DO BANKING SYSTEM TRANSPARENCY AND COMPETITION AFFECT NON-PERFORMING LOANS IN CHINESE BANKING SECTOR

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1 DO BANKING SYSTEM TRANSPARENCY AND COMPETITION AFFECT NON-PERFORMING LOANS IN CHINESE BANKING SECTOR Usman Bashir Researcher, School of Management, University of Prof. Yugang YU Professor, School of Management, University of Muntazir Hussain Researcher, School of Management, University of Ahmed Ali Researcher, School of Management, University of Abstract: Increasing importance of transparency practices and improving bank competition status in china remain rarely explored in NPL literature. Thus, purpose of this study is to examine the banking system transparency and competition along with macroeconomic and bank specific variables as determinates of NPL. We use the bank fixed effect model for Chinese banks using annual data for the period of 2000 to Our results indicate that higher transparency in Chinese banking system leads to decrease the poor quality assets and higher competition increases NPL. Moreover we find mixed results in context to macroeconomic and bank specific variables. Our study have practical implications for risk management practices and macro prudentional policies. JEL Classification: G21, G28, C23 Keywords: Banking system transparency, nonperforming loans, determinants, competition INTRODUCTION Research studies on non-performing loans reported its adverse impact on banking economy and therefore emphasized minimization of non-performing loans to keep sound financial stability of banking system (Ghosh, 2015, Barseghyan, 2010, Zeng, 2012) Proportion of non-performing loans in banking sector is dependent, to a large extent, on the policy making of regulators (Ghosh, 2015). Consistent and progressive reforms, such as entrance in WTO and implementation of Basel Accord, in Chinese banking sector have fabricated renewed outlook both of banks competitiveness and transparency practices. However, empirical results of such reforms and its impact on non-performing loans are remarkably missing from the literature. Current study investigates the impact of transparency practices, competition along with macroeconomic and bank specific variables on nonperforming loans of Chinese banks. The recent world financial crisis highlighted the importance of transparency practices in the banking sector. Transparency practices enhance banks financial stability(nier, 2005). Bellver and Kaufmann (2005) argue that transparency is revelation of banking operations related information to the extent that enables market agents to make appropriate assessment about bank s stability and fundamental activities. In recent years, scientific research on transparency have gained significant attention in quite a few world economies (e.g. (Chen and Hasan, 2006, Leuz and Wysocki, 2008, Landier and Thesmar, 2011, Moreno and Takalo, 2012). 89

2 Implementation of Basel Accord in Chinese banking sector since 2000 revitalized importance of mandatory disclosure. Higher transparency practices work in two directions; on one side it enables depositors and regulators to keep a check on bank lending operations, on the other side, banking staff senses greater responsibility of risk if approved loans do not perform well. Despite the fact that Basel Accord has been fully implemented in Chinese banking sector, academic assessment on transparency and non-performing loans in Chinese banking industry remain scarce. Therefore, the first purpose of this research is to examine the effect of transparency practices on non-performing loans. The literature on franchise value suggests that competition erodes franchise value of banks by engaging in riskier loaning practices (Jiménez et al., 2013). High competition threatens market concentration that compels banks to pursue more risky lending practices in order to maintain profitability. Such loaning practices enhance the chances of nonperforming loans. Consequently, greater competition in banking sector hinders stability of banking systems. Contrary to this, low competition enhances profits as banks consider only high-quality borrowers " skim the cream" and avoid potentially riskier customers that ultimately reduces the proportion of non-performing loans and improves banks financial stability(beck et al., 2006). Scientific inquiry on banking competition in China surprisingly remained absent from academic literate(fungáčová et al., 2013). A plausible reason for such paucity is the existence of non-competitiveness in Chinese banking market prior to reform implementation. However, profound buildup reforms made in last two decades have remarkably changed Chinese banking system and facilitated greater competitiveness. For example, in the late 1990s, the inception of recapitalization process for nonperforming loans; in early 2001, joining WTO embarked entrance of foreign banks and implementation of Basel Accord. A clear indication of increasing competition can be deduced from the market concentration status of five major banks of China 1 that was reduced from 83.41% (2001) to 45.86% (2013) 2. Current situation of Chinese banking sector noticeably indicates emerging trends of competitiveness, thus, the second purpose of this research is to investigate the impact of competition and non-performing loan. Following empirical literature we have also used macroeconomic and bank specific determinants. Such determinants are taken from recent empirical studies see (Dimitrios et al., 2016, Ghosh, 2015, Louzis et al., 2012). Theoretical Background Transparency practices are referred to the set of information disclosure requirement lied down by Basel II and third Pillar of Basel II. It is observed that depositors avoid keeping funds in banks with more risk oriented practices and tend to switch to the banks with greater reliability. In this case, funds are redistributed from risky banks to more reliable banks (Semenova, 2012). Non-Performing Loans are the one of the prime indicators of banks riskiness (Jiménez et al., 2013). Industrial-organizational theory suggests that transparency leaves little space for organizational members to make any haphazard decisions that might yield negative outcomes as they have to make mandatory disclosure afterward. Consequently, lending practices set direction in more efficient manners that the chances of riskier loaning start declining. In order to streamline banking sector practices, (OECD, 1998) report on Australian banking sector suggests mandatory disclosure practices to curb fraud and unfair practices. (Barth et al., 2009) reported that information sharing practices reduce chances of fraudulent lending practice in banks. Earlier work on competition and loaning practices e.g. (Marcus, 1984, Chan et al., 1986)reported that increased competition erodes banks profits by losing high-quality borrowers and forces banks to consider potentially riskier customers which ultimately escalate non-performing loans. Similar results reported in later research that higher competition threatens banks stability by pursuing greater proportion of lending to low-quality borrowers (Marquez, 2002). On the other hand, in the case of lower competition banks have relatively secured stability and lesser chances of systematic crisis. Beck et al. (2006)conducted a study on 69 countries for the period of 20 years and found results that higher concentrated banking systems are 90

3 more stable with lower chances of systemic crisis. To complete our empirical model we have also added macroeconomic and bank specific determinants according to recent literature (Dimitrios et al., 2016, Ghosh, 2015, Louzis et al., 2012). Methodology Our study reexamines the determinants of nonperforming loan for Chinese banking system. we use the variables which are classified into the following categories, bank specific determinants, macroeconomic determinants, bank competition and banking system transparency in the following econometric specification. we use six different model specifications based on our variables classification where is the constant term and is a vector for banking industry specific variables for bank i at time period t, are the macroeconomic conditions and determinants for china at time t where as and are the Transparency Index, Lerner Index and Boone Indicator for Chinese banking sector at time t. we are first to introduce the use TI and bank competition variable as determinants of nonperforming loan which lacks in the empirical literature regarding determinants of NPLs. we use mandatory information disclosure as a proxy for banking system transparency leading us to hypothesize that higher the transparency reduces poor asset quality. The data to construct the transparency index is taken from the WB Bank Regulation and Supervision Survey following the methodology of (Semenova, 2012, Andrievskaya and Semenova, 2014). In competition-risk nexus literature the NPLs are used as proxy for measuring the bank credit risk. Such relationship can be found in study of (Jiménez et al., 2013). Bank Competition has not been used directly as a determinant of NPL. We have used annual data from 2000 to 2014 by constructing unbalanced panel. The number of observation varies in different model depending on the number of variable included in each specification. The bank level data is taken form Bank Scope while country level data has been obtained form World Bank and Federal Reserve Bank of St. Louis. The brief construction of variables is given in table below. Table 1: Description of variables Variable Name Measurement Expected Sign Credit Growth Total loans divided by + total assets Bank Return of total assets - Profitability ratio (ROA) Cost to Income Cost to Income ratio + Ratio Size Natural Log of Total assets + Net Interest Interest Incomeinterest - Margin expense / total assets Inflation Rate Consumer Price Index + (CPI) Unemployment rate Unemployment rate + Real Interest Bank Prime Loan rate + Rate charged on loan to business/firms Federal Public % changes in federal + Finance budget or surplus as percent of GDP Real GDP Real GDP growth % - growth % Transparency Constructed index - Index following method of reference (Semenova, 2012) Lerner Index Calculated learner + Taken from Federal 91

4 Reserve Bank of St. Louis Boone Indicator Calculated Boone + Indicator Taken from Federal Reserve Bank of St. Louis We have tried to estimate the (Arellano and Bond, 1991) difference GMM method to estimate six models in Stata. Unfortunately, our lagged dependent variable is not statistically significant. We have used fixed effect regression to estimate these six models instead of difference GMM. In empirical literature, in such situation, scholar argues that difference GMM is not beneficial when lagged dependent variable is not statistical significant. So simple fixed effect model can produce more robust results (Fungáčová et al., 2014). Table 2: Bank Effect Fixed Model results for Chinese banking sector Variable Spec I Spec II Spec III Spec IV Spec V Spec VI INF (0.943)*** (1.014)*** (1.003)*** (1.128)*** (1.146)*** UNEMP (1.241) (1.202) (1.192)** (1.396)*** (1.303)* RIR (0.593)*** (0.650)*** (0.639)*** (0.711)*** (0.721)*** FISCAL (0.874)* (0.893) (0.877)* (1.084) (1.218)* RGDP (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** LG (8.431) (8.345)** (8.178)** (8.795)** (8.956)** ROA (1.838)*** (1.961)*** (2.182) (2.230) (2.255) CIR (0.061) (0.061) (0.060) (0.070) (0.070) SIZE (0.862) (1.754)*** (1.827)*** (2.243)*** (2.270)*** NIM (1.407) (1.437) (1.444) (1.511) (1.517) TI (0.849)*** LERNER (5.933)* BI ( ) Constant (5.811) (18.887) (37.020)*** (37.810)*** (47.098) (48.203)*** R-sq

5 Joint F-Test (Prob Banking > F) years Fixed Effect yes Yes yes yes yes yes Annual data for Chinese banking sector from 2000 to The table presents Bank fixed effect model, Spec I to VI are the specification of estimations on the basis of our criterion variables. Inf is the Inflation, UNEMP is the Unemployment, RIR is the Real Interest Rate, RGDP is the Real Gross Domestic Product, LG is the Loan Growth, ROA is the Return on Asset Ratio, CIR is the Cost Income Ratio, NIM is the Net Interest Margin, TI is Transparency Index, LERNER is the Lerner Index and BI is the Boone Indicator. *,**, and *** refer to the p-values regarding level of significant at 0.10, 0.05, and 0.01 levels, respectively. In parentheses we have standard errors. Results The results output for our specifications are given in Table 1. As we can see that INF has a positive and significant coefficient in our all econometric specifications, suggesting that high inflation causes NPLs to increase which is in line with the theory that rise in inflation can cause domestic units and companies cost to increase, lowering their ability to pay debts (Škarica, 2014). RIR is positively significant in all specifications at 1% which indicates that higher real interest rates can lead to higher debt cost which could reduce the debt servicing ability of borrowers leading to defaults. We have week negative but significant effect of FISCAL on NPLs in specification I,IV and VI. This relationship suggests that increase fiscal deficit (expenditure) is negatively affecting NPLs meaning that increase in government expenditure leads to lowering of NPLs. It is not surprising that Chinese government had injected billions RMB to help the bank in NPL problems. Such evidence capital injections can be found in study of (Zhang et al., 2016, Okazaki, 2007). RGDP has significant negative relationship with NPLs in all specifications suggesting that increase in GDP is reduces NPLs which is in line with the determinants of NPL's literature. UNEMP has negative coefficient in all specifications which is not according to economic rationale. The coefficient for LG is significant in specification III, to VI indicating that banks while increasing the loan supply reduce their loan spreads and lowering their credit policy standards which leads to higher NPLs (Keeton, 1999). The ROA coefficient is significant and in accordance with (Berger and DeYoung, 1997) bad management hypothesis for our specification II and III which relates to those banks earning high profitability have less inclination towards risky lending. Size is positively significant in all our specifications. Larger the bank higher is the NPL in case of China. Chinese largest banks are either government owned or protected by government in case of bad situations. Such banks in China have higher incentive to increase debt to equity ratios beyond regulatory and market discipline controls which in turn increase NPLs for similar results on different economies (Ghosh, 2015, Stern and Feldman, 2004). CIR and NIM are not significant in Chinese banking industry context. The coefficient of transparency index is significant at 1% percent and negatively related to NPLs. It suggests that increased transparency leads to lowering of NPLs. In Chinese banking sector Basel Accord III was implemented in 2007 which emphasizes on mandatory disclosure requirements which is one of the reason that justify our economic rationale for such results. Similarly, our novel variable Lerner is statistically positive and significant. It suggests that higher competition in banking market induce bank to take risky credit policies which results in higher NPLs. Such evidence can be found in study of(jiménez et al., 2013). Boone indicator is not a statistically significant measure of competition for Chinese banking industry. Conclusion This study empirically tested the determinants of NPL for Chinese banking system. We have found that higher banking system transparency reduces NPL; whereas, high competition in banking market increases NPL. Moreover, Macroeconomic determents have significant effect on NPL especially inflation, real interest rate and real gross domestic product. lastly, Bank specific 93

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