Date of Award

8-11-2020

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Economics

Abstract

This dissertation explores people's decision-making process of residential location and housing. The first chapter examines how environmental amenities and health affect an individual's decision about where to live. Chapters 2 and 3 investigate a historical housing market in Southern China using unique datasets. These two chapters provide insights into house price determinants and the behavior of the housing market in the short and long run, as well as the economic function of lineages.

Chapter 1 provides an important implication for epidemiology, as it implies a naive estimation of the adverse effect of air pollution on health will be biased, as people sort based on air quality differences. This paper provides direct evidence that air-pollution-related health shocks change how a household evaluates clean air and, as a result, incentivize relocation towards better air quality. I employ a spatial equilibrium model, in which a household chooses a county to live in based on the county-level characteristics including air pollution. Using NLSY79 data, I create a panel tracking respondents’ respiratory health shocks and county-level location for over three decades. The estimates from a multinomial mixed logit model support the hypothesis that households move toward cleaner air after a female adult is diagnosed with asthma or becomes pregnant. I find that households react more strongly to a new asthma diagnosis for an adult than to a child's diagnosis. For illustration, the probability that a household with an adult diagnosed with asthma chooses to live at a place such as Los Angeles county would decrease from 0.0464 to 0.0459 (a 0.97% decrease) as a result of a 10-point increase in local Air Quality Index (worsened air quality). If a family has a pregnant member, the respective decrease in probability would be 0.39%. The estimated increase in expected marginal willingness to pay for a one-unit reduction in AQI contributed by adult asthma and pregnancy are $14.08 and $4.97, respectively (in 1982-1984 dollars).

Chapter 2 uses a unique housing transaction dataset that I collect from the historic Huizhou prefecture in China's Anhui Province. The richness of these records provide a distinct opportunity to study economic life in late imperial China. In this paper, I focus on house and residential land transaction records to develop price indices for the Ming Dynasty, the Qing Dynasty and the Republic of China era using a hedonic regression model. The current sample includes more than 1000 records and is continuing to expand. The price indices show that house and land prices are correlated with the layout of the property, familial ties between sellers and buyers, location of the house, and whether the seller is in an urgent financial situation.

Chapter 3 aims at examining the effect of historical shocks, such as natural disasters and political turmoils, on the short and long run performance of lineages in the Huizhou area, and how lineages with different attributes respond differently to the shocks. The effect of a lineage's characteristics on the socioeconomic outcomes of its descendants has attracted attention in the economic history literature. However, identification of the long term effect is limited by the availability of historical individual-level data. This chapter obtains detailed individual-level labor and education information from genealogy records collected in China. The outcome variables that describe lineage performance include wealth, imperial civil examination results, and the labor market achievements of lineage members. The current paper does not have the complete lineage-level information; it uses the housing information and discusses the effect of historical shocks on housing prices. The current results find that in the first ten years following the shock, floods decrease house prices; national and local political turbulences have heterogeneous effects on house prices; famines drastically decrease house prices.

DOI

https://doi.org/10.57709/18661478

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