Abstract
This chapter provides a comprehensive assessment of structural change patterns in the world economy. It uses a new dataset on sectoral employment produced by the International Labour Organization, which is complemented by national accounts and population data from the United Nations Department of Economic and Social Affairs. The sample includes 169 countries, representing about 99% of the world’s output and population in 2013. One of the main contributions of this chapter is its focus on the sub-regional level, which has been hitherto absent from the literature. We provide an assessment of 13 sub-regions in Africa, Asia and Latin America in order to offer deeper and richer insights into the recent dynamics of structural change. Overall, our results suggest that within-sector productivity improvements were the key driver of output per capita growth in most sub-regions. Nonetheless, structural change has also played a critical role in enhancing economic performance since 2002—mainly through services. Changes in the demographic structure and employment rates have also contributed to the recent performance, albeit to a much lesser extent. Accelerating the pace of structural change—by exploiting existing productivity gaps—will be crucial to sustain current economic growth rates in developing regions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
This chapter is based on the gross domestic product (GDP) production approach, rather than the (perhaps more common) expenditure approach. Therefore, instead of assessing whether it is consumption, investment or exports that is stimulating economic growth, we investigate which economic sectors are driving economic performance.
- 2.
The Shapley decomposition considers the marginal effect on a variable (in our case, output per capita growth) of sequentially eliminating each of the contributory factors, and then assigns to each factor the average of its marginal contributions in all possible elimination sequences (Sorrocks 2013). See also World Bank (2015).
- 3.
For instance, McMillan et al. (2014) use \(\Delta w = \sum\nolimits_{i = 1}^{n} {\Delta w_{i} (s_{i,t = 0} )} + \sum\nolimits_{i = 1}^{n} {\Delta s_{i} (w_{i,t = 1} )}\), while Timmer et al. (2015) use an empirically equivalent decomposition that further disaggregates the between-sector component into static and dynamic reallocation effects.
- 4.
The observed decline in labour productivity is partly due to stronger employment growth in public utilities (section E)—which is observed across all regions.
- 5.
It should be noted that South Africa accounted for 91% of Southern Africa’s GVA in 2013 and 85% of employment, while Nigeria represented 76% of Western Africa’s GVA in 2013 and 45% of employment.
- 6.
In 2013, China accounted for 78% of Eastern Asia’s GVA and 96% of employment; while India was responsible for 70% of Southern Asia’s GVA and 71% of employment.
- 7.
In 2013, Mexico accounted for 88% of Central America’s GVA and 74% of employment; while Brazil represented 49% of South America’s GVA and 52% of employment.
- 8.
A sector provides a positive contribution to the between-sector component if: (i) its labour productivity is above the aggregate average and its employment share increases or (ii) its labour productivity is below the aggregate average and its employment share declines (this is often the case of agriculture).
- 9.
The structural change component is intrinsically linked to the employment share (Ei/E), while the employment component relates to the (sectoral) employment rate (Ei/A).
- 10.
In fact, agriculture is the least productive sector in all regions (and sub-regions).
- 11.
McMillan and Harttgen (2015) also report results for an expanded African sample (19 countries), but disaggregated into four sectors only. The findings are broadly similar to the main results.
- 12.
Such a decomposition yields an output per worker growth rate of 3.4% per year for 1991–2013, of which 72% is due to within-sector improvements and the remaining 28% is due to structural change.
- 13.
For instance, China accounts for most of GVA and employment in Eastern Asia. As a comparison, GVA per worker growth declines from 7.6% (our result) to 5.3% (when unweighted) in 2002–2013, while the between-sector effect drops from 1.8 percentage points to 0.9 percentage points. Similar discrepancies emerge when McMillan and Harttgen (2015) apply employment weights and Kucera and Roncolato (2012) apply GDP weights to their respective results.
- 14.
A single weight needs to be used across all components to ensure consistency, but while output would probably be more suitable for weighing within-sector effects, employment is likely to be more appropriate for between-sector effects. This can be problematic, since a country’s weight may vary considerably according to which variable is chosen. For example, D.R. Congo accounts for 50% of Middle Africa’s employment, but only 14% of GVA.
References
Chenery, H. (1960). Patterns of Industrial Growth. American Economic Review, 50, 624–653.
Chenery, H., & Taylor, L. (1968). Development Patterns: Among Countries and Over Time. The Review of Economics and Statistics, 50(4), 391–416.
Gollin, D., Lagakos, D., & Waugh, M. (2014). The Agricultural Productivity Gap. The Quarterly Journal of Economics, 129(2), 939–993.
Herrendorf, B., Rogerson, R., & Valentinyiet, A. (2014). Growth and Structural Transformation. In P. Aghion & S. Durlauf (Eds.), Handbook of Economic Growth (Vol. 2, pp. 855–941). Amsterdam, North Holland: Elsevier.
Kucera, D., & Roncolato, L. (2012). Structure Matters: Sectoral Drivers of Growth and the Labour Productivity Employment Relationship (ILO Research Paper 3). Geneva: ILO.
Kuznets, S. (1957). Quantitative Aspects of the Economic Growth of Nations: II. Industrial Distribution of National Product and Labor Force. Economic Development and Cultural Change, 5(Suppl. 4), 1–111.
Kuznets, S. (1966). Modern Economic Growth. New Haven: Yale University Press.
McMillan, M., & Harttgen, K. (2015). What Is Driving the African Growth Miracle (NBER Working Paper No. 20077).
McMillan, M., & Headey, D. (2014). Introduction—Understanding Structural Transformation in Africa. World Development, 63, 1–10.
McMillan, M., Rodrik, D., & Verduzco-Gallo, I. (2014). Globalization, Structural Change, and Productivity Growth, with an Update on Africa. World Development, 63, 11–32.
Shorrocks, A. (2013). Decomposition Procedures for Distributional Analysis: A Unified Framework Based on the Shapley Value. The Journal of Economic Inequality, 11(1), 99–126.
Timmer, M., de Vries, G., & de Vries, K. (2015). Patterns of Structural Change in Developing Countries. Groningen Growth and Development Centre, Research Memorandum 149.
UNCTAD. (2014). The Least Developed Countries Report 2014—Growth with Structural Transformation: A Post-2015 Development Agenda. New York and Geneva: United Nations.
World Bank. (2015). Job Generation and Growth Decomposition Tool: Understanding the Sectoral Pattern of Growth and Its Employment and Productivity Intensity Reference Manual and User’s Guide Version 1.0. World Bank Group. Available via http://siteresources.worldbank.org/INTEMPSHAGRO/Resources/JoGGs_Decomposition_Tool_UsersGuide.pdf. Accessed mid-April 205.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix
Appendix
Structure of output and employment by region
Labour productivity gaps, 2013
Rights and permissions
Copyright information
© 2019 The Author(s)
About this chapter
Cite this chapter
Martins, P.M.G. (2019). Sub-Regional Perspectives on Structural Change. In: Elhiraika, A., Ibrahim, G., Davis, W. (eds) Governance for Structural Transformation in Africa. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-03964-6_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-03964-6_3
Published:
Publisher Name: Palgrave Macmillan, Cham
Print ISBN: 978-3-030-03963-9
Online ISBN: 978-3-030-03964-6
eBook Packages: Economics and FinanceEconomics and Finance (R0)