Skip to main content
Top
Published in: Empirical Economics 6/2021

06-08-2020

Estimation of firm productivity in the presence of spillovers and common shocks

Authors: Shunan Zhao, Man Jin, Subal C. Kumbhakar

Published in: Empirical Economics | Issue 6/2021

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Productivity is largely estimated ignoring the potential impact of spillovers and common shocks in the literature, and therefore, the estimates may be subject to the omitted variable bias and internal inconsistency. In this paper, we estimate a nonparametric production function, in which technology spillovers and common shocks have persistent effects on productivity and are controlled for through spatial networks and a factor structure in the productivity evolution process. We synthesize the proxy variable method to structurally identifying the production functions using the semiparametric common correlated effect estimator. The proposed model is then applied to the Chinese computer and peripheral equipment firms. We find that the annual productivity growth rate in this high-technology sector is about 15%. While firms are cross-sectionally dependent via both spatial and non-spatial connections, the productivity growth is largely explained by firms’ own effort, and mildly explained by the neighbors’ activities. Productivity is found to be higher in the areas of agglomeration, and the common shock effects on productivity are not necessarily correlated with the spatial variables.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Appendix
Available only for authorised users
Footnotes
1
Note that we deviate from Gandhi et al. (2017) in this second step. Specifically, when deriving Eq. (2.14), we substitute for \(\omega _{it-1}\) using the inverted material demand, whereas Gandhi et al. (2017) proxies for \(\omega _{it-1}\) using \({\mathscr {Y}}_{it-1} +\phi _{t-1}(K_{it-1}, L_{it-1})\). We deviate in this way to avoid estimating a nonparametric sieve approximation within another sieve approximation. As a consequence, our procedure involves more unknown parameters to be estimated because the unknown inverted material demand has 3 additional variables.
 
2
When the nonparametric functions we approximate have subscript t, which means they are changing over time such as the function \(\beta _{Mt}(K_{it}, L_{it}, M_{it})\varLambda \), a time trend variable is added as an additional regressor, although it is not explicitly written.
 
3
The time averages of the z variables are excluded from \(h_{t-1}\) to avoid potential collinearity, because \(z_{it-1}\) and \({\bar{z}}_{-i,t-1}\) have already been included on the right-hand side of the estimated equation (see eq. 3.4).
 
4
The numbers of the entire manufacturing sector comes from Jin et al. (2019).
 
5
We use the “haversine” formula to calculate the shortest distance between two points, which assumes a spherical earth and ignores ellipsoidal effects. Haversine formula is implemented in R programming in the package “geosphere”.
 
6
To compare with the productivity of the entire manufacturing sector in China, we refer to the findings by Malikov et al. (2020).
 
7
The Pearl River Delta is located in the south of China. It is one of the most densely urbanized regions in the world. This metropolitan region includes Guangdong province, Hong Kong and Macau. Its dominant language is Cantonese. It has a population of about 60 million. The Yangtze River Delta is located in the middle east of China. This metropolitan region includes Shanghai, southern Jiangsu province and northern Zhejiang province. Its dominant language is Mandarin. It has a population of about 115 millions.
 
8
In Fig. 6a, we normalize the factors by making their average 100 in the year of 1999.
 
Literature
go back to reference Ackerberg DA, Caves K, Frazer G. Identification properties of recent production function estimators. Econometrica. 2015;83(6):2411–51.CrossRef Ackerberg DA, Caves K, Frazer G. Identification properties of recent production function estimators. Econometrica. 2015;83(6):2411–51.CrossRef
go back to reference Bai J. Inferential theory for factor models of large dimensions. Econometrica. 2003;71(1):135–71.CrossRef Bai J. Inferential theory for factor models of large dimensions. Econometrica. 2003;71(1):135–71.CrossRef
go back to reference Bai J. Panel data models with interactive fixed effects. Econometrica. 2009;77(4):1229–79.CrossRef Bai J. Panel data models with interactive fixed effects. Econometrica. 2009;77(4):1229–79.CrossRef
go back to reference Bailey N, Holly S, Pesaran MH. A two stage approach to spatiotemporal analysis with strong and weak cross-sectional dependence. J Appl Econ. 2016;31(1):249–80.CrossRef Bailey N, Holly S, Pesaran MH. A two stage approach to spatiotemporal analysis with strong and weak cross-sectional dependence. J Appl Econ. 2016;31(1):249–80.CrossRef
go back to reference Bailey N, Kapetanios G, Pesaran MH. Exponent of cross-sectional dependence: estimation and inference. J Appl Econom. 2015;31(6):929–60.CrossRef Bailey N, Kapetanios G, Pesaran MH. Exponent of cross-sectional dependence: estimation and inference. J Appl Econom. 2015;31(6):929–60.CrossRef
go back to reference Baltagi BH, Egger PH, Kesina M. Firm-level productivity spillovers in China’s chemical industry: a spatial Hausman–Taylor approach. J Appl Econom. 2016;31(1):214–48.CrossRef Baltagi BH, Egger PH, Kesina M. Firm-level productivity spillovers in China’s chemical industry: a spatial Hausman–Taylor approach. J Appl Econom. 2016;31(1):214–48.CrossRef
go back to reference Chudik A, Pesaran MH, Tosetti E. Weak and strong cross-section dependence and estimation of large panels. Econom J. 2011;14(1):C45–90.CrossRef Chudik A, Pesaran MH, Tosetti E. Weak and strong cross-section dependence and estimation of large panels. Econom J. 2011;14(1):C45–90.CrossRef
go back to reference Ciccone A, Hall R. Productivity and the density of economic activity. Am Econ Rev. 1996;86(1):54–70. Ciccone A, Hall R. Productivity and the density of economic activity. Am Econ Rev. 1996;86(1):54–70.
go back to reference Coe DT, Helpman E. International R&D spillovers. Eur Econ Rev. 1995;39(5):859–87.CrossRef Coe DT, Helpman E. International R&D spillovers. Eur Econ Rev. 1995;39(5):859–87.CrossRef
go back to reference Coricelli F, Driffield N, Pal S, Roland I. When does leverage hurt productivity growth? A firm level analysis. J Int Money Finance. 2012;31:1674–94.CrossRef Coricelli F, Driffield N, Pal S, Roland I. When does leverage hurt productivity growth? A firm level analysis. J Int Money Finance. 2012;31:1674–94.CrossRef
go back to reference Craven P, Wahba G. Smoothing noisy data with spline functions. Numer Math. 1979;31(4):377–403.CrossRef Craven P, Wahba G. Smoothing noisy data with spline functions. Numer Math. 1979;31(4):377–403.CrossRef
go back to reference Dai M, Maitra M, Yu M. Unexceptional exporter performance in China? The role of processing trade. J Dev Econ. 2016;121:177–89.CrossRef Dai M, Maitra M, Yu M. Unexceptional exporter performance in China? The role of processing trade. J Dev Econ. 2016;121:177–89.CrossRef
go back to reference De Loecker J. Detecting learning by exporting. Am Econ J Microecon. 2013;5(3):1–21.CrossRef De Loecker J. Detecting learning by exporting. Am Econ J Microecon. 2013;5(3):1–21.CrossRef
go back to reference Doraszelski U, Jaumandreu J. R&D and productivity: estimating endogenous productivity. Rev Econ Stud. 2013;80:1338–83.CrossRef Doraszelski U, Jaumandreu J. R&D and productivity: estimating endogenous productivity. Rev Econ Stud. 2013;80:1338–83.CrossRef
go back to reference Eberhardt M, Helmers C, Strauss H. Do spillovers matter when estimating private returns to R&D? Rev Econ Stat. 2013;95(2):436–48.CrossRef Eberhardt M, Helmers C, Strauss H. Do spillovers matter when estimating private returns to R&D? Rev Econ Stat. 2013;95(2):436–48.CrossRef
go back to reference Ertur C, Musolesi A. Weak and strong cross-sectional dependence: a panel data analysis of international technology diffusion. J Appl Econom. 2017;32:477–503.CrossRef Ertur C, Musolesi A. Weak and strong cross-sectional dependence: a panel data analysis of international technology diffusion. J Appl Econom. 2017;32:477–503.CrossRef
go back to reference Fons-Rosen C, Kalemli-Ozcan S, Sorensen BE, Villegas-Sanchez VVC. Foreign investment and domestic productivity: identifying knowledge spillovers and competition effects. Cambridge: National Bureau of Economic Research; 2017.CrossRef Fons-Rosen C, Kalemli-Ozcan S, Sorensen BE, Villegas-Sanchez VVC. Foreign investment and domestic productivity: identifying knowledge spillovers and competition effects. Cambridge: National Bureau of Economic Research; 2017.CrossRef
go back to reference Gandhi A, Navarro S, Rivers D. On the identification of production functions: how heterogeneous is productivity? J Polit Econ 2017; (forthcoming). Gandhi A, Navarro S, Rivers D. On the identification of production functions: how heterogeneous is productivity? J Polit Econ 2017; (forthcoming).
go back to reference Glass AJ, Kenjegalieva K. A spatial productivity index in the presence of efficiency spillovers: evidence for U.S. banks, 1992–2015. Eur J Oper Res. 2019;273(3):1165–79.CrossRef Glass AJ, Kenjegalieva K. A spatial productivity index in the presence of efficiency spillovers: evidence for U.S. banks, 1992–2015. Eur J Oper Res. 2019;273(3):1165–79.CrossRef
go back to reference Glass AJ, Kenjegalieva K, Sickles RC. A spatial autoregressive stochastic frontier model for panel data with asymmetric efficiency spillovers. J Econom. 2016;190(2):289–300.CrossRef Glass AJ, Kenjegalieva K, Sickles RC. A spatial autoregressive stochastic frontier model for panel data with asymmetric efficiency spillovers. J Econom. 2016;190(2):289–300.CrossRef
go back to reference Gonçalves S, Perron B. Bootstrapping factor models with cross sectional dependence. J Econom. 2020; (forthcoming). Gonçalves S, Perron B. Bootstrapping factor models with cross sectional dependence. J Econom. 2020; (forthcoming).
go back to reference Guariglia A, Liu X, Song L. Internal finance and growth: microeconometric evidence on Chinese firms. J Dev Econ. 2011;96(1):79–94.CrossRef Guariglia A, Liu X, Song L. Internal finance and growth: microeconometric evidence on Chinese firms. J Dev Econ. 2011;96(1):79–94.CrossRef
go back to reference Holly S, Pesaran MH, Yamagata T. A spatio-temporal model of house prices in the USA. J Econom. 2010;158(1):160–73.CrossRef Holly S, Pesaran MH, Yamagata T. A spatio-temporal model of house prices in the USA. J Econom. 2010;158(1):160–73.CrossRef
go back to reference Hou Z, Jin M, Kumbhakar SC. Productivity spillovers and human capital: a semiparametric varying coefficient approach. Eur J Oper Res. 2020. Hou Z, Jin M, Kumbhakar SC. Productivity spillovers and human capital: a semiparametric varying coefficient approach. Eur J Oper Res. 2020.
go back to reference Jin M, Zhao S, Kumbhakar SC. Financial constraints and firm productivity: evidence from Chinese manufacturing. Eur J Oper Res. 2019;275(3):1139–56.CrossRef Jin M, Zhao S, Kumbhakar SC. Financial constraints and firm productivity: evidence from Chinese manufacturing. Eur J Oper Res. 2019;275(3):1139–56.CrossRef
go back to reference Kahle D, Wickham H. ggmap: spatial visualization with ggplot2. R J. 2013;5(1):144–61.CrossRef Kahle D, Wickham H. ggmap: spatial visualization with ggplot2. R J. 2013;5(1):144–61.CrossRef
go back to reference Kapetanios G, Pesaran MH, Yamagata T. Panels with non-stationary multifactor error structures. J Econom. 2011;160(2):326–48.CrossRef Kapetanios G, Pesaran MH, Yamagata T. Panels with non-stationary multifactor error structures. J Econom. 2011;160(2):326–48.CrossRef
go back to reference Kelejian HH, Prucha IR. A generalized moments estimator for the autoregressive parameter in a spatial model. Int Econ Rev. 1999;40(2):509–33.CrossRef Kelejian HH, Prucha IR. A generalized moments estimator for the autoregressive parameter in a spatial model. Int Econ Rev. 1999;40(2):509–33.CrossRef
go back to reference Keller W. Geographic localization of international technology diffusion. Am Econ Rev. 2002;92:120–42.CrossRef Keller W. Geographic localization of international technology diffusion. Am Econ Rev. 2002;92:120–42.CrossRef
go back to reference Keller W. International technology diffusion. J Econ Lit. 2004;42(3):752–82.CrossRef Keller W. International technology diffusion. J Econ Lit. 2004;42(3):752–82.CrossRef
go back to reference Lall SV, Shalizi Z, Deichmann U. Agglomeration economies and productivity in Indian industry. J Dev Econ. 2004;73(2):643–73.CrossRef Lall SV, Shalizi Z, Deichmann U. Agglomeration economies and productivity in Indian industry. J Dev Econ. 2004;73(2):643–73.CrossRef
go back to reference Levinsohn J, Petrin A. Estimating production functions using inputs to control for unobservables. Rev Econ Stud. 2003;70(2):317–41.CrossRef Levinsohn J, Petrin A. Estimating production functions using inputs to control for unobservables. Rev Econ Stud. 2003;70(2):317–41.CrossRef
go back to reference Li Q, Racine JS. Nonparametric econometrics: theory and practice. Princeton: Princeton University Press; 2007. Li Q, Racine JS. Nonparametric econometrics: theory and practice. Princeton: Princeton University Press; 2007.
go back to reference Lu D. Exceptional exporter performance? Evidence from Chinese manufacturing firms. Working Paper, University of Chicago; 2010. Lu D. Exceptional exporter performance? Evidence from Chinese manufacturing firms. Working Paper, University of Chicago; 2010.
go back to reference Lu J, Lu Y, Tao Z. Exporting behavior of foreign affiliates: theory and evidence. J Int Econ. 2010;81:197–205.CrossRef Lu J, Lu Y, Tao Z. Exporting behavior of foreign affiliates: theory and evidence. J Int Econ. 2010;81:197–205.CrossRef
go back to reference Ma Y, Tang H, Zhang Y. Factor intensity, product switching, and productivity: evidence from Chinese exporters. J Int Econ. 2014;92(2):349–62.CrossRef Ma Y, Tang H, Zhang Y. Factor intensity, product switching, and productivity: evidence from Chinese exporters. J Int Econ. 2014;92(2):349–62.CrossRef
go back to reference Malikov E, Sun Y. Semiparametric estimation and testing of smooth coefficient spatial autoregressive models. J Econom. 2017;199(1):12–34.CrossRef Malikov E, Sun Y. Semiparametric estimation and testing of smooth coefficient spatial autoregressive models. J Econom. 2017;199(1):12–34.CrossRef
go back to reference Malikov E, Zhao S, Kumbhakar SC. Estimation of firm-level productivity in the presence of exports: evidence from China’s manufacturing. J Appl Econom. 2020; (forthcoming). Malikov E, Zhao S, Kumbhakar SC. Estimation of firm-level productivity in the presence of exports: evidence from China’s manufacturing. J Appl Econom. 2020; (forthcoming).
go back to reference Moll B. Productivity losses from financial frictions: can self-financing undo capital misallocation? Am Econ Rev. 2014;104(10):3186–221.CrossRef Moll B. Productivity losses from financial frictions: can self-financing undo capital misallocation? Am Econ Rev. 2014;104(10):3186–221.CrossRef
go back to reference Musolesi A. Basic stocks of knowledge and productivity: further evidence from the hierarchical bayes estimator. Econ Lett. 2007;95:54–9.CrossRef Musolesi A. Basic stocks of knowledge and productivity: further evidence from the hierarchical bayes estimator. Econ Lett. 2007;95:54–9.CrossRef
go back to reference Olley GS, Pakes A. The dynamics of productivity in the telecommunications equipment industry. Econometrica. 1996;64(6):1263–97.CrossRef Olley GS, Pakes A. The dynamics of productivity in the telecommunications equipment industry. Econometrica. 1996;64(6):1263–97.CrossRef
go back to reference Ord J. Estimation methods for models of spatial interaction. J Am Stat Assoc. 1975;70:120–6.CrossRef Ord J. Estimation methods for models of spatial interaction. J Am Stat Assoc. 1975;70:120–6.CrossRef
go back to reference Pesaran MH. General diagnostic tests for cross section dependence in panels. Cambridge Working Papers in Economics No. 0435. 2004. Pesaran MH. General diagnostic tests for cross section dependence in panels. Cambridge Working Papers in Economics No. 0435. 2004.
go back to reference Pesaran MH. Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica. 2006;74(4):967–1012.CrossRef Pesaran MH. Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica. 2006;74(4):967–1012.CrossRef
go back to reference Pesaran MH. Testing weak cross-sectional dependence in large panels. Econom Rev. 2015;34:1089–117.CrossRef Pesaran MH. Testing weak cross-sectional dependence in large panels. Econom Rev. 2015;34:1089–117.CrossRef
go back to reference Pesaran MH, Tosetti E. Large panels with common factors and spatial correlation. J Econom. 2011;161(2):182–202.CrossRef Pesaran MH, Tosetti E. Large panels with common factors and spatial correlation. J Econom. 2011;161(2):182–202.CrossRef
go back to reference Serpa JC, Krishnan H. The impact of supply chains on firm-level productivity. Manag Sci. 2018;64(2):511–32.CrossRef Serpa JC, Krishnan H. The impact of supply chains on firm-level productivity. Manag Sci. 2018;64(2):511–32.CrossRef
go back to reference Su L, Jin S. Sieve estimation of panel data models with cross section dependence. J Econom. 2012;169(1):34–47.CrossRef Su L, Jin S. Sieve estimation of panel data models with cross section dependence. J Econom. 2012;169(1):34–47.CrossRef
go back to reference Triplett J. The Solow productivity paradox: what do computers do to productivity? Can J Econ. 1999;32(2):309–34.CrossRef Triplett J. The Solow productivity paradox: what do computers do to productivity? Can J Econ. 1999;32(2):309–34.CrossRef
go back to reference Vidoli F, Canello J. Controlling for spatial heterogeneity in nonparametric efficiency models: an empirical proposal. Eur J Oper Res. 2016;249(2):771–83.CrossRef Vidoli F, Canello J. Controlling for spatial heterogeneity in nonparametric efficiency models: an empirical proposal. Eur J Oper Res. 2016;249(2):771–83.CrossRef
go back to reference Zhao S, Liu R, Shang Z. Statistical inference on panel data models: a kernel ridge regression method. J Bus Econ Stat. 2019; (forthcoming). Zhao S, Liu R, Shang Z. Statistical inference on panel data models: a kernel ridge regression method. J Bus Econ Stat. 2019; (forthcoming).
Metadata
Title
Estimation of firm productivity in the presence of spillovers and common shocks
Authors
Shunan Zhao
Man Jin
Subal C. Kumbhakar
Publication date
06-08-2020
Publisher
Springer Berlin Heidelberg
Published in
Empirical Economics / Issue 6/2021
Print ISSN: 0377-7332
Electronic ISSN: 1435-8921
DOI
https://doi.org/10.1007/s00181-020-01922-3

Other articles of this Issue 6/2021

Empirical Economics 6/2021 Go to the issue

Premium Partner