After discussing various factors related to bank lending, we are now ready to specify variables for the empirical models. Following loan pricing literature(Bharath, et al, 2010 Lin et al, 2011; among others), loan rate can be measured by the spread over a benchmark interest rate. The natural choice of the benchmark rate in china is the benchmark lending rate set by PBC, which is also widely used in loan pricing in practice. The empirical model can be written as Spread t =Bo+B, MP_+B, Mr+B, FCi+B.LCit-l+Bs ADi+a+ur( 8) Where Spread t is the price spread over the corresponding benchmark lending rate for firm i at time t. All explanatory variables are lagged one quarter in order to avoid endogeneity caused by simultaneousity. MP- indicates monetary policy instruments such as benchmark deposit rate and rrr in the period t-l. MRit- represents the free market interest rate which is the market interest rate for bank lending. FCit-denotes various firms characteristics such as the firm's industry, firms ownership, and various financial characteristics such as return of equity, total asset, total employment, liquid asset, debt-to-asset ratio, equity-to-debt ratio and profit margin. LC,- includes features of a specific loan such as loan maturity, the type of bank and collateral. AD- denotes macroeconomic variables including fixed-asset investment growth, money supply and market liquidity. a represents unobservable firm characteristics such as reputation of the firm, relationship between firm and bank etc. u t is the idiosyncratic shock which is not correlated with any explanatory variables Similarly, we can write down an empirical model for loan quantity as follows L=Φ。+Φ1MP1+Φ2MRa-1+中3FC+中LC+中ADn++En(9 Where L is the loan quantity for firm i at time t. u; represents some unobservable fixed effect impacting the loan size and e is the idiosyncratic shock. Other variables are the same as Equation( 8). Since the dataset is a panel dataset, it is good to remove observable time-constant features such as the firms industry and ownership. Moreover, the panel also enables us to eliminate unobservable time-constant features such as the firms' reputation and the relationship between banks and firms. Empirically, the easiest way to remove these fixed effects is to take the first differencing of Equation(8 )as follows △ Spread=B1△MP1+B2△MR1+B3△FC-1+B4△LCi-1+B5△ADn-1+△an(10) In practice, prices in loan contracts in China are as a certain amount of interest rate For different maturities. PbC has set different be rates. The spread of a loan is calculated based on its corresponding benchmark lending rate
11 After discussing various factors related to bank lending, we are now ready to specify variables for the empirical models. Following loan pricing literature (Bharath, et al, 2010; Lin et al, 2011; among others), loan rate can be measured by the spread over a benchmark interest rate. The natural choice of the benchmark rate in China is the benchmark lending rate set by PBC, which is also widely used in loan pricing in practice.8 The empirical model can be written as it it it it it it i it Spread = Β + Β MP + Β MR + Β FC + Β LC + Β AD + + u 0 1 −1 2 −1 3 −1 4 −1 5 −1 α (8) Where it Spread is the price spread over the corresponding benchmark lending rate for firm i at time t 9 . All explanatory variables are lagged one quarter in order to avoid endogeneity caused by simultaneousity. MPit−1 indicates monetary policy instruments such as benchmark deposit rate and RRR in the period t −1 . MRit−1 represents the free market interest rate which is the market interest rate for bank lending. FCit−1 denotes various firm’s characteristics such as the firm’s industry, firm’s ownership, and various financial characteristics such as return of equity, total asset, total employment, liquid asset, debt-to-asset ratio, equity-to-debt ratio and profit margin. LCit−1 includes features of a specific loan such as loan maturity, the type of bank and collateral. ADit−1 denotes macroeconomic variables including fixed-asset investment growth, money supply and market liquidity. αi represents unobservable firm characteristics such as reputation of the firm, relationship between firm and bank etc. it u is the idiosyncratic shock which is not correlated with any explanatory variables . Similarly, we can write down an empirical model for loan quantity as follows: Lit MPit MRit FCit LCit ADit i it = Φ + Φ + Φ + Φ + Φ + Φ + µ + ε 0 1 −1 2 −1 3 −1 4 −1 5 −1 (9) Where Lit is the loan quantity for firm i at time t . µi represents some unobservable fixed effect impacting the loan size and it ε is the idiosyncratic shock. Other variables are the same as Equation (8). Since the dataset is a panel dataset, it is good to remove observable time-constant features such as the firm’s industry and ownership. Moreover, the panel also enables us to eliminate unobservable time-constant features such as the firms’ reputation and the relationship between banks and firms. Empirically, the easiest way to remove these fixed effects is to take the first differencing of Equation (8) as follows: it it it it it it it ∆Spread = Β ∆MP + Β ∆MR + Β ∆FC + Β ∆LC + Β ∆AD + ∆u 1 −1 2 −1 3 −1 4 −1 5 −1 (10) 8 In practice, prices in loan contracts in China are typically written as a certain amount of interest rate spreads over the corresponding benchmark lending rate set by PBC. 9 For different maturities, PBC has set different benchmark lending rates. The spread of a loan is calculated based on its corresponding benchmark lending rate
Where ASpread, Spreadt -Spreadi-I. The same first differencing applies to other variables. Incorporating what we discussed before, a full model with all explanatory variables can be written as △ Spread=B△dn-1+B2△RR-1+B2△ repo-1+B4△ naturity+B3△bank +B6△coll-4+B,△ FAl1+B△m2n-1+B△xn-1+B104fxn+B1△rOen-1+B12△ liquid +B3Atan-1+A4△ daratio-1+B13△ equity+B6△ m In-1+B12△ employee+△un (11) Detailed definitions for all above variables can be found in Table 1. Similarly, the first difference of Equation(9)can be written as △Ln=y△din-+y2△RRR-1+y3△ repo1+y4△ maturity-1+y△ bank +y6△coll-y△FAln-1+y△m2a-+y△xn-1+%o4fxn-1+%1 Aroe-1+y12△ liquid +%3△tan+y4△ daratio+ys△ equity-1+y6△ narg in-1+yn△ employee-+△Ea Table 1: Definition of Variables Name of variables Variable definition Loan spread( spreadi Loan interest rate spread charged by a bank over the corresponding benchmark lending rate set b PBC Loan size(l,) Loan amount Benchmark deposit rate( dr,) Benchmark deposit rate set by PbC RRR (RRR) Reserve requirement ratio set by PBC 7-day Repo rate(repo Indicator for the market interest rate: 7-day repurchase agreement rates in money market Maturity maturity Loan maturty Dummies for different banks(l=the large four state-owned bank, 2=joint-venture banks Bank type( bank 3=foreign banks. 4=rural credit cooperation, 5=city commercial banks, 6=private credit company, 7=other financial company, 8=development banks d=others Collateral(coll) Dummy variable to indicate whether the loan is Investment( FAl Fixed asset investment growth Money supply(m2) Money supply growth Inflation (I,) Inflation rate 10 The large four banks are Industrial and Commercial Bank of China(ICBC), China Construction Bank (CCB), Bank of China(BOC)and Agricultural Bank of China(ABC)
12 Where ∆ it = it − it−1 Spread Spread Spread . The same first differencing applies to other variables. Incorporating what we discussed before, a full model with all explanatory variables can be written as: it it it it it it it it it it it it it it it it it it it ta daratio equity m in employee u coll FAI m fx roe liquid Spread dr RRR repo maturity bank + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ ∆ = ∆ + ∆ + ∆ + ∆ + ∆ − − − − − − − − − − − − − − − − − 13 1 14 1 15 1 16 1 17 1 6 1 7 1 8 1 9 1 10 1 11 1 12 1 1 1 2 1 3 1 4 1 5 1 arg 2 β β β β β β β β β π β β β β β β β β (11) Detailed definitions for all above variables can be found in Table 1. Similarly, the first difference of Equation (9) can be written as: it it it it it it it it it it it it it it it it it it it ta daratio equity m in employee coll FAI m fx roe liquid L dr RRR repo maturity bank γ γ γ γ γ ε γ γ γ γ π γ γ γ γ γ γ γ γ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ ∆ = ∆ + ∆ + ∆ + ∆ + ∆ − − − − − − − − − − − − − − − − − 13 1 14 1 15 1 16 1 17 1 6 1 7 1 8 1 9 1 10 1 11 1 12 1 1 1 2 1 3 1 4 1 5 1 arg 2 (12) Table 1: Definition of Variables Name of variables Variable definition Loan spread ( it Spread ) Loan interest rate spread charged by a bank over the corresponding benchmark lending rate set by PBC Loan size( Lit ) Loan amount Benchmark deposit rate( it dr ) Benchmark deposit rate set by PBC RRR ( RRRit ) Reserve requirement ratio set by PBC 7-day Repo rate ( it repo ) Indicator for the market interest rate: 7-day repurchase agreement rates in money market Maturity( maturityit ) Loan maturity Bank type ( it bank ) Dummies for different banks (1=the large four state-owned bank10, 2=joint-venture banks, 3=foreign banks. 4=rural credit cooperation, 5=city commercial banks, 6=private credit company, 7=other financial company, 8=development banks, 9=others Collateral ( it coll ) Dummy variable to indicate whether the loan is backed by collateral ( Yes=1, No=0) Investment ( FAIit ) Fixed asset investment growth Money supply ( m2 ) Money supply growth Inflation (π it ) Inflation rate 10 The large four banks are Industrial and Commercial Bank of China (ICBC), China Construction Bank (CCB), Bank of China (BOC) and Agricultural Bank of China (ABC)
Foreign asset(fx) Foreign asset purchase position Return of equity( roe) Return of equity Liquidity (liquid) Total asset(ta) Total asset of the firm Debt-to-asset ratio( daratio Ratio of total debt to total asset quity-to-debt ratio (equity Equity-to-debt ratio Profit margin( marg in;) Earnings before interest rate and tax/total revenue Total employment(employee) Total employment of the firm 4.3 Does the impact of monetary policy instruments on bank lending vary with firm size? The above empirical models try to answer how monetary policy affect loan pricing and oan size after controlling for firm characteristics and microeconomic conditions from the results, we are able to quantify the partial impact from the policy instruments and the market interest rate. However, is the impact on bank lending symmetric to large firms and small firms? In order to answer this question, we need to enable the parameters before monetary policy instruments to be flexible in terms of firm size. Specifically speaking, we need to add interaction terms between policy instruments and firm size(we use total asset as the indicator of the firm size)as follows: △ Spread=Bl1△hnx1+B2△RRn1+f3△ repo1+B4△dr1-1×tan-1) +B5△(RR1×1an-1)+P6△( repo-×an-1)+B2△ maturity+Bs△b +B,△cOl-1+B1o△FAln1+B1Mm2n+B12△兀n+B13Axnx1+B14△roen-1 +Bs△ quid1+B16an1+Bn△ daratio1+Bls△ equity-+B1△ Im in +B20△ employee1+△ Similarly, the loan size equation with more flexible parameters before monetary policy instruments can be written as △Ln=yl△dn+y2△RRn1+y3△ . repo+y4△(dn-1×tana-) +y5△(RRR1-1×tan-)+y6△( repo-×tan-1)+y7△ maturity+y8△ bank-1 +y△coll-1+yo△FAln1+y1△m2+y12△m1+y134fx1-1+y14△ Aroe+yls△ iquid=1 +y6△an-1+y17△ daratio1+y1s△ equity+y1 Am arg in-1+y2△ employee+△El 4.4 Does the impact of monetary policy instruments on bank lending vary with monetary stance? The impact from policy instruments and the market interest rate on bank lending could also vary with monetary stance since the monetary policy transmission mechanism could be asymmetric between monetary tightening and loosening. In order to answer this question, we can add similar interaction terms between monetary policy instruments and 13
13 Foreign asset ( it fx ) Foreign asset purchase position Return of equity ( it roe ) Return of equity Liquidity ( it liquid ) Total liquid assets Total asset ( it ta ) Total asset of the firm Debt-to-asset ratio ( it daratio ) Ratio of total debt to total asset Equity-to-debt ratio ( it equity ) Equity-to-debt ratio Profit margin( it marg ) in Earnings before interest rate and tax/total revenue Total employment ( it employee ) Total employment of the firm 4.3 Does the impact of monetary policy instruments on bank lending vary with firm size? The above empirical models try to answer how monetary policy affect loan pricing and loan size after controlling for firm characteristics and microeconomic conditions. From the results, we are able to quantify the partial impact from the policy instruments and the market interest rate. However, is the impact on bank lending symmetric to large firms and small firms? In order to answer this question, we need to enable the parameters before monetary policy instruments to be flexible in terms of firm size. Specifically speaking, we need to add interaction terms between policy instruments and firm size (we use total asset as the indicator of the firm size) as follows: it it it it it it it it it it it it it it it it it it it it it it it it it employee u liquid ta daratio equity m in coll FAI m fx roe RRR ta repo ta maturity bank Spread dr RRR repo dr ta ' ' ' ' ' ' ' arg ' ' ' 2 ' ' ' ' ( ) ' ( ) ' ' ' ' ' ' ( ) 20 1 15 1 16 1 17 1 18 1 19 1 9 1 10 1 11 1 12 1 13 1 14 1 5 1 1 6 1 1 7 1 8 1 1 1 2 1 3 1 4 1 1 + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ × + ∆ × + ∆ + ∆ ∆ = ∆ + ∆ + ∆ + ∆ × − − − − − − − − − − − − − − − − − − − − − − − β β β β β β β β β β π β β β β β β β β β β Similarly, the loan size equation with more flexible parameters before monetary policy instruments can be written as it it it it it it it it it it it it it it it it it it it it it it it it it ta daratio equity m in employee coll FAI m fx roe liquid RRR ta repo ta maturity bank L dr RRR repo dr ta ' ' ' ' arg ' ' ' ' ' 2 ' ' ' ' ' ( ) ' ( ) ' ' ' ' ' ' ( ) 16 1 17 1 18 1 19 1 20 1 9 1 10 1 11 1 12 1 13 1 14 1 15 1 5 1 1 6 1 1 7 1 8 1 1 1 2 1 3 1 4 1 1 γ γ γ γ γ ε γ γ γ γ π γ γ γ γ γ γ γ γ γ γ γ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ × + ∆ × + ∆ + ∆ ∆ = ∆ + ∆ + ∆ + ∆ × − − − − − − − − − − − − − − − − − − − − − − − 4.4 Does the impact of monetary policy instruments on bank lending vary with monetary stance? The impact from policy instruments and the market interest rate on bank lending could also vary with monetary stance since the monetary policy transmission mechanism could be asymmetric between monetary tightening and loosening. In order to answer this question, we can add similar interaction terms between monetary policy instruments and
monetary stance. However, since the monetary stance is not announced by pbc publicly, we need to define the monetary policy stance according to monetary policy changes. For sake of simplicity, we define the monetary policy stance according to the benchmark lending rate changes announced by PBC. Therefore, the empirical models can be written as follows. △ Spread=B1Mn+B2△RRn1+B"'△ repo+'4△(dn×l +B'5△(RR1-Xln-1)+B'6△( repo-1×an-1)+p"△ maturity-+p"s△ bank +B△coln1+B1o△FAm1+B"Mm2m+B12△xa+'134fx1+B"14△roen +B'5△ iquid1+p'16△an-4+B'1n△ aratio+p"ls△ equity-+B' Am arg in-1 +B"2a0△ employee-+△'"a Similarly, the loan size equation looks as follows △Ln=y1△n-1+y2△RRa-1+y3△epon-1+y4△n×ln) +y5△(RRR-1tan-1)+y6△( repo-1×tan-1)+y7△ maturity-1+y8△ bank-1 +y△coln-+y1o△FAla1+y1△m2i-1+y12△rn+y14xn-1+y14△roen1 +yls5△ liquid1+y16△tan-1+y1n△ duration+ys△ equity+y△ n arg in-1 +y2△ employee+△e" 5. Data and Sample selection The data is a hand-collected loan level panel dataset from WINd financial data service ranges from 2002Q3 to 2011Q4. The reasons we chose companies on the SZSE are d o he dataset includes 672 listed firms in Shenzhen Stock Exchange and the sample perie follows: First of all. firms listed on the szse are more diverse in terms of firm size compared to those listed on the Shanghai Stock Exchange(SHSE). Second, the loan level information of listed firms must be collected one by one manually and hence is very time consuming. The data of listed firms from shse can be used and included in the study in future While the data on firms characteristics and financial conditions can be obtained relatively easily from quarterly financial reports of listed firms, individual loan data such as loan rate and loan size can only be collected manually from appendices and footnotes of financial reports. Most details on loans are available after 2007 since the Chinese Securities Regulatory Commission(CSRC) imposed stricter regulations on financial information disclosure in early 2007, requiring listed firms to report any important changes(including fund raising and bank loans)related to their financial conditions. 12 However, not all listed firms on the Szse report their loan information in a valid format Some firms only partially report information concerning their borrowings such as the I There are multiple ways to define monetary stance in China, more information about this can be found in He and Pauwels. 2008 Detailed information on the new regulation(Information Disclosure Regulation for Companies Offering SecuritiestothePublic2007No.9)canbefoundinhttp://www.csrc.gov.cn/pub/newsite
14 monetary stance. However, since the monetary stance is not announced by PBC publicly, we need to define the monetary policy stance according to monetary policy changes. For sake of simplicity, we define the monetary policy stance according to the benchmark lending rate changes announced by PBC.11 Therefore, the empirical models can be written as follows: it it it it it it it it it it it it it it it it it it it it it it it it it employee u liquid ta daratio equity m in coll FAI m fx roe RRR ta repo ta maturity bank Spread dr RRR repo dr ta '' '' '' '' '' '' '' arg '' '' '' 2 '' '' '' '' ( ) '' ( ) '' '' '' '' '' '' ( ) 20 1 15 1 16 1 17 1 18 1 19 1 9 1 10 1 11 1 12 1 13 1 14 1 5 1 1 6 1 1 7 1 8 1 1' 1 2 1 3 1 4 1 1 + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ × + ∆ × + ∆ + ∆ ∆ = ∆ + ∆ + ∆ + ∆ × − − − − − − − − − − − − − − − − − − − − − − − β β β β β β β β β β π β β β β β β β β β β Similarly, the loan size equation looks as follows: it it it it it it it it it it it it it it it it it it it it it it it it it employee liquid ta daratio equity m in coll FAI m fx roe RRR ta repo ta maturity bank L dr RRR repo dr ta '' '' '' '' '' '' '' arg '' '' '' 2 '' '' '' '' ( ) '' ( ) '' '' '' '' '' '' ( ) 20 1 15 1 16 1 17 1 18 1 19 1 9 1 10 1 11 1 12 1 13 1 14 1 5 1 1 6 1 1 7 1 8 1 1 1 2 1 3 1 4 1 1 γ ε γ γ γ γ γ γ γ γ γ π γ γ γ γ γ γ γ γ γ γ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ + ∆ × + ∆ × + ∆ + ∆ ∆ = ∆ + ∆ + ∆ + ∆ × − − − − − − − − − − − − − − − − − − − − − − − 5. Data and Sample Selection The data is a hand-collected loan level panel dataset from WIND financial data service. The dataset includes 672 listed firms in Shenzhen Stock Exchange and the sample period ranges from 2002Q3 to 2011Q4. The reasons we chose companies on the SZSE are as follows: First of all, firms listed on the SZSE are more diverse in terms of firm size compared to those listed on the Shanghai Stock Exchange (SHSE). Second, the loan level information of listed firms must be collected one by one manually and hence is very time consuming. The data of listed firms from SHSE can be used and included in the study in future. While the data on firms’ characteristics and financial conditions can be obtained relatively easily from quarterly financial reports of listed firms, individual loan data such as loan rate and loan size can only be collected manually from appendices and footnotes of financial reports. Most details on loans are available after 2007 since the Chinese Securities Regulatory Commission (CSRC) imposed stricter regulations on financial information disclosure in early 2007, requiring listed firms to report any important changes (including fund raising and bank loans) related to their financial conditions.12 However, not all listed firms on the SZSE report their loan information in a valid format. Some firms only partially report information concerning their borrowings such as the 11 There are multiple ways to define monetary stance in China, more information about this can be found in He and Pauwels, 2008. 12 Detailed information on the new regulation (Information Disclosure Regulation for Companies Offering Securities to the Public 2007 No. 9) can be found in http://www.csrc.gov.cn/pub/newsite