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Business cycle accounting of the BRIC economies

Suparna Chakraborty
  • Corresponding author
  • Department of Economics, University of San Francisco, 2130 Fulton Street, San Francisco, California 94117, USA
  • We are deeply grateful to the editor, Dr. Tiago Cavalcanti, and two anonymous referees for their suggestions to improve the paper. We also thank Robert Dekle, Guillaume Vandenbroucke, Joel David, and Jagjit Chaddha for helpful suggestions and comments. We have also benefited from comments by audiences at the University of Southern California, 8th Annual Conference on Economic Growth and Development, Indian Statistical Institute 2012, and the 2012 Asian Meeting of the Econometric Society, Delhi School of Economics. Our special thanks to Tetsuaki Takano for excellent research assistance. Chakraborty gratefully acknowledges financial support from the University of San Francisco Faculty Development Fund. All remaining errors are our own.
  • Email:
/ Keisuke Otsu
  • School of Economics, University of Kent, Canterbury, Kent, UK
  • We are deeply grateful to the editor, Dr. Tiago Cavalcanti, and two anonymous referees for their suggestions to improve the paper. We also thank Robert Dekle, Guillaume Vandenbroucke, Joel David, and Jagjit Chaddha for helpful suggestions and comments. We have also benefited from comments by audiences at the University of Southern California, 8th Annual Conference on Economic Growth and Development, Indian Statistical Institute 2012, and the 2012 Asian Meeting of the Econometric Society, Delhi School of Economics. Our special thanks to Tetsuaki Takano for excellent research assistance. Chakraborty gratefully acknowledges financial support from the University of San Francisco Faculty Development Fund. All remaining errors are our own.
Published Online: 2013-09-12 | DOI: https://doi.org/10.1515/bejm-2012-0129

Abstract

Drawing upon the experiences of Brazil, Russia, India and China (BRIC), we apply the Business Cycle Accounting methodology to study the phenomenon of rapid economic growth. We document that while efficiency wedges do contribute in a large part to growth, especially in Brazil and Russia, there is an increasing importance of investment wedges especially in the late 2000s, noted in China and India. The results are typically related to the stages of development with Brazil and Russia coming off a recession in the 1990s to grow in the 2000s, while India and China were on a comparatively stable growth path. Our results suggest, at least for the BRICs examined, that while efficiency wedges play a major role in jump-starting recovery, investment wedges are equally important for sustaining the recovery. Relating wedge patterns to institutional and financial reforms, we find that financial market developments and effective governance in BRICs in the last decade are consistent with improvements in investment and efficiency wedges that led to growth.

This article offers supplementary material which is provided at the end of the article.

Keywords: BRIC; business cycle accounting; efficiency; investment adjustment costs; market frictions; trend shocks; JEL Codes: E32; E66

References

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About the article

Corresponding author: Suparna Chakraborty, Department of Economics, University of San Francisco, 2130 Fulton Street, San Francisco, California 94117, USA, Tel.: +1-415-422-4715, e-mail:


Published Online: 2013-09-12

Published in Print: 2013-01-01


Tables 1 and 2 are from the IMF and Angus Maddison’s (2005) online data resources. Details of the historial timeline are in the online appendix.

Per capita GDP growth rate is low in Brazil compared to the aggregate GDP growth due to an expanding population.

In a closed economy set-up, net exports are added to government consumption. We also consider a small open economy setting in which net exports are separately defined.

For example, if BCA exercise identifies efficiency wedges as a major player, the interpretation is that whatever primary factors are responsible for output growth, they work by improving the nation’s efficiency (or productivity).

The role of labor and government consumption wedges turn out to be somewhat sensitive to model specifications.

We explain the details in the online appendix.

We also conduct an exercise with Cobb-Douglas preferences with higher elasticity of substitution presented in the appendix.

Note that under the traditional BCA architecture (CKM 2007), the labor and capital wedges are modeled as taxes on labor income and capital income respectively, yielding the usual government budget constraint.

Aggregate demand is comprised of household consumption, gross domestic capital formation and government consumption.

We follow Gollin (2002) and compute the income share of capital from national income statistics. These are 0.474, 0.475, 0.294, and 0.401 for Brazil, Russia, India and China respectively. We further adjust for the imputed service income from consumer durables as explained in the data appendix.

We used total population for China since we do not have adult population data.

We construct the total capital stock series as the sum of net fixed capital stock and household durables and the total investment series as the sum of gross domestic capital formation and household expenditures on durables.

This assumption is not important as the preference weight and steady state level of labor wedges do not appear in the linearized system of equations.

We cannot make a distinction between

and ωk in the benchmark model so the estimated values are the same. However, they are distinguishable in the alternative models we explore in the following section.

The detailed procedure is explained in the appendix.

The variables are plotted as log deviations from their 1990 value (1992 in case of Russia) and detrended using the average growth rate during the sample period. We also conduct a robustness check detrending all countries by a common rate of 1.5% in the appendix.

As defined in CKM (2007), a “kth lag” is the correlation between the tk th value of the variable of interest with output at period t.

Equilibrium conditions are listed in the appendix.

Rahmati and Rothert (2011) further adds debt price wedges in the international capital market similar to Otsu (2010a) and Lama (2011).

On estimating the stochastic process, we impose a restriction on the lag matrix P such that there is no spill-over into/from the trend wedge from/into the other wedges.

China had its own share of political troubles brewing from the Tiananmen Square massacre of 1989.

Data is collected from the IMD World Competitiveness Yearbook (henceforth, WCY)


Citation Information: The B.E. Journal of Macroeconomics, ISSN (Online) 1935-1690, ISSN (Print) 2194-6116, DOI: https://doi.org/10.1515/bejm-2012-0129. Export Citation

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