Automation and Labor Markets in Developing Countries
Creator
Rodrigo Flores, Rodimiro
Advisor
Mukoyama, Toshihiko
Abstract
In these essays, I analyze the implications of automation for labor markets in developing countries. The first two chapters aim to understand the dynamic consequences for firms and workers of adopting a new technology, focusing on Brazil. In Chapter 1, I find that productivity increases with a five-year lag after the adoption of industrial robots. This adjustment period to technology adoption is known as the productivity paradox. Combining employer-employee matched data with a novel measure of robot adoption, I provide evidence of establishment-level labor reorganization and organizational capital depreciation induced by the automation process during the first five years after the adoption. When these processes stop, the productivity gains reach their maximum. To provide a comprehensive answer to the productivity paradox, in Chapter 2, I estimate a general equilibrium model with heterogeneous firms, endogenous robot adoption, and organizational capital accumulation. The model accounts for the productivity dynamics, robots diffusion, and the change in the aggregate skilldemand in Brazil. The model highlights the role of organizational costs accompanying the adoption of new technologies. In Chapter 3, co-authored with Adriana Kugler et. al., we analyze whether automation by a key trading partner can hurt workers in developing countries. We find that U.S. robots decrease employment and earnings for Colombian workers. Importantly, local labor markets which exported the most to the U.S. in the past, are also the most affected by the increased adoption of U.S. robots, suggesting that Colombian workers may be losing employment to automated jobs reshored back to the U.S. Our estimates also suggest that there may be sectors that benefit from automation due to productivity gains as the general equilibrium effects are nil at the local labor market level.
Description
Ph.D.
Permanent Link
http://hdl.handle.net/10822/1064607Date Published
2022Subject
Type
Publisher
Georgetown University
Extent
127 leaves
Collections
Metadata
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