Tuesday, March 13, 2018

Loan waivers as fresh start in bankruptcy

Farmer distress is in the news again. The causes of distress may be many - ranging from low productivity on small and marginal farms, crop failure due to weather fluctuations, to low market price due to a bumper harvest. The usual policy response, however, seems to be a loan-waiver announcement. For example, several loan-waivers were announced in the second half of 2017. The stated rationale for loan-waivers is that if debt burdens are alleviated in this one instance, then it provides consumption relief and makes it possible to start the next farming season on a clean state. The assumption seems to be that the problems that caused distress this season, will somehow, not manifest themselves again.

Even if one were to believe that a one time loan-waiver is a solution to problems of agrarian distress, the efficacy of the mechanism depends on its implementation. This article makes the case that it is possible, and even desirable, that the process of loan-waivers be handled through procedures in the personal insolvency sections of the Insolvency and Bankruptcy Code (IBC). The Code which became functional for corporates last year, has still not been made available for individuals. Implementing loan waivers through the IBC would essentially mean that the decision to avail a loan waiver would cease to be a "political decision" applicable to the collective of farmers, and become an "individual's decision".

Why mass scale loan-waivers are a bad idea?

Ad-hoc loan waiver announcements are usually politically motivated. Their implementation leaves a lot to be desired. For example, a CAG report has demonstrated large scale mismanagement in a previous loan waiver scheme. By December 2017, stories had surfaced about implementation issues in Maharashtra. In January this year, the Punjab government launched its loan-waiver scheme even as farmers protested alleging irregularities in the selection of beneficiaries.

Large scale loan-waivers create moral hazard problems which are detrimental to the development of a credit culture. If debtors expect that there will be a loan-waiver announcement in the future, then there is little incentive to repay on time, as has been demonstrated by empirical research (Kanz, 2016, Chakraborty and Gupta, 2017). Anecdotes suggest that loan-waivers have contagion effects on other sources of credit such as micro-finance. Lenders (other than public sector banks that are forced to lend through priority sector lending targets) become wary of venturing into these markets making borrowers more credit constrained. Credit becomes more expensive for everyone, and not just those who benefited from the waiver.

Waivers also have fiscal consequences - money spent on waivers is not spent on measures that may improve agricultural productivity in the long run. For the costs they impose in terms of the fisc as well as poor development of credit markets, their benefits seem uncertain. It is not clear who is benefiting from the loan waivers, and whether intended beneficiaries are actually getting the waivers.

 

Personal insolvency in the IBC

Before we study how waivers would be handled through the IBC, it is useful to understand the processes of personal insolvency in the Code. The IBC has an entire section on personal insolvency which consists of three processes, two of which are similar to that in corporate insolvency. This includes the Insolvency Resolution Process (IRP) wherein the debtor who defaults on loan repayment, proposes an alternate repayment plan to her creditors, and subsequently gets a discharge from her debts. The second is the Bankruptcy Process (BP), wherein the non-exempt assets of the debtor are liquidated to pay off her creditors, leading to a discharge.

Once the personal insolvency sections of the IBC are notified, it would become possible for farmers to approach the courts to ask for a re-negotiation of their debts from their lenders through the IRP. Such a demand may be made by creditors as well. If the IRP fails, farmers could undergo the bankruptcy process. Both these processes do not include an element of a waiver. It is expected that the debtor will make some repayments to the creditor - either through a repayment plan that most likely promises payments out of future wage (or other) income, or through liquidation of the non-exempt personal assets. Such a system disciplines borrowers and brings difficult questions on the feasibility of a particular venture, including low productivity farming, to the forefront. The process also disciplines creditors by constraining them from forceful credit recovery, and by bringing them to the table to re-negotiate with the debtor, and take a haircut on their loans.

 

Debt relief in the IBC

The third process, called Fresh Start, aims to provide debt-relief to people who fall below certain asset, income and debt thresholds. The eligible debtor, in this case, the farmer, can trigger this process. On acceptance of the farmer's petition, her debts will be waived off. That is, the creditors will not be able to initiate a recovery process on this debt, and will have to do a write-off. A record of default will be kept by the Insolvency and Bankruptcy Board of India (IBBI) for a certain prescribed time-period, and future creditors will have access to these facts. Thus, the fresh start process provides full waiver to the farmer, while containing moral hazard through a record of default. The decision on whether to avail of the waiver rests with the individual.

 

Why a fresh start?

One may wonder why a fresh start at all. If loan waivers are expected to create moral hazard problems, then why make provisions for such a process? There are two good reasons for allowing complete debt relief.
  1. Very often the costs of going through an insolvency resolution process or a bankruptcy may be higher than the amount that can be expected to be collected. In such an event it is more efficient for the system to just write-off those loans.
  2. Bankruptcy systems often provide an element of social insurance, as it is believed that distress can be the result of circumstances beyond one's control. In the case of personal insolvency, especially, studies in the US have shown that events such as medical emergencies account for a number of financial distress cases. In such circumstances, it might be optimal to provide a mechanism to discharge one's debt without undergoing a resolution process. The provision of insurance might actually encourage households to take debt, and engage in entrepreneurial ventures. This may be true of farmers who may be in distress due to events such as crop failure, and may wish to avail of a write-off of their debts.

 

Why is the fresh start better than a loan-waiver?

The fresh start process is superior to the loan-waiver programs as it offers a more systematic way of resolving distress. Each farmer can weigh the trade-offs between obtaining debt relief through the fresh start process, but potentially more expensive credit in the future, versus offering a resolution plan or going through a bankruptcy process for a better credit score. It thus contains the moral hazard problem and might yield better credit markets.
As the application is made by the intended beneficiary, there will be very little possibility of selective provision to beneficiaries, or leakages in administration of the waiver program. The application for a fresh start does not depend on the state government declaring a loan-waiver scheme - it is possible to obtain this in the ordinary course of life. It contains the fiscal problem as the state does not rush in to pay the banks for the loans that have been waived. If the state government still wishes to pay for the waivers, then it can always pay the banks, but use the IBC to actually implement it. In doing so, it still is able to constrain the moral hazard problem as those who avail of waivers will have a record.

 

Challenges of implementing fresh start

The advantage of fresh start lies in formalising the process of debt-waiver, and putting the decision in the hands of the farmers who can weigh the consequences of the decision. This may sound simple in theory, but as with most things, may turn out to be difficult in practice for the following reasons:
  1. The roll out of the process rests on the institutional machinery of the IBC being in place. This requires setting up new Debt Recovery Tribunals as well as improving their procedure, and training of Resolution Professionals. Farmers in remote villages need a way to access this institutional infrastructure. If the machinery is not in place, then the existence of such a process is moot.
  2. The decision to undergo a fresh start may be a complex if one is financially not very savvy. This requires a cadre of credit counselors who work in the interest of the debtors, in advising on the application process as well as on understanding the substantive implications of the Code. There is currently no such cadre of "advisors" who can guide farmers on these decisions.
  3. As currently defined, the thresholds for a fresh start eligibility are rather narrow. Only debtors with gross annual income of less than Rs.60,000, assets less than Rs.20,000, debts less than Rs.35,000, and no home-ownership, are eligible to get a complete waiver of debts. It is not clear how many farmers will qualify for a fresh start under these conditions. It might be useful for the government to revisit these thresholds in light of how important fresh start may be for solving the problem of loan distress in India.
  4. In an environment where lending to agriculture is politically motivated (see Cole, 2009), large scale use of fresh start might get the banks in further trouble - as on one hand they will be forced to write off these loans, but on the other also forced to lend to farmers through priority sector lending requirements. For the fresh start mechanism to work, we need to move towards a more thorough-going reform in public sector banking in general and agricultural credit in particular.

 

Conclusion

Indian agriculture seems to repeatedly encounter large scale agrarian distress. In this environment, the proclivity to announcing farm loan waivers, the inability of waivers to reach intended beneficiaries, while causing adverse consequences on credit culture suggests that we find more efficient ways of resolving this distress. Enacting the personal insolvency sections of the Indian Bankruptcy Code may be a useful mechanism to address this problem. In particular, the provisions on fresh start, which provide a complete waiver of debts, may be extremely useful in providing relief to farmers in a systematic way, and by confronting the problem of loan write-offs, may pave the way for reform in agricultural lending.

I thank Josh Felman and Anjali Sharma for useful comments.This article first appeared on Ajay Shah's blog, March 12, 2018.

PenCalc: A tool for simulating pension outcomes

Policy decisions on pensions should be shaped by an evaluation of the link between various parameters of the pension scheme and potential outcomes. For example, the setting of fees, investment guidelines, annuity policies, should be designed after a careful study of how these will affect the pension received. This is especially important in defined-contribution pension systems where there is considerable uncertainty about returns that may be obtained. Pension outcomes must, therefore, be understood through the lens of the risk-return trade-off.
This article presents penCalc, a new open source software system developed for conducting simulations on pension outcomes. It allows the key variables that may affect the pension to be changed, and presents the user with a range of possible pension amounts. This can help policy makers evaluate the impact of a policy change on pension outcomes. This can also be used by individuals for retirement planning.

penCalc

penCalc simulates pension scenarios based on assumptions on age of entry, exit, wage growth, contribution rate, portfolio allocation, asset returns, annuity prices, and inflation. It is developed using R, an open source programming language and software environment for statistical computing, supported by the R Foundation for Statistical Computing. The package may be installed as follows:
devtools::install_github("renukasane/penCalc")

Assumptions

The default assumptions made in penCalc are shown in Table 1. They have been chosen to be as close as possible to the National Pension System (NPS) in India. For example, the age at exit is the current retirement age. The returns assumptions are derived from a study of the Indian financial environment. The life-cycle allocation is sourced from the Deepak Parekh Committee Report set up by the PFRDA in 2009 on investment allocations. In a life-cycle portfolio allocation, the exposure to equity is very high at younger ages, and gradually reduces as one approaches retirement. The fees and expenses also reflect the current AUM charges in the NPS, as well as the flat fee charged by the Centralised Record-keeping Agency (even though this may not be exactly INR 100). The annuity price is taken from the current offerings of the Jeevan Akshay policy of the Life Insurance Corporation of India.
Table 1: Assumptions
Age
Age of entry 25
Age of exit 60
Wages and contributions
Starting wage INR 25,000 per month.
Wage growth (nominal) 8% per annum
Contribution rate 20% of wage
Initial amount (already in the account) 0
Inflation (mean, sd) (4%, 0)
Investment portfolio Life-cycle
Returns (nominal)
GOI bonds (mean, sd) (7%, 0)
Corporate bonds (mean, sd) (10%, 0)
Equities (mean, sd) (16%, 25%)
Fees
AUM 0.01%
Flat fee INR 100 p.a.
Annuity parameters
Percent to be annuitised 40%
Price for an INR 1 a day nominal
annuity
INR 4,087

These default numbers can be changed to reflect different views on NPS rules as well as the Indian macroeconomic environment. The tool can also be used for pension income simulation with assumptions that reflect the environment in different countries.

Using penCalc

The structure of the code is given below. The function consists of various parameters, and the default values set against the parameters. For example, age.entry is set to 25, while age.exit is set to 60. All of these parameters can be changed.
  x <- pencalc(age=list(age.entry=25,     
                        age.exit=60),         
       wage=list(25000,            
                 0.08,                  
                 0.2,                   
                 initial.amount=0),    
       inflation=list(c(0.04,0), real=TRUE),        
       inv.weights=list("lc"),    
       returns=list(data.frame(mean=c(0.07, 0.10, 0.16), 
                                      sd=c(0, 0, 0.25)),
                    c(monthly.fees.expenses=0.01, 100)),
         annuity=list(perc.annuitised=0.4, value=4087))

How the model works

The starting wage and the yearly growth rate in wages are used to generate a vector of wages for the years the subscriber is expected to be in the system. The number of years is calculated as the difference between the age of entry and exit. In this particular instance, the number of years is 60-25+1, that is 36 years.
The contribution rate is then used on this vector of wages to arrive at the rupee value of contributions made each year in the NPS. The wages are expected to stay the same in each month of the year. For example, in this case, the contributions will be 20% of the wage of INR 25,000 in the first year.

The returns on each instrument are simulated from a normal distribution with the mean and standard deviation of that particular instrument. The investment weights and returns are used to arrive at a portfolio return. The monthly fees and expenses are deducted from the portfolio returns. The contributions and returns are accumulated over each year in the system and give us the total accumulation in the pension account.

If the user has entered the "real=TRUE" option, then the rate of inflation is subtracted from all inputs. The results of the model in such a case will be in terms of today's rupee value, and not nominal values. The default inflation rate is 4%, but as discussed earlier, this can be easily changed.

The simulation is done 1,000 times and generates a distribution of accumulated amounts in the NPS account. The amount to be annuitised (for example 40%) is subtracted from this accumulation. The annuity price is used to arrive at the monthly pension that can be purchased with this amount. The remainder (for example 60%) is available as a lump sum withdrawal. The model has the following outputs:
  1. In hand accumulation: This is the average amount of lump sum withdrawal available at retirement. In the case of 40% annuitisation, the in hand accumulation is the remainder 60% of the total accumulated balances. In the case of full annuitisation, this amount will be zero, as the entire accumulation is turned into an annuity.
  2. Monthly pension: This is the rupee value of the average monthly pension the retiree can expect to get after the purchase of the annuity.
  3. Replacement rate: This is the ratio of the pension to the last drawn wage. The replacement rate only makes sense for government employees. For those with varied contributions over their lifetime, it is not sensible to divide the pension with the last wage. The replacement rate should be ignored for subscribers other than regular salaried employees.

Example 1: Portfolio dominated by GOI bonds

This example demonstrates the use of the calculator for an investment allocation between government bonds and equity of 85%and 15% respectively. We have chosen the real=TRUE option. Hence all the results are in 2018 rupees.
Since the example is using all the default values and only changing the investment weights (as the default weights are the life cycle model), we change that parameter in the model. We first create a weightmatrix where we specify the portfolio allocation into government debt and equity. We then supply the weightmatrix to inv.weights. The code is as follows:
 
library(penCalc)
weightmatrix <- data.frame(goi_bonds=rep(0.85, 36), 
                              corp_bonds=rep(0,36),
                              equity=rep(0.15,36))
set.seed(111)
# 40% annuity
x <- pencalc(inflation=list(c(0.04,0),real=TRUE),
     inv.weights=list(weightmatrix))

# 100% annuity
y <- pencalc(inflation=list(c(0.04,0), real=TRUE),
             inv.weights=list(weightmatrix),
             annuity=list(perc.annuitised=1, value=4087))

Table 2 describes the results. The first three columns show the results for 40% annuitisation, while the next three show the results for 100% annuitisation. The results are in "real" terms. The numbers in the bracket represent the standard deviation - this reflects the uncertainty around the average lump sum and pension amounts.
Table 2: Portfolio dominated by GOI bonds
40%
annuitisation
100% annuitisation

Average 10th percentile 90th percentile Average 10th percentile 90th percentile
Monthly Pension (in Rs.) 23,297 (828) 22,196 24,361 58,242 (2072) 55,491 60,902
In hand accumulation (in Rs. million) 4.7 (0.17) 4.4 4.9 0.0 (0.0) 0.0 0.0
Replacement rate 23.6 (0.80) 22.5 24.7 59.0 (2.1) 56.2 61.7

With 40% annuitisation, the average pension at the age of 60 is INR 23,297. This provides an average replacement rate of 24%and also provides a lump sum withdrawal of INR 4.7 million. Pension at the 90th percentile of the distribution is INR 24,361, while at the 10th percentile is INR 22,196. The replacement rates are 25% and 22% respectively.

With 100% annuitisation, the average monthly pension increases to INR 58,242 and the replacement rate to 59%. The 90th percentile of this distribution is INR 60,902, with a replacement rate of 62% while the 10th percentile is 55,491 with a replacement rate of 56%.

Example 2: Life-cycle portfolio investment

The previous example is heavily skewed towards government bonds. Given the huge equity premium in India, it is useful for the NPS to invest more heavily in equities. One way of increasing equity exposure is through a life-cycle portfolio allocation. The current example uses the default life-cycle portfolio weights indicated by the "lc" option. However, these weights can also be changed. The code is as follows:
 
library(penCalc)
set.seed(111)
# 40% annuity
x <- pencalc(inflation=list(c(0.04,0), real=TRUE),
             inv.weights=list("lc"))
     
# 100% annuity
y <- pencalc(inflation=list(c(0.04,0),real=TRUE),
             inv.weights=list("lc"),
             annuity=list(perc.annuitised=1, value=4087))

Table 3 describes the results. The average pension at the age of 60 is INR 36,744 with 40% annuitisation. This provides a replacement rate of 37% and also leaves a lump sum amount of INR 7.4 million. The average here is higher than that obtained using a portfolio dominated by government bonds. However, the standard deviation is also higher, suggesting that the risk is higher. This is not surprising because the exposure to equity is higher in the life-cycle investment portfolio.
Table 3: Life-cycle portfolio investment
40% annuitisation 100% annuitsation

Average 10th percentile 90th percentile Average 10th percentile 90th percentile
Monthly Pension (in Rs.) 36,744.3 (3702.4) 32,017.9 41,462.0 91,860.8 (9256.1) 80,044.7 103,654.9
In hand accumulation (in
Rs. million)
7.41 (0.75) 6.45 8.35 0.0 0.0 0.0
Replacement rate 37.2 (3.80) 32.4 42.0 93.1 (9.4) 81.1 105.1

Pension at the 90th percentile of the distribution is INR 41,462, with a replacement rate of 42%, but at the 10th percentile is INR 32,018 with a replacement rates of 32%. Full annuitisation provides an average monthly pension of INR 92,000 and a replacement rate of 93%. At the 10th percentile, the replacement rate drops to 81%, but at the 90th percentile it jumps up to 105%.

Example 3: Varying contribution rates

The assumption of a constant contribution rate is not realistic in the case of informal sector workers. The model handles this by using a vector of wages, and a contribution rate of 100% in the model. This effectively makes the values entered in the wage the actual contribution. In the example described below, we simulate 36 values for wages from a normal distribution with a mean of INR 3,000 and a standard deviation of INR 100. We then use a contribution rate of 100%. The code is as follows:
library(penCalc)
wage = round(rnorm(36, 3000, 100),0)
# 40% 
set.seed(111)
x <- pencalc(wage=list(wage,
                       0,
                       1,
                       initial.amount=0),
              inflation=list(c(0.04,0),real=TRUE))
      
#100 %
set.seed(111)
     y <- pencalc(wage=list(wage,
                     0,
                            1,
                            initial.amount=0),
                  inflation=list(c(0.04,0), real=TRUE),
           nnuity=list(perc.annuitised=1, value=4087))
 
Table 4 presents the results. As the replacement rate is meaningless in this context, it is not shown in the table. An informal sector worker with average monthly contribution of INR 3,000 every year for 36 years, can expect an average monthly pension of INR 13,454 with 40% annuitisation, or an average monthly pension of INR 33,635 with 100% annuitisation.
Table 4: Varying contribution rates
40%
annuitisation
100% annuitsation

Average 10th percentile 90th percentile Average 10th percentile 90th percentile
Monthly Pension (in Rs.) 13,454 (1698.3) 11,305.8 15,623.4 33,635.2 (4,245.9) 28,264.5 39,058.5
In hand accumulation (in
Rs. million)
2.7 (0.34) 2.3 3.1 0.0 (0.0) 0.0 0.0

Conclusion

penCalc is a new open source software system developed to model pension outcomes. It allows the key variables of interest to be changed - and sets out a range of plausible outcomes using data on returns, equity premium and income from annuities purchased at retirement. The results are averages from the simulation. It is, therefore, useful to also look at the standard deviation to get a complete picture of the possible outcomes. As has been demonstrated in the examples, the outcomes can vary considerably, and retirees must factor in this uncertainty as they do their financial planning.

A recent working paper, Simulating Pension Income Scenarios with penCalc: An Illustration for India's National Pension System, demonstrates many examples of the use of this tool for different assumptions of equity returns, and annuity prices. We hope this software becomes a useful tool for policy makers and regulators as they develop pensions policy.

I thank Arjun Gupta for collaboration on the software development, William Price for collaboration on the working paper. The work was supported through the FIRST Initiative in funding the engagement with India's Pension Fund Regulatory and Development Authority. This article first appeared on Ajay Shah's blog, 25 January, 2018.

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