I document a significant deindustrialization trend in recent decades that goes considerably beyond the advanced, post-industrial economies. The hump-shaped relationship between industrialization (measured by employment or output shares) and incomes has shifted downwards and moved closer to the origin. This means countries are running out of industrialization opportunities sooner and at much lower levels of income compared to the experience of early industrializers. Asian countries and manufactures exporters have been largely insulated from those trends, while Latin American countries have been especially hard hit. Advanced economies have lost considerable employment (especially of the low-skill type), but they have done surprisingly well in terms of manufacturing output shares at constant prices. While these trends are not very recent, the evidence suggests both globalization and labor-saving technological progress in manufacturing have been behind these developments. The paper briefly considers some of the economic and political implications of these trends.
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The bulk of R&D and patents originates from manufacturing. In Europe, for example, close to two-thirds of business R&D spending is done in manufacturing even though the sector is responsible for only 14–15 % of employment and value added in aggregate (Veugelers 2013, p. 8).
These numbers come from Timmer et al. (2014), which is the principal data source I will use in the paper.
See also Amirapu and Subramanian (2015), who document premature deindustrialization within Indian states.
The only exception is West Germany, for which there are no data after 1991 and constant-price series are at 1991 prices. Since all my regressions include country fixed effects, this difference in base year will be absorbed into the fixed effect for the country.
I am grateful to Jesus Felipe for making these data available to me.
These differences are statistically significant. The 95% confidence intervals for log incomes at which manufacturing shares peak, computed using the delta method, are as follows: manemp [8.45, 8.97]; nommva [8.79, 9.58], and realmva [10.16, 12.27]. The confidence interval for manemp (and nommva) does not overlap that for realmva. The series for manemp and nommva easily pass the Lind and Mehlum (2010) test for the presence of an inverse U-relationship in log GDP per capita, while realmva fails it because the extremum occurs outside the observed income range.
An alternative, and more efficient form of estimation would be to introduce periodXgroup dummies in a single, global regression. However, the results in Tables 4-6 suggest considerable heterogeneity in the estimated coefficients on population and income terms across groups. So allowing these coefficients to vary seems worth the price of potentially reduced power. Since the period dummies in the group-specific regressions are estimated tightly for the most part, the loss in efficiency does not appear to make much practical difference.
For a similar chart, see Felipe and Rhee 2014.
Amirapu and Subramanian (2015) present similar charts, using industrial employment data from the World Development Indicators.
I assume that manufactures are the only goods that are traded. In reality, many services are also traded, and the share that crosses national borders has increased over time. Still, even though services dominate the domestic economy, they amount to less than a quarter of global trade. For measurement and other issues posed by trade in services, see World Trade Organization (WTO 2010).
Matsuyama (2009) shows that cross-country results have to be interpreted with caution when economies are globally integrated. In particular, faster productivity gains need not be correlated with more rapid decline in manufacturing across countries, even if productivity change is globally responsible for manufacturing’s decline. See also Uy et al. (2013) which develops a model with productivity and trade cost shocks under various assumptions about demand, and uses it to explain South Korea’s pattern of structural change. The authors find that non-homothetic demand, more rapid productivity growth in manufacturing, and the decline in manufacturing trade costs do a good job of explaining structural change, with the exception of the decline in manufacturing after 1990.
Aid inflows operate similarly to resource booms in so far as they drive up the price of non-traded goods, and reduce the relative price of manufactures. For an examination of these issues in the Sub-Saharan African and Latin American contexts, respectively, see Rajan and Subramanian (2011) and Palma (2014).
I am grateful to an anonymous referee who suggested the closed-economy analogy.
Young (November 2013 and forthcoming) raises some important questions about gaps in inter-sectoral productivity and in manufacturing versus nonmanufacturing productivity growth, arguing that these gaps may be due to selection based on unobserved worker skills. To the extent that manufacturing is more productive because it employs the more capable workers, it loses its “specialness.” In particular, more labor absorption in manufacturing would not raise economy-wide productivity, as marginal workers drawn into manufacturing would be of the lower-productivity type. Even if selection effects are present, however, it is not clear they can explain why manufacturing industries that are further away from the frontier experience more rapid labor productivity growth (as in Rodrik 2013). A recent paper by Franck and Galor (2015) suggests early industrialization may have adverse long-run effects, within a country: these authors find that regions in France that adopted industrial technology earlier eventually ended up with lower incomes and human capital levels. It is not clear what the implications of such results to growth patterns across countries are, however. France is a post-industrial country, and the cross-region findings are conditional on industrialization and within-country convergence having taken place.
A full welfare evaluation of the trends discussed in this paper must take into account other effects in addition to the foregone productivity gains due to premature deindustrialization. For developing countries that are net importers of manufactures, the global reduction in the relative price of manufactures due to technological progress in advanced countries represents a terms-of-trade benefit and a (static) welfare gain. (For developing countries that are net exporters of manufactures, there is a corresponding terms-of-trade loss.) The fall in manufacturing prices may also reduce the cost of capital-goods in developing countries, and thereby spur investment. Where private investment is sub-optimal due to credit-market or other failures, this would represent an additional source of welfare gain. It is in principle possible to attach some quantitative magnitudes for representative countries to each one of these effects, using the results here and in Rodrik (2013). However, such an effort would take this paper too far afield, and I leave it to future work.
It is possible that these trends will be reversed, as manufacturing migrates from Asia to low-wage countries. Anecdotal evidence (e.g. the rise of Chinese manufacturing investment in countries such as Ethiopia and Rwanda) as well as some of the more systematic evidence in McMillan et al. (2014) suggest that manufacturing may have a renewal of sorts in Sub-Saharan Africa. But the fact that we rarely see double humps in the manufacturing curve should make us skeptical of this eventuality.
On Africa, for example, see African Center for Economic Transformation (2014), which emphasizes the need for productive diversification and structural transformation if recent growth rates are to be sustained.
I show in Rodrik (2014) that the vast majority of the countries that experienced growth rates of 4.5 percent or more for at least three decades are those that underwent rapid industrialization. The list is composed essentially of two categories of countries: some in the European periphery during the 1950s and 1960s (e.g., Spain, Portugal, Israel), and some in East Asia since the 1960s (e.g., South Korea, Taiwan, Malaysia). The exceptions are some small, but resource-rich countries (e.g., Botswana, Oman, Equatorial Guinea), many of which experienced reversals eventually. In that paper, I propose a framework that distinguishes between two channels of growth, with overlapping but distinct requirements: a “fundamentals” channel that relies on the accumulation of economy-wide skills and institutional capabilities, and a “structural transformation” channel that relies on industrialization. I argue that slow-to-moderate growth is possible with the former, but that rapid convergence requires the latter.
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I am grateful to Elias Sanchez-Eppler, Russell Morton, and Juan Obach for expert research assistance, Robert Lawrence and Arvind Subramanian for useful conversations, David Romer for comments, and Jesus Felipe for sharing his data set. Four referees have provided very constructive suggestions that led to improvements.
Country and variable coverage in the GGDC 10-Sector Database
|Acronym||Country||Value added in current prices||Value added in constant prices||Employment by sector|
|NGA(alt)||Nigeria (2014 GDP revision)||2010–2013||2010–2013 (in 2010 prices)|
|USA||United States of America||1947–2010||1947–2010||1950–2010|
|DEW||West Germany||1968–1991||1950–1991 (1991 prices)||1950–1991|
Source Timmer et al. (2014). See http://www.rug.nl/research/ggdc/data/10-sector-database
Countries included in the Socio Economic Accounts of the World Input-Output Database (WIOD)
Austria, Germany, Netherlands, Canada, China, Belgium, Greece, Poland, United States, India, Bulgaria, Hungary, Portugal, Japan, Cyprus, Ireland, Romania, South Korea, Czech Republic, Italy, Slovak Republic, Australia, Denmark, Latvia, Slovenia, Brazil, Taiwan, Estonia, Lithuania, Spain, Mexico, Turkey, Finland, Luxembourg, Sweden, Indonesia, France, Malta, United Kingdom, Russia.
Source Timmer (2012), latest update available at http://www.wiod.org/protected3/data/update_sep12/SEA%20Sources_June2014.pdf.
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Rodrik, D. Premature deindustrialization. J Econ Growth 21, 1–33 (2016). https://doi.org/10.1007/s10887-015-9122-3
- Economic growth