UCLA

The first chapter of this dissertation examines the impact of highway expansion on aggregate productivity growth and sectoral reallocation between cities in China. To do so, I construct a unique dataset of bilateral transportation costs between Chinese cities, digitized highway network maps, and firm-level census. I first derive and estimate a market access measure for cities in China from 1995 to 2005. I then examine the channels through which the highway infrastructure affected economic outcomes. The results suggest that highways promoted aggregate productivity growth by facilitating the entry of new firms and reallocation among existing firms. I estimate the aggregate economic impact of China's national highway system and find that eliminating all highways in China would decrease aggregate productivity by 3.2%. There is also evidence that the national highway system led to a sectoral reallocation between cities in China.

In the second chapter, I investigate to what extent firms in developing economies improve their productivity by importing foreign technology. I examine the effects of machinery importing on firm productivity for Chinese manufacturing firms, or "upgrading by importing". To do so, I develop an algorithm to merge the Chinese firm-level census data with the Chinese Customs data. To address endogeneity concerns on importing decisions, I use a propensity score matching approach to identify the impact of machinery imports on firm productivity. Finally, I estimate a simple empirical model to examine the heterogeneous effects and to quantify the aggregate impact of machinery importing. I find that machinery and equipment imports improved firm productivity in China and could potentially generate large gains in aggregate productivity. The results in this paper suggest that importing foreign machinery goods is an important channel for technology diffusion.

The third chapter analyzes the impact of fiscal and structural policies on gender inequality for countries at different stages of development. Using Bayesian Model Averaging, in addition to frequentist methods, we address model uncertainty due to the large number of possible determinants previously highlighted in the microeconomics literature. We find that better sanitation facilities, low adolescent fertility, narrower marriage age gaps and higher public spending in education contribute to closing the gender gap in education. Better infrastructure, more equal legal rights, low adolescent fertility rates, and a stronger institutional environment boost female labor force participation. At lower levels of labor market protection, stronger protection is associated with narrower labor force participation gaps, but excessive labor market rigidity weighing on female labor force participation.