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Structural Change and Development
The first part of this lecture will present some broad facts about growth and development and motivate the use of models of structural change to study development. The second part will briefly survey benchmark models of structural change and their key implications. The core of the lecture will focus on industrialization patterns in recent developing economies, including a number of Asian economies. I will document the heterogeneity in industrialization experiences, and interpret them in the context of benchmark models of structural change. I will close by presenting some new evidence on cross country differences in manufacturing productivity and their implications for closing aggregate productivity gaps.
Some relevant papers (click on the title to view):
1) Growth and Structural Transformation
Structural transformation refers to the reallocation of economic activity across the broad sectors agriculture, manufacturing and services. This review article synthesizes and evaluates recent advances in the research on structural transformation. We begin by presenting the stylized facts of structural transformation across time and space. We then develop a multi–sector extension of the one–sector growth model that encompasses the main existing theories of structural transformation. We argue that this multi–sector model serves as a natural benchmark to study structural transformation and that it is able to account for many salient features of structural transformation. We also argue that this multi–sector model delivers new and sharper insights for understanding economic development, regional income convergence, aggregate productivity trends, hours worked, business cycles, and wage inequality. We conclude by suggesting several directions for future research on structural transformation.
2) Heterogeneous Paths of Industrialization
Industrialization experiences differ significantly across countries. We use a bench-mark model of structural change to shed light on the sources of this heterogeneity and, in particular, the phenomenon of premature deindustrialization. Our analysis leads to three key findings. First, benchmark models of structural change robustly generate hump-shaped patterns for the evolution of the manufacturing sector. Second, heterogeneous patterns of catch-up in sectoral productivities across countries can generate variation in industrialization experiences similar to those found in the data, including premature deindustrialization. Third, differences in the rate of agricultural productivity growth across economies can account for a large share of the variation in peak manufacturing employment shares.
3) New Evidence on Sectoral Productivity: Implications for Industrialization and Development
Moving labor from agriculture to manufacturing – “industrialization” – is often viewed as essential for the development of poor countries. We present new evidence on the channels through which industrialization can help poor countries close the productivity gap with rich countries. To achieve this, we leverage recent data releases by the Groningen Growth and Development Centre and build a new dataset of comparable labor productivity levels in agriculture and manufacturing for 64 mostly poor countries during 1990–2018. We find two key results: (i) cross-country labor productivity gaps in manufacturing are larger than in the aggregate and (ii) there is no tendency for manufacturing labor productivity to converge. While these results challenge the notion that expanding manufacturing employment is essential for the development of today’s poor countries, we also find that higher labor productivity growth in manufacturing is associated with higher labor productivity growth in the aggregate and in several key sectors.
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