Silviase/jgov_v1.4_full_ann huggingface dataset:
asymmetric information between banks and their potential shareholders. $ ^{7} $ $ ^{8} $

#### 2.2. The Japanese Credit Crunch of 1997-1998 and JASME’s Policy Responses

Measuring a bank’s capital adequacy by the differential between the bank’s actual capital to asset ratio and its (hypothetical) target, Watanabe (2007a) finds that in FY 1997, in aggregate, the bank’s insufficient capital adequacy reduced lending to the manufacturing industry and the lending to “healthy” non-manufacturing industries, which exclude the industries to which the share of loans that became non-performing was higher than the industry wide average, by 5.7% and 8.5%, respectively, confirming that the credit crunch made the access to bank credit by relatively healthy firms challenging. $ ^{9} $

The primary objective of the JASME, which was established in August 1953, was to make loans to SMEs whose access to private credit is relatively limited. By laws, the JASME was stipulated to lend long-term loans with maturity no less than one year. The loans outstanding of the JASME stood at 1,820 billion yen as of the end of FY 1997. The JASME was disestablished in October 2008 and was consolidated into the JFC, a newly established GFI, as its Small and Medium Enterprise Unit.

As a credit crunch became increasingly evident, the Government of Japan took a wide range of actions to ease the stress felt by the firms, particularly bank dependent SMEs that were having increasing difficulty in meeting their financing needs. The Government announced three comprehensive policy packages in which the adverse
effects of the credit crunch from late 1997 through 1998 were explicitly addressed. The three are the “Emergency Economic Measures to Clear a Path for the 21st Century” (hereafter referred to as the “Emergency Economic Measures”) released at the Meetings of Ministers for Economic Measures in November 1997, the “Comprehensive Economic Measures” released in April 1998 and the “Outline of the Measures for SMEs Affected by the Banks’ Less Willingness to Lend”, which the Cabinet approved in August 1998.

Among these three, it is the first package, the “Emergency Economic Measures”, that lead to the creation of the largest counter credit crunch program of the JASME. This package specifically requested GFIs including the JASME to launch the lending programs targeting SMEs potentially having a hard time financing because of, for instance, having difficulty securing working capital after undergoing substantial changes in transactions with (private) financial institutions. In response to the request in this package, by inaugurating the working capital targeting “Fund to Respond to Changes in Financial Environments”, the JASME became more committed to greatly expanding its policy lending to SMEs likely adversely affected by the credit crunch.

The amount of JASME’s loans outstanding under the “Fund” at 336 billion yen as of FY 1998 is far greater than the total amount of its loans outstanding under various measures the JASME employed under two later packages. Thus, our primary interests lie in the JASME’s lending behavior after it established the “Fund” in December 1997. As Figure 1 shows, the JASME’s working capital loans the “Fund” targeted grew more rapidly during the period from FY 1997 through FY 1999 than before, while its equipment loans did not. $ ^{10} $
#### 2.4. The Relevant Literature about State Owned Banks

The extant studies using various levels of data are not very conclusive about SOBs' lending along the business cycle, particularly in times of crisis, but generally report the counter-cyclical nature of SOBs in developed economies. The World Bank (2013) reports that, in developed economies, the asset share of state owned banks in the financial system increased from 6.7 percent during the period 2001-2007 to 8 percent during the period 2008-2010, while in developing economies the share decreased from 20.5 percent to 17.3 percent. Cull and Peria (2013) find that, during the crisis period of 2008 and 2009, lending by SOBs was counter-cyclical in Latin America but that it was not in Eastern Europe. Bertay et al. (2015) find that lending by SOBs is less pro-cyclical than lending by private banks in developing countries and that it is counter-cyclical in developed economies. Duprey (2015) find that SOBs are less cyclical than private banks in high income and middle-income countries but are not in low-income countries. Using the same JFC provided data as ours, Ogura (2015) finds that, during the period of the global financial crisis, Japanese SMEs increased the share of borrowing from GFIs if their main banks were large banks whose loans outstanding to SMEs decreased in aggregate.

Another concern about SOBs is whether their lending, particularly their countercyclical lending during the crisis period, help firms become more productive or profitable. Coleman and Feler (2015), Bonomo et al. (2015) and Lazzarini et al. (2015) find that increased government-oriented lending in Brazil after 2008 is not statistically significantly associated with various firm productivity measures. Lin et al. (2015),
however, find that GFI's lending to a listed Japanese firm is positively associated with the firm's contemporaneous investment and its ROA one year later and these associations are stronger in the period from 1991 through 1994 when the real GDP growth slowed down. Carvalho (2014) finds that, in Brazil, the firms eligible for borrowing loans from SOBs shift their employment to the states politically attractive to incumbent election candidates but do not expand the overall employment during the non-crisis period. Eslava et al. (2014) find that small Colombian firms that borrowed loans from a public development bank are associated with larger employment, larger investment and larger output during the period from 2004 through 2009. Ru (2017) finds that, during a period from 1998 through 2009, a state owned bank's lending to a local government is associated with greater employment by SOEs and smaller employment by private firms in the locality.

### 3. Data and Methodology

#### 3.1. The Hypothesis and the Empirical Models

Our primary objective is to examine the efficacy of the JASME’s policy to expand lending aimed at mitigating adverse effects the credit crunch inflicted on SMEs. If the JASME’s lending achieved its policy objective, it should have lent more aggressively to a firm that faced a financial constraint by the credit crunch more greatly, and thereby played a role of compensating private financial institutions (banks). Therefore, the hypothesis we need to test on is, “the amount of loans the JASME lent to a firm whose main bank cut bank on more lending supply was greater.”

The empirical model to test on this hypothesis we employ is the following equation (1).
$$ JASME_{i}=\alpha_{0}+\alpha_{1}CAPSUR_{i}+\alpha_{2}Z_{i}+\alpha_{3}Industry_{i}+\varepsilon_{i}\quad\cdots(1) $$

$ JASME_{i} $ is a measure for the amount of loans the JASME extended to firm i during the period from December 1, 1997 through March 31, 1999, which we call the (JASME credit crunch) policy period. $ ^{11} $ $ Z_{i} $ is a set of control variables. $ Industry_{i} $ and $ \varepsilon_{i} $ are a set of industry dummy variables and an error term, respectively. Following Gopalan et al. (2011), we employ, as control variables, the logarithm of total assets, ROA as defined by net income divided by total assets and the leverage as defined by total liabilities, which equals total assets less net wealth, divided by total assets. $ ^{12} $ $ ^{13} $ $ ^{14} $ These financial statement based variables are measured as of the fiscal year closing for a firm between April 1997 and March 1998 if the earliest loan contract was extended until March 1998, and are measured as of FY closing for a firm between April 1998 and March 1999 if the earliest contract was extended after April 1998. CAPSUR $ _{i} $ is the growth rate of lending (supply) by firm i's main bank due to the bank's capital adequacy in excess of its target. We will explain the way CAPSUR is constructed shortly.

For $ JASME_{i} $ , we examine amounts of two types of loans grouped in the JFC contract data, equipment loans and working capital loans as well as total loans that are a sum of amounts of equipment loans and working capital loans. If the JASME extends multiple
loans to a firm during the policy period, we obtain the total amount of loans for each loan type by summing loan amounts.

We construct CAPSUR based on the regression run by Watanabe (2007a). Watanabe estimates the following regression equation using the data about domestically licensed banks extracted from the Nikkei NEEDS bank financial data.

$$ \Delta l n L_{j,97}=\beta_{0}+\beta_{1}\Delta l n L_{j,96}+\beta_{2}\left\{\frac{K_{j,97}}{A_{j,97}}-\left(\frac{K_{j}}{A_{j}}\right)^{t a r g e t}\right\}+\beta_{3}X_{j}+\epsilon_{j}\quad\cdots\left(2\right) $$

Where $ ln\Delta L_{j,97} $ is the growth rate of bank j's loans excluding loans to “troubled” industries that consist of real estate, construction, services and wholesale and retail industries, which are the industries where the share of non-performing loans exceeds the average across the entire industries. $ \frac{K_{j,97}}{A_{j,97}} $ is the ratio of capital to total assets of bank j, $ \left(\frac{K_{j}}{A_{j}}\right)^{target} $ is its time invariant target as estimated by the time series average of bank j's ratio of capital to total assets over the three year period from FY 1992 through FY 1994. $ X_{j} $ is a set of dummy variables for such bank types as city banks, trust banks and regional banks while regional 2 banks are a base group. $ \epsilon_{j} $ is an error term. Watanabe (2007a) identifies the estimate of $ \beta_{2} $ , $ \widehat{\beta_{2}} $ by employing a bank's share of lending to the real estate industry in FY 1989 and the bank's 10 year-growth of lending share to the real estate industry since FY 1980 as instrumental variables that are independent of the business cycle driven correlation between bank capital and borrowing demand. $ ^{15} $
CAPSUR is constructed as the product of the differential between the actual ratio of capital to total assets and its target, which Watanabe calls the capital surplus, $ \left\{\frac{K_{j,97}}{A_{j,97}} - \left(\frac{K_{j}}{A_{j}}\right)^{target}\right\} $ and $ \widehat{\beta_{2}} $ , $ \widehat{\beta_{2}}\left\{\frac{K_{j,97}}{A_{j,97}} - \left(\frac{K_{j}}{A_{j}}\right)^{target}\right\} $ . CAPSUR is the growth rate of loans excluding loans to “troubled” industries that can be explained by the capital surplus of a bank. A negative CAPSUR means that to what extent a bank’s inadequate capital slowed the bank’s lending growth. Thus, (the negative of) CAPSUR is a measure for the extent of bank j’s reduction in lending supply due to poor capital adequacy, which is a variable to measure the extent of the credit crunch a firm that borrows from bank j faces. $ ^{16} $

We also attempt to assess the JASME’s policy lending by examining the effect of its lending on a borrowing firm’s concurrent and ex-post real and financial behavior as well as its ex-post performance. In practice, we run the following regression.

$$ y_{i}=\gamma_{0}+\gamma_{1}JASME_{i}+\gamma_{3}W_{i}+\gamma_{4}Industry_{i}+\nu_{i}\quad\cdots(3) $$

Where $ y_{i} $ is a variable to measure firm i's real or financial behavior or its performance as of FY1998 or later. $ JASME_{i} $ is the logarithm of total loans firm i borrowed from the policy period from December, 1997 through March, 1999. $ W_{i} $ is a set of additional independent variables. For y, we employ the following six variables, two variables to measure the firm profitability, the ROA, the EBITDA to total assets ratio, the investment rate, a change in the cash to capital stock ratio, the borrowing growth and
the employment growth. For each of these dependent variables employed as $ y_{i} $ , we employ a different set of additional independent variables as represented by $ W_{i} $ . $ Industry_{i} $ and $ \nu_{i} $ are a set of industry dummy variables and an error term, respectively. We instrument $ JASME_{i} $ by independent variables employed in equation 1 including CAPSUR so as to identify the JASME's counter credit crunch lending supply and run two-stage least squares regressions (2SLS). We add more instrumental variables if other endogenous variables are included in W. We explain a dependent variable as well as control variables along with additional instrumental variables employed for them.

## Firm profitability

We employ two measures for firm profitability, namely ROA as defined by current income divided by total assets, and EBITDA to total assets ratio. For additional independent variables, we employ the logarithm of total assets to control for firm size. We run two stage least squares (2SLS) regressions where the excluded instrumental variables are independent variables in equation (1), CAPSUR, the logarithm of total assets, ROA and the leverage measured during the policy period as described before. When running regressions, we intentionally exclude the logarithm of the lagged total assets as an instrumental variable because it is an ex post variable that is measured after the JASME extended all the loans.

Methodologically, our empirical approach most closely resembles that employed by Aiyar et al. (2014). Using the bank level data of the United Kingdom, they run the regressions of the lending growth by a foreign bank branch not subject to the Basel capital requirements on the local lending growth by domestic banks subject to the requirements instrumented by changes in their regulatory requirements in order to examine the effects of changes in capital requirements on unregulated foreign banks competing with
regulated domestic banks through the (negative) impacts of the more stringent requirements on lending by domestic banks. We believe that our approach improves over theirs because a dependent variable is a firm level measure for new borrowing rather than the bank level lending growth employed by them. This is because our firm level measure for a firm's borrowing from the JASME is constructed by summing all the loans each firm borrowed from the JASME during the fixed policy period and is a measure for the JASME's new lending to that firm, whereas the lending growth is not a secular measure for new lending but is affected by any other changes in the amount of total loans outstanding including loan write-offs and redemptions of loans.

## I nvestment rate

The investment rate is defined as an increase in fixed assets plus depreciation divided by capital stock, which is defined by fixed assets less lands, at the previous fiscal year end. For a set of additional independent variables, we employ (a proxy of) marginal q, cash flow to capital stock ratio and CAPSUR. Among these variables, CAPSUR is an only variable treated as exogenous. For a definition of the marginal q denoted as Mq, we follow Ogawa (2003) and Imai (2016), where the q for firm i is constructed by the following formula.

$$ M q=\frac{\pi_{i}}{P^{I}}\frac{1+r_{i}}{r_{i}+\delta} $$

Where $ \pi_{i} $ and $ r_{i} $ are the profit rate as defined by the ratio of the operating profit to the capital stock at the previous fiscal year end and the discount rate as defined by the ratio of the interests paid and the discounts on note to the sum of the short-term loans outstanding, the long-term loans outstanding and the notes payable outstanding. $ P^{I} $ and $ \delta $ are the investment good deflator published by the Bank of Japan and the depreciation rate set to equal 0.0772, respectively. The cash flow to capital stock ratio is the sum of
current income and depreciation divided by the capital stock at the previous fiscal year end. We can think of (a negative of) CAPSUR and instrumented JASME as exogenous shocks to tighten and relax a firm's financial constraint, respectively. In the investment literature starting from Fazzari et al. (1988), the positive coefficient of the cash flow to capital stock ratio, which is often called the cash flow sensitivity of investment as an evidence of a firm's financial constraint when making investment but a counterargument to this that the cash flow is a proxy for a firm's investment opportunities and thus endogenous, has become dominant. $ ^{17} $ The excluded instrumental variables are independent variables in equation (1) excluding CAPSUR, the logarithm of total assets, ROA and the leverage measured during the policy period as well as the logarithm of sales during the policy period, which is added to ensure the overidentification of the 2SLS regressions.

## Cash to capital stock ratio

For the cash regressions, we follow the regression model of Almeida et al. (2004). A dependent variable is a change in the sum of cash and deposits divided by the capital stock at the previous fiscal year end as a dependent variable. The additional independent variables are the marginal q and the cash flow to capital stock ratio, which are treated endogenous and the logarithm of total assets and CAPSUR, which are treated as exogenous. The excluded instrumental variables are the same as those employed for the investment rate regression described immediately above.

## Borrowing growth

For the borrowing growth regressions, a dependent variable is the log growth of total
loans outstanding a firm is owed. The additional independent variables and excluded instrumental variables are the same as those employed for the investment rate regressions. Independent variables treated as endogenous and those treated as exogenous are also the same as those for the investment rate regressions.

## Employment growth

For the employment growth regressions, a dependent variable is the log growth of employment. An additional independent variable is the logarithm of employment in FY 1998 that is treated as exogenous. Excluded instrumental variables are all the independent variables in equation (1).

#### 3.2. Data

The data used in this study are primarily firm level and contract level micro data provided by the JFC. We select contracts agreed over the abovementioned one year and four month long policy period from December 1, 1997 through March 31, 1999.

The JFC provided us with three databases, the database recording details of loan contracts extended by the JFC such as the facility size and the date of loan execution (the JFC contract database) along with the database recording financial statements of the firms at dates of their financial closing (the JFC financial statement database) and the database recording the details about financial institutions each firm borrows from including the JFC at dates of their annual fiscal closing (the JFC financial institution database). Our loan contract data are not randomly sampled but cover all the contracts extended by the JFC.

Firms’ industries are provided by the JFC in a separate file. Our dataset is compiled by merging the data about firms extracted from the JFC financial statement database, the
data about firms' lenders extracted from the JFC financial institution database and the data about contracts that were extended during the policy period extracted from the JFC contract database so that every firm recorded in it has at least one contract the JFC extended to during this period.

We link the abovementioned dataset constructed based on the data provided by the JFC with the data about firms' main banks. We utilize the data about firms' main banks used by Watanabe (2007a) originally collected from the Nikkei NEEDS databank. As Watanabe analyzes 126 domestically licensed banks under the Banking Act that operated as of the end of FY 1997, we drop the contracts extended to the firms whose main bank was not a domestically licensed bank under the Banking Act such as a shinkin bank. $ ^{18} $ After consolidating multiple contracts for a firm, we are left with 2,061 firms in our base sample. $ ^{19} $

Although the JFC financial institution database records the information about a firm’s lenders that are neither the firm’s main bank nor the JFC, we utilize the information about the firm’s main bank only because in our view it is when the firm’s main bank becomes less willing to lend to the firm that the firm is the most severely adversely affected by the credit crunch. $ ^{20} $ $ ^{21} $
### 4. Results

#### 4.1. Descriptive statistics

Table 1-1 presents descriptive statistics of variables used to construct dependent variables and independent variables used in equation (1) and sales used as an additional instrumental variable when running regressions of equation (3).

As for measures for loans we use for dependent variables, amounts of total loans, working capital loans and equipment loans are on average 78 million yen, 61 million yen and 16 million yen, respectively. The median of the amount of equipment loans is 0 presumably because equipment loans are borrowed to replace equipment such as machineries every several years. Total assets are on average 1.62 billion yen.

As for independent variables, CAPSUR, a measure for the lending growth due to a firm’s main bank’s capital surplus is on average negative at -2.4 percent. The ratio of capital to total assets of main banks of our sample firms is on average falls short of its target at the end of FY 1997 so that they reduced lending. The average ROA and the average leverage of our sample firms are -0.008 and 0.88, respectively, suggesting that our sample firms on average incur small accounting losses and are highly leveraged.

Table 1-2 presents the descriptive statistics of dependent and independent variables used when running the regressions of equation (3). $ ^{22} $

#### 4.2. The JASME Loans Regressions

Table 2 presents the regression results of equation (1) when a dependent variable is either the logarithm of total loans, that of equipment loans or that of working capital loans.
The regressions are run using the OLS. $ ^{23} $ $ ^{24} $

The coefficient of CAPSUR, which is a measure for the loan growth by a firm’s main bank caused by the bank’s capital surplus, is negative and significant when a dependent variable is either the logarithm of total loans or that of working capital loans, but it is not when the dependent variable is the logarithm of equipment loans. These results show that the JASME made a larger amount of new working capital loans to a firm whose poorly capitalized main bank reduced lending. The coefficient of CAPSUR is not significant for the regression with the logarithm of equipment loans most likely because it is working capital loans rather than equipment loans that were targeted by the JASME’s “Fund”. The coefficient estimate of -0.971 for total loans means that a decrease in CAPSUR by one standard error (3.1 percent) is associated with an increase in total loans by about 3.1 percent, or 2.4 million yen when evaluated at the sample mean of the amount of total loans. Similarly, a decrease in CAPSUR by one standard error is associated with an increase in working capital loans by 5.1 percent or 3.1 million yen when evaluated at the sample mean of the amount of working capital loans. $ ^{25} $ A monetary increase in total