i in country c between the crisis and pre-crisis periods. Shareic;Pre is the share of value added of industry i to total value added of all industries in country c, averaged over the pre-crisis period 2006–07. Financial dep: is a measure of an industry dependence

A. Mirzaei, R. Grosse

Table 5 The impact of the interaction between quantity and quality ﬁnance on industry performance during the crisis: Robust to controls. This table reports the results estimating.

QL on external ﬁnance. FinanceQN c; Pre is a country-speciﬁc proxy for quantity of ﬁnance (i.e. domestic credit), averaged over the pre-crisis period 2006–07 and Financec; Pre is a vector of country-speciﬁc quality of ﬁnance (i.e. bank efﬁciency, integrity in bank lending, and bank private monitoring), averaged over the pre-crisis period 2006–07. X is a vector of control variables, averaged over the pre-crisis period 2006–07. Speciﬁcally, Market cap. is stock market capitalization of listed companies to GDP. FDI is total foreign direct investment as a percentage of GDP. Competition is bank competition as measured by the Boone indicator. Foreign bank is the extent to which the banking system’s assets are foreign owned. Property right is the degree to which a country’s laws protect private property rights. Corruption is the index of corruption (with higher values denoting less corruption). Law and order is the index of law and order tradition in the country. GDPPC is the logarithm of GDP per capita. See Appendix I for detail deﬁnition of variables. Regressions are estimated using OLS with industry and country dummies. The statistical inferences are based on robust standard errors (associated t-values reported in parentheses) clustered by industry level. ***, **, and * denote statistical signiﬁcance at the 1%, 5%, and 10% levels, respectively. Our sample includes 28 industries with three-digit ISIC, Rev.2 for 63 countries. Sample size varies across regression speciﬁcations because not all variables are available for all industries or all countries.

5a: Bank efﬁciency

506

Foreign credit

Bank competition

Bank ownership

Property right

Corruption

Law and order

Economic development

All

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

Share

0.6073*** (-2.92)

0.4284** (-2.27)

0.6787*** (-3.09)

0.6048** (-2.74)

0.6815*** (-3.11)

0.6823*** (-3.11)

0.6787*** (-3.09)

0.6776*** (-3.12)

0.3422 (-1.63)

Financial dep. FinanceQN FinanceQL

0.0029** (2.66)

0.0030** (2.55)

0.0026** (2.33)

0.0029** (2.38)

0.0027** (2.31)

0.0028** (2.38)

0.0026* (1.95)

0.0026** (2.36)

0.0033** (2.65)

Financial dep. FinanceQN

0.0019* (-1.72) 0.2876*** (-2.84) 0.0116 (0.96) 0.0005 (1.13)

0.0016** (-2.38) 0.3295** (-2.33) 0.0031 (1.18)

0.0018** (-2.60) 0.2525** (-2.10) 0.0029 (1.10)

0.0016** (-2.43) 0.3045* (-2.04) 0.0030 (1.11)

0.0018** (-2.18) 0.2559* (-1.86) 0.0030 (1.14)

0.0020** (-2.25) 0.2867** (-2.16) 0.0030 (1.16)

0.0017 (-1.64) 0.2456 (-1.45) 0.0029 (1.13)

0.0016** (-2.18) 0.2357* (-1.97) 0.0029 (1.12)

0.0020 (-1.54) 0.3722** (-2.45) 0.0088 (0.79) 0.0001 (0.20) 0.0005 (0.55) 0.1702 (0.83) 0.0005 (0.34) 0.0020 (0.80) 0.0991 (0.66) 0.0047 (-0.03)

Financial dep. FinanceQL FinanceQN FinanceQL Financial dep. Market cap. Financial dep. FDI Financial dep. Competition Financial dep. Foreign bank Financial dep. Property right Financial dep. Corruption Financial dep. Law and order

0.0012 (0.99) 0.5116* (1.86) 0.0001 (0.09) 0.0007 (0.43) 0.0302 (0.94) 0.0033 (0.07)

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International Review of Economics and Finance 64 (2019) 493–512

Equity credit

5a: Bank efﬁciency Equity credit

Foreign credit

Bank competition

Bank ownership

Property right

Corruption

Law and order

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9] 0.0802*** (-2.84) 0.1495 (-0.24)

Financial dep. GDPPC

Economic development

All

Constant

0.3696 (-0.55)

0.0714 (0.69)

0.0940 (0.89)

0.0728 (0.67)

0.0812 (0.76)

0.0875 (0.83)

0.0845 (0.80)

0.0021 (-0.09) 0.0860 (0.78)

Industry FE Country FE # Countries # Industries Observations Adj. R2

Y Y 63 28 1344 0.149

Y Y 63 28 1369 0.144

Y Y 63 28 1445 0.137

Y Y 63 28 1334 0.161

Y Y 63 28 1445 0.136

Y Y 63 28 1445 0.136

Y Y 63 28 1445 0.136

Y Y 63 28 1445 0.136

Y Y 63 28 1217 0.178

Equity credit

Foreign credit

Bank competition

Bank ownership

Property right

Corruption

Law and order

Economic development

All

A. Mirzaei, R. Grosse

Table 5 (continued )

5b: Integrity in bank lending

507

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

0.5135 (-0.94)

0.3401 (-0.96)

0.6235 (-1.45)

0.5861 (-1.45)

0.6244 (-1.46)

0.6216 (-1.45)

0.6218 (-1.45)

0.6178 (-1.45)

0.1289 (-0.30)

Financial dep. FinanceQN FinanceQL

0.0074* (1.79)

0.0074*** (3.57)

0.0073*** (2.93)

0.0072*** (2.96)

0.0077*** (2.83)

0.0077*** (3.25)

0.0075** (2.68)

0.0070*** (3.14)

0.0089** (2.32)

Financial dep. FinanceQN

0.0063* (-1.97) 0.1112 (-0.30) 0.0111 (-0.71) 0.0002 (-0.40)

0.0069*** (-4.52) 0.0805 (-0.30) 0.0177 (0.72)

0.0063*** (-3.30) 0.2078 (-0.73) 0.0174 (0.72)

0.0066*** (-3.72) 0.1529 (-0.51) 0.0165 (0.68)

0.0070*** (-3.25) 0.2444 (-0.65) 0.0176 (0.72)

0.0070*** (-3.65) 0.2287 (-0.88) 0.0176 (0.72)

0.0067*** (-2.99) 0.1862 (-0.58) 0.0175 (0.72)

0.0060*** (-3.83) 0.1441 (-0.54) 0.0175 (0.72)

0.0076** (-2.74) 0.4958 (-0.95) 0.0110 (-0.71) 0.0004 (-0.40) 0.0095*** (4.46) 0.8015* (-1.90) 0.0029*** (-3.57) 0.0026 (0.61) 0.1669 (0.71)

Financial dep. Finance

QL

FinanceQN FinanceQL Financial dep. Market cap. Financial dep. FDI Financial dep. Competition Financial dep. Foreign bank Financial dep. Property right Financial dep. Corruption

0.0050 (1.42) 0.2272 (-0.50) 0.0009 (-1.05) 0.0010 (0.48) 0.0156 (0.37)

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International Review of Economics and Finance 64 (2019) 493–512

[1] Share

5b: Integrity in bank lending Equity credit

Foreign credit

Bank competition

Bank ownership

Property right

Corruption

[1]

[2]

[3]

[4]

[5]

[6]

Financial dep. Law and order

Law and order

Economic development

[7]

[8]

[9]

0.0143 (-0.63)

0.1245 (-0.80) 0.0624 (-1.01)

0.0020 (0.06)

Financial dep. GDPPC

All

Constant

0.4269 (0.80)

0.1521 (-0.43)

0.1445 (-0.42)

0.1249 (-0.36)

0.1456 (-0.42)

0.1398 (-0.40)

0.1423 (-0.41)

0.1328 (-0.38)

0.5132 (0.97)

Industry FE Country FE # Countries # Industries Observations Adj. R2

Y Y 37 28 763 0.185

Y Y 37 28 804 0.169

Y Y 37 28 864 0.153

Y Y 37 28 842 0.162

Y Y 37 28 864 0.153

Y Y 37 28 864 0.152

Y Y 37 28 864 0.152

Y Y 37 28 864 0.153

Y Y 37 28 725 0.210

A. Mirzaei, R. Grosse

Table 5 (continued )

5c: Bank private monitoring

508

Foreign credit

Bank competition

Bank ownership

Property right

Corruption

Law and order

Economic development

All

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

Share

0.6033*** (-2.86)

0.4602** (-2.25)

0.6477*** (-3.24)

0.6013** (-2.66)

0.6465*** (-3.25)

0.6476*** (-3.25)

0.6413*** (-3.24)

0.6421*** (-3.26)

0.3860* (-1.75)

Financial dep. FinanceQN FinanceQL

0.0009** (2.08)

0.0009** (2.50)

0.0008** (2.32)

0.0007*** (3.13)

0.0008** (2.57)

0.0008** (2.45)

0.0009*** (2.84)

0.0009*** (2.79)

0.0013*** (3.90)

Financial dep. FinanceQN

0.0070** (-2.43) 0.0552 (-1.52) 0.0050*** (5.34) 0.0001 (-0.23)

0.0075*** (-2.84) 0.0581** (-2.06) 0.0001 (-1.36)

0.0069** (-2.69) 0.0544* (-1.84) 0.0001 (-1.42)

0.0059*** (-3.44) 0.0341 (-1.65) 0.0001 (-1.43)

0.0071** (-2.65) 0.0549* (-1.85) 0.0001 (-1.42)

0.0071** (-2.75) 0.0540* (-1.84) 0.0001 (-1.44)

0.0073** (-2.76) 0.0666** (-2.23) 0.0001 (-1.36)

0.0072*** (-2.82) 0.0613** (-2.16) 0.0001 (-1.38)

0.0092*** (-4.95) 0.0950*** (-2.87) 0.0049*** (5.42) 0.0007 (-1.19) 0.0012 (1.30) 0.0867 (0.30) 0.0003 (0.18) 0.0041

Financial dep. FinanceQL FinanceQN FinanceQL Financial dep. Market cap. Financial dep. FDI Financial dep. Competition Financial dep. Foreign bank

0.0016 (1.15) 0.3189 (1.15) 0.0003 (0.22) 0.0001

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International Review of Economics and Finance 64 (2019) 493–512

Equity credit

5c: Bank private monitoring Equity credit

Foreign credit

Bank competition

Bank ownership

Property right

Corruption

Law and order

Economic development

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

Financial dep. Property right

(-0.03)

Financial dep. Corruption

All [9] (1.26)

0.0047 (0.21)

Financial dep. Law and order

0.0458 (-1.24)

Financial dep. GDPPC Constant

5.4909*** (-5.22)

0.1856 (1.62)

0.2064* (1.76)

0.1578 (1.42)

0.2008* (1.72)

0.2000* (1.72)

0.2117* (1.80)

0.0175 (-0.73) 0.2254* (1.86)

Industry FE Country FE # Countries # Industries Observations Adj. R2

Y Y 56 28 1218 0.143

Y Y 56 28 1254 0.139

Y Y 56 28 1297 0.136

Y Y 56 28 1208 0.154

Y Y 56 28 1297 0.136

Y Y 56 28 1297 0.136

Y Y 56 28 1297 0.137

Y Y 56 28 1297 0.136

0.2725 (1.58) 0.2702 (-1.56) 0.1053*** (-2.84) 5.2964*** (-5.12)

Y Y 56 28 1102 0.177

A. Mirzaei, R. Grosse

Table 5 (continued )

509 International Review of Economics and Finance 64 (2019) 493–512

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International Review of Economics and Finance 64 (2019) 493–512

restricted set of controls ϕR2 (those reported in Column 1 of Tables 4a, 4b and 4c) to the coefﬁcient with a full set of controls ϕF2 (those reported in Column 9 of Tables 5a, 5b and 5c). As the ratio increases, it indicates that unobservable variables must have a larger impact on ϕ2 relative to observed variables in order to explain away the results. We ﬁnd that the ratio is negative (4.71, 5.93 and 2.60, respectively), implying that controls even strengthen the results (Fenske, 2015). Thus, it implausible that our estimates can be fully attributed to omitted variables. Finally, our results also are robust to the following tests (these results are not reported but available from the authors upon request). First, we use averages of the quantity and quality of ﬁnance for the pre-crisis period 2001–07, rather than averages of 2006–07. Our results remain unchanged. Second, we use output growth rates instead of value-added growth rates, as the dependent variable. We conﬁrm our main ﬁnding, but it is only statistically signiﬁcant when ﬁnancial quality is measured in terms of bank efﬁciency and bank private monitoring. Third, we employ an alternative measure of external-ﬁnance dependence. In our baseline results, we use a proxy for dependence from Laeven and Valencia (2013), which in turn applies the Rajan and Zingales methodology, using US data for the period 1998–2010. We now use original data from Rajan and Zingales (1998). However, we use data for young ﬁrms, as one might expect that if the quality of ﬁnance matters for ﬁnancially dependent industries, this should be more pronounced for ﬁnancially vulnerable young ﬁrms. We ﬁnd that this alternative ﬁnancial dependence does not alter the main message that if the quantity of ﬁnance is accompanied with a quality of ﬁnance, it makes ﬁnancially dependent industries more resilient to ﬁnancial crises. 5. Conclusions The global ﬁnancial crisis of 2008–9 demonstrated very clearly that both greater ﬁnancial deepening and ﬁnancial openness are not strategies that ensure sustained economic development – either in emerging markets or in the traditional industrial nations. The idea of openness to avoid monopoly power on the part of ﬁnancial institutions and also to avoid government policy favouritism, is quite reasonable in principle – but does not take into account the inadequacies of the regulatory environment or those of the ﬁnancial institutions in their attempts to monitor and limit (excessively) high-risk behaviour. Our argument is that ﬁnancial quality is most important in times of crisis, while ﬁnancial quantity per se is inadequate for protecting consumers during such phases. The recent ﬁnancial crisis that originated in the U.S. spread around the world, affecting banking systems in many other countries. This systemic crisis was a disruptive event, not only to ﬁnancial sectors, but also to the real economy. The crisis greatly affected industry performance in most countries. Yet, neither all countries, nor even all industries within countries, were affected to the same extent. Our analysis therefore considered differences in industry performance across countries and within countries, in order to explore how ﬁnancial quality helps to explain the severity of the crisis. We ﬁnd that the pre-crisis levels of ﬁnancial development (quantity of ﬁnance) and bank efﬁciency, integrity in bank lending, and bank private monitoring (quality of ﬁnance) enhance our understanding of the cross-country intensity of the crisis. Speciﬁcally, we ﬁnd that industries which are more dependent on external ﬁnance performed disproportionately better during the crisis, if located in countries where a high quantity of ﬁnance during the pre-crisis period was accompanied with better quality of ﬁnance. Overall, our results suggest that although ﬁnancial development and ﬁnancial liberalization contribute to long-term economic growth, they also lead national ﬁnancial systems to be more susceptible to cross-border ﬁnancial crises, if not backed by the requisite quality of ﬁnance. Our results further indicate that within countries, industrial sectors are differently affected by sudden terminations of credit supply. Those sectors that are more dependent on external ﬁnance, particularly those in construction-related sectors, are normally affected more severely by systemic ﬁnancial crises. This indicates that the external ﬁnancial needs of such non-ﬁnancial ﬁrms should be addressed through the allocation of a greater quantity of ﬁnance that is also accompanied by a higher quality of ﬁnance. Appendix I Deﬁnition and source of variables. Variable Industry performance Value added

Output

Deﬁnition

Source

Change in real value added growth from pre-crisis to crisis period. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. UNIDO reports nominal data in U.S. dollars. Nominal value added deﬂated using U.S. producer price index of ﬁnished goods index (taken from Economic Research, Federal Reserve Bank of St. Louis). Change in real output growth from pre-crisis to crisis period. UNIDO reports nominal output data in U.S. dollars. Nominal value added deﬂated using U.S. producer price index of ﬁnished goods index (taken from Economic Research, Federal Reserve Bank of St. Louis).

UNIDO database, and own calculation.

Other industry characteristics Share The value added of each sector divided by the total value added of all sectors in a country. Financial dep. It denotes Rajan and Zingales’s (1998) measure of industry reliance on external ﬁnance, deﬁned as 1 minus industry cash ﬂow over industry investment of large publicly traded U.S. ﬁrms over 1980–2006. Quantity ﬁnance

"

" Laeven and Valencia (2013).

(continued on next page)

510

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International Review of Economics and Finance 64 (2019) 493–512

(continued ) Variable

Deﬁnition

Source

Domestic credit

Domestic credit to private sector which refers to ﬁnancial resources provided to the private sector by ﬁnancial corporations, as % of GDP.

World Bank-World Development Indicators.

Inverse of overhead costs to total assets ratio of banking system, as measured by operating expenses as a share of the value of all held assets. Inverse of lending corruption, which is the degree to which a ﬁrm manager views lending corruption in banking as an obstacle for the operation and growth of their business, with no obstacle (1), a minor obstacle (2), a moderate obstacle (3), or a major obstacle (4). Private monitoring index measures whether there are incentives/abilities for the private monitoring of ﬁrms. It takes value between 0 and 12, with higher values indicating more private monitoring.

World Bank: The Global Financial Development Database. World Business Environment Survey (WBES).

Quality ﬁnance Bank efﬁciency Integrity of bank lending

Bank private monitoring

Controls Market cap.

FDI Competition

Foreign bank Property right

Corruption

Law and order

GDPPC

Stock market capitalization of listed companies to GDP. Market capitalization is calculated by multiplying a company’s shares outstanding by the current market price of one share. Total foreign direct investment as a percentage of GDP. A measure of degree of bank competition based on proﬁt-efﬁciency in the banking market (that is the Boone indicator). It is calculated as the elasticity of proﬁts to marginal costs. An increase in the Boone indicator implies a deterioration of the competitive conduct of ﬁnancial intermediaries. The extent to which the banking system’s assets are foreign owned which is the fraction of the banking system’s assets that is 50% or more foreign owned. Property right measures the degree to which a country’s laws protect private property rights and the degree to which its government enforces those laws. It ranges from 0 to 100. A higher score indicates better protection of property rights and signify greater protection of private property rights. It reﬂects perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as “capture” of the state by elites and private interests. Reﬂects perceptions of the extent to which agents have conﬁdence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence. Natural logarithm of real GDP per capita.

World Bank Surveys on Bank Regulation.

World Bank: World Development Indicators. " World Bank: The Global Financial Development Database.

World Bank Surveys on Bank Regulation. Heritage Foundation Data base.

World Bank: Worldwide Governance Indicator. "

World Bank: World Development Indicators.

Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.iref.2019.08.010.

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The interaction of quantity and quality of finance: Did it make industries more resilient to the recent global financial crisis?

The interaction of quantity and quality of finance: Did it make industries more resilient to the recent global financial crisis?

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