Since my last
article, on the announcement made by the Rajasthan government to implement the old pension scheme (i.e., pay as you go-defined benefit—PAYG-DB) for state government employees who were appointed on or after 1 January 2000, the issue has now attracted considerable attention.
Other states have also copied, or are in the process of copying, this decision. States ruled by Bharatiya Janata Party (BJP), notably Madhya Pradesh (MP) are under immense pressure to reverse the decision taken in 2003 to phase out the old PAYG-DB scheme.
However, before I begin to address the critical open-ended issue mentioned in my last article – the size of implicit pension liability—created by this decision, I acknowledge a very sharp comment on my previous blog.
One of the readers mentioned that “the situation [becomes] even more dangerous, [as] there is a leveling off in population. That would surely affect PAYG schemes, as they have around the world.”
This, indeed, is a very sharp comment and I agree with this. However, the implications of this leveling of demographic dividend will not be uniform across states. The Economic Survey mentioned that southern states will age faster than northern states and, if we superimpose the possible distributional effect of the digital technology, there will emerge some states which will be net losers. This regional analysis is not attempted here today.
This brings us to the more important issue of the cost of this retrospective decision on the economy and how the liability will pan out in future.
Working with states-level figures has always been a challenge. In respect of National Pension Scheme (NPS), data on the state-level participants is not available. Consolidated data given by NPS Trust informs us that there are 51,40,504 contributing state-level employees as of March 2021. The implicit pension liability is estimated for this cohort for ease although the current head count of the state government employees is 5,514,516 as of January 2022. The assumption is that all states migrate to the old scheme.
The implicit pension debt (as against the explicit debt) is an assessment of the future obligations of the government on account of its contractual obligation to provide pension benefits. Since pension liability cash-flows resemble typical bond with uncertain coupon, the term ‘debt’ is used. However, at the most basic level, a pension liability is a cash-flow contingent on survival and, hence, involves the use of actuarial methods to assess the long-term liability. In many foreign countries, treasury offices measure this liability on a 100-year horizon and are transparently disclosed.
In order to calculate this liability without going into technical details (for technical details refer Life Insurance Mathematics, Hans U. Gerber, Springer), some basic information is needed: the age structure of the population under study, salary escalation rate, and basic plus DA for each participant. Typically, government pension also includes survivor pension and spouse gets life annuity at a reduced rate till death—mostly 30%. Liability on account of survivors is not considered here as no demographic data on spouse is available.
The actual data used is as follows: the per capita basic + dearness allowance (DA) (= salary) for state government employees is derived from the data given on NPS Trust website. The annual salary per capita is Rs401,392. The salary escalation rate is taken from Reserve Bank of India (RBI)’s ‘State Finances: A Study of Budgets, Statement 29: Expenditure on Wages and Salaries’.
The 5-year average works out at 13% per annum (pa). The age structure of the population under study is approximated using the table 2.3 (d) of ‘Payroll Reporting in India: An Employment Perspective—December 2021’ published by Ministry of Statistics & Programme Implementation.
The mortality is projected using Institute of Actuaries of India’s ‘Indian Individual Annuitant’s Mortality Table (2012-15)’ for retirement phase and ‘Indian Assured Lives Mortality (2006-08) Ult’ for the working phase. Projected benefit obligation method is used to arrive at the cash-flow profile. Pension benefit is assumed at 50% of last drawn salary.
The average working years of the population works out at 21.1 years. The saw-teeth pattern in total profile is due to the assumption that the entire age structure is described by five representative ages 20, 24, 27, 32 and 48.
The present value of the projected obligation as on today is Rs36,45,278 crore discounted at 7.30%, yield on 40-year government security. This constitutes 15.42% of the 2022 GDP at current prices.
If we assume that entire transfers to the old pension scheme is partially funded, as in the state governments withdraw the funds from NPS Trust, then the solvency position estimated as the ratio of the current AUM for state government employees by present value of the liability is 9.7% (Rs3,54,922/ 36,45,278). That is, even with partial funding the liability will be 90% unfunded.
Some comments on the above results are made. First, the above figure is an underestimate because around 370,000 state government employees are not included as mentioned above. Second, around 500,000 employees are estimated to die as per mortality table before 2033. This liability has not been accounted for. Second, if we account for joint annuity, liability will extend further in future. Lastly, the inflation and wage indexation that happens in government sector on account of pay fixation will also increase the liability. The only factor that may reduce the liability is adjustment to mortality due to COVID-19. However, this factor may even out with universal immunisation.
In conclusion, the general equilibrium consequences of this decision will pan out over a long period. It will surely dent the savings rate and by 2033, the impact will be felt; peak will occur in 2060. The intergenerational impact of this decision will have to be assessed as current cohort of working population ravaged by COVID-19 will carry disproportionately high burden of this liability.
In a nutshell, only God can add some nectar to Amrit Kal.
(The author is an economist in the banking system. The views expressed here are personal.)
