nach oben

Empirical Economics

Erschienen in:

08.12.2023

Is output growth of Chinese manufacturing firms input or productivity driven? A flexible production function approach with endogenous inputs

verfasst von: Subal C. Kumbhakar, Mingyang Li

Erschienen in: Empirical Economics | Ausgabe 4/2024

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

In this paper, we focus on estimating output growth and its components attributed to quasi-fixed inputs, variable inputs, and productivity change, while treating variable inputs as endogenous. We consider two approaches. In the classical approach the time trend variable proxies for technical (productivity) change, while in the productivity (proxy variable) approach, we add a “productivity” term which is correlated with the variable inputs. Therefore, estimation strategies in the two models are different. Instead of using a Cobb–Douglas production function, we employ a translog production function to add flexibility. We showcase our theoretical results with Chinese manufacturing as an empirical exercise.

Vorheriger Artikel Migration and economic activity at origin: the role of female household headship in rural Bangladesh

Nächster Artikel A semiparametric alternative to the Heckman correction: application with left-censored data on parental transfers

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

Jetzt informieren

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!

Jetzt informieren

Nur mit Berechtigung zugänglich

Endogeneity is addressed while using either the cost and profit functions. It can also be addressed in a system consisting of the production function and the first-order conditions of cost minimization/profit maximization. Note that we are not referring to production models with inefficiency which is not included here. This is because we are considering a modeling approach that includes productivity (instead of inefficiency).

This is often attributed as total factor productivity, the calculation of which uses factor shares in the total cost. Note that the TFP computed this way does not require any econometric estimation.

It is possible to treat all the inputs as variable in this formulation.

To make things comparable across the two models, we treat M as endogenous although the reason for their endogeneity is different.

The parameter \(\theta \) is not identifiable or separable from \(\varepsilon _{M, it}\) which contains an intercept term. For example, if the production function is Cobb–Douglas, \(\varepsilon _{M, it} = \beta _M\) which is a constant and it cannot be separated from the other constant \(\theta \) either in level or in log.

Note that we added subsidy, s, in the Markov process because we use it in our application. However, in principle any variable can be used in the \(g(\cdot )\) function. For example, R &D, FDI, etc., are policy variables that can be used in \(g(\cdot )\). We used s instead of R &D and FDI because these variables were either not available or mostly missing in our data set.

One can argue that treating \(h(\cdot )\) as a fully non-parametric function ignores the parametric relation (\(R(t-1)-TL(t-1,\beta _2)\)) inside the non-parametric function \(h(\cdot )\). However, this is not the case. Since \(R(t-1)-TL(t-1,\beta _2)\) has unknown parameters (\(\beta _2\)) in it, we cannot include it directly in the \(g(\cdot )\) function as a variable. That is, if we denote, \(q_{it-1} = R(t-1)-TL(t-1,\beta _2)\), and write \(g(\cdot )\) as \(g(q_{it-1}, s_{it})\) we cannot use it in estimation because \(q_{it-1}\) cannot be treated as a known variable (like \(s_{it}\)) which is necessary to consider \(g(q_{it-1}, s_{it})\) as a non-parametric function.

This is also called the power series. For instance, \(\text {poly}_2(m_{it-1}, k_{it-1}, l_{it-1}, s_{it})\) is \( \text {poly}_2(m_{it-1},\) \(k_{it-1},\) \(l_{it-1},\) \(s_{it}) =\) \((1, m_{it-1}, k_{it-1},\) \(l_{it-1}, s_{it},\) \(m_{it-1}^2, k_{it-1}^2,\) \(l_{it-1}^2, s_{it}^2,\) \(m_{it-1}k_{it-1}, m_{it-1}l_{it-1},\) \(m_{it-1}s_{it},\) \(k_{it-1}l_{it-1}, \) \(k_{it-1}s_{it},\) \(l_{it-1}s_{it}).\)

We choose k separately for each of the industries in our empirical application.

Output and inputs are frequently measured in monetary units instead of physical units and we do the same here. Following the literature (e.g., Levinsohn and Petrin 2003; Doraszelski and Jaumandreu 2013; Ackerberg et al. 2015), we assume perfect competition and homogeneous prices in the input and output markets, which allows using the deflators to convert the monetary units into physical units for all the inputs and output.

Note that we aggregate the values for all firms, all years, for each industry. To give some information about the variations, we report the quartile values. The boxplots of the components for each industry give more information. We report these results only for 4 industries to save space. Results for other industries are provided in “Appendix B”.

Balk (2014, 2016) discusses several decomposition methods, including the Olley-Pakes method discussed here, to get an aggregate productivity measure using a bottom-up approach. His decompositions distinguish between continuing, exiting, and entering firms. He also discussed the effect of using industry-level deflators instead of firm-level deflators on the estimation of production functions and productivity change. However, he does not talk about econometric estimation of production functions.

Ackerberg DA, Caves K, Frazer G (2015) Identification properties of recent production function estimators. Econometrica 83(6):2411–2451MathSciNetCrossRef

Balk BM (2014) Dissecting aggregate output and labour productivity change. J Prod Anal 42(1):35–43CrossRef

Balk BM (2016) The dynamics of productivity change: a review of the bottom-up approach. In: Greene WH, Khalaf L, Sickles R, Veall M, Voia M-C (eds) Productivity and efficiency analysis. Springer, Cham, pp 15–49CrossRef

Bates DM, Watts DG (1988) Nonlinear regression: iterative estimation and linear approximations. In: Nonlinear regression analysis and its applications. Wiley, pp 32–66

Bates DM, Chambers JM (1992) Nonlinear models. In: Statistical models, vol 10. Springer

Brandt L, Van Biesebroeck J, Zhang Y (2012) Creative accounting or creative destruction? Firm-level productivity growth in Chinese manufacturing. J Dev Econ 97(2):339–351CrossRef

Cai H, Liu Q (2009) Competition and corporate tax avoidance: evidence from Chinese industrial firms. Econ J 119(537):764–795CrossRef

Chen X (2007) Large sample sieve estimation of semi-nonparametric models. In: Heckman J, Leamer E (eds) Handbook of econometrics, vol 6. Elsevier, pp 5549–5632CrossRef

Cowing TG, Stevenson R (1981) Productivity measurement in regulated industries. Academic, New York

Craven P, Wahba G (1979) Estimating the correct degree of smoothing by the method of generalized cross-validation. Numer Math 31:377–403CrossRef

Diewert WE (1981) The theory of total factor productivity measurement in regulated industries. In: Cowing T, Stephenson R (eds) Productivity measurement in regulated industries. Academic Press Inc, New York, pp 17–44

Doraszelski U, Jaumandreu J (2013) R &D and productivity: estimating endogenous productivity. Rev Econ Stud 80(4):1338–1383CrossRef

Flynn Z (2020) Identifying productivity when it is a factor of production. RAND J Econ 51(2):496–530CrossRef

Fuss M, McFadden DL (eds) (1978) Production economics: a dual approach to theory and applications. North-Holland, Amsterdam

Gandhi A, Navarro S, Rivers DA (2020) On the identification of gross output production functions. J Polit Econ 128(8):2973–3016CrossRef

Hastie TJ, Tibshirani RJ (1990) Generalized additive models, vol 43. CRC Press

Jorgenson D (ed) (2009) The economics of productivity. Edward Elgar Publishing

Kumbhakar SC (2021) Modeling technical change: theory and practice. In: Ray SC, Chambers RG, Kumbhakar SC (eds) Handbook of production economics, vol 1. Springer Nature, Singapore, pp 1–53

Levinsohn J, Petrin A (2003) Estimating production functions using inputs to control for unobservables. Rev Econ Stud 70(2):317–341CrossRef

Malikov E, Zhao S, Kumbhakar SC (2020) Estimation of firm-level productivity in the presence of exports: evidence from China’s manufacturing. J Appl Econ 35:457–480MathSciNetCrossRef

Moré JJ (1978) The Levenberg–Marquardt algorithm: implementation and theory. In: Watson GA (ed) Numerical analysis. Springer, pp 105–116CrossRef

Mundlak Y (1961) Empirical production function free of management bias. Am J Agr Econ 43(1):44–56

Olley GS, Pakes A (1996) The dynamics of productivity in the telecommunications equipment industry. Econometrica 64(6):1263–1297CrossRef

Solow RM (1956) A contribution to the theory of economic growth. Q J Econ 70(1):65–94CrossRef

Solow RM (1957) Technical change and the aggregate production function. Rev Econ Stat 39(3):312–320MathSciNetCrossRef

Yu M (2015) Processing trade, tariff reductions and firm productivity: evidence from Chinese firms. Econ J 125(585):943–988CrossRef

Titel: Is output growth of Chinese manufacturing firms input or productivity driven? A flexible production function approach with endogenous inputs
verfasst von: Subal C. Kumbhakar
Mingyang Li
Publikationsdatum: 08.12.2023
Verlag: Springer Berlin Heidelberg
Erschienen in: Empirical Economics / Ausgabe 4/2024
Print ISSN: 0377-7332
Elektronische ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-023-02505-8

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 4/2024

Nonlinear responses of crude oil prices to the US dollar exchange rates: the role of inventories

Uncertainty and long-run economy: the role of R &D and business dynamism

Direct and indirect effects of regional economic stimuli

The evolution of the natural rate of interest: evidence from the Scandinavian countries

A semiparametric alternative to the Heckman correction: application with left-censored data on parental transfers

Migration and economic activity at origin: the role of female household headship in rural Bangladesh

Premium Partner