legal reform and loan repayment: the microeconomic impact...

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American Economic Journal: Applied Economics 2009, 1:3, 59–81http://www.aeaweb.org/articles.php?doi=10.1257/app.1.3.59

This paper estimates the effect of judicial quality on the repayment behavior of borrowers and the lending decisions of banks. Although judicial enforcement

mechanisms in many developing, and some developed, countries are ineffective and wastefully slow (Maria Dakolias 1999; Simeon Djankov et al. 2008), we know little about the microeconomic mechanisms through which they affect economic out-comes. A recent literature has exploited national differences in judicial quality and legal institutions to estimate their relationship with financial and economic outcomes (Rafael La Porta et al. 1998; Asli Demirgüç-Kunt and Vojislav Maksimovic 1998). However, cross-country differences in judicial quality are likely to be correlated with unobservable country-specific factors that also affect firm performance, growth prospects, and financing decisions, making it difficult to identify the true effects. Although cross-state analyses control for national-level institutions, even they are usually unable to investigate the micro-level mechanisms through which judicial institutions affect economic outcomes (Magda Bianco, Tullio Jappelli, and Marco Pagano 2002; Matthieu Chemin 2007). Unlike previous work, this paper uses a loan-level dataset and the institutional features of a reform introduced within a country

* Department of Economics, Boston University, 270 Bay State Road, Boston, MA 02215 (e-mail: [email protected]). I am grateful to Charles Calomiris, Rajeev Dehejia, Rohini Pande, and Miguel Urquiola for their guidance and encouragement, and several faculty members and colleagues at Columbia University and Boston University, various seminar participants, and two anonymous referees for helpful comments. My thanks to R. Chandrasekar, Maharukh Dastur, Krishnava Dutt, Nachiket Mor, V. R. Sahasrabuddhe, and Bhavna Sharma for their help dur-ing the data collection, and to M. A. Batki, N. V. Deshpande, and G. S. Hegde at the Reserve Bank of India for informative discussions. This research was supported by the Center for International Business Education at Columbia University, the Social Science Research Council’s Program in Applied Economics with funds provided by the John D. and Catherine T. MacArthur Foundation, and the Institute for Financial Management Research. The responsibility for all errors is mine.

† To comment on this article in the online discussion forum, or to view additional materials, visit the articles page at: http://www.aeaweb.org/articles.php?doi=10.1257/app.1.3.59.

Legal Reform and Loan Repayment: The Microeconomic Impact of Debt Recovery Tribunals in India†

By Sujata Visaria*

In 1993, the Indian government introduced debt recovery tribu-nals to speed up the resolution of debt recovery claims larger than a threshold. This paper exploits the staggered introduction of tri-bunals across states and the link between overdues and claim size to implement a differences-in-differences strategy on project loan data. It finds that the tribunals reduced delinquency for the average loan by 28 percent. They also lowered the interest rates charged on larger loans, holding constant borrower quality. This suggests that the speedier processing of debt recovery suits can lower the cost of credit. (JEL G21, K41, O16, O17)

60 AmErIcAn EconomIc JournAL: AppLIEd EconomIcs JuLy 2009

to identify one such mechanism. It finds that bank credit becomes less costly when courts uphold the legal right of banks to enforce debt contracts.

The institutional context for this finding is as follows. Prior to 1993, all debt recov-ery cases in India were processed in civil courts, where they were subject to extremely long delays. To reduce the time taken to resolve cases, in 1993, the government of India enacted a national law providing for a new judicial institution called Debt Recovery Tribunals (DRTs). DRTs follow a new streamlined procedure aimed at lowering pro-cessing times in the adjudication and execution of the verdict. An analysis of 49 ran-domly sampled legal cases, described later in this paper, shows that there were large reductions in the duration of legal proceedings after DRTs were established.

By speeding up the judicial process, the reform effectively increased the value of collateral that the lender could seize in the event of default. In the standard credit model with moral hazard, since the borrower would now lose more in the bad state, he would exert more effort and lower the probability of default. Assuming that banks are perfectly competitive and holding all else constant, increased repayment would make bank profits positive. The increase in profits could be passed on to borrow-ers in the form of lower interest rates (Yuk-Shee Chan and Anjan V. Thakor 1987; Pranab Bardhan and Christopher Udry 1999).

Most existing studies that exploit plausibly exogenous variation in judicial quality do not use loan-level data and, so, cannot examine the effects of such a reform at this micro level. This paper fills that gap.1 Two institutional features of the reform are used to analyze a large loan-level dataset. Since DRTs were introduced in dif-ferent states at different times, there is variation in the date when loans in different states were exposed to them. In addition, only debt recovery claims larger than Rs 1 million are eligible for DRTs and the bank’s claim is likely to be related to the overdues on the loan, which can be observed in the data. Together, these allow a differences-in-differences strategy, where a loan installment is said to be exposed to a DRT if it becomes due after the DRT is established, and exposure increases in the previously accumulated overdues on the loan. The analysis shows that loan repay-ment improved after debt recovery tribunals were introduced. The probability that a loan repayment installment would be paid on time increased by about 28 percent for the average loan in the sample exposed to the tribunals. The results are obtained on a sample of loans originated before the DRT law was enacted and, therefore, are not being driven by endogenous changes in borrower or loan quality. They are robust to controls for state-specific time trends and national-level changes in the repayment of overdue loans, and are significant even after controlling for all unobservable time-invariant loan and borrower characteristics.

The staggered timing of DRT establishment is also used in a differences-in- differences strategy to estimate the effect on the size of new loans issued and the interest rate charged on new loans. Compared to loans given out before DRTs were established, those given out after were charged lower interest rates per unit of loan.

1 Notable exceptions that do use loan-level data are Jeremy Berkowitz and Michelle J. White (2004), who find a nonmonotonic relationship between state-level bankruptcy exemption laws and small firms’ access to credit, and Ana Carla A. Costa and Joao M. P. De Mello (2006), who find a positive relationship between the legal valid-ity of payroll deductions as collateral and the volume of personal credit.

VoL. 1 no. 3 61VIsArIA: LEgAL rEform And LoAn rEpAymEnT

This finding is also robust to controls for time-invariant borrower quality. Thus, the results conform with the predictions of a simple moral hazard model with perfectly competitive lenders.

The rest of this paper is organized as follows. Section I describes the judicial reform being studied here, including the institutional features of DRTs, the pattern of their establishment, and suggestive evidence that they sped up legal case proceed-ings. Section II discusses the empirical strategy and data used for the analysis of repayment behavior. Section III discusses the empirical analysis of loan size and interest rates. Section IV concludes.

I. Debt Recovery Tribunals

Before DRTs were set up, all loan recovery cases were processed in the civil court system, where they were subject to long delays. In 1997, 3.2 million original civil cases were pending in district-level civil courts, of which 34 percent had been in process for longer than three years (Government of India 2000). Disputes about asset liquidation appear to take even longer. Government of India (1988) reported that more than 40 percent of the liquidation cases in 1985 had been pending longer than 8 years.

While legal scholars point to various reasons for the inefficiency of the Indian court system, it is widely acknowledged that procedural rigidities and loopholes are an important factor. Civil courts follow the Code of Civil Procedure 1908, which allows for numerous applications, counter applications, and “special leaves” by the plaintiff and the defendant. Evidence must be presented orally, and hearings tend to be long. Judges have wide latitude in determining whether hearings should be adjourned or whether new claims can be added to the complaint (Wolfgang Klaus Colin Köhling 2002).2

A. The drT Law

Debt Recovery Tribunals were introduced in the wake of India’s financial sec-tor reforms of the early 1990s. The DRT law came into force on June 24, 1993.3 It allows the Government of India to establish debt recovery tribunals “for expeditious adjudication and recovery of debts due to banks and financial institutions,” and to specify their territorial jurisdiction.

DRTs are different from civil courts in a few ways. First, unlike civil courts, DRTs are specialized courts for debt recovery.4 Second, they follow a streamlined

2 The Code of Civil Procedure was legislated during an ambitious drive for codification by the British colonial rulers. Sandra M. den Otter (2000) argues that this was aimed at establishing authoritarian rule, and as a result, “… Indian law was much more codified and systematized than British law was … India served as a laboratory for British legal experiments.”

3 The records of parliamentary proceedings from the period show that the bill was introduced into the lower house of parliament (Lok Sabha) on May 13, 1993. It appears to have met with no opposition in parliament, and was passed on August 10, 1993. It came into effect retrospectively.

4 Research on US courts suggests that although they have high relative operational costs and lower overall coordination, specialized courts are better able to assign cases to judges in line with expertise and have better decision making mechanisms than consolidated courts (David B. Rottman and William E. Hewitt 1996).


“summary procedure” laid out in the DRT Rules (Government of India 1993), that demands faster processing and greater accountability by the litigants. Defendants are given less time to respond to summonses, defense must be provided in writing, and counter-claims against the bank must be made at the first hearing.5 Third, a suit can only be filed in a DRT if the claim is larger than Rs 1 million (approxi-mately $20,000). Although there is no indication that there were any economic rea-sons for choosing this particular lower bound on claim size, it effectively lowers the number of claims that can be filed and allows DRTs to process cases involving large volumes of credit.6 Based on Reserve Bank of India (1996), it is estimated that roughly 4 percent of loans comprising about 75 percent of outstanding bank credit were potentially affected by the reform. Fourth, DRTs have wider ranging powers. They can make interim orders to prevent defendants from transferring or disposing of their assets before the final judgement, an authority they share with select civil courts. Specialized personnel (receivers) are appointed to attach, manage, and sell the defendants’ property, in order to speed up the execution of the verdict. DRTs can also obtain a police warrant to arrest the defendant.

In other respects, DRTs are similar to the civil courts. The substantive laws govern-ing the cases remain the same as before, and lawyers do not require special training or qualifications to argue cases. The presiding officers (judges) in DRTs are usually retired civil court judges. Also, consistent with the civil court system, the DRT law allows for appeals against a judgement. Either party can appeal against a DRT’s rul-ing in the Debt Recovery Appellate Tribunal (DRAT). However, the defendant must deposit 75 percent of the awarded amount with the DRAT before the hearing can take place. The deposit is returned if the DRAT rules in the defendant’s favor.

B. Establishment of drTs

Although welcomed by bankers as well as economists, the act met with oppo-sition. Soon after Delhi received a DRT in July 1994, the Delhi Bar Association challenged the DRT law in the Delhi high court.7 In August 1994, the high court announced a prima facie view that the act may not be valid, and required the Delhi DRT to stay its operations. In its final verdict on March 10, 1995, it ruled that DRTs violated the independence of the judiciary and the executive. It also ruled that the act had other flaws: the lack of provisions for counter-claims by the defendant, and the transfer of cases from one DRT to another.

The national government moved the Supreme Court against this judgement. On March 18, 1996, the Supreme Court issued an interim order that, notwithstanding any stay order passed in any writ petitions, DRTs should resume functions. It also asked

5 The original DRT Act of 1993 did not allow counter claims. These were introduced in the 2000 Amendment, described in detail in the next subsection.

6 In the late 1990s, an amendment bill was introduced in parliament to reduce the monetary threshold to Rs 500,000. It was not taken up for discussion, however.

7 The case was made on the following grounds: (a) since the presiding officers of DRTs were appointed by the Ministry of Finance, the act violated the Directive Principle of State Policy that the executive and judiciary be independent; (b) the act was discriminatory because it did not allow borrowers to make counter claims against banks; (c) there was no rationale for making suits admissible on the basis of their pecuniary claim; and (d) the Constitution did not allow the legislature to establish tribunals for the purpose of debt recovery.


the central government to amend the law to address certain anomalies, and the gov-ernment complied in 2000. The amendment allowed the defendant to make counter claims, and also strengthened the independence of DRTs from the executive branch of government.8 In its final ruling on March 14, 2002, the Supreme Court stated that the DRT Act was constitutional, and the amended act was to be allowed. At this time all pending cases about the constitutional validity of the act were dismissed.

The opposition to the DRT law led to a particular pattern of establishment of DRTs that is useful for the empirical strategy. Since the reform was introduced through a national law that applies to all states of India, individual states do not have the author-ity to choose whether or not to establish these tribunals.9 The national government also chooses when to give a particular state access to a DRT. Beginning in April 1994, the national government set up five tribunals in quick succession. The Delhi High Court’s ruling interrupted this process (Reserve Bank of India 1998), and no new DRTs were established in 1995. It was only after the interim order of the Supreme Court in 1996 that DRTs were established again. All the remaining states received DRTs after this, and by 1999, all states had access to a Debt Recovery Tribunal.

Table 1 lists the dates when DRTs were established. There is no clear pattern in the timing of DRT introduction across states. For example, states with larger populations, urban, or industrial sectors were not more likely to receive early (or late) DRTs. It also appears unlikely that the timing was driven by states lobbying the national government for DRTs at certain times. In fact, the events suggest that in the

8 The amendment stipulated that the chief justice of India be the ex-officio chair of the selection committee for presiding officers. It also allowed counter claims to be filed against the bank. These two features could have made the DRT law less biased in favor of creditors.

9 Like many other laws, the DRT law does not apply to the state of Jammu and Kashmir since it has special autonomous status.

Table 1—Dates of DRT Establishment

City of DRT(1)

Date of establishment(2)

Jurisdiction(3)

group 1 states

Kolkata April 27, 1994 West Bengal, Andaman and Nicobar Islands

Delhi July 5, 1994 Delhi

Jaipur August 30, 1994 Rajasthan, Himachal Pradesh, Haryana, Punjab, Chandigarh

Bangalore November 30, 1994 Karnataka, Andhra Pradesh

Ahmedabad December 21, 1994 Gujarat, Dadra & Nagar Haveli, Daman and Diu

group 2 states

Chennai November 4, 1996 Tamil Nadu, Kerala, Pondicherrya

Guwahati January 7, 1997 Assam, Meghalaya, Manipur, Mizoram, Tripura, Arunachal Pradesh, Nagalandb

Patna January 24, 1997 Bihar, Orissa

Jabalpur April 7, 1998 Madhya Pradesh, Uttar Pradesh

Mumbai July 16, 1999 Maharashtra, Goa

a The Chennai DRT’s jurisdiction was expanded to include Lakshadweep on December 5, 1997. b The Guwahati DRT’s jurisdiction was expanded to include Sikkim on December 5, 1997.


absence of legal difficulties, DRT establishment might have been quick, providing almost no difference in timing. In many cases, access was increased by requiring neighboring states to share the services of a single tribunal. A common DRT was established for each region that had a common high court (these are called DRT regions in what follows). For DRTs to have been assigned in response to lobbying, neighboring states would have had to have colluded. While this is indeed possible, it would have been costly and difficult, given that Indian states are distinct geo-graphical and political entities and often competitors for financial resources from the national government, as well as rivals for the use of natural resources.

Even so, in Table 2, I test to see if state-level observable factors can explain the probability that a state had a functional DRT in any given year.10 I run linear probability regressions using state GDP per capita and its growth rate as explana-tory variables. I also include the probability of a functional DRT on the number of court cases pending per capita, the number of high court judges per capita and on indicators for the political party of the state government, and for whether the state government was an ally of the party in power at the national level.11 These variables are largely unable to predict the establishment of DRTs in bivari-ate regressions.12 When economic, judicial, and political variables are included simultaneously, and region-level fixed effects are controlled for (column 8), none of these variables is significant. Thus, there is no evidence that DRT establishment was driven by trends or levels in other factors that would have increased repayment contemporaneously.

The break in DRT establishment caused by the 1995 Delhi high court ruling suggests that DRTs set up after 1996 were different from those set up before 1996. The early DRTs may have functioned less effectively because they were new and the uncertainty about their legality had not been resolved. On the other hand, they could have been more effective at recovering claims early on because the government may have installed judges who were more likely to rule in the banks’ favor.13 DRTs set up after the Supreme Court issued its interim order would have been more credible since their verdicts were more likely to be binding. On the other hand, the Supreme Court’s instructions to allow borrowers to file counter claims, and for the judiciary to oversee the appointment of judges, could have reduced the bias in favor of creditors. To examine how the impact differs, the analysis will estimate separate effects for DRTs set up before and after 1996.

10 To account for serial correlation, standard errors are clustered by regions that share DRTs. All regressions contain year dummies to account for national changes in the probability that DRTs would be established.

11 From 1995 to 1999, India had three different national governments. The coalition governments during this period were heavily reliant on support from smaller regional parties, and state political parties are likely to have had considerable influence on the actions of the national government.

12 In additional results, omitted here due to space constraints, I include population, volume of credit, and credit per capita (separately). Both population and the volume of credit have a negative impact on the probability of a DRT, but credit per capita is not significant.

13 Tom Chang and Antoinette Schoar (2007) find that in US bankruptcy courts, there are significant differ-ences across judges in the propensity to rule in favor of creditors or debtors.


C. performance of drTs

By January 31, 2002, approximately 57,000 cases had been filed with DRTs. By March 31, 2003, claims worth Rs 314 billion (approximately US $7 billion, roughly 4 percent of total bank credit to the commercial sector in 2003) had been disposed of, and Rs 79 billion (approximately US $2 billion) had been recovered (Government of India 2003).14

To examine, first hand, the effect of DRTs on judicial efficiency, one would need comprehensive data on court cases filed in DRTs and civil courts. Since these data are not available from the courts, Table 3 presents statistics on processing times and

14 In contrast, in the United States, only 1–7 percent of business loans were reported delinquent in any given quarter between 1991 and 2006 (Federal Reserve Board 2006). Bankruptcy cases are also processed much faster.In the period 2000–2002, the pendency rate was about 10 percent (author calculations are based on US Courts 2006). Pendency rate is the number of cases pending at the end of the year as a fraction of cases filed in a year.

Table 2—Predicting the Pattern of DRT Incidence

(1) (2) (3) (4) (5) (6) (7) (8)

GDP per capita 0.07(0.05)

GDP per capita growth rate 0.00 0.00 0.05 − 0.00(0.00) (0.00) (0.04) (0.00)

Cases pending per capita 0.00 0.01 0.15 − 0.01(0.00) (0.01) (0.11) (0.03)

Number of judges per capita − 0.15 3.22 27.18 − 0.42(4.14) (7.38) (62.35) (5.65)

state government

Congress and allies 0.05 0.05 0.54 0.04(0.11) (0.13) (0.91) (0.13)

Janata and allies 0.08 0.13 1.67 0.06(0.12) (0.14) (0.86)* (0.16)

Communist party 0.09 0.10 0.97 0.04(0.12) (0.13) (0.85) (0.15)

Regional party 0.08 0.10 1.01 − 0.05(0.11) (0.13) (0.82) (0.17)

Ally of the center − 0.05 − 0.05 − 0.53 − 0.10(0.06) (0.08) (0.61) (0.07)

Regression OLS OLS OLS OLS OLS OLS Probit Region FE

Observations 184 161 184 184 184 161 69 161r2 0.77 0.73 0.77 0.77 0.77 0.74 0.32a 0.81

notes: The dependent variable takes a value of one if state i had a functional DRT in year t, and zero otherwise. Year dummies in all columns are not reported. Observations correspond to 8 years of data (1993–2000) for 23 states. Union territories are excluded. GDP growth rates are not available for 1993. In column 8, the group vari-able is DRT region. Standard errors in parentheses are clustered by DRT region.

a Pseudo r2

*** Significant at the 1 percent level. ** Significant at the 5 percent level. * Significant at the 10 percent level.


case resolution for 49 debt recovery suits filed by the bank.15 These data were only available for cases filed in the state of Maharashtra.16 Although this does not allow me to separate the effect of DRTs from region-specific factors, which might have lowered case processing times during this period, there is strong suggestive evidence for the higher effectiveness of debt recovery tribunals. The data include both cases open at the time of data collection in 2005, as well as cases that had already closed. Twenty-two of the cases had originally been filed in the Mumbai high court and were

15 In the original sample of 50 cases, one outlier with a claim size of Rs 11 billion had been filed in the high court and was dropped from the analysis. Including it does not change the results but increases the mean claim size in high courts by a significant amount.

16 Details of the judicial process and the data collection are in Appendix A1.

Table 3—Processing Times for Debt Recovery Suits Processed in Civil Courts and DRTs

Bombay high court(1)

Mumbai/Pune DRTs(2)

Difference(3)

p-value(4)

case characteristics

Number filed 22 27

Mean file date June 1997 April 2003

Median file date September 1998 August 2003

Mean claim size Rs 176 million Rs 192 million − 16 million 0.600

Median claim size Rs 110 million Rs 140 million − 30 million 0.656

case processing duration

Mean time to summons 449 days 56 days 393** 0.019

Mean time to first effective hearing 1,329 days 199 days 1,131*** 0.000

Mean time to applicant’s evidence 2,460 days 602 days 1,858*** 0.000

Mean time to defendant’s evidence 2,818 days 643 days 2,175*** 0.001

Mean time to arguments starting 2,226 days 686 days 1,540*** 0.009

Probability that original case is closed 0.33 0.38 − 0.05 0.749

Interim measures

Probability that interim relief granted 0.50 0.41 0.09 0.527

Mean time to interim relief 394 days 60 days 334 0.133

resolution

Probability that court issues recovery certificate

0.27 0.18 0.09 0.481

Mean fraction of claim awarded 0.78 0.99 − 0.21 0.115

Median fraction of claim awarded 0.86 0.99 − 0.13 0.294

Probability that court issues consent decree

0.00 0.09 − 0.09 0.244

Probability of out-of-court settlement 0.13 0.06 0.07 0.391

notes: All cases in the sample were transferred to the DRTs after they were established. Case characteristics are reported by classifying the case according to the venue where it was filed. Case processing, interim relief, and res-olution details are reported by classifying case according to the venue where the summonses were issued.



transferred to the DRT after it had been established in 1999. The remaining 27 cases had been filed directly in a DRT, and therefore had later filing dates on average.

The following facts emerge. First, there is no significant difference in the size of claims filed in the DRT versus the high court. Second, there are large differences between the high court and DRTs in the time taken to process cases. For cases filed in the high court, summonses were issued, on average, 449 days after filing, whereas in the DRT they were issued within 56 days. The difference of 393 days is significant at the 2 percent level. Similar differences can be seen in the times to first effective hearing, presentation of evidence, and beginning of arguments. These steps took, on average, six to seven years when they were processed by the high court, compared to two years in DRTs. Cases were also resolved much quicker in DRTs. Despite the fact that cases in the DRT system entered later, on average, they are just as likely as those in the civil court system to be closed by the time of data collection. Third, there is no statistically significant difference in the use of other legal measures to improve recovery. When there are long delays in the case, the bank’s lawyers often apply for “interim relief.” If

Table 4—Descriptive Statistics for Loans Originated Before June 24, 1993

Mean(1)

Standard deviation(2)

Loan level statistics (n = 772)Size of loan (rupees million) 24.30 44.90

Interest rate (percent) 15.46 5.68

Year of sanction 1,989.69 3.42

Borrower’s cash flow (rupees million) 19,104.94 64,625.68

Borrower’s assets (rupees million) 9,002.09 13,968.28

Industry

Agriculture and allied industries 0.07 0.26

Petroleum and mining 0.003 0.51

Textiles, paper, rubber, wood, etc. 0.20 0.40

Chemicals and drugs 0.11 0.32

Metal products 0.12 0.33

Nonmetal manufactures 0.13 0.33

Machinery and equipment 0.09 0.29

Vehicle manufacturing 0.04 0.20

Furniture, consumer durables 0.02 0.16

Construction, infrastructure, utilities 0.02 0.13

Retail trade 0.004 0.06

Services 0.08 0.27

Transport, post, telecommunications 0.01 0.09

Loan-quarter level statistics (n = 14,244)State DRT 0.63 0.48

Amount overdue (rupees million) 0.23 0.67

All paid within 180 days 0.66 0.47 if state DRT = 0 (n = 5,349) 0.57 0.49 if state DRT = 1 (n = 8,978) 0.71 0.45

note: Information on the assets of borrowers is available for 743 loans.


granted, it freezes the borrower’s assets and prevents him from disposing of them before the case is resolved. Although the probability that interim relief was granted is larger in high courts (but the difference is not statistically significant), it took nearly a year longer for the high court to process the application than it took the DRT. Finally, the overall outcome of the case does not appear to be significantly different for cases filed in the civil court system versus the DRT. The probability that a recovery certificate is issued (i.e., the case is resolved in favor of the bank) is slightly higher in the high court, but the difference is not significant. Instead, the case is more likely to end with a consent decree if processed in a DRT (again, the difference is not significant). There is also some evi-dence that a greater fraction of the claim is awarded in the recovery certificate in DRTs (significant at the 12 percent level). Cases were more likely to be settled out of court when processed in the civil court system than when processed in DRTs.17, 18

II. Empirical Analysis of Repayment Behavior

In what follows, I implement a differences-in-differences strategy on a loan-level dataset to examine the effects of the judicial reform on the market for large loans. First, I describe the nature of the data used. Next, I describe how the institutional features of the reform are used in the empirical analysis. Then, I report the results.

A. data

The loan-level data used in this paper come from the records of a large Indian private sector bank with branches throughout the country. This bank was estab-lished in 1994 as a wholly-owned subsidiary of a public sector development finance institution that had specialized in long-term and medium-term project financing of business enterprises since 1955. In 2002, the bank bought its parent institution and inherited its portfolio of loans. The bank continues to manage the old project loans and to issue new ones.

The data contain detailed records of the history of all project loans in the bank’s accounting database as of June 2003.19 These include loans given to corporate borrowers for various long-term purposes, such as the setting up of new projects, expansion and modernization of pre-existing projects, diversification of business, and guarantees to other lenders. They also include some long-term loans given to rehabilitate firms, or to adjust overruns on previous loans. Project loans in this bank go through three stages: loan origination, commitment (which will be referred to as a loan below), and disbursement. Repayment is due in installments, generally on a quarterly basis.

For each quarter when repayment became due, I measure whether all install-ments on the loan were paid within 180 days of the due date. This is the dependent

17 In his case study of two northern Indian district courts, Robert S. Moog (1997) argues that formal suits take so long to resolve in the civil judicial system that most cases are settled out of court through informal means.

18 Not all cases had been resolved at the time of data collection, so the statistics on “resolution” are calculated in a smaller sample.

19 Appendix A describes the change in the bank’s database system in September 2000, the bias this may lead to, and how I select the sample to avoid the bias.


variable for the regressions on repayment behavior. Paying within 180 days keeps the loan “standard” in the bank’s accounts. In this sense, it is the time frame to pay up an installment in order to avoid a negative impact on the bank’s performance as reported to the central bank.

Table 4 presents descriptive statistics of the loan-level data. Only loans that origi-nated before the DRT Act was passed are included in the regressions on repayment behavior. This restricted sample contains 772 loans, given to 427 distinct borrow-ers. The average year of loan origination is 1990, and we observe repayment on loans for an average of 19 quarters. The average size of loan origination is Rs 24 million, and the average amount overdue is Rs 0.23 million. Borrowers belong to a wide variety of industries. The loans were given for projects in all parts of India to states that received DRTs early as well as those that received them late. In quarters before a state DRT was established, loans were more likely to be delinquent on loan repayment. There was a 57 percent probability that invoices were repaid on time. In quarters after the DRT was established, this probability was higher (71 percent).

B. Empirical specification

DRTs were introduced across states at different times, and, as discussed earlier, there is no evidence to suggest the timing was driven by other factors affecting repayment. A simple differences-in-differences regression could rely on this varia-tion to estimate the effect of DRTs. It would compare loan repayment in a state after it received a DRT with repayment in another state which had not yet received a DRT, in the same time period. However, the analysis here also builds on two other aspects of the reform. One, as discussed earlier, the changes in the DRT law imposed by the 1996 Supreme Court order could have caused debt recovery tribunals established before and after 1995 to be substantially different. To examine how the impact dif-fers across these two groups of DRTs, I classify the states into two groups. Group 1 states received a DRT before the 1995 interruption in DRT establishment, and group 2 states received the DRT after. In each regression model, the effects of DRTs are estimated separately for each group.

Two, in the regressions on repayment behavior, I use the fact that DRTs impose a monetary threshold for claims to be eligible, whereas in the civil court regime, no monetary threshold existed. In the absence of clear rules about how claim size should be determined, the bank chooses the size of the claim when it files the case. However, the firm can dispute the claim size, which may cause it to be revised. In any event, I do not observe the exact claim size, since the loan records do not contain information about judicial proceedings. However, potential claim size should also matter, even if the case is not actually taken to court. Discussion with the bank’s law-yers suggests that legal cases are more likely to be filed when the overdue amounts are larger, and the claim size is influenced by the overdue amount. Thus, the equa-tions include a triple interaction of group, overdues, and state-time to identify the effects of DRTs. This is based on the idea that when a DRT is established, loans with larger overdues perceive a higher likelihood of facing legal action in the DRT. Therefore, we expect that the threat of DRTs becomes more effective as the amount overdue increases. Figure 2 illustrates how the relationship between the probability


of timely repayment and overdues changed after DRTs were set up. As expected, before DRTs were established, there was a strong negative correlation between these two variables. However, since loans with larger overdues are more susceptible to legal action in DRTs, we expect that after DRTs were established, loans with larger overdues improve their behavior. Indeed, the relationship between repayment and overdues becomes flatter. This change in slope after DRTs were established can be attributed to the debt recovery tribunals.

A few points are in order. One, in the analysis, all cases in a region are consid-ered to be exposed to DRTs once a DRT is established in a region. This is because the law requires that all eligible open cases be transferred to the appropriate debt recovery tribunal once it is set up.20 Two, loans are assigned to territorial jurisdic-tions depending on the region where the project is located. This is derived from the rule that a claim can be filed in the location where the cause of action arises or where the defendants reside.21 Three, in terms of timing of exposure to DRTs, loans that fall under the DRT’s jurisdiction are considered treated in the quarters occurring after the DRT was established and untreated in the quarters before.22

20 There is no information about legal proceedings in the loan records, and, so, it is not possible to measure actual exposure of loans to the DRTs.

21 Borrower location information is less complete than project location information in this dataset. However, using state of borrower location gives similar results. Geographic distance to DRT may also matter, but the data do not contain this information.

22 Even after all states had received a DRT, new DRTs continued to be established. Jurisdictions were sub-divided among the new and the old DRTs, reducing the number of cases each DRT would handle. However, at any point in time, a loan always faces only one DRT. To capture this, I define treatment as a binary variable that switched from zero to one when a loan went from no exposure to a DRT to exposure to a DRT. Using an alterna-tive treatment variable where exposure to DRT varies with the annual rate of case disposal gives qualitatively similar results.

Den

sity

0 2 4 6 8 10

Amount overdue

1992 Q2

1992 Q3

Kernel density of amount overdue

Figure 1. Density of Amount Overdue in 1992: Q2 and 1992: Q3

notes: Overdues are measured for all loans in the sample in the second quarter of 1992, and in the third quarter of 1993. The Kolmogorov-Smirnov test fails to reject the null of equal distributions ( p = 0.99).


Four, to identify the effects cleanly, it is important that loans do not select into the treatment group endogenously. Since overdues can change endogenously every quarter, I measure overdues one year before the DRT law was passed.23 In addi-tion, since new loans given after the enactment of the DRT law could be different in unobservable ways, I only estimate changes in repayment behavior for loans that originated before the date of the law.

Table 5 presents a simple version of the empirical specification. The left panel refers to loans given in group 1 states, whereas the right panel refers to loans given in group 2 states. Loans are classified into two categories: “high overdues” (above the sample mean) and “low overdues” (below the sample mean). For each loan, quar-ters occurring before DRTs were established fall in the category “before DRT” and those occurring after DRTs were established fall in the category “after DRT.” The table shows the mean and standard error of the probability of timely repayment in each cell. We can see that in group 2 states, for loans that had high overdues, install-ments due before the state DRT was established were paid on time in 37 percent of the cases. In contrast, for loans with low overdues, repayment was on time in 69 percent of the cases. After the DRTs were set up, delinquency declined by 10 percentage points for the low overdue cases, but by 37 percent for the high overdue cases. The 27 percentage-point difference in these differences represents the effect

23 Parliament passed the law within a few months of its introduction and borrowers did not have a long hori-zon to anticipate the law. Density plots of amount overdue measured four and five quarters before the act suggest that loans were not sorted to avoid DRTs (see Figure 1). The Kolmogorov-Smirnov test fails to reject equality of distributions ( p-value 0.99).

0.2

0.3

0.4

0.5

0.6

0.7

Pro

babi

lity

0 1 2 3 4

Amount overdue

Probability installment paid within 180 days

After state DRT

Before state DRT

Figure 2. Probability of Timely Repayment

notes: All loan quarters in the sample are classified according to whether they occurred before or after the state DRT was established. In each category, a locally weighted regression of the simple mean of the variable allpaid is run on the amount overdue on that loan. Dots represent predicted values from these regressions. Bandwidth = 0.4.


of DRTs. Similar analysis for group 1 states shows a smaller effect; the difference-in-differences is 8 percent.24

This leads to the following regression specification, which controls for other observed and unobserved factors affecting repayment behavior and allows more precise estimates:

(1) allpaidijt = β0 + β1drT tj + β2overdues τij + β3 ( drT t

j × overdues τij )

+ β4 ( group 2 × drT tj ) + β5 ( group 2 × overdues τij )

+ β6 ( group 2 × drT tj × overdues τij )

+ ( Jj × Tt ) + ( overduesτij × Tt ) + γ1Xi + γ2Xit + εijt .

The dependent variable allpaid is an indicator variable that takes value one if all installments due on loan i in state j in quarter t were paid within 180 days.25 drT t

j is an indicator for quarters occurring after a DRT was introduced in state j. overduesτ

ij is the amount overdue on the loan. This amount is measured one year before the DRT law was passed to ensure that it is not endogenous to the timing of DRT placement across states. Group 2 is a binary variable that takes value one if the loan is for a project in a group 2 state, and zero otherwise. The vector Xi represents time-invariant borrower and loan origination-level controls. These include the quarter of loan origination, the size of the loan origination, and the borrower’s

24 The same analysis done for loans originated two years prior to the DRT law gives very similar results.25 In results not reported here, I also run the same regression for the extent of delay in cases where repayment

continues to be delinquent. The results suggest that delay decreases in response to DRT. These results are not significant, however.

Table 5—Means of Probability of Timely Repayment by Overdues Category and Timing of DRT Establishment, for Group 1 and Group 2 States

Group 1 states Group 2 states

High overdues(1)

Low overdues(2)

Difference(3)

High overdues(4)

Low overdues(5)

Difference(6)

After state DRT 0.55 0.70 0.15*** 0.73 0.79 0.05**(0.02) (0.01) (0.02) (0.02) (0.01) (0.02)

Before state DRT 0.26 0.50 0.24*** 0.37 0.69 0.32***(0.02) (0.02) (0.02) (0.02) (0.01) (0.02)

Difference 0.29 0.20 0.08** 0.37*** 0.10*** 0.27***(0.03) (0.02) (0.03) (0.03) (0.01) (0.03)

notes: States are classified into Group 1 (Group 2) states if DRTs were established before (after) 1996. Loans are classified into the high (low) overdues category if overdues measured one year before the date of the DRT law were above (below) the national mean. Loan quarters are classified into the after state DRT (before state DRT) category if the quarter occurred after (before) a state DRT was established in the state where the project is located. Standard errors are in parentheses.



industry. Xit represents borrower’s cash flow. Vectors Jj and Tt represent state and quarter dummies respectively.

In equation (1), β1, β2, and β3 capture the effects on group 1 loans, whereas the remaining β coefficients capture the differential effects on group 2 loans. For exam-ple, β1 estimates the effect of state DRT establishment on group 1 loans that have no overdues. Parameter β2 measures how repayment behavior varies with the amount overdue in group 1 states, before DRTs were set up. In other words, it estimates the slope of allpaid with respect to the amount overdue in the absence of DRTs. β3 captures how this slope changes after DRT establishment. The corresponding esti-mates for group 2 states are β1 + β4, β2 + β5, and β3 + β6. To control for correlated errors and serial correlation over time, standard errors are clustered within state-loan size category cells (Brent R. Moulton 1990; Marianne Bertrand, Esther Duflo, and Sendhil Mullainathan 2004).

C. results

Exposure to DRTs increased the probability that an installment was paid on time for the second wave of DRTs. This can be seen in Table 6, which reports estimates of β1–β6 for different versions of equation 1. I begin with simpler specifications and progressively add more stringent controls. In column 1, dummy variables Jj and

Table 6—Effect of DRT Establishment on the Probability of Timely Repayment for Loans Originated Before June 24, 1993: Levels

(1) (2) (3) (4) (5)After state DRT 0.005 0.004 − 0.060** 0.000 0.000

(0.045) (0.045) (0.029) (0.000) (0.000)Overdues − 0.016 − 0.017 − 0.054 0.025 0.011

(0.035) (0.035) (0.042) (0.037) (0.046)After state DRT × overdues − 0.013 − 0.010 0.035 − 0.053* − 0.030

(0.033) (0.033) (0.041) (0.032) (0.038)Group 2 × after state DRT − 0.018 − 0.015 0.077** 0.000 0.000

(0.051) (0.051) (0.031) (0.000) (0.000)Group 2 × overdues − 0.083* − 0.082* − 0.031 − 0.120** − 0.121**

(0.043) (0.043) (0.041) (0.050) (0.051)Group 2 × after state DRT × overdues 0.143*** 0.142*** 0.136*** 0.190*** 0.210***

(0.043) (0.043) (0.048) (0.042) (0.051)

Borrower’s cash flow No Yes Yes Yes YesState dummies × quarter dummies No No No Yes YesOverdues × quarter dummies No No Yes No Yes

Observations 14,244 14,244 14,244 14,244 14,244r2 0.188 0.191 0.163 0.257 0.261

notes: The unit of observation is a loan quarter. All columns include as controls the logarithm of the size of the loan origination, dummies for the type of project and borrower’s industry, and a quadratic polynomial in the quar-ter of loan origination. All columns control for state where project is located and quarter when installment is due with dummy variables. In columns 3–5, dummy variables are interacted as indicated. In addition, columns 2–5 include the borrower’s cash flow. Standard errors in parentheses are clustered by state × loan size category.



Tt are included separately to control for state characteristics and a national time trend, and borrower cash flow is omitted to avoid bias caused by borrowers endog-enously reporting a lower cash flow to avoid repaying on time. In column 2, cash flow is included, which makes little difference to the estimates. The establishment of DRTs had no effect on loans with zero overdues, either in group 1 or group 2 states. DRTs cause loans with higher overdues to improve repayment. This effect is small and insignificant in group 1 states, possibly because the debate over the legal validity of DRTs made them ineffective here. However, in group 2 states there is a 13 percentage point (or roughly 20 percent) increase, which is significant at the 1 percent level. Column 3 controls for any time-varying repayment patterns that vary with the amount overdue, such as national-level pressure on banks to lower their nonperforming loans that could have led high overdue loans to repay faster, regard-less of debt recovery tribunals. The effect in group 2 states continues to be large and significant at 14 percentage points. Column 4 controls for all observable and unobservable factors that vary over time within a state. This removes any effects caused by states receiving DRTs at the same time as economic growth or other pol-icy changes improved repayment. The effect in group 2 states continues to be strong at 14 percentage points. Finally, column 5 controls for both national changes in the repayment of overdue loans and state-specific changes across all loans, by running the regression in equation (1). The effect in group 2 states becomes stronger; there is an 18 percentage point, or a 28 percent, increase in the marginal effect of overdues on the likelihood of timely repayment.

The analysis here is only done on loans that originated before the reform took place, and, so, we do not worry that the results are driven by better borrowers or projects having been funded after the reform. However, the reform could have been implemented so as to affect those borrowers or projects that were always better able to repay. The borrower fixed effects regressions in columns 1 and 2 of Table 7 com-pare the repayment behavior of a borrower after a DRT is established to his own repayment behavior before DRTs were established, thus removing the effect of any time-invariant borrower characteristics. Similarly, columns 3 and 4 run loan fixed effects regressions, which control for any fixed loan characteristics affecting repay-ment behavior. Although this reduces the size of the effect, it continues to be signifi-cant and large. There is an 8 percentage point increase in the slope of allpaid with respect to amount overdue in group 2 states.

III. Empirical Analysis of Lending Behavior

Next, I examine the effect of the reform on the average size of the loan origination and the interest rate.

A. data

The sample is now enlarged to include loan originations and disbursements which occurred after DRTs were established. A caveat is in order. The data do not contain information on loan applications that were rejected, and, therefore, the effects on


the demand and supply of credit cannot be separated. It is possible that the type of borrowers or projects financed changed endogenously in response to DRT establish-ment. Although the regressions control for time-invariant observable characteristics and borrower-level time-invariant unobservable characteristics, they cannot control for unobservable changes in the composition of loans over time.

B. Empirical specification

The analysis of the size of loan origination relies on the time variation in DRT establishment across states. An observation is a loan origination, since that is the stage at which loan size is determined. The following simple differences-in-differ-ence regression is estimated:

(2) yijt = β0 + Jj + Tt + β1drT tj + β2 (group 2 × drT t

j ) + γ Xijt + εijt .

Here, yijt measures the logarithm of the size of the loan origination. The right-hand side of the regression includes state dummies, quarter dummies, and controls at the loan and borrower level. The coefficient of interest β1 captures the effect of state DRT establishment.

To estimate the effect on interest rates, I use the idea that larger loans are more likely to generate large claim sizes, and, so, are more likely to be processed in the

Table 7—Effect of DRT Establishment on Probability of Timely Repayment on Loans Originated Before June 24, 1993: Fixed Effects

(1) (2) (3) (4)Overdues 0.000 0.001

(0.015) (0.015)After state DRT − 0.001 − 0.001 − 0.007 − 0.007

(0.044) (0.044) (0.044) (0.044)After state DRT × overdues − 0.001 − 0.001 0.002 0.001

(0.024) (0.024) (0.025) (0.025)Group 2 × after state DRT 0.006 0.006 − 0.008 − 0.007

(0.052) (0.052) (0.054) (0.054)Group 2 × after state DRT × overdues 0.080** 0.079** 0.087** 0.087**

(0.038) (0.038) (0.038) (0.038)

Borrower’s cash flow No Yes No YesFixed effects Borrower Borrower Loan Loan

Observations 13,306 13,306 11,335 11,335r2 0.531 0.531 0.538 0.538

notes: The unit of observation is a loan quarter. All columns include as controls dummies for the quarter when installment is due. In addition, columns 1 and 2 include the logarithm of the size of the loan origination, dummies for the type of project, a quadratic polynomial in the quarter of loan origination, and dummies for the state where the project is located. In addition, columns 2 and 4 include the borrower’s cash flow. Borrower (loan) fixed effects regressions are run for the subsample of borrowers (loans) that are observed before and after the state DRT was set up. Standard errors in parentheses are clustered by state × loan size category.



DRTs. If banks foresee this, they might price the loans accordingly. Therefore, the regression estimated is

(3) yijt = β0 + Jj + Tt + β1drT tj + β2 Log ( Size )ijt + β3 { drT t

j × Log ( Size )ijt }

+ β4 ( group 2 × drT tj ) + β5 { group 2 × drT t

j × Log ( Size )ijt }

+ γ Xijt + εijt .

Here, yijt is the interest rate charged on loan i, disbursed in state j, in quarter t. Coefficient β1 captures the effect of DRT establishment on the smallest loan in group 1, and β3 estimates the change in the relationship between loan size and interest rate as a result of DRT establishment. The corresponding effects in group 2 states are β1 + β4 and β3 + β5.

In both equations, dummies for state of project location and quarter dummies control for state characteristics and time trends. Additional explanatory variables control for the type of project, borrower’s industry, and borrower’s fixed assets at the time the loan origination (or loan disbursement for the interest rate regressions) took place. In addition, the interest rate regressions include dummy variables for the currency of the loan and the timing of the origination and the commitment. Standard errors are clustered at the state level to control for correlated disturbance terms.

C. results

Table 8 reports results from a crosssection as well as borrower fixed effects regres-sions on loan origination size. Although, in group 2 states, the coefficient has a posi-tive sign, it is not significant. There is no evidence that DRTs changed the average loan size.26

Table 9 shows the results for interest rates. Note first that, on average, larger loans are charged higher interest rates. Columns 1 and 2 differ only in that column 2 includes the borrower’s fixed assets. The establishment of DRTs did not change inter-est rates on the smallest loan. However, in group 2 states, it changed the relationship between the interest rate and loan size. On average, interest rates rise by 0.4 percent-age points less for a 1 logarithmic point increase, or 1.36 percentage points less for a Rs 10 million increase in loan size. Columns 3 and 4 control for borrower fixed effects and are run for the subsample of borrowers who received loans both before and after DRTs were established, with and without controls for assets, respectively. These are likely to be select borrowers who are better at repayment than the average, and we might expect that DRTs have a smaller effect on such borrowers. Indeed, like before, in group 1 states, there is no significant effect. However, the estimated effect in group 2 states is 0.5 percentage points, and an f-test of β3 + β5 shows that it is significant at the 10 percent level.

26 Quantile regressions (not reported) also show no significant effect, suggesting that the size of individual loans did not change in any part of the distribution.


Table 8—Effect of DRT Establishment on the Logarithm of the Size of Loan Origination

(1) (2) (3) (4)After state DRT − 0.193 − 0.133 − 0.026 − 0.036

(0.181) (0.155) (0.335) (0.318)

Group 2 × after state DRT 0.236 0.259 0.445 0.426(0.357) (0.366) (0.396) (0.426)

Borrower’s assets No Yes No Yes

Fixed effects None None Borrower Borrower

Observations 2,018 2,018 600 600r2 0.420 0.425 0.619 0.619

notes: The unit of observation is loan origination. Controls include dummies for project type, state of project loca-tion, and quarter of loan origination in all columns. In addition, columns 1 and 2 include dummies for the bor-rower’s industry, and column 2 includes the borrower’s assets. The borrower fixed effects regression in column 3 is run on the subsample of borrowers who are observed before and after the state DRT was set up. Standard errors in parentheses are clustered at the state level.


Table 9—Effect of DRT Establishment on Interest Rates Charged on Loans: Levels and Fixed Effects

(1) (2) (3) (4)Log size 0.723*** 0.734*** 0.666*** 0.666***

(0.070) (0.074) (0.177) (0.176)After state DRT 0.183 0.167 0.357 0.357

(0.548) (0.558) (0.807) (0.805)After state DRT × log size − 0.212 − 0.192 − 0.279 − 0.278

(0.124) (0.128) (0.216) (0.224)Group 2 × after state DRT 3.194*** 2.793*** − 0.397 − 0.410

(0.891) (0.919) (1.333) (1.311)Group 2 × after state DRT × log size − 0.509*** − 0.419* − 0.258 − 0.255

(0.173) (0.204) (0.253) (0.235)

Borrower’s assets No Yes No Yes

Fixed effects None None Borrower Borrower

f-test: β 3 + β 5 = 0 [0.000] [0.001] [0.083] [0.095]

Observations 2,169 2,169 797 797

r2 0.381 0.382 0.665 0.665

notes: The unit of observation is a loan. Controls include dummies for project type, state of project location, quar-ter of loan disbursement and currency, and quadratic polynomials in the quarter of origination and commitment in all columns. In addition, columns 1 and 2 include dummies for the borrower’s industry, and column 2 includes the borrower assets. Borrower fixed effects regressions in columns 3 and 4 are run on the subsample of borrowers who are observed both before and after the state DRT was set up. p-values from the test of significance of β 3 + β 5 are reported in brackets. Standard errors in parentheses are clustered at the state level.



IV. Conclusion

In this paper, I use a micro dataset on project loans to examine the effect of a reform aimed at speeding up the legal process to resolve disputes between banks and defaulting borrowers. The results show that the establishment of the new DRTs causes loans with high overdues to improve repayment. A loan with average over-dues of Rs 0.23 million increased the probability of timely repayment by about 28 percent. Loans disbursed after DRTs were set up are also affected. Since larger loans are more likely to be affected by DRTs in the future, interest rates now increase with loan size at a slower rate.

The data in this study come from a single bank. Since the reform applied equally to all banks in the system, there is no reason to think that the effects are confined only to this bank or its borrowers. However, before interpreting this as a move down the demand curve for credit, we need evidence not only on interest rates, but also on the volume of credit. Since I do not have data on all loans given by this bank, or on all loans in the banking system in general, this paper does not analyze these effects.

The motive of the Indian government for establishing DRTs was to improve the legal channels for loan recovery without incurring the costs of overhauling the entire judicial system. The data suggest that even this limited reform was successful at reducing delays in the judicial process, and reducing delinquency in loan repayment. Given that banks in several emerging market economies have high volumes of non-performing loans, this example can have important implications.

The paper also demonstrates a mechanism through which such reform may affect the credit market. The establishment of DRTs appears to have led this bank to charge lower interest rates on new project loans than it otherwise would have, holding con-stant time-invariant features of the project and borrower. Future work will attempt to disentangle the effects on different types of borrowers and projects, and to examine the general equilibrium effects of this reform, if any.

Appendix A: Legal Data

Here, I briefly describe the legal data on performance of courts versus DRTs. A case is filed in a court (DRT) by submitting a plaint (original application). This states the particulars of the case, and makes a request to the court/tribunal for remedial action. The bank can also file an urgent application and request “interim relief,” which is usu-ally an injunction preventing the borrower from disposing of its assets while the matter is sub-judice. The Mumbai high court also follows the practice of appointing a court receiver, who can seize the defendant’s assets. Once the application has been filed, the court issues summons to the defendant, specifying a date when he/she should appear in court. In the Mumbai high court, filing an original application requires invoking the Letters Patent. This is done in a hearing, and, hence, a first hearing often takes place before the summonses are issued. Once the summonses are issued, the defendant appears in court and replies to the summons by presenting his side of the case and/or filing a counter-claim against the borrower. At each such appearance, the court sets a date for the next hearing. Both parties may or may not appear on this date, and it is possible that several dates are set before a hearing actually takes place. Over the course


of these hearings, both applicant and defendant submit evidence and make oral argu-ments before the judge or presiding officer. At any point, the process could end. The borrower could agree to consent terms (where a consent decree is issued by the court but executed privately) or settle with the bank out of court. Alternately, the borrower could file an application with the Board of Industrial and Financial Reconstruction (BIFR), a national authority that investigates if the company is “sick” and can order a restructuring. This effectively freezes the case in court until the BIFR makes a deci-sion. (Although summary statistics indicate that the number of cases filed per year in the BIFR has risen since 1996, they are also being processed in a shorter time.)

After all arguments have been heard, the judge arrives at a verdict. He or she could either rule against the bank, or rule that the firm owes the bank a certain sum. Either party can appeal this decision in the higher court or the appellate tribunal. If there is no appeal, in a DRT, a recovery certificate is issued, and the recovery officer starts the process of recovering this sum. In a civil court, the final verdict is executed by the court receiver. This involves independent valuation of the assets, proclamation of sale, and actual sale through auction. The proceeds of the sale (up to the amount in the verdict) are then transferred to the bank.

The data here pertain to 49 legal cases filed by the bank with civil courts or DRTs in Maharashtra. I was not allowed to see the case records, and, so, the law firm that handles the cases was asked to draw a random sample of cases from those filed in DRTs and those filed in the Mumbai high court. Employees of the law firm coded the data and masked the names and particulars of the borrowers so that I have no identifying information.

The data consist of 22 cases that had been filed with the Bombay high court and 27 cases filed with a DRT. Note, however, that once a DRT is established, all live cases in that jurisdiction’s civil court must be transferred to the DRT. Therefore, all of the 22 Bombay high court cases were transferred to a DRT at some point in their lifetime. (There is variation in the stage at which they were transferred, however.) The data contain information about the dates on which various steps in the legal process took place: date of filing, date when summons to the defendant was issued, date of all “substantial” hearings (i.e., hearings where both parties attended), date when evidence was filed, arguments began, arguments ended, and the final resolu-tion of the case (if resolved). A brief description of the bank’s legal argument is also provided.

Appendix B: Data Cleaning

In September 2000, the bank moved its project loan database to a new database system. Only loans that were active at the time of migration were transferred to the new system. All loans sanctioned after the date of migration are in the new system. For any active loan the entire repayment schedule is available and can be used to reconstruct the history of repayment as described in the text. My data consist of all loans that existed in the new database at the time of data collection (May 2003), cur-rently active or not. However, the removal of currently inactive loans at the time of database migration causes the following problem due to systematic attrition in the data.


The objective of this paper is to examine delinquency, or delays in loan repay-ment. If a loan is delinquent, the account will remain active for longer since the bank will employ various methods to obtain the payment until the payment is made, or the loan is written off the books. Therefore, at any point in time, if we look only at active loans, they are disproportionately likely to be delinquent, and so the loans transferred to the new system are likely to include a disproportionately large number of delinquent loans. Instead, we observe the entire population of loans sanctioned after the database migration, and, so, these loans have the true proportion of delin-quent loans. This would artificially make it appear as if delinquency had decreased over time.

To address this problem, I take advantage of the fact that the entire repayment schedule is available for each loan. I restrict the sample to loans for which the last invoice date was scheduled to occur after the date of migration. Barring prepay-ment, all of these loans would have to be in the new database regardless of past performance.27

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