Working Papers
Political Orientation and Household Climate Adaptation: Evidence from Building Permits (March 2026)
I study how political orientation affects households' decisions to invest in climate adaptation. I focus on adaptation to wind-related risk in Florida, arising from hurricane exposure. Hurricanes are the most severe and costly weather hazard in the state, which directly impacts the value of many households' most important asset, their houses, and to which households can adapt by investing in provably effective measures such as impact-resistant windows, storm shutters, and structural reinforcements of their homes. The recent politicization of climate change, however, has arisen as a potentially important -- and understudied -- force pushing back against these efforts. In this paper, I provide the first large-scale evidence that political orientation indeed does affect household adaptation investments. I use a novel dataset of 13 million building permits for Florida and use textual analysis of permit descriptions to identify approximately 580,000 wind-adaptation permits. I then link adaptation decisions to political orientation by linking this data to voter registration data. I show that the probability that registered Republicans invest in wind-adaptation measures is 4-7% lower than that of registered Democrats, controlling for a rich set of individual, property and geographic characteristics (including comparing households that purchase the same property at different points in time). These effects are economically meaningful, equivalent to the effects of two to three income deciles, and hold up under a variety of specifications. Evidence on mechanisms points toward a belief channel: the gap is present both in areas where building code regulations partially constrain homeowner decisions and in areas where these regulations don't apply; in addition, the adaptation gap widens sharply at the start of the first Trump administration before narrowing after the relaunch of Florida's "My Safe Florida Home" subsidy program, that direct incentivizes this kind of adaptation measures.
Award: Best PhD Poster Award at the Baruch-JFQA Climate Finance and Sustainability Conference
Household Adaptation and Climate Beliefs: Evidence from Building Permits (March 2025)
I study how beliefs about climate change shape households’ climate adaptation decisions. Parsing the textual description of 152 million building permits for single-family residential housing, I identify which ones are intended to protect the house against three major climate-related hazards: wildfires, hurricanes, and floods. These three types of permits directly reflect households’ adaptation to these risks. I validate these measures of adaptation by showing that, in the cross-section of U.S. counties for the period 2000-2023, they strongly correlate with the local exposures to these risks: for example, wildfire-related permits are significantly more frequent in wildfire-exposed areas, and especially so in the riskiest areas. The main result of the paper focuses on the link with climate change beliefs, which is related to long-term exposure to these hazards: I show that, controlling for the current exposure to these hazards, wildfire and hurricane-related permits are more frequent precisely where people are more worried about climate change (which makes expected future risks higher); the role of climate beliefs in driving adaptation becomes stronger in more exposed areas. Flood adaptation is less well measured by permitting activity and does not show a clear link with climate beliefs. This analysis suggests that where households are more aware about climate risks and its link to future risks of wildfire and hurricanes, they respond by actively taking individual adaptation measures.
Measuring Inequality from Top to Bottom (September 2014)
This paper presents a new methodology to measure inequality that optimally combines household survey information and tax records to construct a complete income distribution. Combining the two data sources is necessary because, on the one hand, household surveys do not accurately represent the wealthiest segment of the population, while tax records do; on the other hand, the opposite is true for the lower end of the income distribution: tax records only include incomes above a certain threshold. The key innovation of the proposed methodology – and the main difference from the existing literature (e.g. Atkinson and Piketty 2007) – is the choice of an optimal income threshold b. The Gini coefficient for the population is then computed combining the conditional income distributions for incomes below b (using household survey data) and above b (using tax records). Central to this methodology is the fact that b is not chosen arbitrarily: it should be determined in such a way as to minimize reliance on household survey data to compute the top of the income distribution. In practice, the optimal b corresponds to the minimum in- come level that triggers mandatory tax filing. The proposed methodology is applied to the case of Colombia.