Wahyoe Soedarmono

Amine Tarazi

Gary S Monroe

Dominic Gasbarro

Keywords: Loan loss provisions, loan growth, information sharing, borrower legal rights JEL classification: G10, G14, G21, G28

In this paper, we examine the role of information sharing and borrower legal rights in affecting the procyclical effect of bank loan loss provisions. Based on a sample of Asian banks, our empirical results highlight that higher non-discretionary provisions reduce loan growth and hence, non-discretionary provisions are procyclical. A closer investigation suggests that better information sharing through public credit registries managed by central banks, but not private credit bureaus managed by the private sector, might substitute the role of a dynamic provisioning system in mitigating the procyclicality of non-discretionary provisions. We also document that higher discretionary provisions in countries with stronger legal rights of borrowers may temper the procyclical effect of non-discretionary provisions. However, these findings only hold for small banks. This suggests that the implementation of a dynamic provisioning system to mitigate the procyclicality of non-discretionary provisions is more crucial for large banks, because such procyclicality cannot be offset by strengthening credit market environments through better information sharing and legal rights of borrowers.

Introduction

During the last three decades, financial crises in both developed and developing countries were mostly preceded by strong macroeconomic performance in an environment with poor bank risk management (e.g., the 1980 US saving and loan crisis, the 1994/1995 Mexican crisis, the 1997/1998 Asian crisis, the 1998 Russian crisis, and the 2008 credit crisis that led to a prolonged global economic downturn). Such developments suggest the increasing need for a sound credit risk management in banking to limit the riskiness of banks and prevent a procyclical effect during economic downturns. The procyclical effect of credit risk management mainly occurs due to the fact that banks tend to underestimate credit risk during cyclical upturns, but overestimate it during cyclical downturns [START_REF] Altman | The Link between Default and Recovery Rates: Theory, Empirical Evidence, and Implications[END_REF]. Consequently, these actions reduce loan availability and deepen economic recessions.

Likewise, overcoming the procyclicality of bank credit risk management has become one of the key issues in the new Basel Accords, particularly related to bank capital regulation. Meanwhile, studies of banks' procyclical behavior have been conducted through two major research avenues. The first strand of literature focuses on the impact of macroeconomic fluctuations on bank capital buffers to examine whether capital buffers are procyclical over the business cycle. In this regard, banks are substantially required to fulfill minimum capital adequacy ratios in order to cope with credit risk.

Since capital requirements are based on risk, banks tend to increase capital buffers and reduce loans during a cyclical downturn when impaired loans materialize. The second strand of literature explores the effect of macroeconomic fluctuations on loan loss provisions and how provisioning affects bank lending behavior.

In spite of a growing literature focusing on the first strand (e.g., [START_REF] Ayuso | Are capital buffers pro-cyclical? Evidence from Spanish panel data[END_REF][START_REF] Borio | Procyclicality of the financial system and the financial stability: issues and policy options[END_REF][START_REF] Estrella | The cyclical behaviour of optimal bank capital[END_REF][START_REF] Jokipii | The cyclical behaviour of European bank capital buffers[END_REF], very limited attention has been given to the second strand of research, particularly regarding the link between loan loss provisions and bank lending. Several studies highlight the presence of the procyclicality of loan loss provisions over the business cycle (e.g. [START_REF] Laeven | Loan loss provisioning and economic slowdowns: too much, too late?[END_REF][START_REF] Bikker | Bank provisioning behaviour and procyclicality[END_REF][START_REF] Craig | Sources of procyclicality in East-Asian financial systems[END_REF], but only [START_REF] Bouvatier | Banks' procyclical behaviour: does provisioning matter?[END_REF] and [START_REF] Bouvatier | Effects of loan loss provisions on growth in bank Lending: Some international comparisons[END_REF] assess how provisioning affects bank lending.

While [START_REF] Bouvatier | Banks' procyclical behaviour: does provisioning matter?[END_REF] focus on European banks, [START_REF] Bouvatier | Effects of loan loss provisions on growth in bank Lending: Some international comparisons[END_REF] extend their previous study by incorporating a sample of banks from emerging markets. By partitioning loan loss provisions into discretionary and non-discretionary loan loss provisions, [START_REF] Bouvatier | Banks' procyclical behaviour: does provisioning matter?[END_REF] document that non-discretionary loan loss provisions exacerbate a procyclical effect because higher non-discretionary provisions reduce bank loan growth. In contrast, discretionary loan loss provisionsparticularly related to income smoothing behaviorhave no significant impact on bank loan growth. In this sense, the adoption of a dynamic provisioning system is desirable, because it allows banks to generate higher statistical provisions under a dynamic provisioning systems to complement discretionary provisions, which cannot directly offset the procyclical effect of non-discretionary loan loss provisions. Moreover, [START_REF] Bouvatier | Effects of loan loss provisions on growth in bank Lending: Some international comparisons[END_REF] further document that the procyclicality of nondiscretionary loan loss provisions in banking is more pronounced in emerging markets.

In parallel, another strand of literature advocates greater information sharing activities to strengthen financial intermediation. Higher lending activities and lower credit risk can be observed in countries where public and private credit bureaus are of better quality [START_REF] Jappelli | Information sharing, lending and defaults: Cross country evidence[END_REF]. [START_REF] Love | Credit reporting and financing constraints[END_REF] also highlight the role of private credit bureaus and public credit registries in reducing firms' financing constraints. Specifically, private credit bureaus have greater contribution than public credit to alleviate firms' financing constraints. [START_REF] Brown | Information sharing and credit: Firm-level evidence from transition countries[END_REF] also document that higher information sharing increases bank lending through a reduction in intermediation cost. [START_REF] Houston | Creditor rights, information sharing and bank risk taking[END_REF] further find that stronger creditor legal rights are associated with higher economic growth. Against this backdrop, our contribution is twofold.

Firstly, given that better information sharing and legal rights might strengthen financial intermediation, this paper is the first to explore whether information sharing and strength of borrower legal rights can mitigate the procyclicality of bank loan loss provisions. Hence, we highlight whether the adoption of a dynamic provisioning system can be substituted by better credit information sharing and borrower legal rights.

Secondly, we specifically assess whether the procyclicality of loan loss provisions, as well as the role of information sharing and legal rights in affecting such procyclicality, differs between large and small banks. Bank size is an important dimension in bank credit risk management because large banks are more prone to be "too big to fail" and to have moral hazard problems [START_REF] Mishkin | How big a problem is too big to fail[END_REF][START_REF] Kane | Incentives for banking megamergers: What motives might regulators infer from event-study evidence?[END_REF]. During economic boom periods, large banks can arguably generate lower loan loss provisions to anticipate unexpected credit risk, because they believe that the government can rescue them in case of failure.

As such, the role of bank size in affecting the procyclicality of loan loss provisions warrants further examination.

To assess these issues, we focus on emerging markets in Asia for at least three reasons. First, bank credit is the predominant source of financing for private sector businesses in Asian countries and, therefore, unsound credit risk management in banking can exacerbate financial disintermediation during a cyclical downturn [START_REF] Adams | Emerging East Asian banking system: Ten years after the 1997/98 crisis[END_REF]Angkomkliew et al., 2009). Second, conflicts of interest between bank regulators and investors are also apparent in Asian banks when banks rely on loan loss reserves to cope with credit risks. [START_REF] Agusman | Accounting and capital market measures of risk: Evidence from Asian banks during 1998-2003[END_REF] document that higher loan loss reserves reduce bank stock returns. In other words, bank regulations related to loan loss reserves and provisioning are subject to conflicts of interest between investors and bank regulators, at least in the Asian context. Hence, assessing the issue of loan loss provisions in Asian banks is relevant to examine how bank regulations and investors' interests might be harmonized, particularly regarding the adoption of a dynamic provisioning system that increases loan loss provisions during economic boom periods. Third, the loan loss provisioning system varies across Asian countries. Although the procyclicality of loan loss provisions became a major issue after the 1997 crisis, the implementation of the dynamic provisioning system are still limited in Asian countries. 1The rest of this paper is structured as follows. Section 2 reviews the existing literature on the use of loan loss provisions for bank credit risk management and its implications. Section 3 describes our data and research method. Section 4 discusses our empirical results and presents the robustness checks, while Section 5 concludes the paper.

Related review of literature and research focus

Banks focus on the use of loan loss provisions as a prudential device to manage credit risk. However, loan loss provisions can be procyclical with the business cycle because loans are likely to default during a cyclical downturn. This, in turn, increases banks' risk aversion that boosts loan loss provisions [START_REF] Altman | The Link between Default and Recovery Rates: Theory, Empirical Evidence, and Implications[END_REF]. From an accounting perspective, there are two types of provisions for bank credit risk: specific and general provisions (Cortavaria et al., 2000). While specific provisions address identified impaired loans through an increase in loan loss reserves, general provisions are associated with a broad assessment of possible future losses on the entire bank portfolio. As banks need to estimate general provisions, such provisions may be influenced by subjective judgements related to managers' discretionary behavior.

The literature documents that general provisions can be further partitioned into nondiscretionary and discretionary components. On the one hand, non-discretionary provisions cover expected credit risks and are considered as a backward-looking component (Whalen, 1994;[START_REF] Beaver | Discretionary behaviour with respect to allowances for loan losses and the behaviour of security prices[END_REF]. On the other hand, the discretionary component is associated with the use of loan loss provisions for managerial objectives. Specifically, the discretionary component is linked to three discretionary actions comprising capital management, income smoothing and signalling [START_REF] Ahmed | Bank loan loss provisions: A reexamination of capital management, earnings management and signaling effects[END_REF]Lobo and Yang, 2001;[START_REF] Kanagaretnam | Managerial incentives for income smoothing through bank loan loss provisions[END_REF][START_REF] Kanagaretnam | Joint tests of signaling and income smoothing through bank loan loss provisions[END_REF][START_REF] Kanagaretnam | Determinants of signaling by banks through loan loss provisions[END_REF]Anandarajan et al., 2007;[START_REF] Hasan | Determinants of the loan loss allowance: Some cross-country comparisons[END_REF].

The Basel definition of capital has emphasized that part of general provisions counts as capital. When loan losses are excessive during a cyclical downturn, increases in specific provisions can be inadequate to cover expected loan losses. Such loan losses can erode bank capital and may, in turn, adversely affect banks' incentive to grant new loans, exacerbating a cyclical downturn. This situation is often referred to as "capital crunch" and has been documented in the literature related to bank capital requirements (e.g., [START_REF] Bernanke | Credit crunch[END_REF]Peek and Rosengren, 1995).

Prior studies have documented that the bank provisioning system is procyclical in general. [START_REF] Laeven | Loan loss provisioning and economic slowdowns: too much, too late?[END_REF] point out that the procyclicality of loan loss provisions can be shown by a negative impact on loan loss provisions of higher loan growth, economic growth, or earnings. In a cross-country setting, [START_REF] Cavallo | Do banks provision for bad loans in good times? Empirical evidence and policy implications[END_REF] also find a negative link between economic growth and loan loss provisions. [START_REF] Bikker | Bank provisioning behaviour and procyclicality[END_REF] document similar evidence for OECD countries. In a single country setting, [START_REF] Arpa | The influence of macroeconomic developments on Austrian banks: implications for banking supervision[END_REF] document the procyclicality of bank loan loss provisions over the business cycle in Austria, while Fernandez de [START_REF] Fernandez De Lis | Credit growth, problem loans and credit risk provisioning in Spain[END_REF] and [START_REF] Pain | The provisioning experience of the major UK banks: a small panel investigation[END_REF] document similar results for Spanish and UK banks, respectively. Only [START_REF] Packer | Loan loss provisioning practices of Asian banks[END_REF], [START_REF] Angklomkliew | Issues and developments in loan loss provisioning: The case of Asia[END_REF] and [START_REF] Craig | Sources of procyclicality in East-Asian financial systems[END_REF] focus on Asian banks and, again, report identical results. Nevertheless, to the best of our knowledge, no prior research examines how loan loss provisions affect bank lending behavior in the Asian context.

Another strand of literature advocates a sound provisioning system in any regulations on bank capital requirements [START_REF] Cavallo | Do banks provision for bad loans in good times? Empirical evidence and policy implications[END_REF][START_REF] De | The financial cycle, factors of amplification and possible policy implications for financial and monetary authorities[END_REF]. This is because a sound provisioning system can avoid credit risk miscalculation in a cyclical downturn due to disaster myopia (Guttentag and Herring, 1984), herd behavior [START_REF] Rajan | Why bank credit policies fluctuate: a theory and some evidence[END_REF], or institutional memory hypothesis [START_REF] Berger | The institutional memory hypothesis and the procyclicality of bank lending behaviour[END_REF]. Fernandez de [START_REF] Fernandez De Lis | Credit growth, problem loans and credit risk provisioning in Spain[END_REF] propose a dynamic or statistical provisioning system that may solve procyclicality issues for Spanish banks. The statistical provisions are not intended to substitute a specific provision, but to complement the loan loss provisioning system. Specific and general provisions are estimated in line with traditional procedures, while the statistical provisions are calculated from the difference between expected loan losses and specific provisions [START_REF] Bouvatier | Banks' procyclical behaviour: does provisioning matter?[END_REF].

As the statistical provisions are estimated to anticipate risks due to business cycle fluctuations, the statistical provisions tend to increase during a cyclical upturn to anticipate a cyclical downturn in the future. Although the statistical provisions increase in a cyclical downturn, the funds obtained from "reserves" generated by the statistical provisions in the earlier period of economic boom can smooth bank profits and losses.

Consequently, incorporating the statistical provisions into a bank provisioning system can mitigate banks' incentives to grant new loans when expected credit risks are underestimated, particularly during a cyclical upturn. As long as banks can improve credit risk evaluation and profit management in their provisioning system, [START_REF] Borio | Procyclicality of the financial system and the financial stability: issues and policy options[END_REF], [START_REF] Mann | Dynamic provisioning: issues and application[END_REF], and [START_REF] Jiménez | Credit cycles, credit risk, and prudential regulation[END_REF] support Fernandez de [START_REF] Fernandez De Lis | Credit growth, problem loans and credit risk provisioning in Spain[END_REF] contention that the procyclicality of bank loan provisions can be resolved.

In order to assess the importance of implementing a dynamic provisioning system for European countries such as Spain, [START_REF] Bouvatier | Banks' procyclical behaviour: does provisioning matter?[END_REF] examine the impact of non-discretionary and discretionary provisions on bank loan growth in several stages. In the first stage, they examine the determinants of bank loan loss provisions (LLP). In doing so, they create a LLP model in which loan loss provisions are regressed on backward-looking indicators related to problem loans (e.g., non-performing loans, loanto-asset ratio, and annual GDP growth), and forward-looking indicators depicting capital management, income smoothing and signalling activities of banks. In the second stage, bank loan loss provisions are subsequently grouped into discretionary and nondiscretionary components. The discretionary component is computed as the fitted values of the LLP model in which forward-looking indicators become explanatory variables. In parallel, the non-discretionary component is computed as the fitted values of the LLP model in which backward-looking indicators become explanatory variables. In the third stage, they create a regression model to examine the impact of estimated discretionary and non-discretionary provisions on bank loan growth. They show that non-discretionary provisions negatively affect bank loan growth, while discretionary provisions have no statistically significant impact on bank loan growth. Hence, only non-discretionary provisions exacerbate the procyclical effect of loan loss provisions over the business cycle.

Building on their previous study, [START_REF] Bouvatier | Effects of loan loss provisions on growth in bank Lending: Some international comparisons[END_REF] modify the procedure to estimate non-discretionary provisions by incorporating an indicator of income smoothing. They argue that loan loss provisions might be used to smooth income as part of bank discretionary behavior, and this behavior might be important to offset a negative impact of non-discretionary provisions on bank loan growth. Their empirical results reveal that greater income smoothing only tempers the negative impact of nondiscretionary provisions on bank loan growth, but such a negative impact remains apparent. In this regard, the use of a statistical provision or a dynamic provisioning system is desirable because greater income smoothing is not enough to mitigate the procyclicality of non-discretionary provisions.

Despite the importance of a dynamic provisioning system, different characteristics of banks (such as bank size) may determine a bank's capacity to implement a dynamic provisioning system. Similarly, each country also has different macroeconomic and institutional environments and, thus, the capacity to adopt a dynamic provisioning system may vary substantially from one country to another. For such reasons, the current study is the first to extend prior research on the link between loan loss provisions and bank lending behavior by considering bank size to account for bank-specific characteristics, as well as information sharing and legal rights to account for country-specific factors.

Information sharing and legal rights are relevant because the issues of financial intermediation are conditional on the extent to which information systems and legal rights are of better quality (e.g., [START_REF] Jappelli | Information sharing, lending and defaults: Cross country evidence[END_REF][START_REF] Love | Credit reporting and financing constraints[END_REF][START_REF] Brown | Information sharing and credit: Firm-level evidence from transition countries[END_REF][START_REF] Houston | Creditor rights, information sharing and bank risk taking[END_REF].

With regards to the influence of legal rights on the link between loan loss provisions and bank loan growth, we consider the influence of the legal rights of borrowers instead of creditor legal rights as in [START_REF] Houston | Creditor rights, information sharing and bank risk taking[END_REF], because we examine the impact of loan loss provisions on bank loan growth from the demand side. Arguably, stronger legal rights of borrowers may increase borrowers' confidence in the banking sector. This in turn reduces the procyclical effect of loan loss provisions on bank loan growth, particularly during economic downturns.

Although we divide loan loss provisions into discretionary and non-discretionary components, our method differs from Bouvatier and[START_REF] Bouvatier | Banks' procyclical behaviour: does provisioning matter?[END_REF]2012). Specifically, we do not distinguish the type of discretionary purposes, i.e., whether income smoothing, capital management or signalling purposes. In this sense, we allow for different types of discretionary behavior to simultaneously offset the procyclicality of non-discretionary provisions, rather than focusing merely on the use of income smoothing as emphasized in [START_REF] Bouvatier | Effects of loan loss provisions on growth in bank Lending: Some international comparisons[END_REF]. We therefore follow [START_REF] Kanagaretnam | Is the market valuation of banks' loan loss provisions conditional on auditor reputation[END_REF] in grouping loan loss provisions into discretionary and nondiscretionary provisions, which is explained in the next section.

Data, methodology and econometric specifications

Data source

From BankScope Fitch IBCA, we construct an unbalanced panel of annual banklevel data from 528 commercial banks in 11 countries in the Asia-Pacific region covering the 2002-2012 period. These countries include China (153), Hong Kong (39), India (60), Indonesia (72), Malaysia (31), South Korea (17), Taiwan (38), Thailand (18), Pakistan (23), Philippines (31), and Vietnam (46). 2 Moreover, we incorporate country-specific data such as real gross domestic product (RGDP) and short-term interest rate (SHRATE) retrieved from Thomson Reuters Datastream International.

As this study also assesses the influence of credit information sharing and legal rights of borrowers on the link between loan loss provisions and bank lending, we also retrieve data country-level data regarding credit reporting system and legal rights strength for each country. These data are collected from the Doing Business database provided by the World Bank. Doing Business 2004-2014 provides data on a country's credit reporting system for January 2003 to January2013. Hence, such information reflects the situation at the end of each year from 2002 to 2012.

To account for the quality of credit information sharing, we consider the degree of credit information covered by private credit bureau (PRIVBUR) and public credit registry (PUBREG) following [START_REF] Tsai | What drives foreign expansion of the top 100 multinational banks? The role of the credit reporting system[END_REF]. PRIVBUR and PUBREG describe the proportion of individuals and firms listed by a private credit bureau and a public credit registry, respectively. Information covered by these credit registries includes repayment history, unpaid debts and credit outstanding. Higher PRIVBUR and PUBREG are associated with better credit information sharing.

However, the information coverage of private credit bureaus is usually greater than that of public credit registries [START_REF] Love | Credit reporting and financing constraints[END_REF]. Private credit registries are more likely to collect information from various sources including non-bank creditors, and store more detailed information on the borrowers. On the other hand, public credit registries tend to collect information only from supervised institutions and the coverage of information is rather limited [START_REF] Love | Credit reporting and financing constraints[END_REF].

We also consider the legal rights strength index (LEGAL) from Doing Business 2004-2014 to account for borrowers' rights protection. Considering the influence of the legal rights of borrowers in examining the relation between loan loss provisions and bank loan growth is relevant for the following reasons. It is widely perceived that stronger depositor protection through deposit insurance can reduces incentives for depositors to discipline bank risk taking (Barth et al., 2006). Arguably, higher borrower protection may also exacerbate entrepreneurial moral hazard, particularly in the presence of information 2 The numbers in parentheses represent the number of banks in our sample for each country. asymmetry on the credit market. As the loan loss provisionining system is part of prudential regulations that aim to cope with bank credit risk, increased borrowers' moral hazard due to stronger borrowers' legal rights may cause risk-shifting from borrowers to banks a la [START_REF] Stiglitz | Credit rationing with imperfect information[END_REF]. As such, the role of loan loss provisions in mitigating credit risk and procyclical effects can be outweighed by stronger legal rights for borrowers. On the other hand, [START_REF] Houston | Creditor rights, information sharing and bank risk taking[END_REF] emphasize that stronger protection of creditor rights is detrimental to financial stability, because it may increase incentives for creditors to undertake excessive risk taking. In this paper, we emphasize the strength of legal rights of borrowers and how it impacts the procyclical effect of loan loss provisions on growth in bank lending. To the best of our knowledge, no prior studies have examined how the legal rights of borrowers affect financial intermediation or stability.

Method

As stated earlier, the objective of our study is threefold. First, we examine the link between loan loss provisions and bank loan growth in order to highlight whether loan loss provisions are procyclical over the business cycle. Second, we examine whether information sharing and legal rights offset the procyclical effect of loan loss provisions, and substitute for the need for a dynamic provisioning system. Third, in examining these issues we consider the effect of bank size (i.e., large and small banks) to address the "toobig-to-fail" issues related to bank risk taking through lending activities.

In doing so, we initially identify the determinants of loan loss provisions for all banks. We then estimate discretionary and non-discretionary loan loss provisions. In the second step, we examine the effect of both discretionary and non-discretionary loan loss provisions on bank loan growth. In the third step, a closer investigation is undertaken to examine the influence of information sharing and legal rights on the link between loan loss provisions and loan growth in banking. Finally, in order to better understand the effect of bank size on these relationships, we repeat our tests for subsamples of large banks and small banks. These steps are explained further in the next section.

The determinants of bank loan loss provisions

In order to disentangle the discretionary and non-discretionary components of the loan loss provisions, we initially estimate a loan loss provisions model with the ratio of loan loss provisions to total loans (LLPL) as the dependent variable following [START_REF] Kanagaretnam | Is the market valuation of banks' loan loss provisions conditional on auditor reputation[END_REF]. We also use the ratio of total loan loss provisions to total assets (LLPTA) as the dependent variable in our tests. Specifically, we estimate the following equations:

t i t i t i t i t i t i t i t i t i t i CHOFFTA CHNPL NPLTA EBTPS LLRTA CHLOAN CAR TIER LLPL , 1 , 7 1 , 7 1 , 6 1 , 5 1 , 4 , 3 1 , 2 1 , 1 0 , 1                   (1a) t i t i t i t i t i t i t i t i t i t i CHOFFTA CHNPL NPLTA EBTPS LLRTA CHLOAN CAR TIER LLPTA , 1 , 7 1 , 7 1 , 6 1 , 5 1 , 4 , 3 1 , 2 1 , 1 0 , 1                            (1b)
Eq. ( 1a) and Eq. ( 1b) represent the loan loss provision models for our two measures of loan loss provisions (LLPL and LLPTA). TIER1 is defined as Tier 1 risk adjusted capital, while CAR is total risk adjusted capital. CHLOAN denotes change in total outstanding loans and LLRTA is loan loss allowance. EBTPS reflects earnings before tax, loan loss provisions, and special items. NPL and CHNPL represent total non-performing loans and change in total non-performing loans, respectively. Finally, LCO represents net loan charge-offs (write-offs). Because we consider both listed and non-listed banks in our sample, all of these variables are scaled by bank total assets rather than market value of equity as in [START_REF] Kanagaretnam | Is the market valuation of banks' loan loss provisions conditional on auditor reputation[END_REF].

We compute the estimated values of discretionary and non-discretionary provisions in period t based on either Eq. (1a) or Eq. (1b). We follow [START_REF] Kanagaretnam | Is the market valuation of banks' loan loss provisions conditional on auditor reputation[END_REF] where non-discretionary provisions in period t are calculated by the fitted values of the LLP equation, while discretionary provisions in period t are represented by the residuals of the LLP equation. We specify NDISCL and NDISCTA to reflect the non-discretionary loan loss provisions estimated from the LLPL and LLPTA models in Eq. (1a) and Eq. (1b), respectively. Likewise, DISCL and DISCTA are discretionary provisions calculated from the LLPL and LLPTA models, respectively.

Bank loan loss provisions and loan growth

Once the estimated discretionary and non-discretionary provisions have been estimated, we assess the impact of non-discretionary and discretionary provisions on bank loan growth. Following [START_REF] Bouvatier | Banks' procyclical behaviour: does provisioning matter?[END_REF], bank loan growth (DLOAN) is defined as the actual change in the ratio of total loans to total assets. More precisely, bank loan growth is measured as:

    1 , , 1 , , , 5 . 0      t i t i t i t i t i TA TA L L DLOAN .
L is total loans. To test for robustness, we also use the simple annual growth of total loans (LOANG) as the dependent variable. LOANG is calculated as the difference between total loans in period t and total loans in period t -1, divided by total outstanding loans in period t -1.

Bank loan growth is regressed against our variables of interest and several control variables that may affect bank loan growth using the following equationsfollowing [START_REF] Bouvatier | Banks' procyclical behaviour: does provisioning matter?[END_REF]:

t i t i t i t i t i t i t i t i t i SHRATE LRGDP SIZE DTA EQTA NDISC DISC DLOAN , , 7 , 6 , 5 , 4 , 3 , 2 , 1 0 ,                   (2a) t i t i t i t i t i t i t i t i t i SHRATE LRGDP SIZE DTA EQTA NDISC DISC LOANG , , 7 , 6 , 5 , 4 , 3 , 2 , 1 0 ,                   (2b)
In Eq. (2a) and Eq. ( 2b), both DISC and NDISC are measured using either LLPL as in Eq. (1.a), or LLPTA as in the Eq. (1b).

To control for bank-specific factors in Eq. (2a) and Eq. (2b), we include several bank-specific variables. The ratio of total equity to total assets (EQTA) is considered as a control variable, because higher capitalization is expected to enhance a bank's capacity to grant new loans as described in the "capital crunch" literature (e.g., [START_REF] Bernanke | Credit crunch[END_REF]Peek and Rosengren, 1995). Building on [START_REF] Olivero | Competition in banking and the lending channel: Evidence from bank-level data in Asia and Latin America[END_REF], we consider the impact of bank liquidity and the "too-big-to-fail" effects on bank loan growth. However, we measure bank liquidity using the ratio of total deposits and short-term funding to total assets (DTA) instead of the ratio of liquid assets to total assets as in [START_REF] Olivero | Competition in banking and the lending channel: Evidence from bank-level data in Asia and Latin America[END_REF] due to data availability reasons. We expect a positive relation between DTA and loan growth because banks with a greater funding base have greater liquidity and more capacity to boost lending activities. Because larger banks tend to behave imprudently due to the "too-big-to-fail" effects [START_REF] Beck | Resolution of failed banks by deposit insurers: Cross-country evidence[END_REF], larger banks might boost loan growth to generate profits. To account for bank size, we use the logarithm of bank total assets (SIZE).

Given that our sample of banks comes from different countries, controlling for country-specific factors is necessary. Following [START_REF] Olivero | Competition in banking and the lending channel: Evidence from bank-level data in Asia and Latin America[END_REF], we include the logarithm of real gross domestic product (LRGDP) and short-term interest rate (SHRATE) to account for the degree of economic development and the impact of monetary policy, respectively. The role of monetary policy in bank lending has been widely discussed in which higher short-term interest rates can temper bank loan growth.

Information sharing, legal rights of borrowers, and the procyclicality of loan loss provisions

In the next stage, we augment the analysis by assessing whether the procyclicality of loan loss provisions is conditional on credit information sharing and strength of the legal rights of borrowers. Our focus is to examine the joint-impact of discretionary provisions and these country-specific factors. From these results, we can ascertain whether the procyclical effect of non-discretionary provisions, if any, can be offset by the countercyclical effect of discretionary provisions, which is also conditional on the extent to which credit information sharing and borrower legal rights are of better quality. In case that the countercyclical effect of discretionary provisionswhich is dependent on the quality of credit information sharing and borrower legal rightscan offset the procyclical effect of non-discretionary loan loss provisions, then statistical provisions generated from a dynamic provisioning system might not be necessary.

In examining the effect of information sharing on bank loan growth, we distinguish between the influence of private credit bureaus and public credit registries instead of investigating the influence of information sharing in general following [START_REF] Brown | Information sharing and credit: Firm-level evidence from transition countries[END_REF]. Our approach in defining information sharing therefore follows [START_REF] Tsai | What drives foreign expansion of the top 100 multinational banks? The role of the credit reporting system[END_REF]. This enables us to determine the type of information sharing mechanism that matters for economic growth and financial stability in overcoming the procyclicality of nondiscretionary loan loss provisions.

For this purpose, we modify Eq. (2a) and Eq. (2b) to incorporate the interaction term between discretionary loan loss provisions and country-specific factors representing information sharing and borrower legal rights, as shown in Eq. (3), Eq. ( 4) and Eq. ( 5) 3 .

t i t i t t t t i t i t i t i t i t i PRIVBUR DISC PRIVBUR SHRATE LRGDP SIZE DTA EQTA NDISC DISC DLOAN , , 9 8 7 6 , 5 , 4 , 3 , 2 , 1 0 , *                       (3) t i t i t t t t i t i t i t i t i t i PUBREG DISC PUBREG SHRATE LRGDP SIZE DTA EQTA NDISC DISC DLOAN , , 9 8 7 6 , 5 , 4 , 3 , 2 , 1 0 , *                       (4) t i t i t t t t i t i t i t i t i t i LEGAL DISC LEGAL SHRATE LRGDP SIZE DTA EQTA NDISC DISC DLOAN , , 9 8 7 6 , 5 , 4 , 3 , 2 , 1 0 , *             (5)
The above equations are also estimated using LOANG as an alternative measure of bank loan growth to replace DLOAN. Moreover, both DISC and NDISC also comprise two different measures depending on the definition of loan loss provisions as stated earlier, from Eq. (1a) and Eq. (1b).

Information sharing, legal rights of borrowers, and the procyclicality of loan loss provisions: Large banks vs. small banks

In the final step, we examine whether large banks and small banks behave differently in terms of their lending behavior in responding to higher loan loss provisions. In order to identify large banks and small banks, we calculate the average of the logarithm of total assets for each bank in the whole period of observation (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012). We then use the 75 th percentile of these average values as a cut-off point. A bank is considered a large bank if its average logarithm of total assets exceeds its 75 th percentile and a bank is considered a small bank if its average logarithm of total assets is less than its 75 th percentile. We construct a dummy variable (DSIZE), taking a value of 1 if a bank is classified as a large bank and 0 otherwise. In turn, we re-estimate Eq. (2a), Eq (2b), Eq. (3), Eq. ( 4), and Eq.

(5) for large and small bank sample separately.

Empirical results

Descriptive statistics and correlation structure

Table 1 reports the descriptive statistics of all variables used in this study.

Descriptive statistics for all variables are also reported separately for large and small banks. The descriptive statistics indicate that small banks exhibit higher loan loss provisions on average (LLPTA and LLPL) than large banks. Small banks also exhibit higher average loan growth than large banks (DLOAN and LOANG). These initial observations highlight differences between small and large banks in terms of their lending behavior and procyclicality. Overall, all of the average values of bank-specific variables for small banks are higher than those for large banks.

[Insert Table 1 here] Table 2 reports the Pearson correlation coefficients for all bank-specific and countryspecific variables used in this study. The correlations suggest that multicollinearity is not likely to be an issue because none of the independent variables are highly correlated.

[Insert Table 2 here]

Regression results

The determinants of bank loan loss provisions

In this stage, we estimate Eq. ( 1) to obtain coefficients related to factors affecting bank loan loss provisions. Table 3 reports our estimation outputs. Non-performing loans (NPLTA), change in non-performing loans (CHNPL) and net loan charge-offs (CHOFFTA) are associated with higher loan loss provisions measured by both LLPTA and LLPL. Meanwhile, the Tier-1 capital ratio (RTIER1) has a significant and positive association with LLPTA but no significant association with LLPL. Changes in total loans (CHLOAN) and loan loss reserves (LLRTA) have a negative association with both measures of loan loss provisions (LLPTA and LLPL). The negative association between CHLOAN and loan loss provisions (LLPTA and LLPL) indicates that loan loss provisions are procyclical because increased loan activities reflecting economic boom periods tend to reduce loan loss provisions. This finding is consistent with [START_REF] Asea | Lending cycles[END_REF] and [START_REF] Bouvatier | Banks' procyclical behaviour: does provisioning matter?[END_REF] who analyze US banks and European banks, respectively.

However, [START_REF] Kanagaretnam | Is the market valuation of banks' loan loss provisions conditional on auditor reputation[END_REF] show that the coefficient of CHLOAN in the loan loss provision regression is positive, suggesting that banks in their sample behave prudently by building up loan loss provisions to cover default risk exposure following the expansion of lending activities. This also indicates that procyclicality of the loan loss provisioning system is less likely to occur in their bank sample.

We also find that the earning variable (EBTPS) has no clear impact on loan loss provisions as reflected in a negative association with LLPTA while having a positive association with LLPL. This result asserts that banks in our sample tend to use loan loss provisions for non-discretionary purposes, particularly to deal with higher nonperforming loans and charge-offs. In other words, banks will increase loan loss provisions, because their expected credit risk also increase.

[Insert Table 3 here]

Bank loan loss provisions and loan growth

In this section, we differentiate the effects of non-discretionary and discretionary loan loss provisions on bank lending. Specifically, we aim to assess what types of provisions amplify business cycle fluctuations through bank lending.

[Insert Table 4 here] The results reported in Table 4 indicate that only non-discretionary loan loss provisions amplify procyclical effects because higher non-discretionary loan loss provisions tend to reduce bank loan growth. This result is robust to our different measures of non-discretionary loan loss provisions and bank loan growth. The bank capital ratio (EQTA) is negatively linked to bank loan growth measured by LOANG, while the deposits-to-assets ratio (DTA) exhibits no significant association with bank loan growth. Bank size (SIZE) has a significant and positive association with bank loan growth. This follows the notion that bank risk takingwhich might come from higher lending activitiesincreases due the "too-big-to-fail" effects, as the asset size of banks increases [START_REF] Beck | Resolution of failed banks by deposit insurers: Cross-country evidence[END_REF][START_REF] Mishkin | How big a problem is too big to fail[END_REF][START_REF] Kane | Incentives for banking megamergers: What motives might regulators infer from event-study evidence?[END_REF].

With regards to country-specific control variables, only short-term interest (SHRATE) is significant and it has a negative association with bank loan growth. This result suggests that the bank lending channel occurs in Asian banks following [START_REF] Olivero | Competition in banking and the lending channel: Evidence from bank-level data in Asia and Latin America[END_REF].

Information sharing, legal rights of borrowers, and the procyclicality of loan loss provisions

To test whether information sharing and borrower legal rights offset the procyclical effect of non-discretionary loan loss provisions, we estimate Eq. (3) to Eq. ( 5). Table 5 presents our results when the effect of private credit bureaus is taken into consideration, while Tables 6 and 7 report our results regarding the influence of public credit registries and borrower legal rights, respectively.

Consistent with our previous results, the results reported in Table 5 initially indicate that non-discretionary loan loss provisions exhibit a procyclical effect, because of the negative link between non-discretionary provisions (NDISCTA and NDISCL) and bank loan growth (DLOAN and LOANG). This procyclical effect cannot be offset by discretionary provisions measured by DISCTA or DISCL, as discretionary provisions do not have a significant association with bank loan growth. In this respect, there is no countercyclical effect of discretionary provisions to offset the procyclicality of nondiscretionary provisions.

[Insert Table 5 here] In the next turn, we examine the influence of information sharing measured by the quality of private credit bureaus (PRIVBUR) and public credit registries (PUBREG) on the link between discretionary provisions and loan growth. Our aim is to test whether the quality of information sharing strengthens or deteriorates the countercyclical effect of discretionary provisions that may in turn offset the procyclicality of non-discretionary provisions.

Table 5 documents the significant and negative interactions between discretionary loan loss provisions (DISCTA or and DISCL) and private credit bureaus (PRIVBUR).

Moreover, we find that the negative coefficients of NDISCTA (or NDISCL) are higher than the sum of the coefficients of DISCTA (or DISCL) and DISCTA*PRIVBUR ( or DISCL*PRIVBUR). In this case, the presence of private credit bureaus is less beneficial to overcome the procyclicality of loan loss provisions, because discretionary provisions also become procyclical. In other words, for countries with better private credit bureaus, the procyclicality of non-discretionary provisions shown by the negative sign of NDISCTA or NDISCL is aggravated by the procyclicality of discretionary provisions.

In contrast, the interaction terms between discretionary loan loss provisions (DISCTA and DISCL) and public credit registries (PUBREG) reported in Table 6 exhibit a positive and significant association with bank loan growth. Moreover, the sum of the coefficients of DISCTA (or DISCL) and such interaction terms are higher than the coefficients of nondiscretionary provisions (NDISCTA and NDISCL). Hence, higher discretionary provisions in countries with better public credit registries in sharing credit information might offset the procyclical impact of non-discretionary provisions on bank loan growth.

As such, a dynamic provisioning system to cope with the procyclicality of nondiscretionary provisions might be replaced by the role of public credit registries.

[Insert Table 6 here] In Table 7, we examine the effect of interaction terms between discretionary provisions (DISCTA or DISCL) and borrower legal rights (LEGAL) on bank loan growth.

Meanwhile, higher discretionary provisions in countries with stronger legal rights of borrowers (DISCTA*LEGAL or DISCL*LEGAL) exhibit a positive impact on bank loan growth. However, the positive sign related to the sum of the coefficients of discretionary provisions and such interaction terms with borrower legal rights are smaller than the negative coefficients of non-discretionary provisions. Accordingly, stronger legal rights of borrowers combined with higher discretionary provisions can only temper the procyclicality of non-discretionary provisions on bank loan growth.

[Insert Table 7 here]

Information sharing, legal rights of borrowers, and the procyclicality of loan loss provisions: Large banks vs. small banks

In this section, we differentiate the effect of information sharing and legal rights of borrowers on the procyclical impact of loan loss provisions with respect to bank size.

Table 8 initially shows that both non-discretionary and discretionary loan loss provisions have a procyclical impact on bank lending for large banks, because both discretionary and non-discretionary provisions are negatively associated with bank loan growth.

[Insert Table 8 here] In Table 9, we document that for large banks, the coefficients of the interaction terms between discretionary loan loss provisions and private credit bureaus (DISCL*PRIVBUR) are higher than the coefficients of discretionary loan loss provisions (DISCL). This indicates that better private credit bureaus strengthen the positive impact of discretionary provisions on bank loan growth. In this regard, discretionary loan loss provisions for large banks are countercyclical, especially for countries with better private credit bureaus.

Nevertheless, the countercyclicality of discretionary provisions in large banks cannot offset the procyclicality of non-discretionary provisions measured by NDISCL, because the negative coefficients of NDISCL are higher than the sum of the coefficients of DISCL and DISCL*PRIVBUR.

[Insert Table 9 here] In Table 10, the coefficients for the interaction terms between discretionary loan loss provisions and public credit registries (DISCTA*PUBREG or DISCL*PUBREG) are not statistically significant for our sample of large banks. Whereas, both discretionary and non-discretionary provisions are negatively associated with bank loan growth. These results highlight that both types of provisions are procyclical, while such procyclical effects cannot be offset by the role of public credit registries.

[Insert Table 10 here] In Table 11, we report that the degree of borrower legal rights has no significant effect on the procyclicality of loan loss provisions. We also find that both discretionary and non-discretionary provisions are procyclical. Overall, we find that the procyclicality of non-discretionary provisions for large banks cannot be offset by discretionary provisions or by the quality of private credit bureaus, private credit registries and borrower legal rights. For large banks, a dynamic provisioning system may be therefore necessary to increase statistical provisions in order to cope with the procyclicality of nondiscretionary provisions that amplify business cycle fluctuations.

[Insert Table 11 here] Moroever, Tables 12 to 15 report the results from re-estimating Eq. (2) to Eq. ( 5) for our sample of small banks. As reported in Table 12, only non-discretionary provisions (NDISCTA and NDISCL) have a significant and negative association with bank loan growth (DLOAN and LOANG). In Table 13, we document that higher information sharing by private credit bureaus exacerbates the procyclicality of non-discretionary provisions in small banks, because the sum of coefficients of the interaction terms (DISCTA*PRIVBUR and DISCL*PRIVBUR) and discretionary provisions (DISCTA and DISCL) are negative, increasing a negative magnitude of the coefficients of non-discretionary provisions.

[Insert Table 12, 13, 14, and 15 here] Tables 14 and15 present our results when we take into account the effect of public credit registries and borrower legal rights, respectively. We find strong evidence that better public credit registries and stronger borrower legal rights combined with higher discretionary provisions offset the procyclicality of non-discretionary loan loss provisions. On the whole, we find that using discretionary loan loss provisions is sufficient for small banks in countries with better quality public credit bureaus and stronger legal rights of borrowers, in order to offset the procyclicality of nondiscretionary loan loss provisions. In other words, a dynamic provisioning system that increases statistical provisions might not be crucial for small banks, if public credit registries and borrower legal rights are of better quality. Strengthening public credit registries and borrower legal rights might therefore be an option to cope with the procyclicality of non-discretionary provisions, instead of forcing small banks to implement a dynamic provisioning system.

Conclusion

This study examines the effect of loan loss provisions on the lending behavior of banks by considering the influence of credit information sharing and legal rights of borrowers. In general, we show that non-discretionary loan loss provisions of Asian banks are procyclical because higher non-discretionary loan loss provisions are significantly associated with a decline in bank loan growth. This procyclical effect for non-discretionary loan loss provisions occurs in both large and small banks. For large banks, we also find that discretionary loan loss provisions exhibit a procyclical effect, as discretionary loan loss provisions in large banks are negatively associated with loan growth.

Furthermore, we generally document that for all banks, credit information sharing and borrower legal rights may reduce the procyclicality of non-discretionary loan loss provisions. Specifically, higher discretionary loan loss provisions of banks in countries with better quality public credit registries (but not private credit bureaus) and stronger legal rights of borrowers may offset, or at least temper, the negative impact of nondiscretionary loan loss provisions on bank loan growth. However, these results only hold for our sample of small banks.

In other words, we find evidence that small banks in countries with better public credit registries and stronger legal rights of borrowers can simply use discretionary loan loss provisions to offset the procyclicality of non-discretionary loan loss provisions. For small banks, higher discretionary loan loss provisionscombined with better quality public credit registries or borrower legal rightsare significantly associated with an increase in loan growth. This positive effect for discretionary loan loss provisions is greater than the negative effect of non-discretionary loan loss provisions on loan growth.

Small banks in countries with better public credit registries and stronger legal rights of borrowers are the ones that do not require a dynamic provisioning system to overcome their procyclical behavior, although we do not discourage small banks from implementing a dynamic provisioning system.

On the other hand, we do not find that high quality private credit bureaus, public credit registries, and legal rights of borrowers offset the procyclicality of nondiscretionary loan loss provisions in large banks. In this regard, the implementation of a dynamic provisioning system is iis more important for large banks than small banks.

Hence, bank regulators need to pay closer attention to large banks with respect to the implementation of a dynamic provisioning system. This is because higher nondiscretionary loan loss provisions in large banks reduce loan growth and such a reduction cannot simply be offset by discretionary loan loss provisions, although the quality of credit information sharing and legal rights of borrowers are strengthened.

Overall, this study identifies conditions where a dynamic provisioning system may mitigate the procyclical behavior of Asian banks. This study is important in the sense that Asian banks do not all have the same capacity to implement a dynamic provisioning system. Similarly, the procyclical effect of loan loss provisions, notably the nondiscretionary provisions may vary from country to country depending on the strength of public credit registries and the legal rights of borrowers. In this respect, each country may have different macroeconomic environments, which, in turn, may determine the effectiveness of the dynamic provisioning system. Notes: DLOAN is the change in the ratio of total loans to total assets calculated from [START_REF] Bouvatier | Banks' procyclical behaviour: does provisioning matter?[END_REF]. LOANG is the annual loan growth rate. DISCL and DISCTA represent discretionary loan loss provisions calculated from the residuals of Eq. (1a) and Eq. (1b), respectively. NDISCL and NDISCTA represent non-discretionary loan loss provisions calculated from the fitted values of Eq. (1a) and Eq. (1b), respectively. EQTA is the ratio of total equity to total assets. DTA is the ratio of deposits and short-term funds to total assets. SIZE is the logarithm of total assets. LGDPR is the logarithm of real gross domestic products. SHRATE is short-term interest rate proxied by the central bank policy rate. Estimations are carried out using Panel Least Squares controlling for both crosssectional and period fixed effects. *** indicates significant at the 1 percent level, while ** and * indicate significant at the 5 percent and 10 percent levels, respectively. Notes: DLOAN is the change in the ratio of total loans to total assets calculated from [START_REF] Bouvatier | Banks' procyclical behaviour: does provisioning matter?[END_REF]. LOANG is the annual loan growth rate. DISCL and DISCTA represent discretionary loan loss provisions calculated from the residuals of Eq. (1a) and Eq. (1b), respectively. NDISCL and NDISCTA represent non-discretionary loan loss provisions calculated from the fitted values of Eq. (1a) and Eq. (1b), respectively. EQTA is the ratio of total equity to total assets. DTA is the ratio of deposits and short-term funds to total assets. SIZE is the logarithm of total assets. LGDPR is the logarithm of real gross domestic products. SHRATE is short-term interest rate proxied by the central bank policy rate. Estimations are carried out using Panel Least Squares controlling for both crosssectional and period fixed effects. *** indicates significant at the 1 percent level, while ** and * indicate significant at the 5 percent and 10 percent levels, respectively. Notes: DLOAN is the change in the ratio of total loans to total assets calculated from [START_REF] Bouvatier | Banks' procyclical behaviour: does provisioning matter?[END_REF]. LOANG is the annual loan growth rate. DISCL and DISCTA represent discretionary loan loss provisions calculated from the residuals of Eq. (1a) and Eq. (1b), respectively. NDISCL and NDISCTA represent non-discretionary loan loss provisions calculated from the fitted values of Eq. (1a) and Eq. (1b), respectively. EQTA is the ratio of total equity to total assets. DTA is the ratio of deposits and short-term funds to total assets. SIZE is the logarithm of total assets. LGDPR is the logarithm of real gross domestic products. SHRATE is short-term interest rate proxied by the central bank policy rate. PRIVBUR is the proportion of individuals and firms listed by a private credit bureau. Estimations are carried out using Panel Least Squares controlling for both cross-sectional and period fixed effects. *** indicates significant at the 1 percent level, while ** and * indicate significant at the 5 percent and 10 percent levels, respectively. Notes: DLOAN is the change in the ratio of total loans to total assets calculated from [START_REF] Bouvatier | Banks' procyclical behaviour: does provisioning matter?[END_REF]. LOANG is the annual loan growth rate. DISCL and DISCTA represent discretionary loan loss provisions calculated from the residuals of Eq. (1a) and Eq. (1b), respectively. NDISCL and NDISCTA represent non-discretionary loss provisions calculated from the fitted values of Eq. (1a) and Eq. (1b), respectively. EQTA is the ratio of total equity to total assets. DTA is the ratio of deposits and short-term funds to total assets. SIZE is the logarithm of total assets. LGDPR is the logarithm of real gross domestic products. SHRATE is short-term interest rate proxied by the central bank policy rate. PUBREG is the proportion of individuals and firms listed by a public credit registry. Estimations are carried out using Panel Least Squares controlling for both cross-sectional and period fixed effects. *** indicates significant at the 1 percent level, while ** and * indicate significant at the 5 percent and 10 percent levels, respectively. Notes: DLOAN is the change in the ratio of total loans to total assets calculated from [START_REF] Bouvatier | Banks' procyclical behaviour: does provisioning matter?[END_REF]. LOANG is the annual loan growth rate. DISCL and DISCTA represent discretionary loan loss provisions calculated from the residuals of Eq. (1a) and Eq. (1b), respectively. NDISCL and NDISCTA represent non-discretionary loan loss provisions calculated from the fitted values of Eq. (1a) and Eq. (1b), respectively. EQTA is the ratio of total equity to total assets. DTA is the ratio of deposits and short-term funds to total assets. SIZE is the logarithm of total assets. LGDPR is the logarithm of real gross domestic products. SHRATE is short-term interest rate proxied by the central bank policy rate. LEGAL is the stength of borrowers legal rights developed by the Doing Business database. Estimations are carried out using Panel Least Squares controlling for both cross-sectional and period fixed effects. *** indicates significant at the 1 percent level, while ** and * indicate significant at the 5 percent and 10 percent levels, respectively. Notes: DLOAN is the change in the ratio of total loans to total assets calculated from [START_REF] Bouvatier | Banks' procyclical behaviour: does provisioning matter?[END_REF]. LOANG is the annual loan growth rate. DISCL and DISCTA represent discretionary loan loss provisions calculated from the residuals of Eq. (1a) and Eq. (1b), respectively. NDISCL and NDISCTA represent non-discretionary loan loss provisions calculated from the fitted values of Eq. (1a) and Eq. (1b), respectively. EQTA is the ratio of total equity to total assets. DTA is the ratio of deposits and short-term funds to total assets. SIZE is the logarithm of total assets. LGDPR is the logarithm of real gross domestic products. SHRATE is short-term interest rate proxied by the central bank policy rate. Estimations are carried out using Panel Least Squares controlling for both crosssectional and period fixed effects. *** indicates significant at the 1 percent level, while ** and * indicate significant at the 5 percent and 10 percent levels, respectively. Notes: DLOAN is the change in the ratio of total loans to total assets calculated from [START_REF] Bouvatier | Banks' procyclical behaviour: does provisioning matter?[END_REF]. LOANG is the annual loan growth rate. DISCL and DISCTA represent discretionary loan loss provisions calculated from the residuals of Eq. (1a) and Eq. (1b), respectively. NDISCL and NDISCTA represent non-discretionary loan loss provisions calculated from the fitted values of Eq. (1a) and Eq. (1b), respectively. EQTA is the ratio of total equity to total assets. DTA is the ratio of deposits and short-term funds to total assets. SIZE is the logarithm of total assets. LGDPR is the logarithm of real gross domestic products. SHRATE is short-term interest rate proxied by the central bank policy rate. PRIVBUR is the proportion of individuals and firms listed by a private credit bureau. Estimations are carried out using Panel Least Squares controlling for both cross-sectional and period fixed effects. *** indicates significant at the 1 percent level, while ** and * indicate significant at the 5 percent and 10 percent levels, respectively. Notes: DLOAN is the change in the ratio of total loans to total assets calculated from [START_REF] Bouvatier | Banks' procyclical behaviour: does provisioning matter?[END_REF]. LOANG is the annual loan growth rate. DISCL and DISCTA represent discretionary loan loss provisions calculated from the residuals of Eq. (1a) and Eq. (1b), respectively. NDISCL and NDISCTA represent non-discretionary loan loss provisions calculated from the fitted values of Eq. (1a) and Eq. (1b), respectively. EQTA is the ratio of total equity to total assets. DTA is the ratio of deposits and short-term funds to total assets. SIZE is the logarithm of total assets. LGDPR is the logarithm of real gross domestic products. SHRATE is short-term interest rate proxied by the central bank policy rate. PUBREG is the proportion of individuals and firms listed by a public credit registry. Estimations are carried out using Panel Least Squares controlling for both cross-sectional and period fixed effects. *** indicates significant at the 1 percent level, while ** and * indicate significant at the 5 percent and 10 percent levels, respectively. Notes: DLOAN is the change in the ratio of total loans to total assets calculated from [START_REF] Bouvatier | Banks' procyclical behaviour: does provisioning matter?[END_REF]. LOANG is the annual loan growth rate. DISCL and DISCTA represent discretionary loan loss provisions calculated from the residuals of Eq. (1a) and Eq. (1b), respectively. NDISCL and NDISCTA represent non-discretionary loan loss provisions calculated from the fitted values of Eq. (1a) and Eq. (1b), respectively. EQTA is the ratio of total equity to total assets. DTA is the ratio of deposits and short-term funds to total assets. SIZE is the logarithm of total assets. LGDPR is the logarithm of real gross domestic products. SHRATE is short-term interest rate proxied by the central bank policy rate. LEGAL is the stength of borrowers legal rights developed by the Doing Business database. Estimations are carried out using Panel Least Squares controlling for both cross-sectional and period fixed effects. *** indicates significant at the 1 percent level, while ** and * indicate significant at the 5 percent and 10 percent levels, respectively. Notes: DLOAN is the change in the ratio of total loans to total assets calculated from [START_REF] Bouvatier | Banks' procyclical behaviour: does provisioning matter?[END_REF]. LOANG is the annual loan growth rate. DISCL and DISCTA represent discretionary loan loss provisions calculated from the residuals of Eq. (1a) and Eq. (1b), respectively. NDISCL and NDISCTA represent non-discretionary loan loss provisions calculated from the fitted values of Eq. (1a) and Eq. (1b), respectively. EQTA is the ratio of total equity to total assets. DTA is the ratio of deposits and short-term funds to total assets. SIZE is the logarithm of total assets. LGDPR is the logarithm of real gross domestic products. SHRATE is short-term interest rate proxied by the central bank policy rate. Estimations are carried out using Panel Least Squares controlling for both crosssectional and period fixed effects. *** indicates significant at the 1 percent level, while ** and * indicate significant at the 5 percent and 10 percent levels, respectively. Notes: DLOAN is the change in the ratio of total loans to total assets calculated from [START_REF] Bouvatier | Banks' procyclical behaviour: does provisioning matter?[END_REF]. LOANG is the annual loan growth rate. DISCL and DISCTA represent discretionary loan loss provisions calculated from the residuals of Eq. (1a) and Eq. (1b), respectively. NDISCL and NDISCTA represent non-discretionary loan loss provisions calculated from the fitted values of Eq. (1a) and Eq. (1b), respectively. EQTA is the ratio of total equity to total assets. DTA is the ratio of deposits and short-term funds to total assets. SIZE is the logarithm of total assets. LGDPR is the logarithm of real gross domestic products. SHRATE is short-term interest rate proxied by the central bank policy rate. PRIVBUR is the proportion of individuals and firms listed by a private credit bureau. Estimations are carried out using Panel Least Squares controlling for both cross-sectional and period fixed effects. *** indicates significant at the 1 percent level, while ** and * indicate significant at the 5 percent and 10 percent levels, respectively. Notes: DLOAN is the change in the ratio of total loans to total assets calculated from [START_REF] Bouvatier | Banks' procyclical behaviour: does provisioning matter?[END_REF]. LOANG is the annual loan growth rate. DISCL and DISCTA represent discretionary loan loss provisions calculated from the residuals of Eq. (1a) and Eq. (1b), respectively. NDISCL and NDISCTA represent non-discretionary loan loss provisions calculated from the fitted values of Eq. (1a) and Eq. (1b), respectively. EQTA is the ratio of total equity to total assets. DTA is the ratio of deposits and short-term funds to total assets. SIZE is the logarithm of total assets. LGDPR is the logarithm of real gross domestic products. SHRATE is short-term interest rate proxied by the central bank policy rate. PUBREG is the proportion of individuals and firms listed by a public credit registry. Estimations are carried out using Panel Least Squares controlling for both cross-sectional and period fixed effects. *** indicates significant at the 1 percent level, while ** and * indicate significant at the 5 percent and 10 percent levels, respectively. Notes: DLOAN is the change in the ratio of total loans to total assets calculated from [START_REF] Bouvatier | Banks' procyclical behaviour: does provisioning matter?[END_REF]. LOANG is the annual loan growth rate. DISCL and DISCTA represent discretionary loan loss provisions calculated from the residuals of Eq. (1a) and Eq. (1b), respectively. NDISCL and NDISCTA represent non-discretionary loan loss provisions calculated from the fitted values of Eq. (1a) and Eq. (1b), respectively. EQTA is the ratio of total equity to total assets. DTA is the ratio of deposits and short-term funds to total assets. SIZE is the logarithm of total assets. LGDPR is the logarithm of real gross domestic products. SHRATE is short-term interest rate proxied by the central bank policy rate. LEGAL is the stength of borrowers legal rights developed by the Doing Business database. Estimations are carried out using Panel Least Squares controlling for both cross-sectional and period fixed effects. *** indicates significant at the 1 percent level, while ** and * indicate significant at the 5 percent and 10 percent levels, respectively.

Table 1 .

 1 Descriptive statistics

	Variables	Mean	All banks Median	Std. Dev.	Mean	Large banks Median	Std. Dev.	Mean	Small banks Median	Std. Dev.
	Loan loss provisions
	LLPL	0.0081	0.0057	0.0316	0.0080	0.0055	0.0096	0.0081	0.0058	0.0365
	LLPTA	0.0049	0.0030	0.0232	0.0045	0.0031	0.0058	0.0050	0.0029	0.0268
	LLRTA	0.0196	0.0107	0.0521	0.0148	0.0096	0.0175	0.0214	0.0114	0.0597
	Capital adequacy ratios
	RTIER1	0.1636	0.1100	0.2594	10.3096	9.7250	3.3961	19.1613	11.9000	30.8939
	CAR	0.1931	0.1381	0.2483	0.1345	0.1308	0.0400	0.2172	0.1441	0.2905
	EQTA	0.1144	0.0789	0.1210	0.0680	0.0645	0.0274	0.1308	0.0898	0.1361
	Loan growth
	DLOAN	0.1013	0.0934	0.1214	0.0881	0.0870	0.0751	0.1061	0.0963	0.1341
	LOANG	0.2224	0.2035	0.2202	0.1871	0.1826	0.1571	0.2360	0.2123	0.2389
	CHLOAN	0.0856	0.0853	0.1065	0.0790	0.0802	0.0660	0.0881	0.0881	0.1178
	Non-performing loans
	NPLTA	0.0256	0.0128	0.0438	0.0206	0.0097	0.0315	0.0276	0.0143	0.0476
	CHNPL	-0.0413	-0.0080	1.9045	-0.1404	-0.0224	1.2089	-0.0009	-0.0005	2.1227
	CHOFFTA	0.0034	0.0012	0.0095	0.0034	0.0015	0.0068	0.0034	0.0010	0.0106
	Information sharing
	PRIVBUR	0.1736	0.0000	0.3039	0.3161	0.1020	0.3805	0.1286	0.0000	0.2660
	PUBREG	0.1244	0.0080	0.1873	0.1344	0.0000	0.2511	0.1222	0.0460	0.1620
	Legal rights of borrowers
	LEGAL	0.0594	0.0500	0.0213	0.0626	0.0500	0.0235	0.0582	0.0500	0.0206
	Control variables
	EBTPS	0.0180	0.0167	0.0275	0.0164	0.0157	0.0084	0.0186	0.0174	0.0317
	DTA	0.8115	0.8510	0.1410	0.8490	0.8681	0.0993	0.7981	0.8430	0.1509
	SIZE	15.1578	15.2605	1.9815	17.4306	17.1988	1.1503	14.3517	14.5815	1.5387
	LGDPR	13.1775	14.1097	2.0738	12.9685	13.3533	2.1604	13.2521	14.2056	2.0420
	SHRATE	0.0507	0.0390	0.0354	0.0361	0.0317	0.0245	0.0548	0.0447	0.0371

Table 2 .

 2 Correlation coefficients

	Variables	LLPL	LLPTA	DLOAN	LOANG	RTIER1	CAR	CHLOAN	LLRTA	EBTPS	NPLTA	CHNPL
	LLPL	1.0000
	LLPTA	0.6447	1.0000
	DLOAN	0.0040	-0.0286	1.0000
	LOANG	-0.0508	-0.0526	0.9048	1.0000
	RTIER1	-0.2496	-0.1030	-0.1284	-0.0138	1.0000
	CAR	-0.2743	-0.1066	-0.1211	0.0088	0.9881	1.0000
	CHLOAN	0.0227	-0.0172	0.9484	0.8889	-0.1508	-0.1421	1.0000
	LLRTA	0.0416	0.0748	-0.1470	-0.1310	0.0885	0.0034	-0.1557	1.0000
	EBTPS	0.0732	-0.0049	-0.0161	0.0485	-0.0039	0.0300	-0.0015	0.0972	1.0000
	NPLTA	0.1560	0.1943	-0.1830	-0.1838	0.1109	-0.0264	-0.1856	0.7914	0.0318	1.0000
	CHNPL	0.2160	0.3658	0.0506	0.0285	-0.1082	-0.1456	0.0416	0.0483	-0.0889	0.1596	1.0000
	CHOFFTA	0.2802	0.1428	-0.1110	-0.0813	-0.0210	0.0376	-0.1010	0.1426	0.0783	0.1360	-0.3791
	EQTA	-0.0878	-0.1289	-0.0852	-0.0093	0.6952	0.6788	-0.1346	0.0805	0.0799	0.1082	-0.1588
	DTA	0.0936	0.0650	0.1200	0.0445	-0.5589	-0.5395	0.1604	-0.1919	-0.1552	-0.1582	0.0623
	SIZE	0.0594	0.0263	-0.0622	-0.0908	-0.4222	-0.4282	-0.0195	-0.1696	-0.0220	-0.2379	0.0326
	LGDPR	0.0317	0.0392	-0.1029	-0.1584	0.0423	0.0420	-0.1142	0.1207	-0.0109	0.1587	0.0782
	SHRATE	0.0437	0.0315	0.0072	-0.0099	-0.1012	-0.0247	0.0075	0.0753	0.0750	0.0721	0.1337
	PRIVBUR	-0.0217	-0.0139	-0.1961	-0.2193	0.1119	0.0893	-0.2013	-0.0579	-0.0907	-0.0954	-0.0239
	PUBREG	-0.0845	-0.0159	0.0095	0.0230	0.0754	0.0735	0.0116	-0.1028	0.0110	-0.1660	0.0116
	LEGAL	-0.0346	-0.0484	-0.0863	-0.0625	0.1226	0.1371	-0.1013	-0.0876	-0.0099	-0.1368	0.0058

Table 3 .

 3 The determinants of loan loss provisions in banking

	LLPTA
	Explanatory variables

Table 4 .

 4 Loan loss provisions and loan growth for all banks

		DLOAN	LOANG	DLOAN	LOANG
	Explanatory variables	Coefficient	t-Statistic	Coefficient	t-Statistic	Coefficient	t-Statistic	Coefficient	t-Statistic
	DISCTA	-0.0299	-0.2471	-0.0936	-0.4024
	NDISCTA	-0.3032***	-2.7095	-0.5797***	-2.6704
	DISCL					-0.4718	-1.1127	-0.6834	-1.3709
	NDISCL					-2.1512***	-3.7347	-4.1589***	-6.2076
	EQTA	-0.2017	-1.1051	-0.4795*	-1.7903	-0.1843	-0.8372	-0.4052	-1.5336
	DTA	-0.0789	-0.8518	-0.1373	-0.8993	-0.1240	-1.1799	-0.1644	-1.0946
	SIZE	0.0711***	3.8038	0.1004***	3.4140	0.0806***	3.5319	0.1163***	3.9901
	LGDPR	-0.0969	-1.5707	-0.0303	-0.2559	-0.1300	-1.6424	-0.0883	-0.7577
	SHRATE	-1.0425***	-5.5349	-2.2065***	-6.0711	-1.0176***	-4.6068	-2.1692***	-6.0741
	Observations	1298		1272		1297		1271
	R-squared	0.5807		0.5519		0.5983		0.5674
	F-statistic	4.1425***		3.5993***		4.4496***		3.8295***

Table 5 .

 5 Loan loss provisions and loan growth for all banks: Do private credit bureaus matter?

		DLOAN	LOANG	DLOAN	LOANG
	Explanatory variables	Coefficient	t-Statistic	Coefficient	t-Statistic	Coefficient	t-Statistic	Coefficient	t-Statistic
	DISCTA	0.1128	0.8156	0.1546	0.6465
	NDISCTA	-0.3977***	-2.9725	-0.7574***	-2.7448
	DISCL					-0.1110	-0.3692	0.1077	0.1695
	NDISCL					-2.1508***	-6.8336	-4.0790***	-6.0902
	EQTA	-0.1698	-1.1329	-0.4619**	-1.9873	-0.1762	-1.5080	-0.4125	-1.5484
	DTA	-0.0537	-0.4989	-0.1133	-0.4561	-0.0899	-1.2377	-0.1423	-0.9247
	SIZE	0.0762***	3.6855	0.1084**	2.5786	0.0843***	5.9946	0.1254***	4.2748
	LGDPR	-0.0699	-0.8925	0.0013	0.0079	-0.1009*	-1.7233	-0.0645	-0.5347
	SHRATE	-1.0373***	-2.6429	-2.1887***	-2.6485	-1.0420***	-5.9289	-2.2129***	-6.1983
	PRIVBUR	0.0453	1.5816	0.0609	0.8408	0.0425**	2.0341	0.0511	1.1575
	DISCTA*PRIVBUR	-1.3869*	-1.6604	-2.6228**	-1.7394
	DISCL*PRIVBUR					-1.4558*	-1.8971	-3.3121**	-2.0066
	Observations	1298		1272		1297		1271
	R-squared	0.5847		0.5545		0.6015		0.5699
	F-statistic	4.1767***		3.6095***		4.4731***		3.8370***

Table 6 .

 6 Loan loss provisions and loan growth for all banks: Do public credit registries matter?

		DLOAN	LOANG	DLOAN	LOANG
	Explanatory variables	Coefficient	t-Statistic	Coefficient	t-Statistic	Coefficient	t-Statistic	Coefficient	t-Statistic
	DISCTA	-0.3619*	-1.6632	-0.5826	-0.9283
	NDISCTA	-0.4567***	-3.4218	-0.8186***	-2.9803
	DISCL					-0.7819***	-2.7717	-1.1705**	-2.0993
	NDISCL					-2.1697***	-6.8640	-4.1665***	-4.8293
	EQTA	-0.1885***	-1.6035	-0.4759***	-2.5399	-0.1813	-1.5697	-0.3919***	-2.9474
	DTA	-0.0877	-1.2254	-0.1589	-0.6466	-0.1289*	-1.8321	-0.1783	-0.7932
	SIZE	0.0782***	5.3323	0.1124***	2.6199	0.0841***	5.8333	0.1241***	3.3402
	LGDPR	-0.0916	-1.5759	-0.0171	-0.0809	-0.1244**	-2.1829	-0.0844	-0.3928
	SHRATE	-1.0288***	-5.7241	-2.1857***	-2.6988	-1.0199***	-5.7944	-2.1686***	-2.8685
	PUBREG	-0.0699	-1.4405	-0.1213	-0.8566	-0.0427	-0.8987	-0.0804	-0.5641
	DISCTA*PUBREG	2.1265**	1.8456	3.1589*	0.8874
	DISCL*PUBREG					3.5615**	2.0675	6.5730**	2.3258
	Observations	1298		1272		1297		1271
	R-squared	0.5830		0.5533		0.6004		0.5689
	F-statistic	4.1478***		3.5909***		4.4522***		3.8213***

Table 7 .

 7 Loan loss provisions and loan growth for all banks: Do borrower legal rights matter?

		DLOAN	LOANG	DLOAN	LOANG
	Explanatory variables	Coefficient	t-Statistic	Coefficient	t-Statistic	Coefficient	t-Statistic	Coefficient	t-Statistic
	DISCTA	-0.0389	-0.0713	-2.3017***	-2.7017
	NDISCTA	-0.2902***	-2.7208	-0.5031**	-2.3780
	DISCL					-3.1598***	-7.7593	-8.1523***	-5.4426
	NDISCL					-2.1996***	-6.8869	-4.2818***	-5.3143
	EQTA	-0.1949*	-1.6528	-0.4959**	-2.4985	-0.1347	-1.3035	-0.4142***	-3.0302
	DTA	-0.0838	-1.1665	-0.1304	-0.5398	-0.1176	-1.2358	-0.1537	-0.7339
	SIZE	0.0747***	5.1287	0.1018**	2.4265	0.0796***	4.1059	0.1146***	2.8792
	LGDPR	-0.1346**	-2.0373	-0.0594	-0.2447	-0.1246	-1.1455	-0.0596	-0.2340
	SHRATE	-1.0686***	-5.8972	-2.2395***	-2.8789	-1.0111***	-3.0959	-2.1220***	-2.9244
	LEGAL	0.0113	1.1724	0.0129	0.6954	0.0007	0.0691	-0.0052	-0.2542
	DISCTA*LEGAL	0.0019	0.0184	0.4251***	2.8051
	DISCL*LEGAL					0.4829***	5.8487	1.3854***	5.3672
	Observations	1298		1272		1297		1271
	R-squared	0.5813		0.5539		0.6028		0.5738
	F-statistic	4.1187***		3.5988***		4.4976***		3.8993***

Table 8 .

 8 Loan loss provisions and loan growth for large bank sample

		DLOAN	LOANG	DLOAN	LOANG
	Explanatory variables	Coefficient	t-Statistic	Coefficient	t-Statistic	Coefficient	t-Statistic	Coefficient	t-Statistic
	DISCTA	-2.6547***	-3.6877	-4.7344***	-3.2464
	NDISCTA	-2.5661***	-3.7126	-4.5915***	-3.2917
	DISCL					-1.4626*	-1.7393	-2.2603**	-2.2973
	NDISCL					-2.8158**	-2.4237	-5.8621***	-4.3522
	EQTA	-0.2059	-0.6861	0.5247	0.8489	-0.2901	-0.6302	0.2191	0.3538
	DTA	-0.2049	-1.2825	-0.2259	-0.7057	-0.2296	-0.7712	-0.2656	-0.8406
	SIZE	0.1432***	5.3932	0.1973***	3.4568	0.1479***	5.9142	0.2177***	3.8657
	LGDPR	-0.1479*	-1.7953	-0.0601	-0.3540	-0.1441	-1.0707	-0.0650	-0.3879
	SHRATE	-1.1256***	-3.4242	-2.3136***	-3.5221	-1.1818*	-2.2693	-2.4619***	-3.7893
	Observations	481		478		481		478
	R-squared	0.3037		0.3380		0.3188		0.3544
	F-statistic	2.9211***		3.2347***		3.0614***		3.4029***

Table 9 .

 9 Loan loss provisions and loan growth for large bank sample: Do private credit bureaus matter?

		DLOAN	LOANG	DLOAN	LOANG
	Explanatory variables	Coefficient	t-Statistic	Coefficient	t-Statistic	Coefficient	t-Statistic	Coefficient	t-Statistic
	DISCTA	-2.7338***	-3.5601	-4.7818***	-3.0670
	NDISCTA	-2.5161***	-3.6469	-4.5319***	-3.2390
	DISCL					-2.7393***	-3.3736	-6.1875***	-3.7755
	NDISCL					-3.3731***	-4.7521	-7.6799***	-5.2858
	EQTA	-0.1107	-0.3684	0.6119	0.9879	-0.1279	-0.4248	0.6346	1.0206
	DTA	-0.2068	-1.2988	-0.2341	-0.7287	-0.2031	-1.2923	-0.1707	-0.5439
	SIZE	0.1459***	5.5253	0.2075***	3.6037	0.1519***	5.8711	0.2272***	4.0834
	LGDPR	-0.0591	-0.6652	0.0450	0.2480	-0.0597	-0.6807	0.0394	0.2225
	SHRATE	-1.2276***	-3.7335	-2.4454***	-3.7014	-1.1299***	-3.4171	-2.1129***	-3.1892
	PRIVBUR	0.1095**	2.5142	0.1484*	1.6659	0.1164***	2.7162	0.1730**	1.9932
	DISCTA*PRIVBUR	0.8618	0.8274	0.9369	0.4044
	DISCL*PRIVBUR					3.8699**	2.0807	12.3646***	3.0703
	Observations	481		478		481		478
	R-squared	0.3130		0.3397		0.3351		0.3727
	F-statistic	2.9707***		3.2111***		3.1794***		3.5534***

Table 10 .

 10 Loan loss provisions and loan growth for large bank sample: Do public credit registries matter?

		DLOAN	LOANG	DLOAN	LOANG
	Explanatory variables	Coefficient	t-Statistic	Coefficient	t-Statistic	Coefficient	t-Statistic	Coefficient	t-Statistic
	DISCTA	-2.4464***	-3.5441	-4.8505***	-3.2048
	NDISCTA	-2.4715***	-5.2810	-4.4946***	-3.6715
	DISCL					-1.3760***	-2.7166	-2.2354**	-2.1128
	NDISCL					-2.7542***	-4.0707	-5.5879***	-4.0624
	EQTA	-0.2309	-0.4939	0.3698	0.3376	-0.3210	-1.0737	0.1548	0.2515
	DTA	-0.2824	-1.0146	-0.4088	-0.8586	-0.3026*	-1.8840	-0.4299	-1.3413
	SIZE	0.1423***	4.9985	0.1966**	2.4953	0.1478***	5.6806	0.2158***	3.8565
	LGDPR	-0.0760	-0.5157	0.0569	0.1758	-0.0794	-0.9315	0.0691	0.3958
	SHRATE	-1.0438**	-2.0522	-2.1135**	-2.0655	-1.0748***	-3.2857	-2.2530***	-3.4541
	PUBREG	-0.1527**	-2.0972	-0.3248***	-2.6401	-0.1483**	-2.4489	-0.3157***	-2.6156
	DISCTA*PUBREG	-1.7936	-0.3292	5.7263	1.1293
	DISCL*PUBREG					-2.4895	-0.6133	-1.7347	-0.2099
	Observations	481		478		481		478
	R-squared	0.3122		0.3497		0.3263		0.3628
	F-statistic	2.9629***		3.3106***		3.0945***		3.4473***

Table 11 .

 11 Loan loss provisions and loan growth for large bank sample: Do borrower legal rights matter?

		DLOAN	LOANG	DLOAN	LOANG
	Explanatory variables	Coefficient	t-Statistic	Coefficient	t-Statistic	Coefficient	t-Statistic	Coefficient	t-Statistic
	DISCTA	-2.2932***	-1.8412	-5.2622***	-2.7008
	NDISCTA	-2.5085***	-3.4616	-4.5227***	-3.3881
	DISCL					-2.7271	-1.1069	-6.1891	-1.3808
	NDISCL					-2.7415***	-2.6865	-5.6859***	-2.6964
	EQTA	-0.2001	-0.5666	0.4818	0.7184	-0.3004	-0.9499	0.1975	0.3338
	DTA	-0.2131	-0.8130	-0.2269	-0.4743	-0.2347	-0.8808	-0.2698	-0.5504
	SIZE	0.1434***	3.5939	0.1986**	2.3122	0.1498***	3.8243	0.2223***	2.6181
	LGDPR	-0.1604	-1.5919	-0.0786	-0.4197	-0.1596	-1.5703	-0.0942	-0.4929
	SHRATE	-1.1378***	-3.7820	-2.3347***	-3.8466	-1.1575***	-3.8925	-2.3722***	-3.9187
	LEGAL	0.4855	0.2998	0.5043	0.1573	0.2645	0.1644	0.2534	0.0799
	DISCTA*LEGAL	-6.0469	-0.2828	12.0482	0.4142
	DISCL*LEGAL					24.9243	0.5191	76.4717	0.8891
	Observations	481		478		481		478
	R-squared	0.3005		0.3348		0.3167		0.3540
	F-statistic	2.8576***		3.1627***		3.0041***		3.3549***

Table 12 .

 12 Loan loss provisions and loan growth for small bank sample

		DLOAN	LOANG	DLOAN	LOANG
	Explanatory variables	Coefficient	t-Statistic	Coefficient	t-Statistic	Coefficient	t-Statistic	Coefficient	t-Statistic
	DISCTA	0.0537	0.4058	0.0105	0.0381
	NDISCTA	-0.3532***	-2.8634	-0.6955***	-2.6839
	DISCL					-0.1626	-0.5680	-0.2189	-0.3574
	NDISCL					-2.3166***	-6.0646	-4.1564***	-5.0061
	EQTA	-0.1999	-1.4749	-0.6045*	-1.9061	-0.1592	-1.1982	-0.4569	-1.4576
	DTA	-0.0330	-0.3944	-0.0651	-0.3543	-0.0932	-1.1322	-0.1104	-0.6107
	SIZE	0.0543***	3.0962	0.0811**	2.2364	0.0635***	3.6761	0.0949***	2.6373
	LGDPR	-0.1528*	-1.7803	-0.1936	-1.0881	-0.2132**	-2.5305	-0.3021*	-1.7147
	SHRATE	-1.1078***	-4.9251	-2.2342***	-4.8611	-1.0835***	-4.9379	-2.2036***	-4.8772
	Observations	817		794		816		793
	R-squared	0.4818		0.4147		0.5069		0.4341
	F-statistic	4.2992***		3.4533***		4.6425***		3.6533***

Table 13 .

 13 Loan loss provisions and loan growth for small bank sample: Do private credit bureaus matter?

		DLOAN	LOANG	DLOAN	LOANG
	Explanatory variables	Coefficient	t-Statistic	Coefficient	t-Statistic	Coefficient	t-Statistic	Coefficient	t-Statistic
	DISCTA	0.1911	1.2811	0.2469	0.8601
	NDISCTA	-0.4462***	-3.3611	-0.8710***	-3.4622
	DISCL					0.2509	0.5589	0.8659	0.7837
	NDISCL					-2.3364***	-4.7392	-4.1274***	-4.8387
	EQTA	-0.1774	-1.2983	-0.5989	-1.6061	-0.1703	-0.8856	-0.4892	-1.3183
	DTA	-0.0189	-0.2148	-0.0365	-0.1500	-0.0712	-0.6993	-0.0996	-0.4154
	SIZE	0.0606***	3.4235	0.0920**	2.1408	0.0688***	3.6111	0.1098***	2.6252
	LGDPR	-0.1363	-1.5463	-0.1672	-0.8863	-0.2000**	-2.2930	-0.2986*	-1.6629
	SHRATE	-1.0856***	-4.8335	-2.1935***	-3.8738	-1.0751***	-4.7233	-2.1857***	-3.9874
	PRIVBUR	0.0309	1.1740	0.0526	0.8475	0.0259	1.1602	0.0367	0.6054
	DISCTA*PRIVBUR	-1.4731**	-2.0282	-2.7274*	-1.8608
	DISCL*PRIVBUR					-1.8905**	-2.3426	-4.9161**	-2.5713
	Observations	817		794		816		793
	R-squared	0.4844		0.4163		0.5096		0.4389
	F-statistic	4.3051***		3.4489***		4.6512***		3.6819***

Table 14 .

 14 Loan loss provisions and loan growth for small bank sample: Do public credit registries matter?

		DLOAN	LOANG	DLOAN	LOANG
	Explanatory variables	Coefficient	t-Statistic	Coefficient	t-Statistic	Coefficient	t-Statistic	Coefficient	t-Statistic
	DISCTA	-0.2113	-0.7748	-0.0201	-0.0345
	NDISCTA	-0.4558***	-2.9749	-0.7069**	-2.1187
	DISCL					-0.4868*	-1.7101	-0.7394	-0.8408
	NDISCL					-2.3535***	-10.1207	-4.2152***	-6.8236
	EQTA	-0.1905	-1.3890	-0.6130*	-1.9035	-0.1630	-0.9884	-0.4728**	-2.0979
	DTA	-0.0346	-0.4119	-0.0640	-0.3471	-0.0907	-0.7138	-0.1095	-0.3849
	SIZE	0.0582***	3.0802	0.0792**	2.0259	0.0646***	3.2073	0.0945**	2.1791
	LGDPR	-0.1527*	-1.7779	-0.1939	-1.0879	-0.2114**	-2.3798	-0.3103	-1.3569
	SHRATE	-1.1046***	-4.9077	-2.2337***	-4.8511	-1.0823***	-3.3289	-2.1940***	-2.9573
	PUBREG	-0.0235	-0.3013	0.0249	0.1539	-0.0025	-0.0274	0.0439	0.2198
	DISCTA*PUBREG	1.5881**	1.1326	0.1512	0.0499
	DISCL*PUBREG					3.2664**	1.8659	6.0932**	1.4283
	Observations	817		794		816		793
	R-squared	0.4813		0.4126		0.5073		0.4336
	F-statistic	4.2633***		3.4116***		4.6177***		3.6250***

Table 15 .

 15 Loan loss provisions and loan growth for small bank sample: Do borrower legal rights matter?

			DLOAN	LOANG	DLOAN	LOANG
	Explanatory variables	Coefficient	t-Statistic	Coefficient	t-Statistic	Coefficient	t-Statistic	Coefficient	t-Statistic
	DISCTA	0.2292	0.3070	-2.2657	-1.1187
	NDISCTA	-0.3540***	-2.7741	-0.6109***	-2.6528
	DISCL					-2.9525***	-3.0522	-8.2554***	-2.9595
	NDISCL					-2.3918***	-4.6287	-4.3927***	-4.7290
	EQTA	-0.1924	-0.9151	-0.6332*	-1.7061	-0.1120	-0.6141	-0.4933	-1.3875
	DTA	-0.0373	-0.3773	-0.0526	-0.2213	-0.0834	-0.8668	-0.0892	-0.3987
	SIZE	0.0565***	2.6917	0.0758*	1.7246	0.0596***	2.9677	0.0852**	1.9941
	LGDPR	-0.1742*	-1.7416	-0.1327	-0.6646	-0.1762*	-1.8119	-0.1837	-0.9499
	SHRATE	-1.1234***	-4.5354	-2.1965***	-3.8244	-1.0807***	-4.6708	-2.1445***	-3.8691
	LEGAL	0.5606	0.3960	-0.7147	-0.2402	-0.3695	-0.2701	-2.2142	-0.7542
	DISCTA*LEGAL	-3.3136	-0.2399	43.1925	1.1136
	DISCL*LEGAL					49.1530***	3.0735	146.8840***	2.6871
	Observations	817		794		816		793
	R-squared	0.4803		0.4147		0.5117		0.4423
	F-statistic	4.2502***		3.4319		4.6820***		3.7189***

See Angklomkliew et al. (2009) for further discussion on the current loan loss provisioning system in various Asian countries in order to respond the 1997/1998 financial crisis.

We interact each variable representing country-specific credit market environments (i.e. private credit bureaus, public credit registries, and borrower legal rights) with discretionary provisions instead of nondiscretionary provisions, because only discretionary provisions can be adjusted by bank managers for capital management, signaling, or income smoothing purposes. Arguably, credit market environments at the country level can affect the extent to which capital management, signaling or income smoothing plays a role, so that bank managers increases bank loan loss provisions. On the other hand, non-discretionary provisions cannot be easily adjusted due to credit market environments, because non-discretionary provisions are merely dependent on the degree of non-performing loans.