My dissertation studies political economy issues in the development of China. Chapter 1 lies in the intersection of urban economics and political economy. This chapter exploits plausibly exogenous variation generated by a unique national policy in China that requires all residential buildings to receive sufficient hours of sunshine to study a central question in urban economics, namely, whether urban density facilitates the diffusion of information. The policy creates higher degrees of restriction on density at higher latitudes, where longer shadows require buildings to be further apart. Data on individual housing projects across China reveal that the cross-latitude variation in regulatory residential Floor Area Ratio can be described quite well by a formula linking structure density to latitude through the solar elevation angle. These differences in building density further induce differences in population density and land prices across latitudes. Using differential topic dynamics on a national petition platform to measure information diffusion, this chapter shows that people respond to shifts in government attention with varying speeds across latitudes. Increases in local government reply rate to a topic raises the volume of subsequent posts on the same topic, exhibiting an S-shaped time trajectory consistent with local information diffusion about shifting government priorities. These responses are systematically faster in southern cities, where density is higher. Survey evidence further indicates that otherwise similar individuals are more likely to gossip about public issues in a southern city. Chapter 2, coauthored with Junyan Jiang and Tianguang Meng and forthcoming at the journal Governance, examines the flip side of the interaction between local governments and citizens studied in Chapter 1. This chapter studies the response of government policies to opinions expressed online. We address this question by studying the patterns and consequences of online participation at a major electronic petition platform in China. Content analysis of around 900,000 petitions reveals that a substantial share of them concern lower-class issues and are originated from less developed rural and suburban areas. Linking variations in petition volumes to an original dataset of government policy priorities, we further show that online participation led governments to place greater emphasis on social welfare policies and to increase the coverage of a key low-income assistance program. These results underscore the potential of online participation as an important mechanism to improve the quality of governance. Chapter 3, coauthored with Amit Khandelwal, Suresh Naidu and Heiwai Tang, turns to a systematic examination of China's reform process. We apply natural language processing methods to analyze a comprehensive corpus of 1.4 million legal documents issued by the Chinese government at central and local levels since 1949, and measure their market orientation in a data-driven fashion. We document an active introduction of market-oriented legal infrastructure from the mid-1980s to around 2000, which slowed down in the last fifteen years. These dynamics are present within fine-grained policy domains. We find that the market orientation of policies explains just an extra 2% of provincial variation in GDP per capita growth beyond province and time fixed effects. Variable selection based on richer representations of the text exhibits similarly limited predictive power for provincial growth. Taken together, these findings suggest the importance of studying the informal arrangements between market participants and government officials.

Chapter 1, “The Impact of Trade Shocks on Local Labor Markets” estimates the effects of increased trade with China on Brazilian local labor markets using longitudinal individual data on the universe of Brazilian formal sector workers. First, I use reduced-form estimation strategies commonly found in the literature to compare my results to previous findings. I show that my results at the regional level mirror those found in prior studies based on cross-sectional data. I argue that these estimates are potentially biased as they do not take into account the flows of factors and goods between regions. I complement the reduced-form approach with a structural analysis based on the model by Caliendo et al. (2015) in order to endogenize such flows and to study welfare effects. I find that in the absence of the Chinese shock the Brazilian Commodities sector would have shrunk while Manufacturing and Services would have expanded. Relative to this baseline, the employment effect of increased trade with China at the national level was a slower reduction in the share of the Commodities sector and a slower growth in the Manufacturing subsectors that were relatively more exposed to Chinese import competition. My analysis suggests that while the average Brazilian worker benefitted from this shock, the welfare effects were very heterogeneous across sectors and across locations. I find that this heterogeneity is vastly underestimated if instead of using data at the level of metropolitan areas I use data aggregated by States and I explain why the choice of spatial units affects these results. Chapter 2, “Agglomeration: A Long-Run Panel Data Approach” studies the sources of agglomeration economies in cities. We begin by incorporating within and cross-industry spillovers into a dynamic spatial equilibrium model in order to obtain a panel data estimating equation. This gives us a framework for measuring a rich set of agglomeration forces while controlling for a variety of potentially confounding effects. We apply this estimation strategy to detailed new data describing the industry composition of 31 English cities from 1851-1911. Our results show that industries grew more rapidly in cities where they had more local suppliers or other occupationally-similar industries. We find no evidence of dynamic within-industry effects, i.e., industries generally did not grow more rapidly in cities in which they were already large. Once we control for these agglomeration forces, we find evidence of strong dynamic congestion forces related to city size. We also show how to construct estimates of the combined strength of the many agglomeration forces in our model. These results suggest a lower bound estimate of the strength of agglomeration forces equivalent to a city-size divergence rate of 1.6-2.3% per decade. Chapter 3, “Gravity estimation with unobserved bilateral flow data” adapts the methodology by Miscio & Soares (2016) to predict domestic trade flows by sector between Brazilian metropolitan areas. This methodology, initially developed to infer commuting flows from aggregate data on population by place of residence and by place of work, relies on moment conditions derived from a general gravity equation and it is consistent with a large class of trade models. I show that it can also be applied to infer domestic trade flows by sector. Before using the methodology on Brazilian data, where we only observe flows between States, I test it on US data from the Commodity Flow Survey, where we observe both flows between States and between finer spatial units similar to metropolitan areas. I argue that the predicted bilateral flows obtained from this methodology are highly correlated with actual flows. Alternative approaches found in the recent literature differ from the one presented here in that they require stronger assumptions and deliver weaker results. In particular, the other approaches only describe aggregate flows (i.e. summing across all sectors) and cannot be used to predict sectoral flows.

Thesis (Ph. D.)--University of Texas at Austin, 2006. / Vita. Includes bibliographical references.

This dissertation consists of three independent chapters. Chapter 1 examines the determinants and welfare implications of the increased geographic of workers by skill from 1980 to 2000. I estimate a structural spatial equilibrium model of local labor demand, housing supply, labor supply, and amenity levels. The estimates indicate that cross-city changes in firms' demands for high and low skill labor were the underlying forces driving the increase in geographic skill sorting. I find that the combined effects of changes in cities' wages, rents, and endogenous amenities increased well-being inequality between high school and college graduates by a significantly larger amount than would be suggested by the increase in the college wage gap alone. / Economics

No description available.

In chapter 1, I study how spillover effects from competitors' choices affect a firm's decision to open a store. Using panel data from the United Kingdom's fast food industry, I propose and estimate a game of entry under incomplete information that incorporates spillover effects between firms' entry decisions. A positive spillover is identified for Burger King - increasing the stock of existing McDonald's by 1 outlet increases Burger King's estimated equilibrium probability of opening a new store by approximately 18 percentage points. Chapter 2 advances our collective knowledge about the impact of learning from others in industry dynamics, and whether it can generate the clustering of rival retailers. Uncertainty about new markets provides an opportunity for learning from others, where one firm's past entry decisions signal to others the potential profitability of risky markets. The setting is Canada's hamburger fast food industry from its inception in 1970 to 2005, where I introduce a new estimable dynamic oligopoly model of entry/exit with unobserved heterogeneity, common uncertainty about demand, learning through entry, and learning from others. I find that the presence of uncertainty induces retailers to herd into markets that others have previously done well in. Finally, chapter 3 (joint with Feng Chi) studies the early adoption of Twitter in the 111th House of Representatives. Our main objective is to determine whether successes of past adopters have the tendency to speed up Twitter adoption, where past success is defined as the average followers per Tweet - a common measure of "Twitter success" - among all prior adopters. The data suggests that accelerated adoption can be associated with favorable past outcomes: increasing the average number of followers per Tweet among past adopters by a standard deviation (of 8 followers per Tweet) accelerates the adoption time by about 112 days.

In chapter 1, I study how spillover effects from competitors' choices affect a firm's decision to open a store. Using panel data from the United Kingdom's fast food industry, I propose and estimate a game of entry under incomplete information that incorporates spillover effects between firms' entry decisions. A positive spillover is identified for Burger King - increasing the stock of existing McDonald's by 1 outlet increases Burger King's estimated equilibrium probability of opening a new store by approximately 18 percentage points. Chapter 2 advances our collective knowledge about the impact of learning from others in industry dynamics, and whether it can generate the clustering of rival retailers. Uncertainty about new markets provides an opportunity for learning from others, where one firm's past entry decisions signal to others the potential profitability of risky markets. The setting is Canada's hamburger fast food industry from its inception in 1970 to 2005, where I introduce a new estimable dynamic oligopoly model of entry/exit with unobserved heterogeneity, common uncertainty about demand, learning through entry, and learning from others. I find that the presence of uncertainty induces retailers to herd into markets that others have previously done well in. Finally, chapter 3 (joint with Feng Chi) studies the early adoption of Twitter in the 111th House of Representatives. Our main objective is to determine whether successes of past adopters have the tendency to speed up Twitter adoption, where past success is defined as the average followers per Tweet - a common measure of "Twitter success" - among all prior adopters. The data suggests that accelerated adoption can be associated with favorable past outcomes: increasing the average number of followers per Tweet among past adopters by a standard deviation (of 8 followers per Tweet) accelerates the adoption time by about 112 days.

Thèse (D.D.R.) -- Université du Québec à Chicoutimi, conjointement avec l'Université du Québec à Rimouski, 2003. / Bibliogr.: f. [519]-547. Document électronique également accessible en format PDF. CaQCU

Thèse (D.D.R) -- Université du Québec à Chicoutimi, conjointement avec l'Université du Québec à Rimouski, 2003. / Bibliogr.: f. 363-380. Document électronique également accessible en format PDF. CaQCU

Thesis (Ph. D.)--University of Texas at Dallas, 1992. / Includes vita. Includes bibliographical references (leaves 116-122).