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How did the largest expansion of unemployment benefits in U.S. history affect household behavior? Using anonymized bank account data covering millions of households, we provide new empirical evidence on the spending and job search responses to benefit changes during the pandemic and compare those responses to the predictions of benchmark structural models. We find that spending responds more than predicted, while job search responds an order of magnitude less than predicted. In sharp contrast to normal times when spending falls after job loss, we show that when expanded benefits are available, spending of the unemployed actually rises after job loss. Using quasi-experimental research designs, we estimate a large marginal propensity to consume out of benefits. Notably, spending responses are large even for households who have built up substantial liquidity through prior receipt of expanded benefits. These large responses contrast with a theoretical prediction that spending responses should shrink with liquidity. Simple job search models predict a sharp decline in search in the wake of a substantial benefit expansion, followed by a sustained rebound when benefits expire. We instead find that the job-finding rate is quite stable. Moreover, we document that recall plays an important role in driving job-finding dynamics throughout the pandemic. A model extended to fit these key features of the data implies small job search distortions from expanded unemployment benefits. Jointly, these spending and job finding facts suggest that benefit expansions during the pandemic were a more effective policy than predicted by standard structural models. Abstracting from general equilibrium effects, we find that overall spending was 2.0-2.6 percent higher and employment only 0.2-0.4 percent lower as a result of the benefit expansions.

The Regression Kink (RK) design is an increasingly popular empirical method for estimating causal effects of policies, such as the effect of unemployment benefits on unemployment duration. Using simulation studies based on data from existing RK designs, we empirically document that the statistical significance of RK estimators based on conventional standard errors can be spurious. In the simulations, false positives arise as a consequence of non-linearities in the underlying relationship between the outcome and the assignment variable, confirming concerns about the misspecification bias of discontinuity estimators pointed out by Calonico et al. (2014b). As a complement to standard RK inference, we propose that researchers construct a distribution of placebo estimates in regions with and without a policy kink and use this distribution to gauge statistical significance. Under the assumption that the location of the kink point is random, this permutation test has exact size in finite samples for testing a sharp null hypothesis of no effect of the policy on the outcome. We implement simulation studies based on existing RK applications that estimate the effect of unemployment benefits on unemployment duration and show that our permutation test as well as inference procedures proposed by Calonico et al. (2014b) improve upon the size of standard approaches, while having sufficient power to detect an effect of unemployment benefits on unemployment duration.

The Regression Kink (RK) design is an increasingly popular empirical method, with more than 20 studies circulated using RK in the last 5 years since the initial circulation of Card, Lee, Pei and Weber (2012). We document empirically that these estimates, which typically use local linear regression, are highly sensitive to curvature in the underlying relationship between the outcome and the assignment variable. As an alternative inference procedure, motivated by randomization inference, we propose that researchers construct a distribution of placebo estimates in regions without a policy kink. We apply our procedure to three empirical RK applications – two administrative UI datasets with true policy kinks and the 1980 Census, which has no policy kinks – and we find that statistical significance based on conventional p-values may be spurious. In contrast, our permutation test reinforces the asymptotic inference results of a recent Regression Discontinuity study and a Difference-in-Difference study. Finally, we propose estimating RK models with a modified cubic splines framework and test the performance of different estimators in a simulation exercise. Cubic specifications – in particular recently proposed robust estimators (Calonico, Cattaneo and Titiunik 2014) – yield short interval lengths with good coverage rates.

The past thirty years have seen a dramatic decrease in the rate of income convergence across U.S. states. This decline coincides with a similarly substantial decrease in population flows to wealthy states. We develop a model where labor mobility plays a central role in convergence and can quantitatively account for its disappearance. We then link this decline in directional migration to a large increase in housing prices and housing regulation in high-income areas. The model predicts that these housing market changes generate (1) a divergence in the skill-specific economic returns to living in rich places, (2) a decline in low-skilled migration to rich places and continued low-skilled migration to places with high income net of housing costs, (3) a decline in the rate of human capital convergence and (4) continued income convergence among places with unconstrained housing supply. Using Census data, we find support for the first three hypotheses. To test the fourth hypothesis, we develop a new state-level panel measure of housing supply regulations. Using this measure as an instrument for housing prices, we document the central role of housing prices and building restrictions in the end of income convergence.