Credit information sharing, corruption and financial development: International evidence

Tran Hung Son; Hoang Cong Gia Khanh; Nguyen Thanh Liem Article Credit information sharing, corruption and financial development: International evidence Cogent Business & Management Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Tran Hung Son; Hoang Cong Gia Khanh; Nguyen Thanh Liem (2020) : Credit information sharing, corruption and financial development: International evidence, Cogent Business & Management, ISSN 2331-1975, Taylor & Francis, Abingdon, Vol. 7, Iss. 1, pp. 1-15, https://doi.org/10.1080/23311975.2020.1851856 This Version is available at: https://hdl.handle.net/10419/245013 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oabm20 Cogent Business & Management ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/oabm20 Credit information sharing, corruption and financial development: International evidence Tran Hung Son, Hoang Cong Gia Khanh & Nguyen Thanh Liem | To cite this article: Tran Hung Son, Hoang Cong Gia Khanh & Nguyen Thanh Liem | (2020) Credit information sharing, corruption and financial development: International evidence, Cogent Business & Management, 7:1, 1851856, DOI: 10.1080/23311975.2020.1851856 To link to this article: https://doi.org/10.1080/23311975.2020.1851856 © 2020 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Published online: 02 Dec 2020. Submit your article to this journal Article views: 568 View related articles View Crossmark data BANKING & FINANCE | RESEARCH ARTICLE Credit information sharing, corruption and financial development: International evidence Tran Hung Son 1,2 , Hoang Cong Gia Khanh 1,2 and Nguyen Thanh Liem 1,2 Abstract: This study investigates the individual impact of credit information sharing and corruption as well as their interaction on financial development, using a sample of 120 countries for the period 2004–2017. Public credit registries (PCR) and private credit bureaus (PCB) are used as proxies for the level of credit information sharing, whereas financial development is measured in terms of size, activity and efficiency. We obtain evidence in support of the following arguments. First, PCB has a negative impact on the size of the financial sector, whereas PCR has an insignificant effect. Second, PCB and PCR have a positive impact on the financial intermediation activity, with the magnitude of the latter being higher. Third, PCB and PCR increase the financial sector efficiency, again with the magnitude of the latter being stronger. Fourth, both PCR and PCB reduce the negative effects of corruption on financial sector development, but PCR tends to be more effective, suggesting that PCR may play a more important role compared to that of PCB. The findings survive a battery of robustness tests, including the use of an alternative corruption indicator and subsample analysis. Finally, policy implications are discussed. Subjects: Economics; Finance; Banking Keywords: information sharing; corruption; financial development Jel: G15; G21; O16 ABOUT THE AUTHORS Dr. Tran Hung Son is currently working for the University of Economics and Law, Vietnam National University, Hochiminh city and Institute for Development and Research in Banking Technology. His research interests cover contemporary issues in banking and finance, financial crisis, financial development. Associate Professor Hoang Cong Gia Khanh is a Vice Rector of University of Economics and Law, Vietnam National University, Hochiminh city. His research interests are in the fields of banking and finance issues. Dr. Nguyen Thanh Liem is a Research Fellow at the Institute for Development and Research in Banking Technology. He mainly works in the field of corporate governance, innovation and bank stability and performance. PUBLIC INTEREST STATEMENT The information about the creditworthiness of the borrowers is shared by public credit registries (PCRs) and private credit bureaus (PCBs). However, whether these agencies can mitigate the information asymmetry and support financial development remains an empirical question to answer. This study uses a sample of 120 countries in the period from 2004 to 2017, and finds that PCBs and PCRs have different impacts on financial development depending on how financial development is measured. However, in general, PCRs have a more positive impact compared to PCBs. We also find that corruption hampers financial development, and both PCBs and PCRs help contain the negative impact of this factor. Interestingly, PCRs tend to be more effective with regard to this. Several implications are provided to promote the effectiveness of PCBs and PCRs in encouraging financial development. Hung Son et al., Cogent Business & Management (2020), 7: 1851856 https://doi.org/10.1080/23311975.2020.1851856 Page 1 of 15 Received: 09 March 2020 Accepted: 29 September 2020 Corresponding author: Tran Hung Son, Institute for Development and Research in Banking Technology, University of Economics and Law, Vietnam National University-HCM, Ho Chi Minh City, Vietnam E-mail: [email protected] Reviewing editor: David McMillan, University of Stirling, UK Additional information is available at the end of the article © 2020 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. 1. Introduction The financial sector plays a vital role in a country’s economy, and the lack of it may adversely affect firms’ performance (Pradhan & Hiremath, 2020a). Nevertheless, the financial sector does not always function properly especially when there is a high level of information asymmetry, causing damages to the economy. In a lending relationship, asymmetric information is a situation in which borrowers have more information than lenders. Asymmetric information adversely affects financial development by reducing the efficient allocation of capital (Jappelli & Pagano, 2002). Informationsharing bureaus have been introduced to reduce concerns over the moral hazard and adverse selection issues in the financial markets (see Triki & Gajigo, 2014). Information-sharing might mitigate issues related to foreign-currency borrowings of firms in developing countries like India (Pradhan & Hiremath, 2019, 2020b), as financial soundness of firms is exposed in more detail for easier credit appraisal. Credit information sharing is the exchange of information on a client’s financial history, covering credit history, credit worthiness and the current debt level of the borrower (Jappelli & Pagano, 2002). Information sharing via private bureaus and public credit registries has become an important part of banking system development (Barth et al., 2009). Previous studies tend to support the argument that credit information sharing exerts a positive effect on financial development. For instance, Jappelli and Pagano (2002) show that information-sharing institutions help to increase the size of the credit market. Djankov et al. (2007) find that public credit registries could lead to higher ratios of private credit to GDP in developing countries. The literature has extolled the benefits of credit information sharing on credit activities. Houston et al. (2010) argue that information sharing leads to higher bank profitability and lower bank risk. Better credit information sharing can reduce the negative effect of abnormal loan growth on bank risk (Soedarmono & Sitorus, 2017), and mitigate financial fragility as well as the detrimental impact of a credit boom on financial fragility (Guérineau & Léon, 2019). Other studies show that information-sharing bureaus may increase corporate access to finance and financial development in terms of depth, efficiency, activity and size (Asongu et al., 2016; Triki & Gajigo, 2014). Besides credit information sharing, corruption can have an impact on the lending behavior of banks (Tran et al., 2020). The “sand the wheels” hypothesis suggests that corruption can adversely affect financial sector activity (see Cooray & Schneider, 2018), and greater corruption may result in a financial crisis (e.g., Park, 2012). Barth et al. (2009) show that information sharing helps mitigate corruption in lending. In this study, we analyze whether the relationship between credit information sharing and financial development depends on the degree of corruption in a country. Using aggregate data from World Bank covering 120 countries over the period 2004–2017, we address two fundamental questions: First, are credit information sharing and corruption significantly associated with financial development? Second, does the relation between credit information sharing and financial development change at high and low levels of corruption? This paper contributes to the existing literature in three important ways. First, the vast majority of extant studies tend to focus on the corruption-economic growth nexus, and little has been researched on the link between corruption and financial development. In this paper, we show that corruption has a negative impact on financial development, considering several facets including the size, efficiency and activity of the financial sector. Second, several studies have discussed the influence of credit information sharing on financial development, but those works only cover particular regions (see for example, Asongu et al. (2016)), so the generalizability of the findings might suffer. We use a sample of 120 countries with the most updated dataset. Finally, we show consistent and robust evidence suggesting that credit information sharing can alleviate information asymmetry associated with corruption, thus allowing more credit availability with lower levels of non-performing loans. Hung Son et al., Cogent Business & Management (2020), 7: 1851856 https://doi.org/10.1080/23311975.2020.1851856 Page 2 of 15 The rest of the paper is organized as follows. Section 2 presents the discussion on the relevant literature on the link between information sharing, corruption and financial development. Section 3 outlines the research methodology, including estimation method, models, hypotheses and variable definition. Section 4 displays the results of the estimation of the models to test the hypotheses. Section 5 concludes the paper with the implications and suggestions for future research. 2. Literature review This section summarizes two strands of empirical literature focusing on: (1) information sharing and financial development and (2) corruption and financial development. Then, we discuss the background on the impact of the interaction of information sharing and corruption on financial development. 2.1. Information sharing and financial development According to Jappelli and Pagano (2002), there has been a substantial theoretical foundation showing that information asymmetry reduces the efficient allocation of capital, thus adversely affecting financial development. Due to the fact that lenders tend to lack information about the genuine situation of borrowers, they are exposed to the risk of adverse selection. The lenders could face more severe losses when they are unable to monitor or control the behaviors of borrowers in the post-credit period. For both insolvent and solvent borrowers, there could be incentives to limit the reimbursement of financial obligations that are associated with the loan. Therefore, to protect the lenders’ capital against such risks, credits are granted only with high interest rates and through costly screening processes, which substantially imposes adverse effect on financial development and the ability of firms to compete well as their costs of debt have increased markedly. Asongu et al. (2016) suggest that these disadvantages could be contained with the advent of information sharing on the borrowers’ credit history and solvency traits. Across the globe, private credit bureaus (PCB) and public credit registries (PCR) have been established to share the much-needed information with banks. By addressing the issue of information asymmetry, information sharing helps promote efficient allocation, lower the possibility of firms encountering credit constraints as long as they have more ability to exchange information with the lenders, and nurture credit market competition (Jappelli & Pagano, 2002), and improve credit availability and lower borrowing cost (Barth et al., 2009; Brown et al., 2009). Guérineau and Léon (2019) find that credit information sharing decreases financial fragility for both advanced and less developed countries, and it also alleviates the negative impact of a credit boom on financial fragility but this effect only exists for more developed countries and for household credit booms. Jappelli and Pagano (2002) opine that bank lending is higher and credit risk is lower in countries where lenders share information about borrowers. Ivashina (2009) examines how the lead bank’s ownership share of a syndicated loan affects the information asymmetry in the lending syndicate. The results find that the information asymmetry problem within a syndicate has an important economic impact on loan spread. Soedarmono and Sitorus (2017) investigate the joint effect of abnormal loan growth and credit reporting system on bank risk. The results find that better information sharing can reduce bank risk one year ahead due to higher abnormal loan growth. A number of studies have assessed the effect of stronger rights to information of creditors on risk-taking behavior of banks as well as bankruptcy likelihood (Djankov et al., 2007; Houston et al., 2010). Examining the effect of stronger rights to information by creditors, Djankov et al. (2007) find that legal creditor rights and information-sharing institutions not only increase credit amount, but also reduces borrowers’ bankruptcy later on. On the contrary, using a sample of approximately 2,400 banks in 69 countries, Houston et al. (2010) show that stronger creditor rights tend to promote greater bank risk taking, while information sharing among creditors beneficially leads to higher bank profitability and lower bank risk. Hung Son et al., Cogent Business & Management (2020), 7: 1851856 https://doi.org/10.1080/23311975.2020.1851856 Page 3 of 15 Information-sharing bureaus are expected to theoretically increase access to finance by promoting the development of the financial sector (Asongu et al., 2016; Triki & Gajigo, 2014). Asongu et al. (2016) examine the relationship between information sharing and financial development in African countries, and show differing findings about the effects of information-sharing bureaus on financial development in terms of depth, efficiency, activity and size. Triki and Gajigo (2014) investigate: (1) the effects of public and private credit registries on firms’ access to finance and (2) the effect of public credit registries’ design on the obstacles in access to finance. Their findings show that (1) access to finance is on average better in economies with more private credit bureaus (PCBs), compared to countries with more public credit registries (PCRs) or countries with no presence of both, and (2) there are considerable differences in firms’ access to finance among countries with PCRs. 2.2. Corruption and financial development “Sand the wheels” hypothesis suggests that corruption negatively affects economic activities. Mauro (1995) shows that corruption degrades the quality of private investment and lowers economic growth. Corruption has a significant negative effect on the efficiency of public expenditure, the accumulation of human capital, foreign direct investment, and the effectiveness of international aids, thus implying a detrimental effect on economic growth (Cieslik & Goczek, 2018; Grundler & Potrafke, 2019; Mo, 2001; Murphy et al., 1993; Shleifer & Vishny, 1993; Wei, 2000). The impact of corruption on the financial system is scantily researched, compared to the corruption-growth nexus. For the financial system, corruption may also have an adverse effect because it can undermine the integrity of the banking system and provide incentives for banks to accept more risks. For example, Khwaja and Mian (2005) and Charumilind et al. (2006) show that firms that own links to officials/politicians will be able to attain bank loans but those firms are more likely to end up with higher default rate, generating high risks for the banks and triggering financial crises. Consistently, M. Chen et al. (2015) show that corruption will increase banks’ risk-taking. Corruption is also a cause for the rise in capital costs (M. Chen et al., 2015; Ciocchini et al., 2003). Akins et al. (2015) show that banks with a higher governmental stake and/or those covered in deposit insurance scheme fail to reduce the adverse impact of corruption in lending activities even when they can identify the risk of capital loss. The quality assets of the banking system will be affected by the inefficient allocation of bank funds. Bougatef (2015) provides evidence that corruption aggravates the problem of impaired financing. Park (2012) and Tran et al. (2020) find that the banking system is a channel that transfers the impact of corruption on economic growth by considering nonperforming loans that are negatively affected by corruption. Cooray and Schneider (2018) examine the relationship between corruption and financial sector development. They find evidence to support the “sand the wheels” hypothesis, that is a decline in corruption is associated with higher levels of financial sector development. The alternative view is that corruption “greases the wheels” of economic activities; in other words, corruption might improve economic development. For instance, efficient projects will be facilitated further by bribing politicians and banks to get credit approvals (see Mauro (1995) for a similar “speed money” argument). Y. Chen et al. (2013) find that bribery enables more productive firms to be granted larger loans. Toader et al. (2017) show that in countries with higher levels of corruption, banks could increase their stability if they implement rigorous corporate governance practices. That view is consistent with the argument that corruption could help counteract bad policies, thereby promoting economic activity in countries with weak institutions (Dreher & Gassenbner, 2013; Johnson, 1975; Lef, 1964; Leys, 1965; Nye, 1967; Wedeman, 1997). In summary, the literature tends to suggest the positive effect of information sharing on financial development, thanks to its ability to tackle information asymmetry. Meanwhile, even though corruption can have both encouraging and detrimental effects on financial development, the findings from previous studies in general support the “sand the wheels” hypothesis. This leads to our following testable hypotheses: Hung Son et al., Cogent Business & Management (2020), 7: 1851856 https://doi.org/10.1080/23311975.2020.1851856 Page 4 of 15 Hypothesis 1: information sharing is positively associated with financial development. Hypothesis 2: corruption is negatively associated with financial development. According to Bermpei et al. (2020), there are several reasons why public corruption could negatively affect lending activity. First, corruption, with its characteristics of illegality and secrecy, can increase the information asymmetry between borrowers and banks (Shleifer & Vishny, 1993). As a consequence, firms in the region with high corruption could become more clandestine and less information is available about them. This could be seen as a defensive move in an effort to guard the firms’ assets against the expropriation by public officials. However, it could also be a result of managers concealing their participation in corruption-related behavior. Empirical studies such as Dass et al. (2016), Dass et al. (2018), and Xu et al. (2019) confirm that firms in the region with higher levels of corruption tend to have lower information transparency. Corruption could also increase information asymmetries between borrowers and lenders through the establishment of a culture that accepts unethical behavior, or create more uncertainty about the cash-flows, or firms’ ability to honor the loans (Bermpei et al., 2020). Therefore, it is expected that credit information sharing should weaken the negative influence of corruption on bank lending: information-sharing bureaus should provide much-needed information about the true financial status, thus mitigating the association between corruption and information asymmetry between borrowers and lenders. This leads us to our third testable hypothesis as follows: Hypothesis 3: credit information sharing weakens the negative influence of corruption on financial development. 3. Data and methodology 3.1. Data This paper investigates 120 countries with data from the Financial Development and Structure Database and World Development Indicators (WDI) of the World Bank for the period 2004 to 2017. The chosen period is constrained by data availability. Specifically, the data on credit information sharing (PCBs and PCRs) from the World Bank are only available from the year 2004. In the meantime, the Financial Development and Structure Database is only updated until 2017. We use the following proxies to gauge different aspects of financial development, in line with the previous literature (Asongu & Nwachukwu, 2017; Asongu et al., 2016; Demirguc-Kunt & Maksimovic, 1996; King & Levine, 1993; Levine & Zervos, 1998): (1) FDGDP, the ratio of liquid liabilities to GDP, which is a measure of the size of the financial sector relative to the economy; (2) Two measures of financial intermediation activity are used: the ratio of domestic credit provided by the banking sector to GDP (BSD) and the ratio of domestic credit provided by the financial sector to GDP (FSD); (3) Nonperforming loans to total gross loans (NPL), which is a measure of financial sector efficiency. A lower level of non-performing loans reflects higher financial sector efficiency. Following previous studies (e.g. Asongu & Nwachukwu, 2017; Asongu et al., 2016; Barth et al., 2009; Triki & Gajigo, 2014), credit information sharing is measured with public credit registries and private credit bureaus as a percentage of adults covered. Our main measure of cross-country corruption is the International Country Risk Guide (ICRG) corruption index. The ICRG index ranges from 0 (totally corrupt) to 6 (not corrupt). As an alternative measurement of corruption, we use the Control of Corruption index (CCI), which is established by the World Bank as a robustness test. CCI ranges from −2.5 to 2.5. Note that higher values of the corruption indices denote better control of corruption. Hung Son et al., Cogent Business & Management (2020), 7: 1851856 https://doi.org/10.1080/23311975.2020.1851856 Page 5 of 15 Five control variables are used in order to account for bias stemmed from variable omission, namely: GDP growth (GDP), financial freedom index (FINFREE), government expenditures, the rate of government expenditures over GDP (GOV), Trade openness, the ratio of the sum of total exports and imports to GDP (TRADE), rule of law (ROL). The choice of these variables is in accordance with the financial development literature (Asongu & Nwachukwu, 2017; Asongu et al., 2016; Cooray & Schneider, 2018; Herger et al., 2008). The positive relationship between economic growth, trade openness and financial development has been recorded in the literature (Herger et al., 2008; Levine & Zervos, 1998). Previous studies show that financial freedom positively relates to financial development through financial liberalization and economic freedom (Chinn & Ito, 2006; Hafer, 2013). High public spending can put constraints on the financial system of a country (Cooray & Schneider, 2018), that is, the private credit provided by the financial system could be lower due to larger government expenditures. The nexus between rule of law and financial development has been assessed by Dima et al. (2018) who have found a positive relationship. 3.2. Methodology To verify the effects of credit information sharing and corruption on financial development, i.e. hypotheses 1 and 2, we use the model as in Equation (1). FDi;tþ1¼α0þα1:PCBi;tþα2:PCRi;tþα3:CORRi;tþβXi;tþεi;t(1) FDi;tþ1¼γ0þγ1:PCBi;tþγ2:PCRi;tþγ3:CORRi;tþγ4:PCBxCORRi;t þγ5:PCRxCORRi;tþθXi;tþui;t (2) where FD is the financial development of a country; PCB, private credit bureaus; PCR, public credit registries; CORR is the corruption index; X is the vector of five control variables. The interaction terms PCBxCORR and PCRxCORR are entered (model 2) to examine hypothesis 3. To estimate the models, pooled OLS is employed to with robust standard errors to control for heteroskedasticity within the panel data. We also use the one-order lead value of the dependent variable, i.e. the period of proxies of financial development is one year behind that of explanatory variables, to tackle the potential endogeneity associated with the two-way relationship between the dependent and independent variables. As the number of the units is approx. 10%percent of the observations, the use of a fixed effects model will lead to a huge loss of a degree of freedom. Furthermore, the use of pooled OLS is in line with several research works on financial developments including Asongu et al. (2019), Hafer (2013), and Djankov et al. (2007). 4. Empirical results 4.1. Descriptive statistics Table 1 provides descriptive statistics on the value of the variables in the paper. The average ratio of liquidity liabilities to GDP (FDGDP) is 68.5%. Domestic credit provided by the banking sector to GDP (BSD) is 52.6%, while the ratio of domestic credit provided by the financial sector to GDP (FSD) for the period examined is 57.2%. The mean value of non-performing loans to total gross loans (NPL) is about 6.18%. Our variables of interest, PCB, Private credit bureau coverage (% of adults), and PCR, Public credit registry coverage (% of adults), recorded a mean of 32.8% and 10.9%, respectively. The corruption index ranges from 0 (totally corrupt) to 6 (not corrupt). The mean value of the ICRG (CORR) corruption index in the sample is 2.69. Table 2 reports pairwise correlation coefficients between variables in the model. The three proxies of the financial sector (FDGDP, BSD, FSD) are positively correlated with the credit information sharing; which suggests that credit information sharing increases the levels of private-sector credit availability and liquid liabilities. The financial sector efficiency (NPL) is negatively correlated Hung Son et al., Cogent Business & Management (2020), 7: 1851856 https://doi.org/10.1080/23311975.2020.1851856 Page 6 of 15 with credit information sharing, suggesting that increasing information sharing reduces the level of bad loans. Three proxies of the financial sector (FDGDP, BSD, FSD) are positively associated with the corruption index (CORR); in other words, lower levels of corruption are positively associated with higher levels of private-sector credit availability and liquid liabilities. Financial sector efficiency (NPL) is negatively correlated with the corruption index, suggesting that corruption increases nonperforming loans. 4.2. Regression results This section presents the results of the estimation of the models presented in section 3. Table 3 provides regression results on the impact of credit information sharing and corruption on financial development. The results in Column (1) show that PCB is significantly and negatively linked to the size of the financial sector (FDGDP), whereas the effect of PCR is not significant. From columns (2) and (3), we find that PCB and PCR have a positive impact on the financial intermediation activity, suggesting that information-sharing bureaus promote credit availability. The more positive coefficients of PCR relative to that of PCB suggest that PCR may play a more significant role compared to PCB for financial development. Finally, Column (4) shows that corruption enhances the ratios of non-performing loans, thus leading to a higher likelihood of a financial crisis. Both PCB and PCR have negative effects on financial sector efficiency, with the negative magnitude of the latter higher on the dependent variable. Regarding the corruption factor (CORR), the results indicate that private-sector credit availability increases as a country becomes less corrupt. Those results provide evidence that informationsharing bureaus reduce non-performing loans or increase the financial sector efficiency. (2) Similarly, the effect of corruption on non-performing loans is negative and statistically significant, indicating non-performing loans decline as the corruption issue in a country is addressed. Those results provide evidence in support of the “sand the wheels” hypothesis (Hypothesis 2). For control variables, the results imply that a country with higher financial freedom, trade openness and stronger rule of law has positive and significant effects on financial sector size and financial intermediation activity. Higher financial freedom, trade openness and better rule of law lead to increases in liquid liabilities and private credit availability, as well as lower ratios of non-performing loans (only in case of financial freedom). In addition, economic growth leads to a decrease in liquid liabilities, private credit and non-performing loans. Table 1. Descriptive statistics Variable Obs Mean Std. Dev. Min Max FDGDP 1,852 68.508 80.752 2.623 938.722 BSD 1,843 52.618 40.758 0.350 218.944 FSD 1,852 57.236 46.678 0.350 260.704 NPL 1,363 6.183 6.697 0.100 54.541 CORR 1,893 2.696 1.167 0.500 6.000 PCB 1,519 32.833 36.994 0.000 100.000 PCR 1,519 10.901 19.656 0.000 100.000 GDP 1,887 3.961 5.172 −62.076 123.140 FINFREE 1,709 56.121 18.551 10.000 90.000 GOV 1,828 15.605 4.837 0.952 30.003 TRADE 1,862 89.797 60.040 0.167 442.620 ROL 1,881 0.149 0.986 −2.090 2.100 Source: Author’s calculation from research dataset Hung Son et al., Cogent Business & Management (2020), 7: 1851856 https://doi.org/10.1080/23311975.2020.1851856 Page 7 of 15 Dass, N., Nanda, V., & Xiao, S. C. (2016). Public corruption in the United States: Implications for local firms. The Review of Corporate Finance Studies, 5(1), 102–138. https://doi.org/10.1093/rcfs/cfv016 Dass, N., Nanda, V. K., & Xiao, S. C. (2018). Geographic clustering of corruption in the US. Working paper. https://papers.ssrn.com/sol3/papers.cfm?Abstract_ id=2981317 Demirguc-Kunt, A., & Maksimovic, V. (1996). Stock market development and financing choices of frms. World Bank Economic Review, 10(2), 341–369. https://doi. org/10.1093/wber/10.2.341 Dima, B., Barna, F., & Nachescu, M. (2018). Does rule of law support the capital market? Economic ResearchEkonomska Istraživanja, 31(1), 461–479. https://doi. org/10.1080/1331677X.2018.1432371 Djankov, S., McLiesh, C., & Shleifer, A. (2007). Private credit in 129 countries. Journal of Financial Economics, 84(2), 299–329. https://doi.org/10.1016/j.jfineco.2006.03.004 Dreher, A., & Gassenbner, M. (2013). Greasing the wheels? The impact of regulation and corruption on firm entry. Public Choice, 155(3–4), 413–432. https://doi. org/10.1007/s11127-011-9871–2 Grundler, K., & Potrafke, N. (2019). Corruption and economic growth: New empirical evidence. European Journal of Political Economy, 60(2019), 101810. https://doi.org/10.1016/j.ejpoleco.2019.08.001 Guérineau, S., & Léon, F. (2019). Information sharing, credit booms and financial stability: Do developing economies differ from advanced countries? Journal of Financial Stability, 40(2019), 64–76. https://doi.org/ 10.1016/j.jfs.2018.08.004 Hafer, R. W. (2013). Economic freedom and financial development: International evidence. Cato Journal, Cato Institute, 33(1), 111–126. Herger, N., Hodler, R., & Lobsiger, M. (2008). What determines financial development? Culture, institutions or trade. Review of World Economics, 144(3), 558–587. https://doi.org/10.1007/s10290-008-0160-1 Houston, J. F., Lin, C., Lin, P., & Ma, Y. (2010). Creditor rights, information sharing, and bank risk taking. Journal of Financial Economics, 96(3), 485–512. https://doi.org/10.1016/j.jfineco.2010.02.008 Ivashina, V. (2009). Asymmetric information effects on loan spreads. Journal of Financial Economics, 92(2), 300–319. https://doi.org/10.1016/j.jfineco.2008.06.003 Jappelli, T., & Pagano, M. (2002). Information sharing, lending and defaults: Cross-country evidence. Journal of Banking & Finance, 26(10), 2017–2045. https://doi. org/10.1016/S0378-4266(0100185–6 Johnson, E. (1975). An economic analysis of corrupt government, with special application for less developed countries. Kyklos, 28(1), 47–61. https://doi.org/10. 1111/j.1467-6435.1975.tb01933.x Khwaja, A. I., & Mian, A. (2005). Do lenders favor politically connected firms: Rent provision in an emerging financial market. The Quarterly Journal of Economics, 120(4), 1371–1411. https://doi.org/10.1162/ 003355305775097524 King, R., & Levine, R. (1993). Finance and growth: Schumpeter might be right. Quarterly Journal of Economics, 108(3), 717–738. https://doi.org/10.2307/2118406 Lef, N. (1964). Economic development through bureaucratic corruption. American Behavioral Scientist, 8(3), 337–341. https://doi.org/10.1177/000276426400800303 Levine, R., & Zervos, S. (1998). Stock markets, banks and economic growth. American Economic Review, 88(3), 1169–1183. http://www.jstor.org/stable/116848 Leys, C. (1965). What is the problem about corruption? Journal of Modern African Studies, 3, 215–230 (Reprinted in). In A. J. Heidenheimer, M. Johnston, & V. T. LeVine Eds., Political corruption: A handbook. 1989 (pp. 51–66). Transaction Books. Mauro, P. (1995). Corruption and Growth. The Quarterly Journal of Economics, 110(3), 681–712. https://doi. org/10.2307/2946696 Mo, P. H. (2001). Corruption and economic growth. Journal of Comparative Economics, 29(1), 66–79. https://doi. org/10.1006/jcec.2000.1703 Murphy, K. M., Shleifer, A., & Vishny, R. W. (1993). Why is rent-seeking so costly to growth? American Economic Review, 83(2), 409–414.http://www.jstor.org/stable/ 2117699 Nye, J. (1967). Corruption and political development: A cost-benefit analysis. American Political Science Review, 61(2), 417–427. https://doi.org/10.2307/1953254 Park, J. (2012). Corruption, soundness of the banking sector, and economic growth: A cross-country study. Journal of International Money and Finance, 31(5), 907–929. https://doi.org/10.1016/j.jimonfin.2011.07.007 Pradhan, A. K., & Hiremath, G. S. (2019). External commercial borrowings by the corporate sector in India. Journal of Public Affairs, 20(1), 1–6.https://doi.org/ 10.1002/pa.1987 Pradhan, A. K., & Hiremath, G. S. (2020a). Do external commercial borrowings and financial development affect exports? Cogent Business & Management, 7(1), 1–22. DOI: 10.1080/23311975.2020.1796269 Pradhan, A. K., & Hiremath, G. S. (2020b). Why do Indian firms borrow in foreign currency? The Journal of Applied Economic Research, 14(2), 191–211.https:// doi.org/10.1177/0973801020904492 Shleifer, A., & Vishny, R. W. (1993). Corruption. The Quarterly Journal of Economic, 108(3), 599–617. https://doi.org/10.2307/2118402 Soedarmono, W., & Sitorus, D. (2017). Abnormal loan growth, credit information sharing and systemic risk in Asian banks. Research in International Business and Finance, 42(2017), 1208–1218. https://doi.org/10. 1016/j.ribaf.2017.07.058 Toader, T., Onofrei, M., Popescu, A., & Andrieș, A. M. (2017). Corruption and banking stability: Evidence from emerging economies. Emerging Markets Finance and Trade., 54(4), 591–617. https://doi.org/10.1080/ 1540496X.2017.1411257 Tran, H. S., Liem, N. T., & Nguyen, V. K. (2020). Corruption, nonperforming loans, and economic growth: International evidence. Cogent Business & Management, 7(1), 1735691.DOI: 10.1080/ 23311975.2020.1735691 Triki, T., & Gajigo, O. (2014). Credit bureaus and registries and access to finance: New evidence from 42 African countries. Journal of African Development, 16(2), 73–101.https://EconPapers.repec.org/RePEc:afe:journl: v:16:y:2014:i:2:p:73-101 Wedeman, A. (1997). Looters, rent-scrapers, and dividend-collectors: Corruption and South Korea, and the Philippines. Journal Of Developing Areas, 31(4), 457–478. https://www.jstor.org/stable/4192713 Wei, S. (2000). How taxing is corruption on international investors? Review of Economics and Statistics, 82(1), 1–11. https://doi.org/10.1162/003465300558533 Xu, H., Dao, M., & Wu, J. (2019). The effect of local political corruption on earnings quality. Review of Quantitative Finance and Accounting, 53(2), 551–574. https://doi. org/10.1007/s11156-018-0758-x Hung Son et al., Cogent Business & Management (2020), 7: 1851856 https://doi.org/10.1080/23311975.2020.1851856 Page 14 of 15 © 2020 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. 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