Research

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Abstract: The real price of housing in the US increased 60.4% from 1980 to 2019. Over this same period, the population grew 45%, and its composition changed as birth rates fell, people lived longer, and migration patterns shifted. I quantify the effects of these demographic changes on the real price of housing in a dynamic, general equilibrium model. I adapt a standard, state-of-the-art incomplete markets model to include time-varying demographics, which I calibrate to exactly match a series of empirical population measures from 1980 to 2019. The model indicates that demographic changes can account for 49.6% of the measured increase in the real price of housing. The effects of demographic change in the model are born mostly by younger households: in 2019, older households achieve a similar standard of living as older households in 1980, while younger households own and consume less than their 1980 counterparts. Using counterfactual demographic evolutions, I separately quantify the effects of changes in three demographic channels—births, aging, and migration—since 1980. Holding births constant at their 1980 level, I find that the real price of housing rises an additional 10.2 percentage points; counterfactuals with constant aging and migration processes produce less price growth than the baseline.

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Evidence shows that the household return on wealth increases with wealth.  One plausible source of this correlation is that wealthier households may hold riskier, higher return portfolios.  I review an established but often overlooked result that, in contrast to the empirical evidence, a textbook income fluctuation model with two assets and standard utility often delivers a return on wealth that is decreasing in wealth.  I propose a simple alternative preference parameterization, and I explore how the stationary equilibrium of the proposed model differs from the standard model in terms of portfolio behavior and wealth inequality.  I demonstrate that these differences are important for policy evaluation by demonstrating that, under the standard model, preferential tax treatment of capital gains is a progressive tax policy, while under my proposed model, the tax policy is regressive.

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We estimate the numbers of always takers, compliers, defiers, and never takers in the sample of people in an experiment, rather than a hypothetical population from which it was drawn, using structure from the randomization design. Our data include only a binary intervention and outcome. We develop a visualization to show that samples with defiers can sometimes generate the data in more ways than samples without defiers, yielding a higher design-based likelihood. We propose a maximum likelihood decision rule that can harness this evidence, which is not captured by standard hypothesis tests, and we provide optimality conditions. We illustrate the output of our decision rule for all possible data in samples of 50 and 200 with half in intervention, demonstrating a pattern in when the MLE includes defiers despite a positive average effect. We provide insights into effect heterogeneity in two published experiments with interventions that could plausibly backfire for some people despite statistically significant positive average effects on takeup of desirable health behaviors. In both, our 95% credible sets include the estimated Frechet bounds, demonstrating that evidence is weak. Yet, our MLE includes no defiers in one; in the other, the MLE includes a count of defiers equal to the estimated upper Frechet bound, over 18% of the sample. The MLE can support a monotonicity assumption or a specific alternative as a step toward improving the average effect of future interventions by targeting them away from noncompliers. Our dbmle package, compatible with Python and Stata, implements our statistics.