Abstract
In this chapter, we provide evidence as to the effects of infrastructure provision on aggregate productivity using industry-level data for a set of developed and developing countries over the 1995–2010 period. A distinctive feature of our empirical strategy is that it allows the measurement of intra- and interindustry resource reallocations which are directly attributable to the infrastructure provision. To achieve this objective, we propose a two-level top-down decomposition of labor aggregate productivity that extends the decomposition introduced by Diewert (Journal of Productivity Analysis 43:367–387) using a time-continuous setting.
This research was partially funded by the Government of the Principality of Asturias and the European Regional Development Fund (ERDF). The authors also thank the Oviedo Efficiency Group, and participants at NAPW 2018 in Miami for their valuable comments to an earlier version of this paper.
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Notes
- 1.
Using a Bennet-type symmetric method, the discrete-time counterpart of our continuous-time decomposition in Eq. (3) can be written as:
$$ {\displaystyle \begin{array}{l} \ln \left(\frac{{\mathrm{X}}_t}{{\mathrm{X}}_{t-1}}\right)=\sum \limits_{n=1}^N\frac{s_{Ynt-1}+{s}_{Ynt}}{2}\mathit{\ln}\left(\frac{{\mathrm{X}}_{nt}}{{\mathrm{X}}_{nt-1}}\right)+\sum \limits_{n=1}^N\frac{s_{Ynt-1}+{s}_{Ynt}}{2}\mathit{\ln}\left(\frac{S_{Lnt}}{S_{Lnt-1}}\right)+\sum \limits_{n=1}^N\frac{s_{Ynt-1}+{s}_{Ynt}}{2}\mathit{\ln}\left(\frac{{\mathrm{p}}_{nt}}{{\mathrm{p}}_{nt-1}}\right)\end{array}} $$We also use a Bennet-type symmetric method to get the discrete-time counterparts of all continuous-time decomposition included in this chapter. Notice that the above productivity decomposition looks like a Törnqvist productivity index. It is worth mentioning that we do not need to introduce in the above decomposition the conventional covariance (or second order) term that appears, for instance, in Baily etal. (1992) and Diewert (2015). The covariance-type terms disappear from the productivity decomposition because the Bennet method is symmetric (Balk 2016a). Diewert (2015) proposed a simplified decomposition with only three terms as in (3) by assigning second- and third-order terms to corresponding first-order terms in a symmetric, even handed manner. He points out in footnote #14 that this assignment scheme is similar to that applied by Bennet (1920).
- 2.
In our empirical application, we cannot compute K n for each industry because we do not have the industry volumes for the capital input, just the economy-wide capital level. To address this issue, we will assume that the temporal evolution of K n is the same in all industries or that \( {\dot{K}}_n=\dot{K},\forall n=1,..,N \).
- 3.
As θ n is a combination of a direct effect through the frontier and an indirect effect through the efficiency term, a portion of the within-industry productivity gains captured by WE is caused by a better reallocation of resources between firms, and hence the WE term cannot be completely interpreted as a term capturing pure (e.g., technological) productivity improvements. The pure productivity within effect can be computed and decomposed using (13) but ignoring the inefficiency term (i.e., using γn instead of θ n).
- 4.
The output share of each industry also depends on capital and labor and the time trend. Therefore, they also generate interindustry reallocation effects.
- 5.
Notice that we have ignored in (18) the subscript for country to simply the notation.
- 6.
Notice that this capital variable has been computed using both private and public investments at country level. Therefore, K is likely correlated with our Z variables.
- 7.
In general, we were not able to find a significant effect for human capital as standard input in previous version of this paper. For this reason, we only include this variable as an inefficiency determinant.
References
Amsler, C., Prokhorov, A., & Schmidt, P. (2017). Endogenous environmental variables in stochastic frontier models. Journal of Econometrics, 199, 131–140.
Asturias, J., García-Santana, M., & Ramos, R. (2017). Competition and the welfare gains from transportation infrastructure: Evidence from the Golden Quadrilateral of India. PEDL Research.
Baily, M. N., Hulten, C., & Campbell, D. (1992). Productivity dynamics in manufacturing plants. Brookings Papers on Economic Activity: Microeconomics, 2, 187–249.
Balk, B. M. (2016a). The dynamics of productivity change: A review of the bottom-up approach. In W. H. Greene, L. Khalaf, R. C. Sickles, M. Veall, & M.-C. Voia (Eds.),. Proceedings in business and economics Productivity and efficiency analysis. Cham: Springer International Publishing.
Balk, B. M. (2016b). Aggregate productivity and productivity of the aggregate: Connecting the bottom-up and top-down approaches. Paper prepared for the 34th IARIW General Conference Dresden, Germany, August 21–27, 2016.
Bennet, T. L. (1920). The theory of measurement of changes in cost of living. Journal of the Royal Statistical Society, 83(3), 455–462.
Cornwell, C., Schmidt, P., & Sickles, R. C. (1990). Production frontiers with cross-sectional and time-series variation in efficiency levels. Journal of Econometrics, 46(1–2), 185–200.
Crescenzi, R., & Rodríguez-Pose, A. (2012). Infrastructure and regional growth in the European Union. Papers in Regional Science, 91(3), 487–513.
Diewert, W. E. (2015). Decompositions of productivity growth into sectoral effects. Journal of Productivity Analysis, 43(3), 367–387.
Färe, R., Grosskopft, S., & Margaritis, D. (2008). Efficiency and productivity: Malmquist and more, Chapter 5. In H. Fried, K. Lovell, & S. Schmidt (Eds.), The measurement of productive efficiency and productivity growth. New York: Oxford University Press.
Feenstra, R. C., Inklaar, R., & Timmer, M. P. (2015). The next generation of the Penn World Table. American Economic Review, 105(10), 3150–3182, Available for download at www.ggdc.net/pwt.
Feng, Q., & Wu, G. L. (2018). On the reverse causality between output and infrastructure: The case of China. Economic Modelling, 74, 97–104.
Foster, A. D., & Rosenzweig, M. R. (1992). Information flows and discrimination in labor markets in rural areas in developing countries. World Bank Economic Review, 6(1), 173–203.
Funkhouser, E. (1996). The urban informal sector in Central America: Household survey evidence. World Development, 24(11), 1737–1751.
Greene, W. (2005). Fixed and random effects in stochastic frontier models. Journal of Productivity Analysis, 23(1), 7–32.
Hadri, K. (1999). Estimation of a doubly heteroscedastic stochastic frontier cost function. Journal of Business and Economic Statistics, 17(3), 359–363.
Kumbhakar, S. C., Asche, F., & Tveteras, R. (2013). Estimation and decomposition of inefficiency when producers maximize return to the outlay: An application to Norwegian fishing trawlers. Journal of Productivity Analysis, 40, 307–321.
McMillan, M., Rodrik, D., & Verduzco-Gallo, Í. (2014). Globalization, structural change, and productivity growth, with an update on Africa. World Development, 63, 11–32.
Moscoso-Boedo, H. J., & D’Erasmo, P. N. (2012). Misallocation, informality, and human capital. Virginia economics online papers 401, University of Virginia, Department of Economics.
Qiang, C. Z.-W., & Rossotto, C. M. (2009). Economic impacts of broadband, information and communications for development 2009: Extending reach and increasing impact (pp. 35–50). Washington, DC: World Bank.
Restuccia, D., & Rogerson, R. (2008). Policy distortions and aggregate productivity with heterogeneous establishments. Review of Economic Dynamics, 11(4), 707–720.
Restuccia, D., & Rogerson, R. (2013). Misallocation and productivity. Review of Economic Dynamics, 16(1), 1–10.
Straub, S. (2012). Infrastructure and development: A critical appraisal of the macro-level literature. Journal of Development Studies, 47(05), 683–708.
Vivarelli, M. (2012). Innovation, employment and skills in advanced and developing countries: A survey of the literature. Discussion paper series, Forschungsinstitut zur Zukunft der Arbeit, No. 6291, Institute for the Study of Labor (IZA), Bonn.
Yang, H. (2000). A note on the causal relationship between energy and GDP in Taiwan. Energy Economics, 22(3), 309–317.
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Orea, L., Álvarez-Ayuso, I., Servén, L. (2020). A Two-Level Top-Down Decomposition of Aggregate Productivity Growth: The Role of Infrastructure. In: Aparicio, J., Lovell, C., Pastor, J., Zhu, J. (eds) Advances in Efficiency and Productivity II. International Series in Operations Research & Management Science, vol 287. Springer, Cham. https://doi.org/10.1007/978-3-030-41618-8_11
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