Elements of the low Indian labour force participation rate: The elderly

by Subhamoy Chakraborty, Renuka Sane and Ajay Shah.

India has a remarkably low rate of labour force participation. The Periodic Labour Force Survey (PLFS) carried out under the Ministry of Statistics and Programme Implementation estimated the labour force participation rate (LFPR), for individuals of age 15 and above, at 49.8% in 2017-18. The CMIE CPHS survey, which has more recent information, has shown a decline in the LFPR by 2019. These numbers suggest that a large part of India is not in the labour market. These magnitudes of non-participation are much larger than the rates seen with unemployment. The grand question of Indian labour economics is that of understanding the low LFPR. The grand question of Indian economic policy lies in obtaining a 50 per cent gain in GDP through a 50 per cent increase in the labour force.

One big element of this LFP problem is women's LFP. The women's LFPR has been falling. In 2011-12, India was already one of the countries with the lowest female labour force participation. This has gotten worse with time. In 2017-18, the female LFPR fell to a historic low of 23.3%. A remarkable feature of the Indian women's LFPR is the comparison against countries like Pakistan (24%) or Bangladesh (36%). For those of us who believe that women's agency in India is ahead of that in Pakistan, this is a bracing fact. The examination of women's LFP is an important crossroads between labour economics and gender studies. It also emphasises the importance of gender studies in thinking about India.

The other two big elements of the LFPR problem are the young and old. In this article, we delve into labour supply of the elderly and establish some basic facts of this field. The positive and normative economics of elderly LFP is an important element of labour economics, given the large and growing share of the elderly in the population. It is also a major issue in ageing studies. High labour force participation by the elderly is well known to contribute to emotional and physical well being. It is generally better for a person to work for a wage of Rs.50 a month rather than obtain a pension of Rs.50 a month. The puzzle of the field lies in devising labour market arrangements that will harness labour supply of the elderly, and avoid the abrupt event of retirement.

The elderly are defined as those above the age of 55. The 55-64 age group is also part of the conventionally defined working age group (age 15 to 64). The 65+ age group constitutes the old elderly.

Data

We study the Consumer Pyramids Household Survey (CPHS), a pan-India panel household survey of about 170,000 households carried out by the Centre for Monitoring Indian Economy. The survey asks about the present employment status of each member above 15 years of age. The response to the employment question is recorded as a 4 point status:

1. Employed
   
2. Unemployed, not willing and not looking for a job
   
3. Unemployed, willing and looking for a job
   
4. Unemployed, willing but not looking for a job
   

Individuals whose employment status is either Employed (1) or Unemployed, willing and looking for a job (3) are considered to be a part of the labour force. In this article, we examine the data for January - April, 2019.

Overall

Table 1 provides estimates of the labour force participation by age group. There are approximately 375 million workers in the 15-54 age group, giving a LFPR of 45%. The LFPR drops slightly to 44% for the 55-64 age group with 51 million workers. The LFPR drops dramatically to 12% for the 65+ age group with only 8.4 million workers.

Table 1: Labour Force Participation: Age Group

Age Group	Population (in millions)	LFP (in millions)	LFPR(%)
15-54	828.3	375.0	45.3
55-64	115.6	50.8	43.9
65 and above	69.1	8.4	12.1

Figure 1 presents the labour force participation rate (LFPR) of those above the age of 55. The labour force participation was a little over 55% at age 55, and fell to about 10% by age 70.

Figure 1: Labour Force Participation (55 and above)

Withdrawal by the elderly from the labour force generally happens for one of the following reasons: (a) people are required to leave their main job at a specific retirement age, (b) are unwilling to work because they value leisure more and access to pensions at a sharply defined age which makes it feasible to stop working (c) are unable to work because of health constraints or (d) the labour market is unfriendly to older workers. In the US, for example, sharp drops in participation are seen at the age of 62 and 65, when access to social security benefits becomes available.

In India, formal pension arrangements are only in place for a small part of the population. Hence, factors "(a)" and "(b)" above should not matter much in India. And yet, we see a sharp drop at age 60. This suggests that reasons such an unfriendly labour market might explain the large drops in labour force participation at older ages.

Changes over time

Figure 2 presents the labour force participation rate in 2016 and 2019. We see that there has been a small increase in the participation rate for the 55-59 age group between 2016 and 2019. However, for all other age categories, there has been a remarkable fall. For example, about 42% of the 60-61 age group participated in the labour market in 2016. This had fallen by about 10 percentage points in 2019. Similarly, in the 65 plus age group, labour force participation was at 28%. By 2019, this had fallen to 12%. This suggests that that stress in the economy has hurt the elderly more than prime-age working males. This may be part of a larger phenomenon, where prime age working males are protected in economic downturns, while all other parts of the labour force (women, the young, the old) seem to lose employment at higher rates.

Figure 2: Labour Force Participation: Over time

International comparison

It is useful to ask how these compare to the numbers in the OECD countries, where formal pension systems shape the decision to retire. Table 1 presents the elderly labour force participation rate (LFPR) in India, US and Japan.

Table 2: LFPR: Comparison with US and Japan

Age Group	India (%) (Jan-Apr 2019)	US (%) (2018)	Japan (%) (2018)
55-59	52.7	72.3	83.4
60-64	29.4	57.1	70.6
65 and above	12.1	19.6	24.7

In 2018 according to the Bureau of Labor Statistics in the US, 72.3% of those in the 55-59 age group were in the labour force. The participation rate fell to 57.1% for age group 60-64 and further declined to 19.6% for those above 65. Meanwhile the labour force participation rate in Japan, as reported by Statistics Bureau of Japan, was 83.4%, 70.6% and 24.7% for age groups 55-59, 60-64 and above 65 respectively. Japan has, in fact, seen a resurgence in elderly labour force participation in recent years owing to better health and education, as well as reduced generosity of social security programs.

Japan is considered one of the best countries in terms of integrating the elderly into the labour market. Suppose we treat Japan as a frontier: the outer limit of what is possible with labour market participation by the elderly. How much would we in India gain if we moved up to this frontier?

If the LFPR of the 55-64 age group in India (which is 43.9%) were to become the same as that of Japan's (77%), then the overall working-age LFPR would go up to 49.15%. This is a 4 percentage point increase in the LFPR owing to increases in the labour force participation of the "young old", and would mean that an additional 38 million individuals would be in the labour force.

An additional 9 million people, of age 65+, would also join the labour force, by matching the Japanese LFPR for the age group of 65+.

Totally, 47 million people would enter the Indian labour force if we moved up to Japanese levels of LFP from age 55 and above. This is an economically significant number. This magnitude of impact will go up in the future as India ages.

Conclusion

The Indian labour market has a remarkable feature: of low labour force participation. In this article, we examine one facet of this problem: the low LFP for the elderly. Despite the prevalence of a large informal sector, and the absence of a formal age of retirement, we find that the elderly labour force participation is low, and has actually fallen between 2016 and 2019.

Withdrawal from the labour market is bad for the elderly and bad for the economy. The examination of the LFP of the elderly is an important crossroads between labour economics and ageing studies. Further research is required in identifying the causes behind the low LFPR of the elderly.

 

The authors are researchers at the National Institute of Public Finance and Policy.

There be dragons: Off-balance-sheet liabilities of the Indian State

by Ila Patnaik and Ajay Shah.

Conventional fiscal stability analysis looks at the stock of debt and wonders whether a country can pay off this debt, under reasonable scenarios for future interest rates and fiscal surpluses. In many countries, though, the fiscal sustainability story has turned on promises made by a government which were not explicitly counted in the debt. There are obvious liabilities that are kept off the books - such as debt in public sector companies or state electricity boards. In this article we look deeper, at less obvious ways in which off-balance-sheet liabilities have arisen, and the checks and balances that can contain them.

Off balance sheet liabilities of the government

Off balance sheet items come in two kinds.

1. A promise that looks like the cashflows on a bond. Example: A pension promise to a person is no different from a series of coupons that are paid out every year. Promising a pension is exactly like issuing that comparable bond.
2. A promise that looks like an option payoff. Example: If a government is in hock to pay the lenders of a firm when it goes bankrupt, it is much like being the seller of an option. When governments write guarantees, this changes the risk profile of the exchequer and generates possibilities of large payouts when those options mature in the money.
   It should be noted that organisations backed by statute are not automatically backed by a government guarantee. As an example, in the UTI crisis of 2001, the government had no legal obligation to make good the losses of investors, but a political decision was made to use fiscal resources to pay half the loss. There is a mixture of financial risk ("Will X get into trouble?") and political risk ("Will the government backstop X?").

A correct reckoning of the liabilities of a government should add in these off-balance-sheet liabilities of both kinds. The FRBM Act brought control on one kind of off-balance-sheet liability of the Indian State: explicit guarantees given by the government. But there is more to the problem of off-balance-sheet liabilities than explicit guarantees.

Differences in cost versus differences in transparency

In the field of pensions, an interesting distinction is made between an unfunded defined benefit program vs. a funded defined benefit program that has assets invested in government bonds. In the conventional wisdom, a funded DB program is always superior to a pay-as-you-go unfunded program.

However, these two approaches are exactly the same in terms of the cashflows: both involve a highly predictable set of claims on the exchequer at future dates. To promise a pension is to implicitly issue a bond. This equivalence, between the cashflows of a bond and the cashflows of a pension, has an interesting implication. Consider a funded DB public pension program that invests in government bonds. The two streams of cashflows cancel out.

This approach to funding (holding government bonds) does not make things cheaper: it is only superior in that it is transparent and connects into the fiscal planning process. Cost savings only come about when a funded DB program invests in higher return assets, such as equities, through which the claims upon the exchequer at future dates are reduced on expectation.

What are the important off-balance-sheet liabilities of the Indian State?

Some important components of the off-balance-sheet liabilities are:

• Promises made for defined benefit pensions of civil servants, in particular the new `one rank one pension' (i.e. wage indexed) pensions for uniformed folk, and the underfunded `Employee Pension Scheme' (EPS) that is run by the EPFO. For the civil servants recruited after 1/1/2004, there is no such problem, as these new recruits are in the New Pension System.
• Promises made in a variety of health-related entitlement programs (Patnaik et. al., 2018).
• The temptation to make good the promises made by public sector financial firms, that experience distress in the future, even when there is no explicit guarantee. Of these, LIC has a balance sheet of Rs.28 trillion.
• The temptation to make good the promises made by private financial firms that experience distress in the future, even when there is no explicit guarantee. As an example, will the failure of IL&FS -- a private financial firm -- induce a direct or indirect fiscal impact upon the exchequer? So far, the government has not put money on the table, but could this change?
• The use of fiscal resources in responding to a full blown financial crisis, that may occur at a future date.
• The Parliament has enacted many laws, which could potentially evolve into large inflexible expenditures. These include `Right to education', `Right to food' and NREGS. On a similar note, the promises which are being made under `minimum support price' (MSP) could turn into large expenditures if the future brings together a certain combination of political pressures, jurisprudence and development of State capacity. Until repeal, these laws are a genotype that could, under the right combination of events at future dates, get expressed in a way that involves major fiscal risk.

These liabilities add up to large sums of money, of the same order of magnitude as the overt stock of public debt. Hence, off-balance-sheet liabilities should become more prominent in the Indian fiscal discourse.

How do the incentives of politicians and officials change?

At present, there is no check-and-balance influencing these opaque promises and risks. Each party in power looks to enter into greater off-balance-sheet obligations so as to get re-elected. How might this change?

The key thing that shapes these incentives is financial repression. At present, government debt is mostly sent into involuntary lenders. When the fiscal system graduates from financial repression to voluntary lenders, off-balance sheet liabilities would matter. There are numerous gains from removing financial repression: voluntary borrowing is more efficient than forced borrowing, the magnitude of resources available in a crisis would become greater, etc. But this requires a government that faces a skeptical bond buyer who demands a risk premium based on the extent to which the Indian State may engineer inflation or default.

In India today, there are many loose ends, which periodically induce fiscal surprises. This creates an adverse risk profile of Indian government bonds, and would drive up the required interest rate for borrowing when faced with voluntary buyers of bonds. In such a world of market discipline, when a government dips into LIC's resources, this would induce a higher cost of borrowing.

In India today, most of the attention in fiscal reforms lies upon tax policy reforms, such as the GST and the Direct Tax Code, and there is some interest in FRBM. There is much more to a mature fiscal system, including the issues of tax administration, debt management, the bond-currency-derivatives nexus, off-balance-sheet liabilities, accrual-based accounting, and the budget process. We need to broaden our research and policy work to address this full range of problems.

Tracking and understanding off-balance-sheet liabilities, communicating them to lenders, and communicating these concerns back into the budget process, is part of the work program of the future Public Debt Management Agency (PDMA) (Pandey and Patnaik, 2017). A Fiscal Council will help. Accrual based accounting will help.

Once we start paying attention to off-balance-sheet obligations, this creates fresh impetus for economic reform in many areas. As an example, if a monsoon failure induces a farm loan waiver paid for by the government, this is like a monsoon derivative that has (maybe) been written by the government. When reforms of personal insolvency and reforms of agriculture remove this possibility, the risk profile of the Indian exchequer will improve, and the cost of borrowing will go down.

Off-balance-sheet liabilities and financial reform

There is a close connection between public finance and finance, centering around the government bond market and the PDMA. For public finance, PDMA and the government bond market are the source of debt. For finance, the PDMA is the biggest investment banker of the country and the government bond market is the tool for low risk transfers of resources across time. What is less widely noticed is the intimate connection, between public finance and finance, through the question of off-balance-sheet liabilities.

How will off-balance-sheet liabilities change when micro-prudential regulation improves and the resolution corporation is setup? Financial firms will face distress less often, we will discern that distress early, and we will have an institutional mechanism to put the distressed firm down. Conversely, under present conditions, we get surprised by the difficulties in an IL&FS or in a UTI. These crises lead to a political question being thrust upon the leadership: Will you make liability-holders happy by using taxpayer money? We should, of course, have a mature political system which is able to turn down such requests most of the time, but we should have a mature financial regulatory system so that these situations do not arise in the first place.

Governments worldwide have faced claims on fiscal resources when dealing with full blown financial crises. The probability of occurrence of such crises, and the severity of such crises, is shaped by the institutional capacity in systemic risk regulation. The FSLRC apparatus for systemic risk regulation -- the Financial Stability and Development Council (FSDC) and its information system, the Financial Data Management Centre (FDMC) -- will reduce fiscal risk and thus the cost of government borrowing. As an example of the work program which should take place through FSDC/FDMC: At present, we have the possibility of runs on mutual funds (Sane et. al., 2018), which can lead to a full blown financial crisis, which requires policy thinking and reforms on a financial system scale.

Our objective in financial economic policy should be: to be as sparing as possible in ever asking for resources from public finance policy. For a sound fiscal system, we require financial sector reform. This will have a beneficial impact upon contingent off-balance-sheet liabilities and thus the cost of borrowing.

The need for a research community and a research literature

A remarkable feature of the existing Indian policy process is that no fiscal estimation was done in the policy process that led up to the announcements  about one rank one pension, or the various health insurance programs.

Even if policy makers had tried to reach into the research community to obtain such estimates, the state of data and knowledge is weak, and it is difficult for policy makers to obtain policy support from researchers. Some early work on the civil servant's defined benefit pension (Bhardwaj and Dave, 2005), one rank one pension (Sane and Shah, 2015) and banking (Shah and Thomas, 2000) is available. Much more needs to be done in this important field.

In an ideal world, record level data would be available from the government which would permit estimation of the value of the implicit debt or the implicit derivatives that the government has issued. The state of information systems and transparency of government is often a bottleneck, and creative research strategies have to be employed. As an example, Bhardwaj and Dave, 2005, utilise data from a national scale household survey to identify present and future beneficiaries of the traditional DB civil servants pension, and extrapolate the sample estimates to an estimate of the implicit pension debt associated with the traditional civil servant's DB pension. Similarly, Shah and Thomas, 2000, exploit information in stock prices to estimate the equity capital gap in banks, which helps overcome the opacity of banks and banking regulation.

A research community is required, which will build a research literature in estimating these expenditures based on exploiting diverse datasets. There will, of course, be multiple different estimates, as different researchers search for useful approximations through different assumptions and modelling strategies. A coherent picture will emerge from these debates. The PDMA, and buyers of government bonds, will be important users of this research community.

Off balance sheet liabilities and GDP growth volatility: A conjecture

There is a big gap between short spurts of GDP growth and sustained GDP growth. A mature market economy is a turtle, it plods along for a century, obtaining a low rate of growth on average, and harnessing the power of compounding. Poor countries fail to get sustained growth. The striking fact in cross-country comparisons is how volatile the GDP growth of poor countries is.

What might be going on? An analogy from a different field is useful. A well known problem in financial portfolio management is the returns that can be obtained, in the short term, by selling out-of-the-money options. For some time, the option seller seems to make a lot of money. But in time, some of those options get exercised and the portfolio gets into a lot of trouble. In similar fashion, for some time, a government that takes on option-like off-balance-sheet liabilities can gain votes and possibly accelerate economic activity, at the cost of sustainability.

Perhaps one element of the high GDP growth volatility of poor countries runs as follows. Mature fiscal systems create checks-and-balances which reduce the extent to which debt or off-balance-sheet liabilities can surge. Perhaps less developed countries have weak institutions, and then the political leadership sees a different optimisation. Short bursts of GDP growth can then be achieved in many bad ways, such as a surge in debt, piling up off-balance-sheet liabilities, etc. But this is not sustained growth: We get a spurt of high growth, and then things go wrong. This yields one more element of the translation of bad institutions into high GDP growth volatility.

References

Bhardwaj, Gautam and Surendra A. Dave, 2005. Towards estimating India's implicit pension debt, Working paper.

Pandey, Radhika and Ila Patnaik, 2017. Legislative strategy for setting up an independent debt management agency. NUJS Law Review, 10(3).

Patnaik, Ila, Shubho Roy and Ajay Shah, 2018. The rise of government-funded health insurance in India. NIPFP Working paper.

Sane, Renuka and Ajay Shah, 2015. What is the cost of one-rank-one-pension? The Leap Blog.

Sane, Renuka, Ajay Shah, Bhargavi Zaveri, 2018. Runs on mutual funds, The Leap Blog.

Shah, Ajay and Susan Thomas, 2000. Systemic fragility in Indian banking: Harnessing information from the equity market. IGIDR Working Paper.



The authors are researchers at the NIPFP in New Delhi. We are grateful to Shubho Roy, M. Govinda Rao and Arbind Modi for useful discussions.

PenCalc: A tool for simulating pension outcomes

by Renuka Sane.

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.

 

Renuka Sane is an associate professor at the National Institute of Public Finance and 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.

Designing the payout phase of the National Pension System

by Renuka Sane.

A pension program must ultimately be judged by the consumption delivered in retirement. In defined contribution (DC) pension systems, such as the NPS, we accumulate wealth over our working life, and draw down this wealth after retirement. When savings are gradually drawn down in order to pay for consumption, there is the possibility of living too long and running out of savings. Buying an annuity eliminates this risk, but it may yield a low consumption per year of life.

In the early years, the focus in pensions policy thinking was on investment. Our task was to get participants going with regular contributions, and sound asset management, so as to build up pension wealth. This thought process has given us the National Pension System (NPS). The prospect of exiting the workforce, and using pension wealth to obtain a stream of consumption, was buried somewhere deep in the future. As a consequence, this part of the pension process has thus far been under-emphasised.

Why do we care about the draw-down phase of NPS?

In India, the NPS forces individuals to annuitise 40% of their pension wealth, and take the remainder as a lump-sum. The subscriber can delay the annuity purchase or lump-sum withdrawal by 3 years. If the accumulated corpus is less than or equal to Rs.200,000, the subscriber can withdraw the entire amount and forgo the annuity purchase. Phased withdrawals are prohibited.

Now that more than 10 years have passed since the first entrants into the NPS, and we are getting closer to the first full cohort retiring, it is time to evaluate the draw-down policy. If there is more clarity and improved design for the draw-down phase, this may induce increased enrollment into the NPS.

The questions

There can be many reasons for exit from the NPS - voluntary retirement, untimely death of the subscriber, and exit at the prescribed retirement age. In this article, we focus mostly on the latter i.e. draw down policy on retirement. Two questions are faced here:

What is the optimal level of mandatory annuitisation?

Different countries have approached the question of mandatory annuitisation differently, and are largely influenced by the existence of a state funded pension which offers protection from poverty in retirement. The Chilean approach, for example, has been to restrict lump-sum distributions, and mandate the use of fixed inflation-indexed annuities or lifetime phased withdrawals. The Australians are more flexible in allowing lump sum withdrawals. Most recently, the UK has done away with its rule of mandating the purchase of an annuity by the age of 75, and allows for programmed withdrawals. The US has very little mandatory annuitisation. Is our mandatory 40% annuitisation optimal, and if not, what should be done?

How do we make the market for annuities work?

Life insurance companies are often reluctant to enter into annuity markets because of the lack of availability of good mortality tables as well as instruments for hedging longevity and inflation risk. Customers may like a survivor annuity that includes the spouse, children and dependent parents, but from a pricing perspective, this may not be feasible. Customers may like inflation indexed annuities, but this requires that the insurance company is able to trade in a market for long dated inflation indexed bonds. We need to understand what are the requirements for enabling an annuity market that is able to provide competitive pricing on its products. The problems of the Indian Bond-Currency-Derivatives Nexus inhibit the emergence of an efficient market for annuities.

Gaps in our knowledge

In order to arrive at an optimal level of annuitisation, we need to have an understanding of what our objective is from the annuitisation. For example, if our objective is to ensure a minimum consumption in retirement, then annuitisation can be mandatory only to the extent that is required to buy the minimum annuity. This requires us to take a view on what constitutes minimum consumption. A nominal annuity may not be able to buy a minimum consumption basket if inflation surprises occur over the lifetime of the retiree. In this case, annuitisation should mandate the purchase of an inflation indexed annuity instead of a nominal annuity. If, on the other hand, we believe that RBI will deliver on its 4% CPI inflation target, this changes the way we think about this. We have not had a larger policy discussion on this question.

Minimum consumption can be thought of either in terms of a minimum replacement rate relative to the average of the last few years of wage or contributions made, or a value that is linked to some consumption index. It may also vary depending on the age at which draw-downs are expected to begin. While the age of retirement is fixed for salaried employees, this may not be the case for informal sector workers. We, therefore, need to take a view on what will be the retirement age, or access age for informal sector workers.

If insurance companies do not take into account person-specific mortality differences, this is unfair on the poor as they die sooner. Under these conditions, mandatory annuitisation may be problematic for poor people. In this case, it might be more prudent to allow for a policy of programmed withdrawals. The trade-offs between an annuity and programmed withdrawal, and the design features of programmed withdrawals need to be better understood.

Finally we need to understand what impedes the development of annuities products. Why is it that insurance companies are reluctant to offer multiple products? What market and regulatory failures need to be addressed so that this market can take off?

What is to be done?

Given the gaps in our knowledge the following elements of work are now required:

Design of annuitisation policy and phased withdrawal policy

The level of annuitisation needs to be thought through from the perspective of consumption as well as the ability of the market to provide such an annuity. This includes questions such as the access age, the level of mandatory annuitisation, the type of annuity, as well as the design of the programmed withdrawal product.

Developing annuity markets

Processes for solving market failures that may impede the functioning of annuities markets need to be set up. For example, an important policy measure that might be in the domain of the PFRDA is the development of mortality tables. PFRDA needs to establish a position on the requirements that the BCD Nexus has to satisfy in order to achieve PFRDA's objectives, and advocate this position with the Ministry of Finance. This includes dealing with questions about long dated bonds, inflation indexed bonds, interest rate derivatives, and instruments to hedge longevity risk.

Procurement procedure for annuity providers

The provision of annuities also depends on the competition in the annuity market, and the price at which the annuity is offered to the subscriber. A focus on low-cost annuity provision needs to be developed. For example, the procurement of annuity service providers should be done via auction, which leads to the lowest prices. This was the approach taken for the appointment of pension fund managers and has led to some of the lowest fund management costs in the world.

The author is a researcher at the Indian Statistical Institute, Delhi.

Concerns about fundamental changes in the New Pension System

by Ashish Aggarwal.

The recent report¹ of the PFRDA constituted Committee headed by former SEBI chairman GN Bajpai to review investment guidelines for NPS schemes has re-opened three important questions. Its recommendations on these appear to be misplaced. From 1999 onwards, a consensus came together about the wisdom of the design of the NPS. PFRDA is now set to jettison the core design concepts of the NPS, without having adequately argued the case for the change. PFRDA needs a much more rigorous approach to the financial regulatory process, than it has demonstrated in the recent years.

Investment management approach: Active or passive?

The NPS has traditionally followed a passive approach to equity investment where Pension Fund Managers (PFMs) replicate the portfolio of a chosen market index. To illustrate, if a fund had tracked BSE Sensex since its inception 36 years ago, it would have delivered annualised returns close to 15.79 per cent. For an example, see the remarkable returns on Nifty and Nifty junior in history. The Bajpai Committee has advocated a shift to active investment management. In this approach, PFMs create a portfolio of stocks and decide the timing of their purchase and sale with an aim to beat passive investment returns.

From 1999 onwards, the policy thinking that led up to the NPS has emphasised passive investment, for good reason. Passive management costs much less than active management. For example, the expense ratio of Nifty BeES, an Exchange Traded Fund (ETF) tracking Nifty, is 0.49 per cent. Globally, index funds and ETFs like Vanguard 500 charge expense ratios of 0.05 to 0.17 per cent. Passive funds costs less as their task is relatively simple and can be largely mechanised. In contrast, actively managed funds have to spend a lot more on human-intensive procedures, and routinely charge expenses of around 2 to 2.5 per cent. A one per cent increase in cost can reduce the pension corpus by 24 per cent over 40 years^a.

Second, global wisdom suggests that while active management can generate higher returns, these are mostly offset by the higher costs. Importantly, active funds that consistently outperform their benchmarks are a rare breed. Here is one recent report² which finds that actively managed funds have generally underperformed their passive counterparts and experienced high mortality rates (i.e. many are merged or closed). Here is another report³ which shows that over a 10 year period, 82 per cent of large cap managers underperformed their benchmark. Mid and small cap managers have fared worse. The data on Indian mutual fund managers shows that about 50 per cent of them have underperformed^b their benchmark.

The GN Bajpai report does not show the rationale in favour of this major change in policy direction. There is a discussion in the report about moving to a `prudent man' regime^c. While the up-side from the change is not obvious, the upward pressure on fund management costs is.

Following the report, PFRDA has gone ahead and changed the investment guidelines⁴ to permit active fund management. The Government sector scheme^d already permits investing in individual
stocks. As a result, there is no scheme now which offers passive management in the NPS. This is a fundamental shift from the concepts of the NPS which had been articulated from 1999 onwards.

Role of fund managers: Should they market the schemes or only manage them?

The Committee recommends that the PFMs should market and sell the NPS, ostensibly in order to grow the customer base. This is a bad idea. Push sales strategies have worked where they are backed by high commissions, opaque products (where costs and benefits are not transparent) or both. NPS is an unbundled design where PFMs focus exclusively on managing investments. POPs (Points of Presence) comprising banks and other distributors are responsible for sales. The Chinese wall between POPs and PFMs ensures that they do not collude to push a particular scheme. This restricts mis-selling.

The issue of mis-selling is often associated with insurance and mutual funds who take a lead in marketing and selling their schemes. The Committee suggests that with the notification of the PFRDA Act, the consequent empowerment of PFRDA through various provisions on investigations, enquiry, penalty, and other enforcement actions besides customer protection measures envisaged in the various regulations under the Act, the issue of mis-selling will be addressed. While the logic of the committee on this count cannot be faulted, a similarly empowered IRDAI and SEBI have been battling this challenge for about two decades. The very design of the NPS was motivated by the problems of the conventional approach seen at SEBI and IRDA.

Allowing PFMs to do marketing is contrary to the basic design of NPS. The committee wants to change this design. To remain within the precincts of the Act, it recommends that, the PFs (PFMs) may canvass the product while the actual on-boarding may be done through the PoPs.

Another Committee, headed by former union finance secretary, Sumit Bose, set up to examine the issue of mis-selling and distributor incentives recently recommended⁵ that the POPs in NPS should be paid an AUM based trail fee. This would provide them the needed incentive and align their interest with the consumer over long term without increasing the risk of mis-selling. This would also leave the Chinese wall between the PFMs and POPs intact. PFRDA should examine these aspects before setting off on solutions to convert the NPS into a conventional SEBI/IRDA style system.

Fund management fees: auction based or fixed?

The Bajpai Committee recommends that the regulator should introduce a fixed and variable component in the fee. The variable fee should depend upon other performance indicators like relative returns generated. It has recommended that PFRDA examine this without compromising on the cost.

It is not apparent how increasing fees will not compromise costs. The Bose committee, has taken a contrary view and specifically recommended against any change of fees for the PFMs as they are
discovered through an auction process. The NPS auction is an transparent and efficient means to achieve lowest pricing in fund management. The remaining contestants have to match this lowest
fee. The consumers get the benefit of lowest cost and can also choose their PFM based on performance. Once the rules of the auction are transparent and apply equally, PFRDA should not have to worry about how to pay PFMs more.

Case for a rigorous approach

PFRDA has been grappling with the above questions over the last few years. It had in 2013⁶ and 2014⁷ re-affirmed passive investment management as the norm. In about a year, it has changed direction towards active management without adequate evidence or rationale. The approach to the issue of PFMs role with regard to marketing and sale of NPS has similarly lacked rigour. In 2013⁸, PFRDA brought in a change and permitted PFMs to market the NPS. Within a few months, in November 2013, this was reversed. This left the PFMs stranded as is evident from the feedback PFRDA received from a PFM:

"Following the new guidelines of 2012 that expanded the permissible activities that can be undertaken by PFs, many PFs made significant investments towards setting up promotion and distribution infrastructure. Clarity about role along with the incentives / revenues available to fulfil this role is a prerequisite to enable the PFs to plan their operations and business plan over a medium to long term."

Clarity on the policy direction on PFM fees is missing. The auction based system was dumped in 2012⁹ in  favour of a fixed fee of 0.25 per cent of assets, a significant increase over the earlier fee of 0.0009 per cent discovered through auction. Since 2014¹⁰, PFRDA has reverted to an auction which again resulted in a low fee of 0.01 per cent. As PFRDA heads for another round of selection for fund managers, it might need to examine the approach to this issue more closely.

On these questions, we should be concerned about the extent to which PFRDA has failed to bring knowledge about pensions into its thinking. The problem runs deeper. If the processes at PFRDA do not produce sound answers on the questions outlined above, they could similarly come up with unsound answers on other issues in the future. As an example, PFRDA might feel like responding to the clamour of assured returns in the NPS.

Rigorous analysis is required before setting sail on such matters. Poor policy decisions are very expensive. Decisions need to be grounded in evidence, be well documented and disclosed transparently.

In the past, sub optimum processes have resulted in sub optimum outcomes in case of PFRDA's regulations¹¹. RBI and SEBI also lag on this¹² count. The government has prepared a Handbook¹³ which lays down sound practices on regulatory governance and lists the procedures that Indian regulators should follow to achieve better governance in regulation making. All financial sector regulators have agreed to comply with the Handbook procedures on framing regulations for: (a) all regulations from 31st October, 2013, and (b) all subordinate legislation -- which includes circulars, notices, guidelines, letters -- from 31st December 2014.

Going by the Handbook, the draft investment circulars/guidelines should have been published by PFRDA with a statement of objectives, the problem that is to be solved, and a cost-benefit analysis (using best practices). Thereafter, comments should have been invited from the public and all comments should have been published on the web site of the regulator.

Conclusion: Undo, rewire and reboot

NPS is over a decade old. It would be useful to close the discussion on fundamental design questions, and bring predictability to the scheme on multi-decade horizons that are required in pension planning. This would increase confidence among consumers, PFMs and POPs. Rapid progress on implementing the best practices laid down in the Handbook would help achieve outcomes in the best interest of consumers. PFRDA could start by applying these to review the questions at hand. Till such time:

• NPS schemes should emphasise passive investment management.
• PFMs should continue to focus only on fund management, while the selling is done by arms length POPs who are neutral between all PFMs.
• The fee for PFMs should continue to be auction based.

Footnotes

1. An annual investment of Rs. 100,000 over 40 years with net annual returns of 11 per cent would result in a corpus of Rs. 64.58 million. An additional one per cent cost would reduce the
   net returns to 10 per cent and the corpus by 24.62 per cent to Rs. 48.68 million. back
2. Over last 10 years, out of 19 mutual funds tracking CNX Nifty, 10 outperformed the benchmark and 9 underperformed. Of the 16 tracking the BSE Sensex, the number of out-performers and under performers were equal. During this period, the category average
   returns by large cap equity mutual funds in India stood at 13.43 per cent per annum. As against this, the reference index, BSE 100 delivered an annualised return of 12.16 per cent. The top performer in the above fund category delivered 18.29 per cent while the bottom performer delivered 7.76 per cent. Flexicap category had similar results. (Category Average: 14.49 per cent, Top Performer: 19.34 per cent, Bottom Performer: 5.53 per cent). Data from Morningstar database. back
3. Under the prudent man rule, if the process followed for taking investment decisions in prudent, then the decisions are prudent. For example, it is imprudent to invest in lottery. The relative prudence does not get affected even if one wins the lottery. back
4. In the government sector scheme, PFMs can invest in individual stocks. Here, NPS follows the pattern notified by the government which permit a maximum of 15 per cent exposure to equity as against 50 per cent in the private sector scheme. The Bajpai Committee has rightly recommended that government employees should have the same scheme option as private sector. This has prompted PFRDA to review the status quo with the government. PFRDA has tied the NPS lite/ Atal Pension Yojana (APY) to the same norms that apply to the government scheme. This should also be reviewed as customers of these schemes too have no scheme choices. back

References

1. PFRDA, Report of the committee to review investment guidelines for NPS schemes in private sector, April 7 2015. back
2. Morningstar,  Active/Passive Barometer, June 2015. In addition to analysing active funds, the report finds that failure tended to be positively correlated with fees (i.e. higher cost funds were more likely to underperform or be shuttered or merged away and lower-cost funds were likelier to survive and enjoyed greater odds of success). back
3. S&P Dow Jones Indices, SPIVA US Scorecard, 2014. back
4. PFRDA, Investment guidelines for NPS schemes (Private Sector), September 10, 2015. The eligible stocks should have a market capitalisation at least Rs. 50 billion and should have derivatives trading in either BSE or NSE. The criteria for being considered for derivatives trading includes being in top 500 stocks in terms of average daily market capitalisation and average daily traded value in previous six months in a rolling basis. NSE currently has 163 eligible stocks for trading in derivative segment. back
5. Ministry of Finance, Report of the Committee to recommend measures for curbing mis-selling and rationalising distribution incentives in financial products, August 7, 2015. back
6. PFRDA, Clarifications on investment guidelines for private sector NPS, April 17, 2013. Prior to 2013 also PFMs were not permitted stock picking. Passive investment management was required to be done through in-house replication of Index funds or ETFs that tracked BSE Sensex or NSE Nifty Index. Investing in ETFs or Index funds of AMCs which charged a management fee was not permitted. Further, investment in equity mutual funds was not permitted. The PFMs were required to choose which index they intended to track in advance on a yearly basis. back
7. PFRDA, Revision of investment guidelines for NPS Schemes, January 29, 2014. back
8. PFRDA (Registration of Pension Fund Managers) 2012 Guidelines, July 12, 2012. back
9. PFRDA, Circular No. PFRDA/CIR/1/PFM/1, August 31, 2012. back
10. PFRDA, Revision of investment management fee for private sector NPS, August 1,
    2014. back
11. Arjun Rajagopal and Renuka Sane, Difficulties with PFRDA's Draft Aggregator Regulations 2014, July 2, 2014.  back
12. Arpita Pattanaik and Anjali Sharma, Regulatory governance problems in the legislative function at RBI and SEBI, September 23, 2015. back
13. Ministry of Finance, Handbook on adoption of governance enhancing and non-legislative elements of the draft Indian Financial Code, December 26, 2013. back

How to think about one rank one pensions

• Renuka Sane and I have a column The way forward with one-rank-one-pension in the Indian Express today. We also presented related ideas earlier in the Mint and on CatchNews.
• Article from 7 July 2015, with associated computer code, which prices the three kinds of annuities at age 35 and at age 60.
• Article from 5 February 2015, about the three kinds of annuities, and the basic tension between high GDP growth and keeping up with the Joneses.
• How to strengthen the New Pension System, by Renuka Sane and Susan Thomas, 9 October 2014.

What is the cost of one-rank-one-pension?

by Renuka Sane and Ajay Shah.

The question

If we switch one person from a simple nominal annuity to `one rank one pension', how much more expensive does the pension become?

Backdrop of India's pension reform

The traditional civil servants pension in India has proved to be very expensive. Bhardwaj and Dave (2006) estimated that the implicit pension debt on account of current civil servants alone, was already 64.5% of GDP. If one were to add new recruits to civil services, and military personnel, this would be even higher. Pension payments were growing sharply. In December 2002, the NDA government made a decision to move new recruits into an individual account defined contribution program, the National Pension System (NPS) [link, link].

The reform was never carried over into defence even though that was long expected to be done the moment NPS had stabilised. As a consequence, we now run two parallel worlds: uniformed defence personnel are on the traditional civil servants pension while others have shifted out to the NPS if they were recruited after 1/1/2004.

The present debate concerns one-rank-one-pension (OROP). In order to understand the fiscal implications of OROP, we must calculate what it costs to produce such a pension.

Calculations about one rank one pension

A pension, which is a stream of payments while the recipient is alive, is an "annuity". There are three kinds of annuities: nominal annuity, inflation indexed annuity i.e. where the annuity value is linked to inflation, and wage indexed annuity, where the annuity value is linked to wage growth. The third is the costliest and is also generally not produced by private insurance companies worldwide. In the extreme, OROP is tantamount to wage indexation i.e. the value of the pension is linked to the wage growth. Hence, in order to price a pension, we have to price the annuity embedded in it.

The full information base required to make these calculations correctly can only be accessed through the government. In the calculations shown here, we make suitable assumptions and proceed. At every step, we have complete transparency about assumptions and computer programs. The gentle reader is requested to actually run the code, and experiment with modified assumptions. The program is written in the free statistics software system, R.

How to price a pension?

Suppose we promise 100 people (all at age 60) that we will pay them Rs.1 per day for the rest of their lives. What is the cost of such a promise today? This depends on two things: the discount rate, and the number of people of this cohort who survive every year. Lets say that the last surviving member lives upto 100 years. This implies a horizon of calculation of 40 years. So in year 1, Rs.1 per day is paid to 100 people. In year two, if 2 people die, this is paid to 98 people and so on.

An approximate survivor function

The rate at which people die away is called the `survivor function'. Our first job is to obtain a survivor function for India and to look at its graph. We use the male mortality rate (as of 2015) from the 2010 the UN Population Projections for India and convert this into the survivor function:

# Convert conditional death probabilities into the survivor function.
calculate.survivorfn <- function(age){
    cooked <- rep(NA, 101)
    cooked[age+1] <- 100
    for(i in (age+2):100) {
        cooked[i] <- cooked[i-1] - a$qxm[i]*cooked[i-1]
    }
    return(cooked)
}

# Work out two survivor functions, starting at age 60 and starting at age 35 --
a <- read.csv("http://www.mayin.org/ajayshah/lfs/india_male_mortality2015.csv", sep=",")
a$cooked.60 <- calculate.survivorfn(60)
a$cooked.35 <- calculate.survivorfn(35)

# Draw a graph with the two survivor functions --
par(mai=c(.8,.8,.2,.2))
plot(35:100,a$cooked.35[36:101], type="l", lwd=2,col="blue", xlab="Age",ylab="Survivors")
lines(60:100,a$cooked.60[61:101], type="l", lwd=2,col="red", xlab="Age", ylab="Survivors")
legend(x="bottomleft",lwd=2,col=c("blue","red"), bty="n",
       cex=.75, legend=c("Starting at age 35","Starting at age 60"))

This shows that as the age of the cohort increases, the number of people surviving decreases. Of the 100 people who start out at age 60, by age 75, roughly half are still alive.

The data used here to calculate the survival function is the mortality rate of the general population. Survival is likely to be better for those with higher income and better access to health care, as is the case with employees of the government. Hence, our use of this survivor function makes annuities appear cheaper than they are in the context of government pensions.

Pricing the annuity using this survivor function

Once we have the survivor function, we work out the NPV of the annuity. Lets say we are paying Rs.1 per day or Rs.365 per year to this cohort, and that the discount rate is 7%. What is the cost of this promise?

# Make the NPV of an annuity p, where people die off based on the
# survivor function S, when the interest rate is r, l is the number of
# years the pension has to be paid.
value.of.pension <- function(p, S, r, l=0) {
  (1/r)^(1:l) %*% (p * S)
}

# As an example: Price an annuity of Rs.1 per day at age 60:
value.of.pension(rep(365,40), a$cooked.60[61:100]/100, 1.07,l=40)

Why has the interest rate of 7% been chosen, for the next 40 years? Here is a long answer. The short answer: Because India now has an inflation target of 4%, and assuming this works, the real return on government bonds may work out to roughly 3 per cent.

The code above yields an answer of Rs.3,163.22. This is a little lower than the price charged by LIC for this annuity, of Rs.3,800. That is to be expected, as our survivor function is of the general population, and not of the annuitant population. Also, our calculations do not take into account the administrative costs of providing the annuity.

This gives us the price of a nominal annuity. Now let's make things more difficult, by introducing inflation indexation and wage indexation.

Pricing inflation indexed and wage indexed annuities

In order to do this, we have to make assumptions about inflation and wage growth.

India now has an inflation target of 4%. This suggests three scenarios for inflation: 3%, 4% and 5%.

We also need to make a range of assumptions for wage growth. We propose three scenarios at 7%, 8% and 9% wage growth. At the baseline scenario of 4% inflation, these correspond to 3%, 4% and 5% real wage growth.

# Do scenarios based on inflation and wage growth --
inflation <- c(0.03, 0.04, 0.05)
wagegrowth <- c(0.07, 0.08,0.09)

# A function to make the stream of pension payments. l is the number
# of years these have to be paid, and index is either the inflation or
# wage index.
make.pension.mat <- function(l, index){
    p1 <- matrix(NA, l, 3)
    p1[1,] <- 365
    for(i in 2:l){
        p1[i,1] <- p1[i-1,1] + index[1]*p1[i-1,1]
        p1[i,2] <- p1[i-1,2] + index[2]*p1[i-1,2]
        p1[i,3] <- p1[i-1,3] + index[3]*p1[i-1,3]
    }
    return(p1)
}

# Now do lots of cases.
                                       # Price indexation
p1 <- make.pension.mat(40, inflation)
value.of.pension(p1, a$cooked.60[61:100]/100, 1.07,l=40)
                                       # Wage indexation
p1 <- make.pension.mat(40, wagegrowth)
value.of.pension(p1, a$cooked.60[61:100]/100, 1.07,l=40)

This gives us the following annuity prices (in Rs.):

• Inflation at 3% - 3940.37
  
• Inflation at 4% - 4269.79
  
• Inflation at 5% - 4644.56
  
• Wage growth at 7% - 5563.41
  
• Wage growth at 8% - 6128.46
  
• Wage growth at 9% - 6781.57
  

How does all this change when retirement is at 35?

The retirement age of the military is different from that of civil services. Approximately 80% of the military retires between the age of 35-40, 18-19% retires between the ages of 54 and 60. Only about 1% retire at the age of 60. This implies that expenditure on pensions will be incurred for a lot longer than if the workforce retired at 60. We estimate the cost of a pension for a person retiring at age 35. As before, we first estimate the survival function, and then the cost of the pension under a price and wage indexed annuity.

# Retirement at 35
value.of.pension(rep(365,65), a$cooked.35[36:100]/100, 1.07,l=65)

                                       # Price indexation
p1 <- make.pension.mat(65, inflation)
value.of.pension(p1, a$cooked.35[36:100]/100, 1.07,l=65)
                                       # Wage indexation
p1 <- make.pension.mat(65, wagegrowth)
value.of.pension(p1, a$cooked.35[36:100]/100, 1.07,l=65)

The code above yields an answer of Rs.4,518.73 for the simple nominal annuity at age 35. This rises to Rs.7,488.54 for an inflation indexed annuity (assuming 4% inflation), and Rs.14,998.25 for the wage indexed annuity (assuming wage growth at 8%.).

These calculations are conservative

All the steps of this calculation have made conservative assumptions:

• The survivor function is for the general population. Civil servants are likely to be healthier than the general population, and uniformed armed forces are likely to healthier than civil servants. When correct survivor functions are plugged into this calculation, annuity prices will go up.
  
• We have used the survivor function for males. Females live longer. Some employees are women. When this is taken into account, annuity prices will go up.
  
• We have used the mortality rate as of 2015. As life expectancy in India improves, this will go down, implying that more people will live till older ages. Annuity prices will go up.
  
• We have assumed that RBI will deliver on its inflation target of 4%.
  

While our assumptions are conservative, we have assumed an extreme form of wage indexation. It is possible that some variant of OROP is constructed without full wage indexation. The estimates of the implicit pension debt would be lower in that case. We have also assumed a constant discount rate of 7%. If the discount rate is higher, the expenditures will be lower than those described here.

Summary of calculations

We treat our computation for the nominal annuity for a 60 year old as the base line. For all other cases, the extent to which it is higher, in per cent, is also shown.

At age 60:
LIC nominal annuity	3800	+20%
Our computation for nominal annuity	3163	+0%
Inflation indexed at 4% inflation	4270	+35%
Wage indexed at 8% wage growth	6128	+94%
At age 35:
Our computation for nominal annuity	4519	+42%
Inflation indexed at 4% inflation	7489	+136%
Wage indexed at 8% wage growth	14998	+374%

We start at the old system: a nominal annuity at age 60. If we change this to an inflation indexed annuity, the implicit pension debt goes up by 35%. If we change this to one-rank-one-pension, the implicit pension debt goes up by 94%. If we do one-rank-one-pension at age 35, the implicit pension debt goes up by 374%.

Please experiment with alternative assumptions

Here's the R program.

Speculation

Civil servants are a tiny slice of the Indian economy. It was a real surprise when Bhardwaj and Dave, 2006, found that the implicit pension debt on account of the civil servants pension came up to 64% of GDP. This was an important impetus for the NPS reform.

Uniformed armed force personnel are also a tiny slice of the economy. Even if all they had was a nominal annuity, this could prove to be quite expensive, as the pension starts at a young age, and the health of this group is very good. On top of this, there is the problem of rapid turnaround. On a horizon of 60 years, we go through four cycles of taking in a person at age 20 who retires at age 35, who will live till 80. Therefore, for each person who is presently serving there will be four alive who are drawing pensions. We may speculate that the implicit pension debt on account of the armed forces pension may also be in the region of 50% of GDP. If so, policy changes which double or triple the value of the annuity map to 50 or 100 percent of GDP.

Policy process

When such questions are being analysed, policy makers should arm themselves with the full calculations, before making decisions.

The calculations that are needed are:

1. Replace the general population survivor function, which we have used, with the actual survivor function for armed folk. We suspect they are much healthier than the general population.
   
2. Use data for the stock of employees and pensioners, and rules about retirement, to work out the implicit pension debt associated with present or potentially modified pension arrangements.
   

Once such calculations are in hand, the political leadership can choose between alternative uses of the same money. E.g. 50% of GDP could pay for complete suburban metro systems for 50 cities, or for 50 aircraft carriers.

References

Towards Estimating India's Implicit Pension Debt by Gautam Bhardwaj and Surendra A. Dave, 2006. The Second International Workshop on The Balance Sheet of Social Security Pensions, Organised by PIE and COE/RES, Hitotsubashi University.

India's pension reforms: A case study in complex institutional change by Surendra Dave, page 149--170 in `Documenting reforms: Case studies from India', edited by S. Narayan, Macmillan India, 2006.

Indian pension reform: A sustainable and scalable approach by Ajay Shah, Chapter 7 in `Managing globalisation: Lessons from China and India', edited by David A. Kelly, Ramkishen S. Rajan and Gillian H. L. Goh, World Scientific, 2006.

Acknowledgments

We thank Ashish Aggarwal, Josh Felman, Shekhar Hari Kumar and Robert Palacios for valuable comments.

Voluntary participation in the National Pension System: What does the evidence show?

by Renuka Sane.

Long-term saving is challenging in most parts of the world. Individuals are impatient, and old age is too far away. Rising life expectancy and potential poverty in old age have led countries to set up state funded pension programs or mandate contributions through the employer. Both these are difficult to implement in India. For example, the EPFO covers only about 8-10 percent of the workforce. This makes the voluntary build-up of savings important. Informal sector workers often do not have access to formal finance, and are unable to save large sums of money in one transaction. Poor people may also find it difficult to forgo current consumption and get invested in illiquid pension assets. There is a case for the State to facilitate a formal savings mechanism, and encourage pension accumulation through co-contribution.

These ideas started gaining ground after the `National Pension System (NPS)' (which used to be called the New Pension System) had been in operation for a few years. The NPS is grounded in the philosophy of self-help and thrift. It is mandatory for central government employees since 2004, and accessible to all citizens of India. The NPS-Lite model was introduced for the informal sector, followed by the launch of the NPS-Swavalamban (NPS-S) scheme in 2010. Under the Swavalamban scheme, if a subscriber in the informal sector contributes a minimum of Rs.1000 in a financial year into her NPS account, she receives a co-contribution of Rs.1000 from the government. The scheme has been operational for four years now, and the co-contribution was promised to last until March 2017.

In the recent Budget, the Finance Minister announced another informal sector pension scheme, the Atal Pension Yojana (APY) which promises a fixed pension of at least Rs.1,000 at age 60 if subscribers contribute pre-defined amounts over their working life. While the APY has several design flaws, it seems likely that it will replace the Swavalamban scheme before 2017, at least for those between 18-40 years of age. NPS-Lite, i.e. the NPS without the co-contribution, is likely to remain in place.

It is important to take stock of what has been the response of the informal sector to NPS-Lite/Swavalamban before taking policy measures on the same. Are people enrolling in the scheme? What kinds of contributions are they able to make? Do we have the policy and processes in place for when customers retire?

How are enrollments and accumulations faring?

There is often skepticism about the ability of poor people to save. However, research has demonstrated that when provided with formal channels, poor people do save, sometimes at high cost. For example, Mukherjee (2014) finds that the willingness of people to save in a co-contribution pension scheme is high.

Aggregate official data also show that subscriptions to the scheme have been rising. According to the 2013-14 Annual Report (Table 1.6, page 22) of the PFRDA, there were a total of 2.8 million customers of NPS-Lite/Swavalamban. They made up 43 percent of the total NPS subscribers, and were the largest category of subscribers - more than government employees who make up a total of 30 percent of the subscriber base. The AUM under NPS-Lite/Swavalamban was Rs.8.4 billion, about 2 percent of the total NPS AUM. The percentage growth of AUM at almost 94 percent, between 2012-13 and 2013-14 was the highest for NPS-Lite/Swavalamban.

Sane and Thomas (2015) analyse participation and contributions of customers over the first three years of the scheme in more detail, using data from one financial services provider, the Kshetriya Grameen Financial Services (KGFS). They find that voluntary participation in an individual account DC pension system is feasible. In fact, it is the relatively poor in the sample that are more likely to open Swavalamban accounts. The evidence on persistence, is however, not as optimistic: only about 50 percent of the participants had managed to contribute more than Rs.1000 at least in one financial year in their NPS-S account. However, non-contribution in one year did not mean dormant accounts - several customers came back the next year. In terms of total contributions, members stop short of contributing more than Rs.1000 in a financial year. Part of this seems to be driven by the scheme becoming centered around the threshold for the Rs.1000 co-contribution. Members could actually contribute larger amounts, but often do not, because the scheme is sold as a Rs.1000 per year contribution scheme.

The problems in the draw-down phase

A pension scheme is ultimately judged by its ability to provide for an adequate consumption in retirement. Accumulations are only one part of the story. Since the accumulated wealth has to provide for a meaningful consumption over the lifetime of the individual, how this wealth is drawn-down becomes important. All the NPS models, including NPS-Lite/Swavalamban require that 40 percent of the account balances be used to purchase an annuity, while the remainder may be drawn-down as a lumpsum. There is currently no option of a programmed withdrawal, where part of the retirement fund is used for a draw-down (as income withdrawal) while leaving the rest of it invested.

Annuities can be expensive for the poor as they have a lower life expectancy than the rich. If they die early, they effectively end up subsidising the rich. We, therefore, need to think more carefully about the choice between annuitisation and programmed withdrawal. Different countries have approached the question of annuitisation differently, and are largely influenced by existence of a state funded pension which offers protection from poverty in retirement. The Chilean approach, for example, has been to restrict lump-sum distributions, and mandate the use of fixed inflation-indexed annuities or lifetime phased withdrawals. The Australians, are more flexible in allowing lump sums. Most recently, the UK has done away with its rule of mandating the purchase of an annuity by the age of 75, and allows for programmed withdrawals. The US has very little mandatory annuitisation.

Life insurance companies are often reluctant to enter into annuity markets because of the lack of availability of good mortality tables as well as instruments for hedging longevity and inflation risk. Lack of good mortality data is especially true in the case of low-income customers. The nominal annuity may also not be able to buy a minimum consumption basket if inflation rises over the lifetime of the retiree. If the administrative costs charged by insurance companies are high, then the value of the annuity will fall further.

Benefit policies of NPS-Lite/Swavalamban thus require a re-think. Enabling the development of mortality tables, market for inflation indexed bonds, changing the procurement of annuity service providers so as to minimise the costs of the annuity, designing default options for those who cannot choose the optimal combination of annuity and lumpsum are some of the policy initiatives that the PFRDA needs to undertake.
Similar questions are pertinent for the NPS as well. However, government employees who were enrolled in the NPS starting 2004 still have some time before they retire. The NPS-Lite/Swavalamban members who enrolled in their late 40s will get to the retirement threshold sooner, and bad design of the draw-down policy or delays in providing benefits can potentially destroy the foundation that has been built for improving informal sector participation.

Conclusion

There are several take-aways from the experience of the NPS-Lite/Swavalamban schemes:

1. The number of people contributing Rs.1000 is gradually increasing.
2. Non-contribution in one year does not mean subsequent non-contribution.
3. There seems to be a hump in contributions at Rs.1000, most likely driven by the threshold design of the co-contribution.
4. Benefit design policies and processes require a re-think.

The NPS-Lite/Swavalamban is gradually taking root, people are beginning to understand the scheme, and intermediaries are learning how to distribute it. Familiarity with the scheme and intermediaries is likely to build the trust that is important in fostering long-term illiquid pension contributions. The infrastructure required for channeling contributions to the fund managers seems to be largely in place.

Analysis shows that the APY by itself is not enough to meet consumption needs in retirement. Thus, even if the APY replaces Swavalamban, intermediaries should consider continuing to distribute the NPS-Lite, as a combination of APY and NPS-Lite may allow customers to enjoy higher returns than the APY alone. A minimum amount of contribution could be made to the APY towards the guaranteed pension, while the remaining can be invested in the NPS-Lite for potentially higher returns. The PFRDA needs to incentivise the sale of both the APY and the NPS-Lite, dislodge the mental threshold of Rs.1000 to encourage contributions of larger amounts, and do a rethink of the draw-down phase.

References

Mukherjee (2014), Micropensions: Helping the Poor Save for Old Age, Paper presented at the 5th Emerging Markets Finance Conference.

Sane, R. and S. Thomas (2015), In search of inclusion: informal sector participation in a voluntary, defined contribution pension system, Journal of Development Studies (forthcoming).