Title: Essays in International and Urban Economics
Description: 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.