borrower distress and debt relief: evidence from a natural

Borrower Distress and Debt Relief:Evidence From A Natural Experiment

Krishnamurthy Subramanian a

Prasanna Tantri a

Saptarshi Mukherjee b

(a) Indian School of Business (b) Stern School of Business, NYU

August, 2017

Borrower Distress and Debt Relief: Evidence From A Natural Experiment

Motivation: Historical

Debt relief for distressed borrowers advocated through the ages:

“If any one owe a debt for a loan, and a storm prostrates thegrain, or the harvest fail, or the grain does not growth for lackof water, in that year he need not give his creditor any grain,he washes his debt-tablet in water and pays no rent for thisyear” - 48th provision of Code of Hammurabi, 1772 BC

One of the first legal codes understood the importance of debt reliefto distressed borrowers.

From 1775 to 1850, many American states passed laws thatprovided for debt moratoria.

During the Great Depression, states passed laws for debt moratoriaof farm mortgages.


Motivation: Developed countries in recent times

In recent times, bankruptcy represents the primary mechanism fordebt relief in developed countries.

In 2010, over 10 percent of all U.S. households filed for personalbankruptcy and received nearly $0.5 trillion in debt relief.


Motivation: Developing countries such as India - 1

In emerging economies such as India, a large proportion of thehouseholds engage in agriculture.

Such households are not only poor but also remain vulnerable toincome shocks. This vulnerability results from:

the income stream from agriculture is highly uncertain(Deaton (2016), Deaton (1989)).weather shocks create significant risks and lead to permanent,

high level of distress among farmers (Jacoby (1997), Datt(2003), Burgess (2011)) anduse of agricultural insurance is limited (Cole (2013))

According to a U.N. report, farmer suicides originating from debttraps represent an important concern in developing countries.1

1Source: www.un.org/esa/sustdev/csd/csd16/PF/presentations/

farmers_relief.pdf


www.un.org/esa/sustdev/csd/csd16/PF/presentations/farmers_relief.pdf

www.un.org/esa/sustdev/csd/csd16/PF/presentations/farmers_relief.pdf

Motivation: Developing countries such as India - 2

Given these vulnerabilities, governments may feel the politicalpressure to alleviate agricultural distress (Dietrich (2015), Beslev(1994), Bolton and Rosenthal (2002) and Rucker (1987)).

Apart from the Indian debt waiver program that we study, recentexamples of such interventions include:

The US $2.9 billion bailout for farmers in Thailand andThe rescheduling of about US $10 billion of agricultural debtin Brazil.


Motivation: Debt relief may get exploited

Debt relief may however be exploited by undeserving borrowers.

Bankruptcy Abuse Prevention and Consumer Protection Act passedin the U.S. in 2005 to

“stop the abuse of the bankruptcy system by debtors whocould pay their debts but instead opted to file for bankruptcyprotection”

Concerns about strategic behaviour by borrowers are well founded:

Mayer, Morrison, Piskorski, and Gupta (2011)Guiso, Sapienza, and Zingales (2013)

To enhance the efficacy of debt relief, important to understand itsimpact on distressed and non-distressed borrowers separately.


Motivation: Conceptual tension

Governments in emerging economies employ scarce fiscal resourcesto serve their narrow political interests (Cole (2009), Khwaja(2005)).

Some existing studies suggest that debt relief programs areineffective (Kanz (2015), Gine and Kanz (2016)).

Yet, theoretical studies advocate the need for such ex-postinterventions to alleviate borrower distress.

Bolton and Rosenthal (2002) contend that debt contracts are highlyincomplete as they do not provide for contingencies arising from anadverse state that is beyond the borrowers’ control.

Therefore, adverse shocks can lead to inefficient foreclosures andthereby create significant deadweight costs.

Political intervention in the form of debt moratoria can avoidinefficient foreclosures and the resultant deadweight costs (Boltonand Rosenthal (2002), Rucker (1987)).


Our study: Key findings

We study the causal effect of debt relief on the loan performance of

distressed andnon-distressed borrowers

Utilize $14.4 bn debt waiver announced by Indian government inFebruary 2008.

Waiver beneficiaries, on average, default about 13.8 to 19.4 % morethan non-beneficiaries.

Broadly consistent with Kanz (2015) and Gine and Kanz(2016).

Distressed beneficiaries default 16.2 to 22.3 % less than comparabledistressed defaulters that did not get the waiver.

Non-distressed waiver beneficiaries under-perform comparablenon-beneficiaries by 11.5 to 29.5 %.


Data

Detailed loan-level data over six years (May 2005 —February 2012).

Provided by a Public Sector Bank

The period includes 3 years before and after the 2008 debt waiver.

Accounts spread over 14 branches across nine districts of AndhraPradesh and Telangana.

All loans in the sample were crop loans of one year maturity(for rice production)


Proxy for loan performance

Use status of loan (current or default) as the main dependentvariable.

All agricultural crop loans in our sample have a maturity of one year.

A loan outstanding for more than 365 days is considered to be indefault.


Proxy for distress

Burgess et al. (2011) use deficient rainfall to measure adverseweather.

Adverse weather causes significant distress among Indianfarmers

Following Burgess et al. (2011), create a standardized variable forrainfall in the kharif season:

r̃kt =rkt − r̄kσ(rk)

,

rkt equals the actual rainfall in Mandal b in year t

r̄k and σ(rk) equal the long-term average and standard deviation ofrainfall in the kharif season in Mandal b


Key aspects of the 2008 debt waiver

All agricultural loans in default as of 31 Dec 2007 (and continued tobe in default as of 28 Feb 2008) qualified for the debt waiver:

The program did not differentiate between distressed andnon-distressed defaulters

The loan waiver was unanticipated.

The previous national level waiver was in 1990-1991 (after anational election)This was the first time that a large scale waiver was announceda year before a scheduled election.


Regression Discontinuity (RD) Design - 1

Use a sharp RD design to study the causal effects of debt relief.

As Lee (2010) argue citing Hahn (2001):

“RD designs require seemingly mild assumptions compared tothose needed for other non-experimental approaches.”

Waiver was awarded to only those borrowers who defaulted on aloan on or before 31st Dec 2007 and continued to be in default until29th Feb 2008.

Borrowers who defaulted on the their loans just before the cut-offdate of 31st Dec 2007 form our treatment group.

Borrowers who defaulted just after the cut-off date form our controlgroup.

31st Dec 2007: cut-off date for implementing the sharp RD design.


Regression Discontinuity (RD) Design - 2

Crucially, borrowers on both sides of the cut-off are defaultersseparated by an artificial cut-off date.

Similar in all dimensions except receiving or nor receiving a waiver.

31st Dec has no significance for agricultural production in India.

Borrowers who defaulted on their loans on or before 31st Dec 2007borrowed their loan before 31st Dec 2006 —14 months before theannouncement of the waiver.

Concerns about self-selection into the program are low.


Regression Setup

Regression equation

Yikt = β0 + βt + βk + t × βk + Γ′Xkt + β1Beneficiaryi + β2Distressedikt

+ β3Beneficiaryi × Distressedikt + εikt

Yikt indicates whether the loan is in default or not

β3 = (Y Waiver Beneficiaries − Y Non-beneficiaries)∣∣

Distress before the waiver

− (Y Waiver Beneficiaries − Y Non-beneficiaries)∣∣

No Distress before the waiver

In all our tests, we include fixed effects for each (branch, year) pair.

Omitted variable has to:

Correlate with the Distress dummy (based on the exogenouscut-off), andVary at a level more granular than (branch, year)

Any time-invarying or time-varying confounding factors that affectdistress at the branch level cannot affect our results.


Table 2: Effect of Adverse Weather on Default

Dependent Variable: Probability of Default

Sample: Pre-Waiver Sample: Full

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

Standardized Kharif Rainfall -0.697*** -0.692*** -0.389***(4.29) (4.37) (3.22)

Standardized Yearly Rainfall -0.666***(3.27)

Drought 0.563*** 0.563***(8.29) (13.56)

Log Loan Amount 0.071*** 0.075*** 0.036*** 0.054*** 0.030***(3.69) (3.26) (5.33) (4.46) (5.70)

Observations 23,723 23,723 23,723 23,723 38,990 38,990R-squared 0.412 0.426 0.419 0.400 0.393 0.441

Branch x Year FE Yes Yes Yes Yes Yes Yes


Figure 1 - Ex-post Loan Performance - DistressedBorrowers


Figure 1 - Ex-post Loan Performance - Non-DistressedBorrowers


Figure 1 - Ex-post Loan Performance - All Borrowers


Table 3: RD Design: Probability of Default (DroughtMeasure) - Distressed Borrowers

Dependent Variable: Post Program Probability of Default(1) (2) (3) (4) (5)

Treatment = 1 -0.162* -0.194*** -0.223** -0.182*** -0.203**(-1.80) (-3.00) (-2.35) (-2.71) (-2.14)

Log Loan Amount 0.019** 0.019** 0.018** 0.018**(2.20) (2.19) (2.07) (2.02)

Standardized Kharif Rainfall -0.270*** -0.270***(-9.99) (-10.01)

Drought 0.472*** 0.472***(14.60) (14.62)

Observations 2,869 2,869 2,869 2,869 2,869R-squared 0.468 0.504 0.504 0.530 0.530

Forcing Polynomial Order 1 1 2 1 2Branch x Year FE Yes Yes Yes Yes Yes


Table 3: RD Design: Probability of Default (DroughtMeasure) - Non Distressed Borrowers


Treatment = 1 0.282*** 0.295*** 0.223*** 0.179*** 0.115*(5.07) (5.35) (3.37) (3.89) (1.91)

Log Loan Amount 0.027* 0.028** 0.020* 0.021*(1.89) (1.97) (1.87) (1.93)

Standardized Kharif Rainfall -0.044 -0.042(-1.30) (-1.26)

Drought 0.604*** 0.603***(21.72) (21.72)




Table 3: RD Design: Probability of Default (DroughtMeasure) - All Borrowers


Treatment = 1 0.192*** 0.194*** 0.139*** 0.138*** 0.145**(5.40) (5.39) (2.62) (4.62) (2.17)

Log Loan Amount 0.020** 0.020** 0.015** 0.015**(2.55) (2.58) (2.14) (2.13)

Standardized Kharif Rainfall -0.184*** -0.184***(-7.65) (-7.63)

Drought 0.534*** 0.534***(22.83) (22.87)




Table 4: RD Robustness

[-10,10] [-15,15] [-20,20] [-25,25](1) (2) (3) (4)

Panel A : Full Sample

Treatment = 1 0.087*** 0.096* 0.113*** 0.119***(2.20) (1.86) (2.76) (3.15)

Observations 1,010 1,516 2,321 3,223R-squared 0.567 0.551 0.514 0.516

Panel B : Distressed Borrowers

Treatment = 1 -0.227*** -0.231** -0.252*** -0.226***(-3.32) (-2.51) (-3.46) (-3.44)

Observations 551 950 1,548 2,208R-squared 0.574 0.540 0.504 0.503

Panel C : Non-Distressed Borrowers

Treatment = 1 0.097** 0.105** 0.169*** 0.145***(1.98) (1.98) (3.24) (2.82)

Observations 459 566 773 1,015R-squared 0.574 0.578 0.547 0.555

Controls Yes Yes Yes YesBranch x Year FE Yes Yes Yes Yes


Figure 3 - Distribution of Waiver for Alternate CutoffDates - 1


Figure 3 - Distribution of Waiver for Alternate CutoffDates - 2


Table 5: Falsification Tests based on Different Cutoff Dates

Dependent Variable: Post Program Probability of DefaultCutoff Date 30 Nov 2007 31 Jan 2008 28 Feb 2008

Panel A : All Borrowers

Treatment = 1 0.010 -0.124 -0.006(0.31) (-0.84) (-0.04)

Observations 3,795 2,476 1,706R-squared 0.523 0.533 0.560

Panel B : Distressed Borrowers

Treatment = 1 0.048 -0.195 0.042(0.71) (-1.48) (0.19)

Observations 2,574 1,689 1,104R-squared 0.512 0.530 0.558

Panel C : Non Distressed Borrowers

Treatment = 1 0.010 0.231 -0.166(0.33) (0.99) (-0.41)

Observations 1,221 787 602R-squared 0.553 0.561 0.581

Forcing Polynomial Order 2 2 2Branch x Year FE Yes Yes Yes


Table 7: Pre-Waiver Performance: RD Analysis

Borrower Category Distressed Non-Distressed

Distress Measure Drought Drought Rainfall Drought Drought Rainfall(1) (2) (3) (4) (5) (6)

Treatment = 1 0.194 0.087 0.083 0.143 0.128 0.153(1.37) (0.52) (0.51) (1.21) (1.08) (1.28)

Log Loan Amount 0.032*** 0.029*** 0.030*** 0.022 0.022 0.024(3.27) (3.02) (2.78) (1.26) (1.29) (1.63)

Standardized Kharif Rainfall -1.110*** -1.110*** -0.892*** -0.891***(-14.47) (-14.48) (-8.49) (-8.56)

Observations 1,416 1,416 1,332 543 543 627R-squared 0.345 0.424 0.416 0.175 0.183 0.220

Branch x Year FE Yes Yes Yes Yes Yes Yes


Figure 5 - Ex-Post Credit Rationing


Policy implications - 1

Our study suggests policy implications that are more nuanced thanthose suggested by the existing empirical studies.

First, a debt waiver that is granted to all borrowers withoutconsidering whether they are indeed distressed or not,

not only wastes scarce fiscal resourcesbut also is counter-productive because it increases loan defaults


Policy implications - 2

Second, our results consistent with the theoretical arguments inBolton and Rosenthal (2002) that debt relief targeted at distressedbeneficiaries is likely to improve loan performance.

Thus, governments may not necessarily be wasting scarce fiscalresources to serve their narrow political interests underlineprovided adebt waiver is targeted towards distressed borrowers.

Though the economic environment we study comprises agriculturalloans in an emerging country, our findings and the attendant policyimplications are similar to those in Mian and Sufi (2014).

They contend that the lack of debt forgiveness on housing loansexacerbated the Great Recession.


Summary

First study to provide evidence of both costs and benefits of debtrelief

Post waiver loan repayment performance improves fordistressed beneficiariesThe loan performance of non-distressed beneficiaries worsensafter the waiver

Study provides policy implications that are more nuanced than thoseobtained from existing studies.


Review of Literature - 1

Our main finding:

Distressed beneficiaries benefit significantly from debt reliefOverall costs from the program were high as benefits werecornered by non-distressed (possibly non-deserving)beneficiaries

The program we examine is unique because the waiver did notdistinguish between distressed and non-distressed borrowers

unlike the HAMP program that Agarwal et al (2016) examine

Therefore, we can disentangle the effect of the waiver on (ex-ante)distressed and non-distressed borrowers.

To our knowledge, first study to do so.



Several studies have examined the costs and benefits of debt reliefthrough bankruptcy

Dobbie and Song (2013); Athreya (2002); Chatterjee andGordon (2012); White et al. (1998); White (2007)

However, a borrower chooses to declare bankruptcy

The decision to file for bankruptcy is also significantly influenced bycredit market conditions

Cohen-Cole et al (2009)

Difficult to disentangle the impact of debt relief and the endogenousborrower circumstances or endogenous market conditions.



Several studies examine large scale government debt relief programsgranted during harsh economic circumstances

Rucker and Alston (1987); Agarwal et al (2016)

Some studies find modest benefits

Hembre (2014); Agarwal et al (2016)

Others have shown that such programs induce moral hazard and donot lead to any improvements in real outcomes

Kanz (2015); Gine and Kanz (2016)

These studies focus either on the benefits of debt relief to distressedborrowers.

Bolton and Rosenthal (2002)

or the costs created by strategic borrowers

Mayer et al (2011); Guiso, Sapienza, and Zingales (2013)



Gine and Kanz (2013) examine the same program as we do

They find waiver leads to no benefit

However, our analysis shows that waiver can benefit distressedborrowers substantially


borrower distress and debt relief: evidence from a natural

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