Local Bank Financial Constraints and Firm Access to External Finance

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The CDSF data is used to construct measures of the actual performance of all loans issuedduring the sample period. A loan originated at time t can be observed at time t+T and itsrepayment status assessed. This assures that the performance measures are in fact linked to theincremental (marginal) investments of banks during the external finance shocks. Finally, it isimportant to note that the CDSF reports both loans and contingent credit lines, but thesecannot be distinguished from each other in the data. Thus, firm level measures of debt willinclude all available credit. Bank level measures of lending will include both actual loansobtained from bank balance sheets, and total loans and commitments obtained fromaggregating the CDSF data at the bank-month level.The measurement of default rates is limited at 24 months after the loan is issued to avoidcensoring due to data limitations (maximum T=24). The CDSF data is not available during2002 and loans issued in December 1999 can be followed at most for 24 months. To checkwhether this limit introduces a substantial measurement error of default rates, I estimate themonthly hazard function of the default rate ―the probability that a loan issued at month tdefaults at month t+T, conditional on not having defaulted at t+T-1. The kernel estimation,plotted in Figure 3, is performed over the sub-sample of loans in the CDSF with at least threeyears of observable repayment history (issued between January and August 1998). The plotshows that the hazard rate peaks before 12 months, and the cumulative hazards indicate that85% (45%) of the loans that default, do so within 24 (12) months of being issued. This indicatesthat the 24 month window allows measuring the default rate without a substantial measurementerror.Column 1 of Table II shows the descriptive statistics of all the loans and credit commitmentexpansions (from now on called loans for conciseness) issued between January 1998 and21
December 1999. There were 750,526 loans issued to 222,146 firms and individuals. The averageloan had a principal of $16,691 and 12.3% of this principal was secured with collateral.Regarding loan performance, 16.8% of all the loans were defaulted within 24 months afterbeing issued. Loan recipients had on average $58,551 in bank debt, and 14.1% of the loans wereissued to borrowers with some non-performing debt in their repayment histories.The third source of data is the MYPES database, collected and managed by the Ministry ofEconomy in Argentina. This database is used to obtain information on the timing and size ofthe program waves used to estimate the expected financing instrument described in theempirical strategy section. The last two rows of Table I show the descriptive statistics of theexpected financing variable in levels and as a proportion of loans outstanding. Expectedexternal financing represents about 7.6% of the stock of loans of participating banks during thesample period. An unreported regression of actual program financing on expected programfinancing, bank and month fixed effects indicates that the coefficient on expected programfinancing is close to one and statistically significant. This indicates that the expected financinginstrument is a good proxy for actual financing both in the time series and cross section, andthat the cross sectional allocation rule was strictly enforced.IV. ResultsA. The Effect of Expected Program Financing on Sources of capital (First Stage)The first empirical test of interest is one that shows that the expected external financinginstrument has a significant and direct impact on bank sources of capital. This is a necessarycondition for the 2SLS estimation of β 0 in specification (II-1), the main relationship of interestand the basis for the proposed test of financing frictions.22
Page 1: Local Bank Financial Constraintsand
Page 7 and 8: anked second among emerging market
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Page 15 and 16: fungibility. In a recent study of a
Page 18 and 19: ―15.4% versus 6.3%, and has an av
Page 20 and 21: The second step is to calculate the
Page 24 and 25: I estimate a regression of bank sou
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Page 28 and 29: interaction term, β C 0 , represen
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Page 42 and 43: higher region and size point scores
Page 44 and 45: Bharath, Sreedhar, Sandeep Dahiya,
Page 46 and 47: Jensen, Michael, 1986, Agency Costs
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Page 58 and 59: Table VIConstraints or Free Cash Fl
Page 60 and 61: Table VIIITesting for Changes in Ba
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