Abstract：In low- and middle-income country cities, poor households often reside in unattractive locations, including flood-prone areas. This can be due to poor information about flood risks or acceptance of these risks in the face of lower housing prices. Poor households are also more vulnerable to floods than richer households given the low-quality housing they occupy. Does information on flood risks help households make better location and housing choices? To what extent will these choices be revised with increased flood risks from climate change? To answer these questions, we develop a polycentric land use model with heterogeneous income groups, formal and informal housing, and flood risks. The model is calibrated to the city of Cape Town (South Africa) and simulations are run to assess the impact of flood risks on land values and income segregation within the city, distinguishing between the effects of three types of flooding (fluvial, pluvial, and coastal). Although total damages from floods are greater for rich households, they represent a larger relative share of poor households’ incomes. Better information encourages the adaptation of poor households up to a certain point, and this allows them to mitigate most of the adverse consequences from climate change. Considering the different nature of flood types is key to understanding their responses.

Distributional Effects with Two-Sided Measurement Error: An Application to Intergenerational Income Mobility∗ (joint with Brantly Callaway, Irina Murtazashvili, Emmanuel S. Tsyawo)

Abstract: This paper considers identification and estimation of distributional effect parameters that depend on the
joint distribution of an outcome and another variable of interest (“treatment”) in a setting with “two-
sided” measurement error — that is, where both variables are possibly measured with error. Examples
of these parameters in the context of intergenerational income mobility include transition matrices, rank-
rank correlations, and the poverty rate of children as a function of their parents’ income, among others.
Building on recent work on quantile regression (QR) with measurement error in the outcome (particu-
larly, Hausman, Liu, Luo, and Palmer (2021)), we show that, given (i) two linear QR models separately
for the outcome and treatment conditional on other observed covariates and (ii) assumptions about the
measurement error for each variable, one can recover the joint distribution of the outcome and the treat-
ment. Besides these conditions, our approach does not require an instrument, repeated measurements,
or distributional assumptions about the measurement error. Using recent data from the 1997 National
Longitudinal Study of Youth, we find that accounting for measurement error notably reduces several
estimates of intergenerational mobility parameters.