Identifier

1097

Document Type

Discussion Paper

Date of Paper

Spring 3-24-2023

Abstract

Structural transformation in most currently developing countries takes the form of a rapid rise in services but limited industrialization. In this paper, we propose a new methodology to structurally estimate productivity growth in service industries that circumvents the notorious difficulties in measuring quality improvements. In our theory, the expansion of the service sector is both a consequence—due to income effects—and a cause— due to productivity growth— of the development process. We estimate the model using Indian household data. We find that productivity growth in non-tradable consumer services such as retail, restaurants, or residential real estate, was an important driver of structural transformation and rising living standards between 1987 and 2011. However, the welfare gains were heavily skewed toward high-income urban dwellers.

Acknowledgements

We are grateful to three referees and to Klaus Desmet and Sebastian Sotelo for their thoughtful discussions at the NBER SI and the ASSA. We thank Daron Acemoglu, Manuel Amador, Marios Angeletos, Treb Allen, David Atkin, Johannes Boehm, Timo Boppart, Fabian Eckert, Reto Foellmi, Penny Goldberg, Doug Gollin, Cormac O’Dea, Gordon Hanson, Peter Klenow, Samuel Kortum, Ramon Marimon, Marc Melitz, Giuseppe Moscarini, Andreas M¨uller, Juan Pablo Nicolini, Rachel Ngai, Ezra Oberfield, Richard Rogerson, Mar´ıa S´aez Mart´ı, Ludwig Straub, Todd Schoellman, Kjetil Storesletten, Aleh Tsyvinski, Danyang Xie, and seminar participants at the ASSA Meeting 2021, the Cowles Macro Conference, the Minneapolis FED, CREi, Dartmouth, Harvard University, HEC Lausanne, IIES, Johns Hopkins SAIS, the NBER Summer Institute, New York University, MIT, Penn State, Peking University, RIDGE, STEG, SED, the Universit´a di Bologna, the Universit´a della Svizzera Italiana, the University of Sankt Gallen, the Workshop on Structural Transformation and Macroeconomic Dynamics, the World Bank, and Yale University. We thank Sarah Moon, Shengqi Ni, Pariroo Rattan, Haonan Ye, Dalton Zhang, and Huihuang Zhu for excellent research assistance. Michael Peters and Fabrizio Zilibotti thank the “Minnesota Opportunity and Inclusive Growth Institute” for its generous hospitality.

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