Eric French on Inequality and the insurance value of transfers across the life cycle
Eric French is a Professor of Economics at University College London and is a Co-Director at the Institute for Fiscal Studies. His research interests lie in better understanding the costs and benefits of social insurance schemes. French’s RePEc/IDEAS profile.
Governments provide social insurance to those who are old, ill, unemployed, disabled, or poor. These programs are costly. For instance, in 2016, Federal US spending on Social Security, and Medicare and Medicaid accounted for 4.9%, 3.2%, and 2.0% of US GDP, respectively, and states spend additional resources for these programs.
This social insurance helps those receiving those benefits at a given point in time, but is potentially very valuable to all households because it ensures that, should they find themselves in difficult circumstances, they will be shielded from extremely low living standards.
These costs are both direct (e.g. the financial cost of the transfers) and indirect (e.g. the provision of benefits reduces the incentives to work and save). Balancing these costs and benefits is a challenge for policy-makers. As governments seek to manage the rising costs of providing social insurance and achieve their redistributive objectives in the context of aging economies with rising inequality, robust evidence is needed on the effects of taxes and transfers on the behaviour and well-being of households.
My research develops and test models of household savings and labour supply to evaluate how reforms to social insurance schemes would impact household behaviour, household well-being, inequality, and public finances. To do this I develop lifecycle models which link household decisions (such as saving, labour supply, participation in social insurance programs, and intergenerational transfers) in each period with their well-being in that period and their consequent opportunities (and well-being) in future periods. I typically then estimate these models using dynamic programming and method of simulated moments.
My research exploits two recent advances in the field of estimation of lifecycle models. First I use administrative data, often linked to survey panel data. Administrative data allow for more accurate measurement. Furthermore, because of their larger sample sizes, administrative data often afford novel identification schemes not available when using only survey data. Second, I use frontier computational methods and computer investments such as large computer clusters and Graphical Processing Units (GPUs) to estimate these models.
The questions that I attempt to answer include:
What are the risks of low wages, poor health, long life and high medical spending late in life? How do these risks impact savings and labor supply behaviour?
How do social insurance schemes such as state pension systems (e.g., Social Security), disability insurance, and health insurance schemes (e.g., Medicare and Medicaid) insure against these risks?
How do these social insurance schemes impact labor supply and savings?
Do these programs provide benefits beyond consumption insurance? Are there health benefits from these programs?
Weighing the costs and benefits of these programs, what is the optimal size of these programs?
In what follows, I address these questions in the context of the different strands of my research by starting with the retirement decision, then savings decisions during the retirement period. Next, given the importance of medical expenses during both of the working and retirement period, I discuss my research on medical expenses in more detail. Finally, I discuss my work on disability insurance.
In Blundell et al. (2016), we review the evidence on retirement and study the role of incentives in the retirement decision. We show that reduced form evidence strongly suggests that many of the mechanisms that might be expected to affect retirement – such as declining health, liquidity constraints, and financial incentives– do influence behavior. However, these factors interact with each other in potentially complicated ways and many public policies work through more than one of these channels. Reduced form papers on their own, therefore, do not tell us much about the mechanisms through which retirement policies or other changes in the economic environment affect retirement behavior.
The retirement decision is inherently a forward looking decision. As a result, the retirement literature has long been at the forefront of estimation of dynamic structural models. Early structural models (e.g., Gustman and Steinmeier (1986)) paid careful attention to the pension incentives for retirement, and to the heterogeneity in those incentives, but did not consider household risk or borrowing constraints. Rust and Phelan (1997) carefully considered the risk that households face, but did not allow for savings. These two frameworks give rise to alternative viewpoints as to the drivers of observed high job exit rates at age 62, which is the Social Security Early Retirement age. In Gustman and Steinmeier framework, the high job exit rates are driven by the actuarial unfairness of certain defined benefit pension schemes. In the Rust and Phelan framework, it is the availability of Social Security benefits at 62: because it is illegal to borrow against future Social Security benefits, many people must work until this age to finance their consumption.
French (2005) presents the first structural model of labor supply and retirement behavior where individuals can save to insure themselves against health and wage shocks as well as for retirement, but cannot borrow against future labor, Social Security, and pension income to smooth consumption in the face of an adverse shock. The paper had several key findings. First, a large fixed cost of work (or commuting time cost) is necessary to explain why so many people work close to 2000 hours or 0 hours and why so few people work part time. However, when accounting for the part time wage penalties (perhaps because of fixed costs on the firm side) estimated in Aaronson and French (2004), estimated fixed costs are more plausible, on the order of 300-400 hours per year. Rogerson and Wallenius (2013) provide nice intuition for the identification of the fixed cost of work in retirement models. Second, borrowing constraints are important for labor supply decisions early in life, but not late in life. As a result, it is unlikely that the relaxation of borrowing constraints coming from Social Security and pension eligibility can be important for driving the high job exit rates near the Social Security early retirement age. Instead, the actuarial unfairness of pension schemes is responsible for the high job exit rates at ages 62 and 65. Third, health is important for understanding retirement behaviour, and can explain about 10% of the decline in employment between ages 50 and 70. Bad health impacts both wages and preferences, and both of these channels are important for understanding why health impacts labor supply.
French and Jones (2011) extend the model in French (2005) to include health insurance and medical spending. In doing so, we provide an empirical analysis of the effects of employer-provided health insurance, Medicare, and Social Security on retirement behavior. Using data from the Health and Retirement Study, we estimated a dynamic model of retirement that accounts for both saving and uncertain medical expenses. Our results suggest that Medicare is important for understanding retirement behavior, and that uncertainty and savings are both important for understanding the labor supply responses to Medicare. Half the value placed by a typical worker on his employer-provided health insurance is the value of reduced medical expense risk. Raising the Medicare eligibility age from 65 to 67 leads individuals to work an additional 0.074 years over ages 60-69. In comparison, eliminating two years worth of Social Security benefits increases years of work by 0.076 years.
French et al. (2018) extend the model further to consider the employment responses to the Affordable Care Act, or Obamacare. Prior to Obamacare, public health insurance of middle age adults was mostly only available to the poor and disabled (Pashchenko and Porapakkarm (2013) and Kitao (2014)). We model the two key channels by which health insurance rates are predicted to change after Obamacare: the Medicaid expansion and the subsidized private exchanges. Consistent with the evidence, the predicted effects of Obamacare are small. The reason for this is that Obamacare has largely just changed the payors of medical care, from the government and hospitals in the form of unsubsidized care to the government in the form of insurance subsidies and Medicaid patients.
We should note that recent retirement research includes many important innovations. For example, O’Dea (2018) extends the model to endogenize the choice of how much to save in a Defined Contribution pension schemes. This is important when considering optimal pensions policy: a large share of fiscal expenditures on pensions is from pension contributions that are tax free.
These dynamic programming models of retirement increasingly can be validated against recent pension reforms. Many European countries have recently been engaged in drastic cuts to pension generosity, potentially increasing labor supply, but also impacting consumption and well being. French, Lindner, O’Dea, and Zawisza (2018) exploit the cohort based introduction of the Polish 1999 pension reform to estimate the effect of pensions on labor supply, consumption, and savings. In 1999 Poland switched its public pension from a Defined Benefit (DB) system to notional Defined Contribution (DC) system. In the new system the link between pension contributions and benefit became stronger. Using a regression discontinuity design we estimate the effect of the reform on employment, hours worked, consumption, and savings. Our preliminary results suggests that employment was significantly affected by the reform, with younger cohorts that were placed into the DC scheme having 8-16 percentage points higher employment, depending on age and the particular cohort selected. We will feed these estimates into a dynamic model of labor supply, consumption, and savings decisions. This model can be used to assess the labor supply and consumption responses to alternative reforms, such as increasing pension generosity or changing the retirement age.
Health has long been known to be an important determinant of retirement, yet it was not until fairly recently that health was built into structural retirement models. French and Jones (2017) survey this research. Blundell et al. (2018a) construct measures of health, aggregating multiple health measures and Blundell et al. (2018b) use them to better understand the dynamics of health and employment. We show that not only is the contemporaneous effect of health that is important for determining employment, but also lagged health. This is potentially due to health shocks not only impacting productivity and preferences immediately, but through a slower process through which health impacts human capital investment.
Late Life Savings, Medical Spending, and Social Insurance
Retirees, especially the high-lifetime income retirees, run down their assets only slowly. These savings patterns of retired U.S. households pose a challenge to the basic life cycle model of saving. In De Nardi et al. (2010) we show that the observed patterns of out-of-pocket medical expenses, which rise quickly with age and income during retirement, and heterogeneous lifespan risk explain a significant portion of U.S. savings during retirement. To do this we construct a model of saving for retired single people that includes heterogeneity in medical expenses and life expectancies, and bequest motives. We estimate the model using Assets and Health Dynamics of the Oldest Old data and the method of simulated moments. The risk of living long and requiring expensive medical care is a key driver of saving for many higher-income elderly. Kopecky and Koreshkova (2014) show it is an important driver of aggregate savings also.
To more precisely disentangle these precautionary saving motives from other saving motives, such as the desire to leave bequests, other papers match additional features of the data. De Nardi et al. (2016) use public insurance choices, while Lockwood (2018) and Hong and Rios-Rull (2012) use private insurance choices. Blundell et al. (2016a) use data from England, where the medical spending risk is less important, and thus where the precautionary savings motive is not as strong. Ameriks et al. (2018) adopt strategic survey questions. Better understanding whether intended bequests left to children and spouses are due to altruism, risk sharing, exchange motivations, or a combination of these factors, is an important direction for future research. See De Nardi et al. (2017a) for a review.
The results in De Nardi et al. (2010) show that the high implicit tax on savings by social insurance programs such as Medicaid rationalizes the low asset holdings of the poorest but also benefit the rich by insuring them against high medical expenses at the ends of their lives. One of the key Social Insurance programs is Medicaid. The old age provisions of the Medicaid program were designed to insure retirees against medical expenses.
Medicaid is an important means-tested insurance program providing medical goods and services to the poor. Its old age provisions were designed to insure retirees against medical expenses. De Nardi et al. (2010) show that its presence rationalizes the low asset holdings of the poorest but also reduces the savings of the high income households by insuring them against high medical expenses at the ends of their lives. De Nardi et al. (2016c) compute the distribution of lifetime Medicaid transfers and Medicaid valuations across currently single retirees. Compensating variation calculations indicate that current retirees value Medicaid insurance at more than its actuarial cost, but that most would value an expansion of the current Medicaid program at less than its cost. These findings suggest that for current single retirees, the Medicaid program may be of the approximately right size.
Most research focuses on retired singles, but many retirees are married. In addition to the risk of poor health, high medical spending, and living past their life expectancy, married retirees face the risk of losing a spouse and the income from that spouse, and also the medical spending and funeral expenses. In De Nardi et al. (2018) we find that not only do retired couples hold more assets than singles, but that high-income couples grow their savings during most of their retirement period. Furthermore, assets change relatively little with age when household structure does not change, but drops significantly at the death of a spouse (see also French et al. (2006)). These observations raise the question: why are they saving so much when the couple is intact and why does wealth drop so much at the time of the death of a household member? To answer these questions, we build a model of retired couples and singles facing uncertain longevity and medical expenses in which couples and singles can have different bequest motives. Both might care about heirs, but couples might also care about their surviving spouse. Using AHEAD data and the method of simulated moments to estimate our model, our paper shows that high medical and other expenses at the time of death of a family member and also altruistic transfers to heirs are both important drivers of these asset drops. French et al. (2017a) documents that medical spending near time of death is high in multiple countries, although in the US the share of medical spending that is paid out of pocket is high (De Nardi et al. (2016b)).
One of the deeper questions that motivates research on bequests is the extent to which individuals are altruistic towards their children or other heirs. Understanding the extent of intergenerational altruism is critical for understanding what kind of Social Security and other reforms individuals would be willing to vote for (see, for example, Fuster et al. (2007)). In De Nardi et al. (2016) we discuss the difficulties in identifying altruism from bequests alone. In Bolt et al. (2018) we are developing and estimating a model to also exploit early life transfers in combination with late life transfers to identify altruism. We exploit British cohort data, which contains detailed information on early life money and time transfers to children, detailed test score information of the children, information on later life wages and other sources of income, as well as cash transfers received later in life. Thus we can estimate how parental time and money investments in children early in life impact children’s income later in life.
Our research on savings and retirement has highlighted the importance of medical spending risk. Even among the elderly, medical spending risk is important. In De Nardi et al. (2016b) we find that while the government pays for 65% of the elderly’s medical expenses, the expenses that remain after government transfers are even more concentrated among a small group of people. Thus, government health insurance, while potentially very valuable, is far from complete.
Most of the estimates of the dynamics of medical spending in the above papers are based on French and Jones (2004), who used HRS data to show that the process for log out of pocket medical expenses is well represented as the sum of a white noise process and a highly persistent AR(1) process. Simulating this model, we find that in any given year, 0.1% of households receive a health cost shock with a present value of at least $125,000. French et al. (2017a) validates the quality of these data by comparing medical spending data across the HRS, MCBS, and MEPS. Jones et al. (2018) update the estimates in French and Jones (2004) to properly model household risks that are insured by Medicaid. We find that, at age 70, households will on average incur over $122,000 in medical spending, including Medicaid payments, over the remaining of their lives. At the top tail, 5 percent of households will incur more than $300,000. Using HRS data linked to Medicare and Medicaid records, Arapkis et al. (2018) extend these estimates to include all payors, including Medicare and Medicaid, and to consider more flexible dynamic processes for medical spending. In addition, we are also estimating the extent of “non-linear persistence”—the extent to which extreme shocks are more persistent than transitory shocks. A recent issue of Fiscal Studies (with key results described in French and Kelley (2016)) estimated medical spending transition matrices for several countries, and found more persistence in the tails of the medical spending distribution.
The United States has two programs for the disabled: Disability Insurance for those with strong previous work histories, and Supplemental Security Income for those with lower previous attachment to the labor market. In 2014, 6.4% of people ages 18-64 and 16.3% of those aged 55-64 were receiving disability benefits from one of these programs. These programs provide high benefit levels and health insurance through Medicare or Medicaid. Relatively few people lose disability benefits for reasons other than death, so the insurance is extremely valuable. An interesting aspect of the program is that many applicants do not receive benefits, but receive benefits upon appeal. These appeals often go before judges, who happen to be for all practical purposes randomly assigned to cases. In French and Song (2014) we exploit this random assignment to estimate the impact of benefit receipt on labor supply, finding non-trivial disemployment effects of the program. In French and Song (2018) we are using this variation, in combination with other variation, to estimate a dynamic programming model. Finally, in Black et al. (2018) we are estimating how disability benefit receipt impacts mortality, finding considerable heterogeneity in the mortality responses to benefit receipt. Marginal Treatment Effects estimates suggest that benefit receipt reduces mortality for inframarginal benefit recipients, which indicates that for maximizing the longevity of current SSDI and SSI applicants, the current disability thresholds for allowance are at about the right level.
My research agenda is fundamentally concerned with understanding how households respond in the face of some of the largest risks that they face over the lifecycle. It is an exciting time to be working in field of dynamic empirical economics. Researchers in the area have made substantial progress in recent years – bringing new and innovative data together with ever-richer models of households’ behavior – substantially adding to the evidence-base for policy-makers.
Aleksander Berentsen is professor of economics at the University of Basel, Switzerland. His research interests lie in monetary theory, in particular cryptocurrencies and unconventional monetary policy, as well as doping in sports. Berentsen’s RePEc/IDEAS profile.
EconomicDynamics: There is a lot of talk about cryptocurrencies. From a purely monetary theory point of view, what differentiates cryptocurrency from textbook money?
Aleksander Berentsen: Let’s focus on Bitcoin which is the first and most famous cryptocurrency. Bitcoin is virtual money issued under competition with decentralized transaction handling. It is substantially different from any other money. Textbook theories of money do not differentiate between money that exists in physical form such as cash and virtual money and in general do not study institutional factors such as governance and the way consensus about ownership is obtained.
The most import and interesting characteristic of Bitcoin is the decentralized nature of transaction handling. Any virtual money requires record keeping. Bitcoin is no exception. Record keeping is decentralized and performed by the miners which compete for the privilege to add the latest transactions to the Bitcoin blockchain where the Bitcoin blockchain is a distributed record of all past transaction of the Bitcoin network.
The current aggregated value of all Bitcoin units is roughly 140 Billion Dollars. At any point in time, all participants, who by the way can remain anonymous, must agree on the ownership of all Bitcoin units. The consensus mechanism developed by the Bitcoin founders is amazingly clever. Bitcoin is the first virtual currency that works without centralized record keeping; there is no boss and no dedicated management, and yet is works. This is mind-blowing. To analyze the dynamic game that makes this work is a must for every game theorist.
ED: How different would monetary policy be in a world with only crypto-currencies?
AB: The answers to this question depend on which cryptocurrency we look at. There are currently more than 1500 cryptocurrencies. Most of them are copies of Bitcoin or some other major cryptocurrency. Some of them are trying to improve on some (perceived) shortcomings of Bitcoin. We are currently seeing a Cambrian explosion in this space and I’m excited to observe live in the coming years which designs will survive.
To answer your question, Bitcoin has a very simple monetary policy since the path of the Bitcoin money supply is pre-determined and the total number of Bitcoins will converge to 21 Million units around 2040. This rigid supply is one of the weaknesses of Bitcoin and responsible for the enormous volatility of the Bitcoin price. When a rigid aggregate supply meets a constantly changing aggregate demand, the result is a highly volatile currency price. This feature will not go away since there is no mechanism in the Bitcoin protocol that can stabilize the price. In contrast, central banks have the mandate to deliver a stable price for their fiat currencies and they do so by providing an elastic supply of money and by managing expectations. Since Bitcoin has no central authority that can adjust the money supply or manage expectations, the Bitcoin price will remain very volatile. For that reason, I believe that Bitcoin might not establish itself as a widely used payment instrument but rather as the first manifestation of an interesting new asset class that will be used for portfolio diversification.
ED: Currently, crypto-currencies seem to be more stores of value than means of transaction. Why is that?
This question is related to the previous question. A reason why people don’t use Bitcoin for daily transactions is the enormous price volatility. Two other reasons are price expectations and high transactions costs. Bitcoin has a scaling problem. The Bitcoin network can currently only handle about seven transactions per second. When there is a lot of trading, a transaction can easily cost 10 Dollar. However, this is only a temporary problem since there are many very talented people working on this issue. For example, the lightening network, which is a second layer built on the Bitcoin network, allows for many more transactions per second. Finally, bad money drives out good money. People holding fiat currency and Bitcoin prefer to spend the “bad” fiat money because they still expect the Bitcoin price to gain in value.
ED: Does the limited supply of a crypto-currency limit its usefulness?
AB: A Bitcoin unit is divisible into 10,000,000 Satoshies. With a Bitcoin price of 10,000,000 dollars, the smallest unit – a Satoshi – would be worth 1 dollar. In this case, Bitcoin would face the same problem that had long plagued commodity money which is the scarcity of small change. Sargent and Velde (2003) have written an entire book on this problem titled “The Big Problem of Small Change”. For now, this is a minor issue with Bitcoin. The main problems are price volatility, scaling, and energy consumed for the proof of work consensus mechanism. There is so much to say about this fascinating new technology. I highly recommend the interested reader to consider a few articles that have recently been published by the Federal Reserve Bank of St. Louis on this topic.
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