Does economic uncertainty affect domestic credits? an empirical investigation

Journal Pre-proofs Does Economic Uncertainty Affect Domestic Credits? An Empirical Investigation Giray Gozgor, Ender Demir, Jaroslav Belas, Serkan Yesilyurt PII: DOI: Reference:

S1042-4431(19)30082-4 https://doi.org/10.1016/j.intfin.2019.101147 INTFIN 101147

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Journal of International Financial Markets, Institutions & Money

Received Date: Revised Date: Accepted Date:

21 February 2019 14 September 2019 9 November 2019

Please cite this article as: G. Gozgor, E. Demir, J. Belas, S. Yesilyurt, Does Economic Uncertainty Affect Domestic Credits? An Empirical Investigation, Journal of International Financial Markets, Institutions & Money (2019), doi: https://doi.org/10.1016/j.intfin.2019.101147

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Does Economic Uncertainty Affect Domestic Credits? An Empirical Investigation Giray Gozgor, Ph.D. Istanbul Medeniyet University, Turkey Email: [email protected] Ender Demir, Ph.D. (Corresponding Author) Istanbul Medeniyet University, Turkey Email: [email protected] Jaroslav Belas, Ph.D. Tomas Bata University in Zlin, the Czech Republic Email: [email protected] Serkan Yesilyurt, Ph.D. Bahcesehir University, Turkey Email: [email protected] Abstract Using the new measure of uncertainty (i.e., the World Uncertainty Index), this paper analyzes the effects of uncertainty on the level of domestic credits in a panel of 139 countries for the period from 1996 to 2017. The findings of the fixed-effects and the system Generalized Method of Moments (GMM) estimations show that a higher level of uncertainty decreases the level of domestic credits. Per capita income and money supply are positively related to the domestic credits., but the current account balance is negatively associated with domestic credit measures. After implementing various sensitivity analyses, i.e., to exclude the outliers and the countries in the different regions as well as to include various controls, the primary evidence remains robust. Keywords: domestic credits; private sector credits; uncertainty shocks; business cycle fluctuations; external imbalances; panel data estimation techniques JEL Classification Codes: E51; E32; G21; C33

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Does Economic Uncertainty Affect Domestic Credits? An Empirical Investigation Abstract Using the new measure of uncertainty (i.e., the World Uncertainty Index), this paper analyzes the effects of uncertainty on the level of domestic credits in a panel of 139 countries for the period from 1996 to 2017. The findings of the fixed-effects and the System Generalized Method of Moments (GMM) estimations show that a higher level of uncertainty decreases the level of domestic credits. Per capita income and money supply are positively related to the domestic credits., but the current account balance is negatively associated with domestic credit measures. After implementing various sensitivity analyses, i.e., to exclude the outliers and the countries in the different regions as well as to include various controls, the primary evidence remains robust. Keywords: domestic credits; private sector credits; uncertainty shocks; business cycle fluctuations; external imbalances; panel data estimation techniques JEL Classification Codes: E51; E32; G21; C33

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1. Introduction How does uncertainty in economic and political decisions affect economic and financial indicators? The researchers have analyzed the effect of uncertainty on decisions of firms, financial intermediaries, governments, and households. For instance, Rodrik (1991) shows that firms withhold their investments when there is uncertainty about the permanence of policy reforms in developing countries. In the recent years, there is a rising interest of literature to investigate the impact of uncertainty on economic and financial indicators (see, e.g., Baker et al., 2016; Bordo et al., 2016). Especially in the last decade, the world economy has witnessed several source uncertainties such as the Global Financial Crisis of 2008, the European Sovereign Debt Crisis, Brexit, and the Trade Wars between the United States (U.S.) and China, (Jiang et al., 2019). The spillover effects of the local and regional uncertainties have consequences for economic decision-makers in almost all countries. The key challenge is to find a suitable proxy for measuring the level of uncertainty. The previous literature uses different uncertainty measures, such as the stock market volatility, the volatility index (VIX), geopolitical risks, and other indicators of political risks. At this point, the index of Economic Policy Uncertainty (EPU) developed by Baker et al. (2016) has been widely used as a proxy for uncertainty since its introduction in 2013. The EPU index considers the frequency of country newspaper articles that include terms related to economy, policy, and uncertainty. The index captures the uncertainty about who will give the economic policy decisions, which policy actions will be taken, and who will be affected by the economic effects of those actions. The previous literature examines the impact of the EPU on financial decisions of firms, the price volatility and return of asset prices (e.g., cryptocurrencies, gold, houses, and stocks) as well as macroeconomic indicators. In a similar vein, we use the new measure of uncertainty (i.e., the World Uncertainty Index-WUI) to examine the impact of uncertainty on the domestic credits. There are previous

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papers in the literature that focuses on the effects of economic policy uncertainty on the credits by controlling traditional determinants. For instance, Bordo et al. (2016) observe that the index of EPU hurts loan growth in the U.S. at the aggregate level and also across individual banks. The authors argue that the effect of EPU in the economy has been occurred by the bank lending channel. A higher level of EPU (usually related to the recessions and the weak recoveries) decreases the bank loan growth; and thereby, the economic activity. To put it differently, the rising EPU slows down the recovery. Caglayan and Xu (2019) also examine the impact of EPU on the credit level, non-performing loans, and loan-loss provisions in the panel dataset of 18 countries. The authors find that rising uncertainty decreases the availability of credit and increases banks’ non-performing loans and loan-loss provisions. On the other hand, He and Niu (2018) document that there is a negative relationship between the EPU and the bank valuations. This negative impact leads to a decrease in bank loan growth due to the rise in the EPU. Therefore, the EPU causes a reduction in bank loan growth, which then decreases the bank valuation. Looking at the Chinese banks, Chi and Li (2017) find that the EPU increases the non-performing loans, the loan concentrations, and the (average) loan size. It is argued that the EPU increases banks’ credit risks while it decreases the loan size. Gong et al. (2018) develop a theoretical model to analyze the determinants of bank lending under uncertainty. Using the data from 19 major economies, the authors find that there is a positive relationship between the loan spreads and the level of uncertainty. This evidence implies that banks ask for an uncertainty premium, which increases the cost of borrowing for firms. As governments and regulatory bodies adjust their economic policies in time, and sometimes they are likely to make new economic decisions or policy reforms. Especially when there is a domestic or global shock in the economy, they can even make a decision, which is opposed to the previous one. This process is generally followed by unpredictability and

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ambiguity, which increase the economic policy uncertainty (Chi and Li, 2017). As the previous studies have provided, the uncertainty affects the decisions of individuals and firms at varying levels. The content of economic policy changes in regulation, trade policy, government spending, monetary, and fiscal policy increase the level of uncertainty in the banking sector. As a result of rising uncertainty, banks can adjust their lending and risk-taking behaviors (Caglayan and Xu, 2019). Inspired by these theoretical models and empirical findings, we investigate the impact of WUI on the level of domestic credits in the panel dataset of 139 countries for the period from 1996 to 2017. For this purpose, we use the new measure of uncertainty of Ahir et al. (2018) (i.e., WUI) as the determinant of domestic credits. Our contributions to the current literature are as follows. To the best of our knowledge, this is the first paper in the literature that uses the new measure of uncertainty (WUI) of Ahir et al. (2018) as a determinant of the level of domestic credits (financial development). For this purpose, we use alternative measures of domestic credits, various sets of controls, and different econometric methodology. So doing, we address a possible “omitted variable bias” and “endogeneity bias.” The findings indicate that a higher level of WUI decreases the level of domestic credits. The rest of the paper is organized as follows. Next section explains the data and econometric methodology. Section 3 provides the findings. Section 4 presents the results of the robustness checks. Section 5 discusses the results and policy implications for different parties. Section 6 concludes the paper. 2. Model, Methodology, and Data 2.1 Empirical Model and Methodology In this paper, we estimate the following equations: 𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 𝐶𝑟𝑒𝑑𝑖𝑡𝑖,𝑡 = 𝛽0 + 𝛽1𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 𝐶𝑟𝑒𝑑𝑖𝑡𝑖,𝑡 ― 1 + 𝛽2 𝑊𝑈𝐼𝑖,𝑡 + 𝛽3 𝑋𝑖,𝑡 + 𝜗𝑡 + 𝜗𝑖 + 𝜀𝑖,𝑡

(1) 6

𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 𝐶𝑟𝑒𝑑𝑖𝑡𝑖,𝑡 = 𝛾0 + 𝛾1𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 𝐶𝑟𝑒𝑑𝑖𝑡𝑖,𝑡 ― 1 + 𝛾2 𝑊𝑈𝐼𝑖,𝑡 ― 1 + 𝛾3 𝑋𝑖,𝑡 + 𝜗𝑡 + (2) 𝜗𝑖 + 𝜀𝑖,𝑡 In Eq. (1) and Eq. (2), 𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 𝐶𝑟𝑒𝑑𝑖𝑡𝑖,𝑡 and 𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 𝐶𝑟𝑒𝑑𝑖𝑡𝑖,𝑡 ― 1 are the current and the lagged domestic credit measures (domestic credit to the private sector and domestic credit to the private sector by banks) in the country i at time t and t–1. 𝑊𝑈𝐼𝑖,𝑡 and 𝑊𝑈𝐼𝑖,𝑡 ― 1 are the current and the lagged index of the uncertainty of Ahir et al. (2018) in the country i at time t and t–1. 𝑋𝑖,𝑡 represents the vector of controls. Finally, 𝜗𝑡, 𝜗𝑡, and 𝜀𝑖,𝑡 denote the “time fixedeffects,” the “country fixed-effects,” and the “error term,” respectively. We also consider the control variables in line with the literature (see, e.g., Caglayan and Xu, 2019; Gozgor, 2018). In the baseline estimations, we use the log per capita gross domestic product (GDP), the money supply (based on the broad money definition), and the current account balance. In the robustness checks, we also include some other controls, which have been used by the previous papers: inflation rate, exchange rate, interest rate (based on both deposit and lending rates), trade openness, annual GDP growth, and the central government total debt. The baseline regressions in Eq. (1) and Eq. (2) are mainly estimated by the fixed-effects estimation, which is the standard estimation technique in the literature (see, e.g., Magud et al., 2014; Le, 2016). In here, we use the robust standard errors clustered at the country level. To eliminate size distortions, we run the “cluster-robust Hausman test” of Kaiser (2015) instead of the Hausman test, and we find that fixed-effects estimations are consistent. According to the research note of Kaiser (2015), the cluster-robust Hausman test is robust to the size distortions, which can be observed in the classical Hausman test in the cases of using robust standard errors.1

Given that uncertainty is significantly interconnected among countries, the issues of cross-sectional dependence and common correlation effect can be significant (Al Mamun et al., 2018; Kutan et al., 2017). Therefore, we implement the cross-sectional dependence test of Pesaran (2004) and the panel unit root test of Pesaran (2007). 1

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The baseline regressions in Eq. (1) and Eq. (2) are also estimated by the system generalized method of moments (GMM) estimations proposed by Arellano and Bover (1995) and Blundell and Bond (1998). The system GMM estimation can eliminate autocorrelation problems and the presence of different orders of integration in the panel datasets.2 We also run the two-stage estimation procedure with consistent estimators to avoid potential multicollinearity among the independent variables. The instruments are collapsed, as this is recommended by Roodman (2009b).3 Besides, the system GMM estimation technique addresses a solution to the potential endogeneity problem between the independent variables and the domestic credit measures by instrumenting them with the respective lagged variables. At this stage, two assumptions must be fulfilled to yield consistent results in the estimations. The first assumption is that the instruments must be uncorrelated with the error terms. Secondly, the instruments must be correlated with the instrumented variables. In this regard, the validity of the first-order and the second-order autocorrelation in the residuals must be obtained, but the validity of the third-order autocorrelation must be rejected. We use the Sargan test statistic to test potential over-identification problem. We also control the country fixed-effects and the time fixed-effects in the system GMM estimations since other heterogeneities across countries might exist during the coverage period. 2.2 Data We focus on two measures as the domestic credit in the estimations of Eq. (1) and Eq. (2): domestic credit to the private sector (% of GDP) and domestic credit to the private sector by banks (% of GDP). The former captures the credit level in the whole financial system (banks, non-financial institutions, and insurance companies), but the latter present the credits are given

We observe the stationary of the main variables. We did not report the related findings to save space. Besides, we could not use the common correlation effect or the Panel ARDL estimations since the variables are stationary. 2 Since it is hard to find a valid instrument for the relationship between WUI and the domestic credits that satisfy the exclusion restrictions, we have not run the instrumental variables (IV) estimations. 3 We consider the two-step system-GMM estimations by running the xtabond2 Stata package proposed by Roodman (2009a).

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by formal banks. The related data are obtained from the World Development Indicators (WDI) dataset of the World Bank (2019). The dataset covers the period from 1996 to 2017, and the starting date is due to the availability of the data. The frequency of the data is annual, and the dataset includes 139 countries.4 We also divide the countries as the low-income and the middleincome countries (97 countries) as well as the high-income countries (42 countries) according to the country and lending group classification of the World Bank. The high-income group in the dataset consists of countries with the Gross National Income (GNI) per capita of higher than $12,056 in the fiscal year of 2018. Another group consists of countries with GNI per capita of $12,056 or less. The list of the countries in the dataset is provided in Appendix I. The main variable of interest is the index of economic uncertainty provided by Ahir et al. (2018). By using the frequencies of the word “uncertainty” (and its variants) in the Economist Intelligence Unit (EIU) country reports, Ahir et al. (2018) introduce the World Uncertainty Index (WUI) for 143 countries from 1996 to 2018. The EIU reports provide major economic and political issues in each country as well as the analysis and the forecasts on political and economic conditions, which are created by domestic analysts and the editorial board of the Economist. The values in the WUI are comparable across the countries since the raw counts are scaled by the total number of words in each report (Ahir et al., 2018). The WUI is superior to other uncertainty measures since it is the first method to construct an index of uncertainty for a panel dataset of developed and developing countries. In short, the novelty of the WUI is that it is the first uncertainty index, which is comparable across countries. Following the spirit of Anderson et al. (2017), we develop the main hypothesis of the paper as uncertainty, probably caused by business cycle fluctuations, will hurt domestic credits. We also consider the lagged WUI for avoiding from a potential reverse causality that is the change in domestic credits can cause uncertainty. Given that WUI is based on actual policy Therefore, we do not purify the business cycles by using the annual frequency data instead of four-year or fiveyear average data. 4

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changes as well as on the perceived/anticipated policy changes from the EIU reports, expectations could be based on past actual policy changes. However, WUI can also be a direct measure of expectations, so a contemporaneous measure of WUI should also work on domestic credits. Therefore, we expect to find the adverse effects of both the current WUI and the lagged WUI on domestic credits. To the best of our knowledge, the WUI has not previously used as the potential determinants of the domestic credits, and we have aimed to fill this gap in the literature. The paper also includes several controls. Mainly, we consider the log per capita GDP, the money supply, and the current account balance, which are based on the models of Gozgor (2014 and 2018). The per capita income and money supply should be positively related to domestic credits, and they represent the “income” and “price” effects of the credit demand, respectively. As income increases, there will be a higher demand for loans. In here, we consider a lagged per capita income for avoiding from a potential reverse causality that is the change in domestic credits can cause the change in income (Magud et al., 2014). A higher money supply meaning that expansionary monetary policy conditions should be related to a higher level of credits (Guo and Stepanyan, 2011). The current account balance is also controlled to include the impact of the rest of the world on the credit supply (Lane and McQuade, 2014). A higher external deficit indicates that there are less domestic savings and thus meaning that higher demand for credits (Davis et al., 2016). In the robustness checks, we include additional controls. According to Magud et al. (2014) and Obstfeld (2012), borrowing at the lower interest rates can help to provide the stability of the exchange rate and to achieve a lower inflation rate. Therefore, we control for interest rate (both deposit and lending rates), inflation rate, and exchange rate. Similarly, a higher level of government debt can create pressure on macroeconomic stability; thus, it can affect the domestic credits. Similarly, a higher annual economic performance (measured by

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economic growth rate) means that a solid economic performance that possibly affects the domestic credits. We consider another control variable: the trade-GDP ratio known as the trade openness. The data for controls are obtained from the WDI dataset. Finally, the details on all variables used in the paper as well as a summary of the descriptive statistics are reported in Table 1. [Insert Table 1 around here] The correlation matrix for the main variables is also reported in Table 2. The correlations between the domestic credit measures (DC and DCB) and WUI vary from –0.04 to –0.06, which are negative, but they indicate a weak relationship. [Insert Table 2 around here] 3. Empirical Results 3.1 Baseline Fixed-effects Estimations Tables 3 presents the baseline regression estimations in Eq. (1) moreover, Eq. (2) for two domestic credit measures as the dependent variables. [Insert Table 3 around here] The results for the DC are reported in columns (I), (II), and (III), while the results for the DCB rate are provided in columns (IV), (V) and (VI). All results imply that the current- and the lagged uncertainty measure (WUI index) decreases the domestic credits, and the coefficients of WUI index are statistically significant at the 1% level or the 5% level. To put it differently, a higher WUI relates to the lower domestic credits in general. Accurately, to analyze the magnitudes of these effects, we observe that a 1% increase in WUI index demonstrates a 2.76% decrease in DC and a 3.47% decrease in the DCB. Among the controls, the per capita GDP and the money supply are positively related to the domestic credit measures. Besides, the current account balance is negatively associated with domestic credit measures. There is also a high level of persistence in the domestic credits. 11

Finally, according to the results of the cluster-robust Hausman test, the fixed-effects estimations are consistent. Our findings indicate that WUI is negatively related to the domestic credit is consistent with the results of Bordo et al. (2016). The novel evidence of this paper is that there is a negative relationship between WUI and domestic credits in 139 countries. We also discuss the results of the fixed-effects estimations for the countries at the different levels of economic development below. 3.2 Results for the Countries at Different Levels of Economic Development We further analyze whether the impact of WUI on credits differs depending on the level of development of countries. To this end, we divide the whole sample into two as the low-income and the middle-income economies (97 countries), and the high-income economies (42 countries). In here, we follow the World Bank’s classification of the country and lending groups, and the details of the classification are provided in Appendix I. Tables 4a and 4b provide the findings of the baseline regressions in Eq. (1) and Eq. (2) for two domestic credit measures as the dependent variables in the low-income and the middleincome economies as well as the high-income economies, respectively. [Insert Tables 4a and 4b around here] The results in Table 4a indicate that two domestic credit measures are negatively associated with WUI in the high-income economies, but the coefficient of the current WUI is statistically insignificant in the high-income economies. The results in Table 4b demonstrate that two domestic credit measures are negatively related to WUI in the low-income and middleincome economies. All main findings are statistically significant. We also divide the sample according to the level of development by analyzing the nonOECD versus the OECD countries. Tables 5a and 5b provide the results of the baseline regressions in the non-OECD economies as well as the OECD economies, respectively.

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[Insert Tables 5a and 5b around here] We find that the negative relationship between domestic credits and WUI in both the non-OECD and the OECD countries. However, the coefficient of the current WUI is statistically insignificant in OECD countries. We also observe that the effects of WUI upon the domestic credit measures are much stronger in the non-OECD countries than in OECD countries. This evidence is reasonable since most of the non-OECD countries are the poor and developing countries, which have a significant informal economy than the high-income economies. Therefore, the impact of WUI on domestic credits may be lower due to the informal economy. Finally, we provide various robustness exercises in the next section. 4. Robustness Checks 4.1 System GMM Estimations Tables 6 provides the results of the system GMM estimations for the baseline regressions in Eq. (1) and Eq. (2) for two domestic credit measures as the dependent variables. [Insert Table 6 around here] In here, we not only aim to use the alternative econometric methodology but also attempt to address the potential endogeneity bias, i.e., there will be a reverse causality issue that is the lower domestic credits can increase WUI.5 The system-GMM estimations can solve this very problem, and the results demonstrate that the required results are obtained for the diagnostics: The results of the Sargan test indicate that there is no over-identification problem in the estimations. The results of the Arellano-Bond autocorrelation test for AR(1), AR(2), and AR(3) illustrate that the first-order and the second-order autocorrelation are statistically significant, but the third-order autocorrelation is not statistically significant. The results also show that there is substantial and very high-level persistence in the dependent variables. Besides, the results in

In here, the results of the Granger causality tests indicate that lagged domestic credits do not significantly affect the current level of WUI. 5

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Table 6 powerfully illustrate that a higher level of the current and the lagged WUI yields the lower domestic credits, which are in line with the previous baseline fixed-effects estimations. 4.2 Robustness to the Inclusion of Other Controls Tables 7 provides the results of robustness checks, including the effects of the various measures to the baseline regressions in Eq. (1) and Eq. (2) for two domestic credit measures as the dependent variables. [Insert Table 7 around here] Note that our baseline model includes the lagged dependent variable, the lagged log per capita GDP, the money supply, and the current account as controls. This set of robustness checks aims to address potential omitted variable bias by including various controls commonly used in the literature. The previous researches indicate that the inflation rate (Bakker and Gulde, 2010), interest rate (Gozgor 2014), trade openness (Gozgor 2018), exchange rate (Magud et al., 2014), and GDP growth (Takats, 2010) are among additional determinants of the domestic credits in different sets of countries. Therefore, we include these variables to the baseline estimations. The related results confirm the baseline results, i.e., the adverse effects of WUI on the domestic credit measures, are robust to the inclusion of these controls. Overall, the coefficients of the WUI index always have a negative sign as well as they are statistically significant in each case in Table 7. 4.3 Robustness to the Outliers Tables 8 provides the results of robustness checks by excluding the outliers and the specific countries from the dataset. These results are also based on the baseline regressions in Eq. (1) and Eq. (2) for two domestic credit measures as the dependent variables. [Insert Table 8 around here] Firstly, we exclude the outliers for the domestic credits and WUI by dropping the observations which are more than two standard deviations away from the mean (Gozgor, 2018).

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As provided in Table 8, the baseline results are robust to exclude the extreme observations from the dataset. Secondly, we aim to analyze whether the effects of WUI on domestic credits can be region-specific as a further robustness check. For this purpose, we separately exclude the observations for the Sub-Saharan African, the Latin American and the Caribbean, the East Asian and Pacific as well as the Middle East and North African countries to check the robustness of the findings. In other words, we re-estimate the baseline regressions by excluding the observations from the Sub-Saharan African, the Latin American and the Caribbean, the East Asian and Pacific as well as the Middle East and North African countries one region at each time.6 The results in Table 8 are robust to the exclusion of each region, and this implies that the baseline results are not dominated by the presence of observations from the specific regions. Overall, various robustness checks indicate that WUI is negatively related to the domestic credit measures in the panel dataset of 139 countries for the period from 1996 to 2017 as we observed in the baseline regressions in Table 3. In this paper, we have considered several robustness checks to illustrate that the relationship between the measures of domestic credits and the indices of WUI is statistically and economically robust. For this purpose, i) we have used alternative dependent variables (alternative measures of domestic credits), ii) we have provided the results for the countries at the different level of income, iii) we have used different sets of controls and have addressed the potential omitted variable bias, iv) we have run different econometric techniques and have addressed the potential endogeneity bias. 5. Discussion on the Results and Policy Implications The main findings in the paper document that a higher level of economic uncertainty decreases the level of domestic credits. This evidence is in line with the recent research of Bordo et al.

Given that we have run the panel data estimations, including the country fixed-effects, we could not consider the dummy variables of the regions. 6

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(2016), which show that EPU has important economic consequences in different aspects. Bordo et al. (2016) mainly conclude that the negative impact of EPU in economic performance is mainly due to the bank lending channel. The novel evidence of this paper is that there is a negative impact of WUI on domestic credits in 139 countries. Per capita income and money supply are positively related to domestic credits. However, the current account balance is negatively associated with domestic credit measures. The previous studies also show that there is a negative impact of the EPU on corporate investment (Gulen and Ion, 2015; Wang et al., 2014). The domestic credits are considered as the primary source of the investments of the private sector and the government (Luca and Spatafora, 2012). While this negative effect can occur based on the decision of firms to postpone their investments to a more confident (or to a less uncertain period), the delay in investment decisions can be associated with the decreasing level of credits provided by the banking sector. Banks and financial institutions might be reluctant to give credit to firms in uncertain periods as they cannot be sure about the prospects of the investment and try to minimize default risk. Therefore, it is safe to conclude that during the times of WUI will constraint the credits available to the private sector, which will dampen the investments; and therefore, the economic growth of the economy (Samargandi and Kutan, 2016). Moreover, the lack of credits might increase the requirements for credit use and can lead to some financial problems for firms. Especially, bank-dependent borrowers (e.g., the Small and Medium Enterprises-SMEs) may find loans more difficulty or costlier (Bernanke and Lown, 1991). The governments should be aware of this fact and should provide additional credit channels to firms during the uncertain periods. For example, Turkey experienced a rise in its economic policy uncertainty in 2017 (Jirasavetakul and Spilimbergo, 2018). During this period, the government has increased the availability of credits available to firms that cannot get a loan due to insufficient collateral by acting as a guarantor for those loans. The SMEs supporting

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public institutions can also create new credit channels with a relatively lower interest rate and a longer maturity. Besides, the previous literature shows that the EPU can be considered as an earlywarning indicator of a recession (Jirasavetakul and Spilimbergo, 2018); therefore, governments or regulatory bodies should act proactively to overcome financing problems of firms and support the corporate investments in providing sustainable economic growth. This proactive action might help to mitigate the effects of slowdown or help to speed up the recovery period. The decreasing credit levels due to rising WUI will also hurt the credit uses of households. Especially in emerging economies, there is a rising trend in the use of credits for the personal loan due to the financing of vehicle purchases and mortgages. This kind of spending can also serve as the engine of growth, and a higher WUI will lower the access to credit for households, which will decrease the demand for credits; and thus, the economic activity. The tighter credit standards will discourage households for credit usage. Governments or regularity bodies should help households by providing refinancing availability or extending the maturity of the credit. During the times of a higher level of WUI, publicly-owned banks can ease the credit conditions to the contrary to private banks. This issue will also affect the revenues of banks. At this point, it is noteworthy to note that this is a short term and temporary solution. In the long-run, the governments should be more transparent in their economic decision-making processes and provide a more predictable and more stable economic environment. This policy will lower the WUI and increases the level of domestic credits; and thus, the economic performance. The findings of the paper also provide important implication for firms. Firms should also be aware of the decreasing credit availability provided by banks in uncertain periods. Therefore, firms should be ready for tightening credit conditions. If firms could successfully anticipate the possible increase in WUI, they can then increase their cash-holdings with a

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precautionary motive (Demir and Ersan, 2017). Cash-holdings can be helpful during the uncertain periods. Given that the WUI is a measure of domestic economic activity, firms can focus more on foreign markets by exporting activities, which can mitigate the adverse effects on WUI on the developments in domestic markets. Likewise, looking for financing alternatives within global capital markets can be an option, especially for large firms. 6. Conclusion In this paper, we used the new measure of uncertainty (WUI) to analyze the impact of uncertainty on the domestic credits in the panel dataset of 139 countries for the period from 1996 to 2017. We utilized the fixed-effects and the system GMM estimations and found that WUI is negatively associated with the measures of domestic credits. We also observed that per capita income and money supply are positively related to the domestic credits., but the current account balance is negatively associated with domestic credit measures. We also addressed the potential “omitted variable bias” and “endogeneity bias.” After implementing various sensitivity analyses, we observed that the main evidence of the paper is economically and statistically robust. Given that WUI indices are provided as the quarterly frequency at WUI dataset, future papers on the subject can utilize time-series techniques to understand the effects of WUI indices on other financial indicators in developing or developed economies (e.g., the BRIC or the G7 economies). References Ahir, H., Bloom, N., & Furceri, D. (2018). The World Uncertainty Index. Mimeo, Retrieved from http://www.policyuncertainty.com/media/WUI_mimeo_10_29.pdf. Al Mamun, M., Sohag, K., Shahbaz, M., & Hammoudeh, S. (2018). Financial Markets, Innovations, and Cleaner Energy Production in OECD Countries. Energy Economics, 72, 236–254.

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Demir, E., & Ersan, O. (2017). Economic Policy Uncertainty and Cash Holdings: Evidence from BRIC Countries. Emerging Markets Review, 33, 189–200. Gong, D., Jiang, T., Li, Z., & Wu, W. (2018). Bank Lending Under Policy Uncertainty: Theory and Cross-Country Evidence. Available at SSRN, No: 3272065. Gozgor, G. (2014). Determinants of Domestic Credit Levels in Emerging Markets: The Role of External Factors. Emerging Markets Review, 18, 1–18. Gozgor, G. (2018). Determinants of the Domestic Credits in Developing Economies: The Role of Political Risks. Research in International Business and Finance, 46, 430–443. Gulen, H., & Ion, M. (2015). Policy Uncertainty and Corporate Investment. The Review of Financial Studies, 29 (3), 523–564. Guo, K., & Stepanyan, V. (2011). Determinants of Bank Credit in Emerging Market Economies. International Monetary Fund Working Paper, No. 11/51. Washington, D.C.: International Monetary Fund. He, Z., & Niu, J. (2018). The Effect of Economic Policy Uncertainty on Bank Valuations. Applied Economics Letters, 25 (5), 345–347. Jiang, Y., He, L., Meng, J., & Nie, H. (2019). Nonlinear Impact of Economic Policy Uncertainty Shocks on Credit Scale: Evidence from China. Physica A: Statistical Mechanics and its Applications, 521, 626–634. Jirasavetakul, L.B.F., & Spilimbergo, M.A. (2018). Economic Policy Uncertainty in Turkey. International Monetary Fund Working Paper, No. 18/272, Washington, D.C.: International Monetary Fund. Kaiser, B. (2015). RHAUSMAN: Stata Module to Perform Robust Hausman Specification Test. Statistical Software Components S457909. Chestnut Hill, MA: Boston College Department of Economics.

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Kutan, A.M., Samargandi, N., & Sohag, K. (2017). Does Institutional Quality Matter for Financial Development and Growth? Further Evidence from MENA Countries. Australian Economic Papers, 56 (3), 228–248. Le, C.H., 2016. Macro-financial linkages and bank behavior: evidence from the second-round effects of the global financial crisis on East Asia. Eurasian Economic Review, 6(3), pp.365-387. Lane, P.R., & McQuade, P. (2014). Domestic Credit Growth and International Capital Flows. The Scandinavian Journal of Economics, 116 (1), 218–252. Luca, O., & Spatafora, N. (2012). Capital Inflows, Financial Development, and Domestic Investment: Determinants and Inter-Relationships. International Monetary Fund Working Paper, No. 12/120, Washington, D.C.: International Monetary Fund. Magud, N.E., Reinhart, C.M., & Vesperoni, E.R. (2014). Capital Inflows, Exchange Rate Flexibility, and Credit Booms. Review of Development Economics, 18 (3), 415–430. Obstfeld, M. (2012). Does the Current Account Still Matter? American Economic Review, 102 (3), 1–23. Pesaran, M.H. (2004). General Diagnostic Tests for Cross Section Dependence in Panels. IZA Discussion Paper, No. 1240. Pesaran, M.H. (2007). A Simple Panel Unit Root Test in the Presence of Cross‐section Dependence. Journal of Applied Econometrics, 22 (2), 265–312. Roodman, D. (2009a). How to Do Xtabond2? An Introduction to Difference and System GMM in Stata. The Stata Journal, 9 (1), 86–136. Roodman, D. (2009b). A Note on the Theme of Too Many Instruments. Oxford Bulletin of Economics and Statistics, 71 (1), 135–158.

21

Samargandi, N., & Kutan, A.M. (2016). Private Credit Spillovers and Economic Growth: Evidence from BRICS Countries. Journal of International Financial Markets, Institution, and Money, 44, 56–84. Takats, E. (2010). Was it Credit Supply? Cross-border Bank Lending to Emerging Market Economies during the Financial Crisis. Bank for International Settlements Quarterly Review, 2, 49–56. Wang, Y., Chen, C.R., & Huang, Y.S. (2014). Economic Policy Uncertainty and Corporate Investment: Evidence from China. Pacific-Basin Finance Journal, 26, 227–243. World Bank (2019). World Development Indicators Dataset. Washington, D.C.: World Bank.

22

Table 1 Descriptive Summary Statistics Variables

Definition

Data Source

Mean

Standard Deviation

Domestic Credit to Private Sector

Percentage of GDP

World Bank, World Development Indicators

46.51

44.44

0.001

233.2

2,792

Domestic Credit to the Private Sector by Banks

Percentage of GDP

World Bank, World Development Indicators

43.50

40.31

0.001

233.2

2,797

GDP per Capita (Current US$)

Logarithmic Form

World Bank, World Development Indicators

8.039

1.666

4.286

11.54

3,075

Broad Money

Percentage of GDP

World Bank, World Development Indicators

53.19

44.73

0.001

376.5

2,534

Current Account Balance

Percentage of GDP

World Bank, World Development Indicators

– 2.101

9.431

–80.05

45.45

2,711

Inflation, Consumer Prices (Annual %)

Percentage

World Bank, World Development Indicators

9.815

82.96

–16.11

4145

2,856

Official Exchange Rate (LCU per US$)

Logarithmic Form

World Bank, World Development Indicators

3.412

2.777

–6.627

22.62

2,811

Deposit Interest Rate

Percentage

World Bank, World Development Indicators

8.223

10.05

–0.210

203.3

2,113

Lending Interest Rate

Percentage

World Bank, World Development Indicators

16.48

23.35

0.500

578.9

2,103

Annual GDP Growth

Percentage

World Bank, World Development Indicators

4.133

5.849

–62.07

123.1

3,075

Trade

Percentage of GDP

World Bank, World Development Indicators

81.66

48.85

0.026

442.6

3,007

Total Central Government Debt

Percentage of GDP

World Bank, World Development Indicators

53.83

36.44

1.893

283.7

748

Economic Uncertainty

Level of Index

The World Uncertainty Index: Ahir et al. (2018)

0.165

0.145

0.000

1.343

3,102

Minimum Maximum Observations

23

Table 2 Correlation Matrix Domestic Credit to the Private Sector

Domestic Credit to the Private Sector by Banks

Log Per Capita GDP

Domestic Credit to the Private Sector

1.000

–

–

Domestic Credit to the Private Sector by Banks

0.949

1.000

Log Per Capita GDP

0.706

Money Supply

Mon ey

Current Account Balance

Econom ic Uncertai nty

–

–

–

–

–

–

–

0.708

1.000

–

–

–

0.776

0.795

0.517

1.000

–

–

Current Account Balance

0.210

0.228

0.382

0.163

1.000

–

Economic Uncertainty

–0.062

–0.044

0.053

0.021

–0.053

1.000

Regressors

Supp ly

Table 3 Benchmark Regressions: Fixed-Effects Estimations for Domestic Credits to Private Sector (1996–2017) (All Countries)

24

Regressors

Domestic Domestic Domestic Domestic Credit Domestic Credit Domestic Credit Credit to the Credit to the Credit to the to the Private to the Private to the Private Private Private Private Sector by Banks Sector by Banks Sector by Banks Sector Sector Sector

Columns

(I)

(II)

(III)

(IV)

(V)

(VI)

Constant Term

5.904*** (0.413)

–13.49*** (2.657)

–13.28*** (2.636)

5.249*** (0.369)

–11.81*** (2.378)

–11.46*** (2.378)

Lagged Domestic Credit to the Private Sector

0.898*** (0.008)

0.755*** (0.040)

0.755*** (0.039)

–

–

–

Lagged Domestic Credit to the Private Sector by Banks

–

–

–

0.907*** (0.008)

0.777*** (0.021)

0.777*** (0.020)

Lagged Log Per Capita GDP

–

2.159*** (0.430)

2.133*** (0.425)

–

1.825*** (0.393)

1.773*** (0.394)

Money Supply

–

0.157*** (0.043)

0.158*** (0.043)

–

0.142*** (0.029)

0.143*** (0.028)

Current Account Balance

–

–0.103*** (0.026)

–0.101*** (0.026)

–

–0.105*** (0.024)

–0.102*** (0.025)

Economic Uncertainty

–1.568** (0.759)

–2.758*** (0.990)

–

–2.128*** (0.776)

–3.472*** (0.906)

–

Lagged Economic Uncertainty

–

–

–3.162*** (0.871)

–

–

–3.306*** (0.844)

Observations

2,651

2,173

2,173

2,657

2,176

2,176

Number of Countries

139

120

120

139

120

120

19.8 [0.000]

23.6 [0.000]

24.1 [0.000]

0.835

0.878

0.878

Hausman Test

R-squared (Within)

18.2 [0.000] 17.9 [0.000] 21.7 [0.000]

0.817

0.864

0.865

Notes: The dependent variables are the domestic credit to the private sector (left-side) and the domestic credit to the private sector by banks (right-side), respectively. The Hausman test shows whether the results of the fixed-effects or the random effects estimations are valid (null hypothesis: the random effects estimations are efficient). The robust standard errors clustered at the country level are reported. The standard errors are in parentheses, and the p–values are in brackets. *** and ** indicate statistical significance at 1% and 5% levels, respectively.

25

Table 4a. Benchmark Regressions (High-Income Economies) Regressors

Domestic Credit to the Private Sector

Domestic Credit to the Private Sector

Domestic Credit to the Private Sector

Domestic Credit to the Private Sector by Banks

Domestic Credit to the Private Sector by Banks

Domestic Credit to the Private Sector by Banks

Constant Term

13.70*** (1.570)

–37.82** (15.14)

–38.85*** (15.05)

12.18*** (1.402)

–33.58** (13.08)

–34.33** (13.13)

Lagged Domestic Credit to the Private Sector

0.874*** (0.016)

0.671*** (0.083)

0.676*** (0.079)

–

–

–

Lagged Domestic Credit to the Private Sector by Banks

–

–

–

0.884*** (0.015)

0.722*** (0.041)

0.723*** (0.038)

Lagged Log Per Capita GDP

–

5.288*** (1.707)

5.417*** (1.691)

–

4.438*** (1.525)

4.541*** (1.525)

Money Supply

–

0.215** (0.080)

0.217** (0.082)

–

0.181*** (0.030)

0.182*** (0.031)

Current Account Balance

–

–0.359*** (0.080)

–0.354*** (0.079)

–

–0.365*** (0.072)

–0.361*** (0.070)

Economic Uncertainty

–5.441** (2.734)

–2.255 (4.821)

–

–7.265*** (2.549)

–5.878 (4.561)

–

Lagged Economic Uncertainty

–

–

–7.597* (4.211)

–

–

–8.760** (4.112)

Observations

729

460

460

730

461

461

Number of Countries

42

26

26

42

26

26

R-squared (Within)

0.800

0.848

0.850

0.818

0.868

0.869

26

Notes: The robust standard errors those are clustered at the country levels are in parentheses. ***, **, and * indicate statistical significance at 1%, 5%, and 10% levels.

Table 4b. Benchmark Regressions (Low-Income & Middle-Income Economies) Regressors

Domestic Domestic Domestic Domestic Credit to Domestic Credit to Domestic Credit to Credit to the Credit to the Credit to the the Private Sector the Private Sector the Private Sector Private Sector Private Sector Private Sector by Banks by Banks by Banks

Constant Term

3.141*** (0.303)

–12.63*** (2.558)

–12.32*** (2.567)

2.785*** (0.270)

–10.90*** (2.286)

–10.54*** (2.305)

Lagged Domestic Credit to the Private Sector

0.925*** (0.009)

0.805*** (0.022)

0.803*** (0.022)

–

–

–

Lagged Domestic Credit to the Private Sector by Banks

–

–

–

0.934*** (0.009)

0.812*** (0.021)

0.811*** (0.021)

Lagged Log Per Capita GDP

–

2.031*** (0.473)

1.973*** (0.475)

–

1.727*** (0.436)

1.658*** (0.441)

Money Supply

–

0.107*** (0.033)

0.108*** (0.033)

–

0.105*** (0.032)

0.106*** (0.032)

Current Account Balance

–

–0.073*** (0.017)

–0.070*** (0.017)

–

–0.071*** (0.017)

–0.068*** (0.017)

Economic Uncertainty

–0.927* (0.559)

–2.742*** (0.832)

–

–1.177* (0.665)

–2.941*** (0.777)

–

Lagged Economic Uncertainty

–

–

–2.174*** (0.643)

–

–

–2.232*** (0.643)

Observations

1,922

1,713

1,713

1,927

1,715

1,715

Number of Countries

97

94

94

97

94

94

R-squared (Within)

0.837

0.886

0.886

0.855

0.895

0.894

27

Notes: The robust standard errors those are clustered at the country levels are in parentheses. ***, **, and * indicate statistical significance at 1%, 5%, and 10% levels.

Table 5a. Benchmark Regressions (OECD Countries) Regressors

Domestic Credit to the Private Sector

Domestic Credit to the Private Sector

Domestic Credit to the Private Sector

Domestic Credit to the Private Sector by Banks

Domestic Credit to the Private Sector by Banks

Domestic Credit to the Private Sector by Banks

Constant Term

14.22*** (1.824)

–36.97* (20.93)

–33.98 (20.58)

12.29*** (1.560)

–22.11 (15.78)

–20.16 (15.08)

Lagged Domestic Credit to the Private Sector

0.880*** (0.018)

0.663*** (0.133)

0.673*** (0.127)

–

–

–

Lagged Domestic Credit to the Private Sector by Banks

–

–

–

0.892*** (0.016)

0.745*** (0.049)

0.748*** (0.043)

Lagged Log Per Capita GDP

–

5.073** (2.322)

4.722* (2.246)

–

2.917 (1.853)

2.719 (1.744)

Money Supply

–

0.271* (0.141)

0.281* (0.145)

–

0.216*** (0.043)

0.221*** (0.044)

Current Account Balance

–

–0.518** (0.217)

–0.538** (0.190)

–

–0.568*** (0.180)

–0.568*** (0.164)

Economic Uncertainty

–4.861* (2.941)

–3.768 (5.406)

–

–7.118*** (2.648)

–8.166 (5.536)

–

Lagged Economic Uncertainty

–

–

–10.90* (5.358)

–

–

–11.92** (5.091)

Observations

533

325

325

534

326

326

Number of Countries

31

18

18

31

18

18

R-squared (Within)

0.828

0.865

0.869

0.850

0.891

0.894

Notes: The robust standard errors those are clustered at the country levels are in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels.

28

Table 5b. Benchmark Regressions (Non-OECD Countries) Regressors

Domestic Credit to the Private Sector

Domestic Credit to the Private Sector

Domestic Credit to the Private Sector

Domestic Credit to the Private Sector by Banks

Domestic Credit to the Private Sector by Banks

Domestic Credit to the Private Sector by Banks

Constant Term

3.851*** (0.347)

–13.71*** (2.515)

–13.38*** (2.520)

3.516*** (0.320)

–12.02*** (2.279)

–11.64*** (2.291)

Lagged Domestic Credit to the Private Sector

0.913*** (0.010)

0.774*** (0.022)

0.773*** (0.022)

–

–

–

Lagged Domestic Credit to the Private Sector by Banks

–

–

–

0.920*** (0.009)

0.780*** (0.021)

0.779*** (0.021)

Lagged Log Per Capita GDP

–

2.154*** (0.433)

2.094*** (0.435)

–

1.866*** (0.402)

1.795*** (0.406)

Money Supply

–

0.129*** (0.031)

0.130*** (0.030)

–

0.127*** (0.030)

0.128*** (0.030)

Current Account Balance

–

–0.102*** (0.023)

–0.100*** (0.023)

–

–0.101*** (0.023)

–0.098*** (0.025)

Economic Uncertainty

–1.051* (0.527)

–2.973*** (0.845)

–

–1.282* (0.750)

–3.204*** (0.789)

–

Lagged Economic Uncertainty

–

–

–2.304*** (0.729)

–

–

–2.432*** (0.719)

Observations

2,118

1,848

1,848

2,123

1,850

1,850

Number of Countries

108

102

102

108

102

102

R-squared (Within)

0.812

0.869

0.869

0.825

0.875

0.875

29

Notes: The robust standard errors those are clustered at the country levels are in parentheses. ***, **, and * indicate statistical significance at 1%, 5%, and 10% levels.

Table 6 System GMM Estimations for Private Sector Domestic Credits (1996–2017) (All Countries) Regressors

Domestic Domestic Domestic Domestic Domestic Domestic Credit to Credit to Credit to Credit to the Credit to the Credit to the the the the Private Private Private Private Private Private Sector by Sector by Sector by Sector Sector Sector Banks Banks Banks

Constant Term

– – 2.889*** 8.064*** 9.238*** (0.640) (0.143) (0.262)

2.615*** (0.826)

–9.519*** (0.940)

–10.56*** (2.242)

Lagged Domestic Credit to the Private Sector

0.968*** 0.895*** 0.897*** (0.008) (0.006) (0.016)

–

–

–

0.969*** (0.009)

0.884*** (0.010)

0.882*** (0.018)

Lagged Domestic Credit to the Private Sector by Banks

–

Lagged Log Per Capita GDP

–

1.342*** 1.674*** (0.213) (0.339)

–

1.515*** (0.136)

1.812*** (0.271)

Money Supply

–

0.062*** 0.057*** (0.007) (0.014)

–

0.066*** (0.004)

0.064*** (0.015)

Current Account Balance

–

– – 0.085*** 0.099*** (0.016) (0.016)

–

–0.089*** (0.021)

–0.103*** (0.027)

–

–2.513*** (0.142)

–2.926*** (0.817)

–

Economic Uncertainty

–

– – 2.289*** 2.324*** (0.155) (0.488)

–

Lagged Economic Uncertainty

–

–

– 10.13*** (1.128)

–

–

–10.04*** (2.056)

Observations

2,408

2,173

2,173

2,413

2,176

2,176

Number of Countries

135

120

120

135

120

120

AR (1) Test Statistic and pvalue

–2.98 [0.003]

–2.77 [0.006]

–2.93 [0.003]

–4.68 [0.000]

–4.29 [0.000]

–4.36 [0.000]

AR (2) Test Statistic and pvalue

–2.65 [0.008]

–2.44 [0.015]

–2.35 [0.019]

–2.43 [0.015]

–2.12 [0.034]

–1.94 [0.052]

AR (3) Test Statistic and pvalue

–0.17 [0.868]

0.11 [0.910]

–0.10 [0.922]

–0.27 [0.785]

–0.03 [0.977]

–0.34 [0.732]

Sargan Test Statistic and pvalue

123.9 [0.336]

114.2 [0.501]

113.6 [0.519]

120.7 [0.413]

112.6 [0.544]

112.4 [0.550]

30

Notes: The robust standard errors clustered at the country levels are in the parentheses. The probability values are in the brackets. ***, **, and * indicate statistical significance at 1%, 5%, and 10% levels.

Table 7 Sensitivity Analysis (Including Additional Controls, All Countries) Sensitivity Analysis Results of the Benchmark Regressions

Including Inflation Rate

Including Exchange Rate

Including Deposit Interest Rate

Including Lending Interest Rate

Including Annual GDP Growth Rate

Including International Trade

Including Central Government Debt

Coefficients

Domestic Credit to the Domestic Credit to the Private Private Sector Sector by Banks

Economic Uncertainty

–2.758*** (0.990)

–3.472*** (0.906)

Lagged Economic Uncertainty

–3.162*** (0.871)

–3.306*** (0.844)

Economic Uncertainty

–2.822*** (1.051)

–3.620*** (0.948)

Lagged Economic Uncertainty

–2.970*** (0.887)

–3.204*** (0.884)

Economic Uncertainty

–2.752*** (0.994)

–3.472*** (0.906)

Lagged Economic Uncertainty

–3.143*** (0.873)

–3.306*** (0.844)

Economic Uncertainty

–2.756** (1.115)

–3.376*** (1.063)

Lagged Economic Uncertainty

–2.562*** (0.809)

–2.795*** (0.803)

Economic Uncertainty

–3.131*** (1.147)

–3.946*** (1.063)

Lagged Economic Uncertainty

–3.325*** (0.952)

–3.634*** (0.944)

Economic Uncertainty

–2.723*** (1.002)

–3.447*** (0.917)

Lagged Economic Uncertainty

–3.136*** (0.864)

–3.287*** (0.842)

Economic Uncertainty

–2.880*** (1.021)

–3.607*** (0.924)

Lagged Economic Uncertainty

–3.078*** (0.862)

–3.287*** (0.841)

Economic Uncertainty

–5.335** (2.335)

–6.528** (2.524)

Lagged Economic Uncertainty

–3.502*** (1.026)

–4.636*** (2.121)

Notes: The constant term, lagged dependent variable, lagged log per capita GDP, money supply, and current account balance are also estimated, but their coefficients are not reported to save space. The robust standard errors those are clustered at the country levels are in parentheses. ***, **, and * indicate statistical significance at 1%, 5%, and 10% levels.

31

Table 8 Sensitivity Analysis (Different Cases, All Countries) Sensitivity Analysis

Results of the Benchmark Regressions

Excluding Extreme Units of Dependent Variables

Excluding Extreme Units of Economic Uncertainty

Excluding Sub-Saharan Africa Countries

Excluding Latin American and Caribbean Countries

Excluding East Asia and Pacific Countries

Excluding the Middle East and North Africa Countries

Coefficients

Domestic Credit to the Private Sector

Domestic Credit to the Private Sector by Banks

Economic Uncertainty

–2.758*** (0.990)

–3.472*** (0.906)

Lagged Economic Uncertainty

–3.162*** (0.871)

–3.306*** (0.844)

Economic Uncertainty

–2.628*** (0.787)

–2.544*** (0.734)

Lagged Economic Uncertainty

–2.345** (0.667)

–2.499*** (0.691)

Economic Uncertainty

–2.362** (1.032)

–3.693** (1.487)

Lagged Economic Uncertainty

–3.433** (1.513)

–3.307** (1.355)

Economic Uncertainty

–2.498** (1.106)

–3.735*** (1.286)

Lagged Economic Uncertainty

–2.993*** (1.302)

–3.424*** (1.278)

Economic Uncertainty

–3.237*** (1.205)

–4.021*** (1.106)

Lagged Economic Uncertainty

–4.393*** (1.035)

–4.344*** (0.999)

Economic Uncertainty

–2.156*** (0.984)

–2.719*** (0.811)

Lagged Economic Uncertainty

–2.745*** (0.867)

–2.662*** (0.793)

Economic Uncertainty

–2.640** (1.075)

–3.412*** (0.980)

Lagged Economic Uncertainty

–3.027*** (0.903)

–3.267*** (0.902)

Notes: The constant term, lagged dependent variable, lagged log per capita GDP, money supply, and current account balance are also estimated, but their coefficients are not reported to save space. The robust standard errors those are clustered at the country levels are in parentheses. ***, **, and * indicate statistical significance at 1%, 5%, and 10% levels.

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Does Economic Uncertainty Affect Domestic Credits? An Empirical Investigation

Highlights:  We examine the impact of uncertainty on the level of domestic credits  We focus on a panel of 139 countries for the period from 1996 to 2017  We consider the new measure of uncertainty (World Uncertainty Index-WUI)  A higher level of uncertainty decreases domestic credits  The primary evidence is robust to alternative specifications and scenarios

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Data Appendix I List of Countries in the Panel Dataset (Country and Lending Groups Classification of the World Bank in 2018) The high-income group in the dataset consists of countries with the Gross National Income (GNI) per capita of higher than $12,056 in the fiscal year of 2018. Another group consists of countries with GNI per capita of $12,056 or less. 97 Low-Income & Middle-Income Countries Afghanistan, Albania, Algeria, Angola, Armenia, Azerbaijan, Bangladesh, Belarus, Benin, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Bulgaria, Burkina Faso, Burundi, Cambodia, Cameroon, the Central African Republic, Chad, China, Colombia, Congo DR, Congo Republic, Costa Rica, Cote d'Ivoire, the Dominican Republic, Ecuador, Egypt, El Salvador, Eritrea, Ethiopia, Gabon, the Gambia, Georgia, Ghana, Guatemala, Guinea, GuineaBissau, Haiti, Honduras, India, Indonesia, Iran, Iraq, Jamaica, Jordan, Kazakhstan, Kenya, the Kyrgyz Republic, Lebanon, Lesotho, Liberia, Libya, Madagascar, Malawi, Malaysia, Mali, Mauritania, Mexico, Moldova, Mongolia, Morocco, Mozambique, Myanmar, Namibia, Nepal, Nicaragua, Niger, Nigeria, North Macedonia, Pakistan, Papua New Guinea, Paraguay, Peru, the Philippines, Romania, Russia, Rwanda, Senegal, Sierra Leone, South Africa, Sri Lanka, Sudan, Tajikistan, Tanzania, Thailand, Togo, Tunisia, Turkey, Uganda, Ukraine, Venezuela, Vietnam, Yemen Republic, Zambia, and Zimbabwe. 42 High-Income Countries Argentina, Australia, Austria, Belgium, Canada, Chile, Croatia, the Czech Republic, Denmark, Finland, France, Germany, Greece, Hong Kong SAR China, Hungary, Ireland, Israel, Italy, Japan, the Korea Republic, Kuwait, Latvia, Lithuania, the Netherlands, New Zealand, Norway, Oman, Panama, Poland, Portugal, Qatar, Saudi Arabia, Singapore, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, the United Arab Emirates, the United Kingdom, the United States, and Uruguay.

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