Current US government policy after large natural disasters is to subsidize rebuilding what existed pre-disaster. While public rebuilding assistance may speed up the economic recovery after a shock, it distorts private relocation and rebuilding incentives. In this paper, I document new facts on household relocation and rebuilding decisions after hurricanes using detailed migration data and a survey of affected individuals. I find that migration is a widespread but costly form of ex-post insurance. Age, wealth, and housing tenure are all important determinants of household choices post-disaster. Motivated by these empirical findings, I propose a dynamic equilibrium model of migration, housing, and infrastructure, with rich household heterogeneity. I find that homeowners, especially those with less home equity at the time of the disaster are the biggest beneficiaries under current policy.

This paper studies sustainable investing using data from a representative survey of German households and a quantitative asset pricing model with heterogeneous investors. About a third of households have green investments worth 11% of household wealth.  Green investments are currently relatively risky, with equity as the main pathway, while green bank accounts are rare.  We find substantial heterogeneity in green taste for both safe and risky green assets throughout the wealth distribution, which can either increase or decrease demand for these assets. Model counterfactuals show that nonpecuniary benefits and hedging demands currently make green equity more expensive for firms.  Nevertheless, the rise of sustainable investing has introduced a greenium of about 1%, as investors who are now aware of green stocks bid up their prices. Many households desire green bank accounts which could substantially increase overall green finance. Feeding treatment effects from an RCT in the survey into our model suggests that greater awareness of climate finance could also lead to a further burst in green equity investment.

This study explores the spending response to tax refunds for Earned Income Tax Credit recipients using a novel dataset combining transaction-based measures of retail spending with administrative IRS data on tax refunds. Our dataset allows us to exploit variation in the timing of EITC refunds, including changes related to the 2017 PATH Act, along with cross-state differences in refund magnitudes to identify spending responses. Results show EITC recipients spend about $0.30 per refund dollar ($1,150 for the average refund) within just two weeks of issuance, suggesting stimulus targeted at this population may provide a quick boost to aggregate demand.

We develop an expectations-based measure of gentrification. Property values today incorporate market participants’ expectations of the neighbourhood’s future. We contrast this with present-oriented variables like demographics. To operationalise the signal implicit in property values, we contrast the percentile rank of a neighbourhood’s average house price to that of its average income, relative to its metropolitan area. We take as our signal of gentrification the rise of a neighbourhood’s house value percentile above its income percentile. We show that a gap between the house value and income percentiles predicts future income growth. We further validate our metric against existing approaches to identify gentrification, finding that it aligns meaningfully with qualitative analyses built on local insight. Compared to existing quantitative approaches, we obtain similar results but usually observe them in earlier years and with more parsimonious data. Our approach has several advantages: conceptual simplicity, communicative flexibility with graphical and map forms and availability for small geographies on an annual basis with minimal lag.