Volume 19, Issue 1 (April 2018)
The EconomicDynamics Newsletter is a free supplement to the Review of Economic Dynamics (RED). It is published twice a year in April and November.
In this issue
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?