UCLA

In the first chapter of this dissertation (joint with Esteban M�ndez-Chac�n), we study the short- and long-run effects of large firms on economic development. To do so, we use evidence from one of the largest multinationals of the 20th Century: The United Fruit Company (UFCo). The firm was given a large land concession in Costa Rica—one of the so-called “Banana Republics”—from 1889 to 1984. Using administrative census data with census-block geo-references from 1973 to 2011, we implement a geographic regression discontinuity (RD) design that exploits a quasi-random assignment of land. We find that the firm had a positive and persistent effect on living standards. Regions within the UFCo were 26% less likely to be poor in 1973 than nearby counterfactual locations, with only 63% of the gap closing over the following 3 decades. Company documents explain that a key concern at the time was to attract and maintain a sizable workforce, which induced the firm to invest heavily in local amenities that likely account for our result.

We then build a dynamic spatial model in which a firm's labor market power within a region depends on how mobile workers are across locations and run counterfactual exercises. The model is consistent with observable spatial frictions and the RD estimates, and shows that the firm increases aggregate welfare by 2.9%. This effect is increasing in worker mobility: If workers were half as mobile, the firm would have decreased aggregate welfare by 6%. The model also shows that a local monopsonist compensates workers mostly through local amenities keeping wages low, and leads to higher welfare levels than a counterfactual with perfectly competitive labor markets in all regions.

In the second chapter of this dissertation, I study an important question in the field of economic growth and development: How developing countries learn to adopt and use new technologies. In particular, the chapter studies how countries learn from each other through international trade. First, I build a panel of bilateral trade flows between industries in different countries. Matching this panel with data on industry-level productivity, I document how productivity grows systematically faster for countries that trade with partners with better technologies, but that this is reducing the gap between local and foreign productivity.

Second, I build a model in which knowledge transfers can occur through imported technology, leading to productivity growth. In my framework, agents have heterogeneous learning abilities: The probability of a producer adopting a technology slightly better than hers is larger than the probability of adopting a much more sophisticated one—the trade-off being that conditional on adoption, more sophisticated technologies lead to higher productivity. I document how the model matches the empirical dependence of productivity growth on productivity gaps across trading partners, and the firm size distribution. The model also highlights how ignoring differences in learning abilities can overestimate the impact of exposure to high-TFP trading partners, leading to suboptimal trade policies. I conclude that developing countries should direct relatively more trade to mid-productive countries—as opposed to very productive ones—to maximize technology transfers and increase growth.