Inferring Labor Income Risk and Partial Insurance From Economic Choices

Abstract

This paper uses the information contained in the joint dynamics of individuals' labor earnings and consumption‐choice decisions to quantify both the amount of income risk that individuals face and the extent to which they have access to informal insurance against this risk. We accomplish this task by using indirect inference to estimate a structural consumption–savings model, in which individuals both learn about the nature of their income process and partly insure shocks via informal mechanisms. In this framework, we estimate (i) the degree of partial insurance, (ii) the extent of systematic differences in income growth rates, (iii) the precision with which individuals know their own income growth rates when they begin their working lives, (iv) the persistence of typical labor income shocks, (v) the tightness of borrowing constraints, and (vi) the amount of measurement error in the data. In implementing indirect inference, we find that an auxiliary model that approximates the true structural equations of the model (which are not estimable) works very well, with negligible small sample bias. The main substantive findings are that income shocks are moderately persistent, systematic differences in income growth rates are large, individuals have substantial amounts of information about their income growth rates, and about one‐half of income shocks are smoothed via partial insurance. Putting these findings together, the amount of uninsurable lifetime income risk that individuals perceive is substantially smaller than what is typically assumed in calibrated macroeconomic models with incomplete markets.

Mortgage Design in an Equilibrium Model of the Housing Market

“…Households retire at age R < T. After retirement, households no longer face idiosyncratic income risk and keep the same idiosyncratic income they had at age R, reduced by ρ log points to account for the decline in income in retirement. This can be thought of as a social security benefit that conditions on terminal income rather than average lifetime income for computational tractability, as in Guvenen and Smith (2014).…”

Inequality in Russia over time and over the life cycle

“…A nearly flat life‐cycle profile of consumption is hard to reconcile with the standard intertemporal theory of consumption when households build a buffer of assets to insure the risk to their disposable incomes (e.g., Deaton, 1991; Carroll, 1997). Standard calibrations of life‐cycle models (e.g., Guvenen and Smith, 2014; Storesletten et al, 2004) assume that the variance of the autoregressive component at the start of working life is zero, so that the variance of idiosyncratic incomes increases over the life cycle due to accumulation of independent persistent or permanent income shocks. We have to depart from this assumption: a flat life‐cycle income profile can be generated by a combination of stationary but persistent incomes and a relatively high initial income variance.…”

“…For example, the RLMS data available for our estimation period reveal that nearly 50 percent of males aged 25-34 are employed in a job that does not match their education/training. 3 Such an approach that uses model-based inference on the income process parameters is similar in spirit to Guvenen and Smith (2014) and Hryshko (2007). we follow the procedure outlined in Schulhofer-Wohl (2018) where we explicitly target the life-cycle consumption and income inequality profiles obtained from the data using the standard restrictions on (i) cohort effects (as we have done in the empirical analysis) and (ii) time effects (following (following Deaton, 1997;Aguiar and Hurst, 2013).…”

The Matching Multiplier and the Amplification of Recessions

“…First, as food is a necessity, its share of total consumption varies across the income distribution (see Appendix Figure A2 and Bonaparte and Fabozzi (2011)) Second, the provision of food stamps potentially distorts food consumption decisions on the margin and likely dampens fluctuations in food expenditure relative to overall expenditures (Hastings and Shapiro (2017)). In order to address these issues, the main measure of expenditure I use throughout the analysis is total expenditure, which I impute using overlapping information in the Panel Study of Income Dynamics and the Consumer Expenditure Survey, or CEX, following the methodology introduced in Blundell, Pistaferri, and Preston (2008) and expanded in Guvenen and Smith (2014). Using the CEX, I estimate a demand for food expenditure as a function of durable consumption, nondurable consumption, demographic variables, and relative prices.…”