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Labor productivity growth: disentangling technology and capital accumulation

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Abstract

How much of the convergence in labor productivity that we observe in manufacturing is due to convergence in technology versus convergence in capital-labor ratios? To shed light on this question, we introduce a nonparametric counterfactual decomposition of labor productivity growth into growth of the capital-labor ratio (K/L), technological productivity (TEP) and total factor productivity (TFP). Our nonparametric specification enables us to model technology allowing for heterogeneity across all relevant dimensions (i.e. countries, sectors and time). Using data spanning from the 1960s to the 2000s, covering 42 OECD and non OECD countries across 11 manufacturing sectors, we find TEP and TFP to account for roughly 46 and −6% of labor productivity growth respectively, on average. While technological growth at the world level is driven primarily by the US and a handful of other OECD countries, we find strong evidence of convergence in both technology and capital-labor ratios. Interestingly, very few of the usual growth determinants are found to enhance the process of technological catching-up.

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Notes

  1. The expression ‘biased technical change’ is used here to refer to forms of technical change directly affecting factor elasticities—i.e. the percent change in output associated to a one percent change in a given factor.

  2. Schelkle (2014) finds similar evidence of experiences of successful catch-up to the United States that are mainly driven by relatively faster factor accumulation. His approach differs from that here in two main dimensions. First his analysis is at the country level rather than the sector level, and second, he explicitly takes into account human capital as a factor of production.

  3. The appeal of these decompositions is that they impose few parametric assumptions on the underlying structure. Further, this style of semi- and nonparametric estimation has recently witnessed an increased interest in the cross-country growth literature (Maasoumi et al. 2007, Henderson et al. 2012, 2013). These methods are invaluable when little a priori information exists regarding the unknown relationship between economic output and the factors of production.

  4. It is worth noting that no a priori assumption in terms of the world frontier (see, for example, Caselli and Coleman 2006) is needed in our approach. Since a different production function is estimated for each country-sector-period, any country-sector-period might be used as benchmark.

  5. We thank an anonymous Associate Editor for helping us think about these issues.

  6. A more detailed description of the database can be found in Rodrik (2013).

  7. The Harberger approach assumes that the initial capital stock is estimated as if the economy was in steady-state in the first period, and thus, output grows at the same rate as the capital stock.

  8. We also used the variable Polity2 (defined as the difference between democracy and autocracy scores) from Polity IV (Marshall et al. 2015) as an alternative to Rule of law given that the time coverage is much longer. Using Polity2 does not yield statistically different results than Rule of law. The results are not reported here, but are available upon request.

  9. A total of 12 observations, marked as outliers by the Billor et al. (2000) algorithm, were dropped at this stage.

  10. In general, the longer the time span, the smaller is the number of countries that can be included, with poorer economies particularly affected.

  11. The three terms in the decomposition, \(\Delta \widetilde{(TEP)}_{t,T}^{cs}\), \(\Delta \widetilde{(K/L)}_{t,T}^{cs}\), and \(\Delta \widehat{(TFP)}_{t,T}^{cs}\), are obtained using the estimated \(\widehat{m}^{cs}_T(\cdot )\) to compute the counterfactual \(m_{T}^{cs}(k_{t}^{cs})\) as the predicted labor productivity based on country c’s time T estimated TEP and time t observed capital per worker.

  12. We thank an anonymous referee for suggesting this exercise.

  13. In principle, IPUMS data on schooling intensity is also available for 2005. However, the two samples are not fully comparable.

  14. Since the decomposition with leader reported in Sect. 6 is formally equivalent to Eq. (3), after re-scaling on US TEP and capital per worker, the robustness checks in this section also apply to the decomposition results of Sect. 6.

  15. We thank an anonymous referee for suggesting this robustness check.

  16. This consideration finds support in aggregate data. For instance, changes at constant prices in Penn World Table 8.0 sum to 147% for output and to 174% for capital. The corresponding numbers in our data are, respectively, 174 and 170%.

  17. We thank an anonymous referee for raising this issue.

  18. Concavity is also a standard assumption for a production function. However, other aspects, such as biased technical change, shift the \(m(\cdot )\) function, which makes concavity in K/L (at fixed technology and human capital levels) difficult to impose/test.

  19. Productivity gaps by sector are available in Online Appendix 2.

  20. Different from usual growth regressions, we use gaps, instead of levels. Alternatively, one might use absolute terms and include the leader shift as an additional regressor.

  21. See Pagan (1984). We obtain qualitatively similar results using jackknife standard errors.

  22. We tried to replace geographic distance with the measure of linguistic distance recently provided by CEPII, but it was never found to be statistically significant or to have a meaningful economic effect on growth. These estimates are available upon request.

  23. A number of other potential variables might be used for this exercise. However, as well as being beyond the scope of our current analysis, this would open the door to the classical growth regression issue of exchangeability (Durlauf 2009). It is however worth noting that we obtain qualitatively similar results using private credit by deposit banks and other financial institutions and the deposit money bank assets ratio as measures of financial development, and using secondary and tertiary enrollment schooling data from Barro and Lee (2013).

  24. In unreported analysis, we verified that even when total trade (not only with the US) is included it is never found to have a statistically or economically significant effect. We also tried to use outward FDI instead of inward flows and found a positive estimated coefficient in the K/L regression and no statistically significant effect in the TEP regression. Outward FDI, while not influencing TEP, acts against capital accumulation and consequently against convergence in labor productivity. These results are available upon request.

References

  • Alvarez, F. E., Buera, F. J., & Lucas, R. E. Jr. (2013). Idea flows, economic growth and trade. NBER working paper no. 19667.

  • Atkinson, A. B., & Stiglitz, J. E. (1969). A new view of technological change. Economic Journal, 79(315), 573–578.

    Article  Google Scholar 

  • Barro, R., & Lee, J.-W. (2013). A new data set of educational attainment in the world, 1950–2010. NBER working paper no. 15902.

  • Barro, R. J., & Sala-i-Martin, X. (1997). Technological diffusion, convergence, and growth. Journal of Economic Growth, 2(1), 1–26.

    Article  Google Scholar 

  • Battisti, M., Di Vaio, G., & Zeira, J. (2013). Global divergence in growth regressions. CEPR discussion working papers DS 9687/2013.

  • Basu, S., & Weil, D. N. (1998). Appropriate technology and growth. Quarterly Journal of Economics, 113, 1025–1054.

    Article  Google Scholar 

  • Beaudry, P., Collard, F., & Green, D. A. (2005). Changes in the world distribution of output per worker, 1960–1988: How a standard decomposition tells an unorthodox story. The Review of Economics and Statistics, 87(4), 741–753.

    Article  Google Scholar 

  • Beck, T., Demirguc-Kunt, A., & Levine, R. (2000). A new database on financial development and structure. World Bank Economic Review, 14, 597–605.

    Article  Google Scholar 

  • Bernard, A. B., & Jones, C. I. (1996a). Technology and convergence. The Economic Journal, 106(437), 1037–1044.

    Article  Google Scholar 

  • Bernard, A. B., & Jones, C. I. (1996b). Comparing apples to oranges: Productivity convergence and measurement across industries and countries. American Economic Review, 86(5), 1216–1238.

    Google Scholar 

  • Billor, N., Hadi, A. S., & Velleman, P. F. (2000). BACON: Blocked adaptive computationally efficient outlier nominators. Computational Statistics & Data Analysis, 34, 279–298.

    Article  Google Scholar 

  • Blinder, A. (1973). Wage discrimination: Reduced form and structural estimates. Journal of Human Resources, 8, 436–455.

    Article  Google Scholar 

  • Bos, J. W. B., Economidou, C., & Koetter, M. (2010a). Technology clubs, R&D and growth patterns: Evidence from EU manufacturing. European Economic Review, 54(1), 60–79.

    Article  Google Scholar 

  • Bos, J. W. B., Economidou, C., Koetter, M., & Kolari, J. W. (2010b). Do all countries grow alike? Journal of Development Economics, 91(1), 113–127.

    Article  Google Scholar 

  • Cameron, G., Proudman, J., & Redding, S. (2005). Technological convergence, R&D, trade and productivity growth. European Economic Review, 49(3), 775–807.

    Article  Google Scholar 

  • Caselli, F. (2005). Accounting for income differences across countries. In P. Aghion & S. Durlauf (Eds.), Handbook of economic growth. North Holland: Elsevier.

    Google Scholar 

  • Caselli, F., & Coleman, W. J. (2006). The world technology frontier. American Economic Review, 96(3), 499–522.

    Article  Google Scholar 

  • Chernozhukov, V., Fernández-Val, I., & Melly, B. (2013). Inference on counterfactual distributions. Econometrica, 81(6), 2205–2268.

    Article  Google Scholar 

  • Ciccone, A., & Papaioannou, E. (2009). Human capital, the structure of production and growth. The Review of Economics and Statistics, 91(1), 66–82.

    Article  Google Scholar 

  • Cohen, D., & Soto, M. (2007). Growth and human capital: Good data, good results. Journal of Economic Growth, 12(1), 51–76.

    Article  Google Scholar 

  • Cohen, D., & Leker, L. (2014). Health and education: Another look with the proper data. New York: Mimeo.

    Google Scholar 

  • Comin, D. A., Dmitriev, M., & Rossi-Hansberg, E. (2012). The spatial diffusion of technology. NBER working papers no. 18534.

  • Crespi, G., & Zuniga, P. (2012). Innovation and productivity: Evidence from six Latin American countries. World Development, 40(2), 273–290.

    Article  Google Scholar 

  • Desmet, K., & Parente, S. L. (2010). Bigger is better: Market size, demand elasticity, and innovation. International Economic Review, 51(2), 319–333.

    Article  Google Scholar 

  • Desmet, K., & Rossi-Hansberg, E. (2014). Spatial development. American Economic Review, 104(4), 1211–1243.

    Article  Google Scholar 

  • Du, P., Parmeter, C. F., & Racine, J. S. (2013). Nonparametric kernel regression with multiple predictors and multiple shape constraints. Statistica Sinica, 23(3), 1347–1371.

    Google Scholar 

  • Durlauf, S. N. (2009). The rise and fall of cross-country growth regressions. History of Political Economy, 41, 315–333.

    Article  Google Scholar 

  • Easterly, W., & Levine, R. (2001). What have we learned from a decade of empirical research on growth? It’s not factor accumulation: Stylized facts and growth models. The World Bank Economic Review, 15(2), 177–219.

    Article  Google Scholar 

  • Färe, R., Grosskopf, S., Norris, M., & Zhang, Z. (1994). Productivity growth, technical progress, and efficiency change in industrialized countries. American Economic Review, 84(1), 66–83.

    Google Scholar 

  • Feenstra, R. C., Inklaar, R., & Timmer, M. P. (2015). The next generation of the Penn world table. American Economic Review, 105(10), 3150–3182.

    Article  Google Scholar 

  • Fiaschi, D., Lavezzi, A. M., & Parenti, A. (2013). On the determinants of distribution dynamics, discussion paper n. 165, Collana di E-papers del Dipartimento di Economia e Management, Universitá di Pisa.

  • Filippetti, A., & Peyrache, A. (2015). Labour productivity and technology gap in European regions: A conditional frontier approach. Regional Studies, 49(4), 532–554.

    Article  Google Scholar 

  • Gerschenkron, A. (1962). Economic backwardness in historical perspective, a book of essays. Cambridge, MA: Belknap Press of Harvard University Press.

    Google Scholar 

  • Harberger, A. C. (1978). Perspectives on capital and technology in less developed countries. In M. J. Artis & A. R. Nobay (Eds.), Contemporary economic analysis. London: Croom-Hel.

    Google Scholar 

  • Henderson, D. J., Papageorgiou, C., & Parmeter, C. F. (2012). Growth empirics without parameters. Economic Journal, 122(559), 125–154.

    Article  Google Scholar 

  • Henderson, D. J., Papageorgiou, C., & Parmeter, C. F. (2013). Who benefits from financial development? New methods, new evidence. European Economic Review, 63, 47–67.

    Article  Google Scholar 

  • Henderson, D. J., & Parmeter, C. F. (2015). Applied nonparametric econometrics. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Henderson, D. J., & Russell, R. R. (2005). Human capital and convergence: A production-frontier approach. International Economic Review, 46(4), 1167–1205.

    Article  Google Scholar 

  • Hurvich, C. M., Simonoff, J. S., & Tsai, C.-L. (1998). Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion. Journal of the Royal Statistical Society, Series B, 60(2), 271–293.

    Article  Google Scholar 

  • Jerzmanowski, M. (2007). Total factor productivity differences: Appropriate technology vs efficiency. European Economic Review, 51, 2080–2110.

    Article  Google Scholar 

  • Jones, C. I. (2003). Population and ideas: A theory of endogenous growth. In P. Aghion, R. Frydman, J. Stiglitz, & M. Woodford (Eds.), Knowledge, information, and expectations in modern macroeconomics. Princeton: Honor of Edmund S. Phelps, Princeton University Press.

    Google Scholar 

  • Jones, C. I. (2005). The shape of production functions and the direction of technical change. Quarterly Journal of Economics, 120(2), 517–549.

    Google Scholar 

  • Knack, S., & Keefer, P. (1995). Institutions and economic performance: Cross country tests using alternative institutional measures. Economics and Politics, 7(3), 207–227.

    Article  Google Scholar 

  • Kumar, S., & Russell, R. R. (2002). Technological change, technological catch-up, and capital deepening: Relative contributions to growth and convergence. American Economic Review, 92(3), 527–548.

    Article  Google Scholar 

  • Li, D., Linton, O., & Lu, Z. (2015). A flexible semiparametric forecasting model for time series. Journal of Econometrics, 187, 345–357.

    Article  Google Scholar 

  • Li, Q., & Racine, J. S. (2004). Cross-validated local linear nonparametric regression. Statistica Sinica, 14, 485–512.

    Google Scholar 

  • Li, Q., & Racine, J. S. (2007). Nonparametric econometrics. Princeton: Princeton University Press.

    Google Scholar 

  • Li, C., Ouyang, D., & Racine, J. S. (2009). Nonparametric regression with weakly dependent data: The discrete and continuous regressor case. Journal of Nonparametric Statistics, 21(6), 697–711.

    Article  Google Scholar 

  • Los, B., & Timmer, M. P. (2005). The appropriate technology explanation of productivity growth differentials: An empirical approach. Journal of Development Economics, 77, 517–531.

    Article  Google Scholar 

  • Maasoumi, E., Racine, J. S., & Stengos, T. (2007). Growth and convergence: A profile of distribution dynamics and mobility. Journal of Econometrics, 136(2), 483–508.

    Article  Google Scholar 

  • Madsen, J. B. (2014). Human capital and the world technology frontier. The Review of Economics and Statistics, 96(4), 676–692.

    Article  Google Scholar 

  • Madsen, J. B., & Timol, I. (2011). Long-run convergence in manufacturing and innovation-based models. The Review of Economics and Statistics, 93(4), 1155–1171.

    Article  Google Scholar 

  • Mankiw, N. G., Romer, D., & Weil, D. N. (1992). A contribution to the empirics of economic growth. Quarterly Journal of Economics, 107, 407–437.

    Article  Google Scholar 

  • Marshall, M. G., Gurr T. R., & Jaggers, K. (2015). Polity IV project: Political regime characteristics and transitions, 1800–2015. Dataset users’ manual. Vienna, VA: Center for Systemic Peace.

  • Nelson, R. R., & Phelps, E. S. (1966). Investment in humans, technological diffusion, and economic growth. American Economic Review, 56(1/2), 69–75.

    Google Scholar 

  • Oaxaca, R. (1973). Male-female wage differentials in urban labor markets. International Economic Review, 14(3), 693–709.

    Article  Google Scholar 

  • Pagan, A. (1984). Econometric issues in the analysis of regressions with generated regressors. International Economic Review, 25(1), 221–247.

    Article  Google Scholar 

  • Parente, S. L., & Prescott, E. C. (1994). Barriers to technology adoption and development. Journal of Political Economy, 102(2), 298–321.

    Article  Google Scholar 

  • Racine, J. S., & Li, Q. (2004). Nonparametric estimation of regression functions with both categorical and continuous data. Journal of Econometrics, 119(1), 99–130.

    Article  Google Scholar 

  • Robinson, P. M. (1983). Nonparametric estimators for time series. Journal of Time Series Analysis, 4(2), 185–207.

    Article  Google Scholar 

  • Rodrik, D. (2011). The future of economic convergence. NBER working papers WP 17400.

  • Rodrik, D. (2013). Unconditional convergence in manufacturing. Quarterly Journal of Economics, 128(1), 165–204.

    Article  Google Scholar 

  • Ruggles, S., Genadek, K., Goeken, R., Grover, J., & Sobek, M. (2015). Integrated Public Use Microdata Series: Version 6.0 [dataset]. Minneapolis: University of Minnesota.

  • Sala-I-Martin, X., Doppelhofer, G., & Miller, R. I. (2004). Determinants of long-term growth: A Bayesian averaging of classical estimates (bace) approach. American Economic Review, 94(4), 813–835.

    Article  Google Scholar 

  • Schelkle, T. (2014). Accounting for convergence between countries. New York: Mimeo.

    Google Scholar 

  • Xia, Y., & Li, W. K. (2002). Asymptotic behavior of bandwidth selected by the cross-validation method for local polynomial fitting. Journal of Multivariate Analysis, 83(2), 265–287.

    Article  Google Scholar 

  • Young, A. (1995). The tyranny of numbers: Confronting the statistical realities of the east Asian growth experience. Quarterly Journal of Economics, 110(3), 641–680.

    Article  Google Scholar 

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Authors and Affiliations

  1. University of Palermo, Palermo, Italy

    Michele Battisti

  2. G.d’Annunzio University and CRENoS, Chieti, Italy

    Massimo Del Gatto

  3. University of Miami, Miami, FL, USA

    Christopher F. Parmeter

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  1. Michele Battisti
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  3. Christopher F. Parmeter
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Correspondence to Christopher F. Parmeter.

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This paper has benefited greatly from comments made by the associate editor, three anonymous referees, and participants at Dynamics, Economic Growth and International Trade (DEGIT), Lima; Structural Change, Dynamics, Economic Growth (SCDEG), Livorno; RCEA Advances in Business Cycles and Economic Growth Analysis, Rimini; and the Cyprus Economics Department Seminar Series. The usual caveat applies.

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Battisti, M., Del Gatto, M. & Parmeter, C.F. Labor productivity growth: disentangling technology and capital accumulation. J Econ Growth 23, 111–143 (2018). https://doi.org/10.1007/s10887-017-9143-1

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