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Empirical Economics

Erschienen in:

04.11.2019

Disaggregate productivity growth sources of regional industries in China

verfasst von: Lan-Bing Li, Cong-Cong Zhang, Jin-Li Hu, Ching-Ren Chiu

Erschienen in: Empirical Economics | Ausgabe 3/2021

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Abstract

This paper extends a global slack-based productivity indicator and constructs a unified framework that consists of global and factor levels of total factor productivity (TFP) to evaluate the performance of regional industries, thus enabling global productivity improvement based on factor-level sources. Evaluating regional industrial performance in China during 1995–2014, the findings reveal that rapid growth of industry in China is not only driven by a huge amount of input, but also by TFP improvement, with industrial productivity driven mainly by technology progress and presenting a gradually increasing trend. Regional productivity performances are imbalanced, in which the east ranks first due to its dual advantages of input and output factors. For source identification, input and output jointly contribute to industrial productivity improvement, but output has a much higher contribution ratio to industrial productivity improvement than input, because it is mainly rooted in desirable output. Finally, on the input side, labor is the primary factor driving input productivity improvement followed by energy, while capital productivity shows very slight growth.

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Since the technology cannot be forgotten, allowing technical regress is a shortcoming of the global approach. The sequential approach can preclude technical regress; for instance, Tulkens and Vanden Eeckaut (1995) point out that with sequential reference sets, only progress can occur. However, the sequential approach grapples with the problem of LP infeasibility, whereas the global approach can appropriately solve it. Therefore, we propose the GSBPI based on the global approach, which can be immune to LP infeasibility, but at the cost of allowing technical regress.

Being similar to many productivity indicators based on nonparametric framework, GSBPI is also a productivity measurement relative to reference production set. Even if the global benchmark technology is employed as the fixed base technology, everything is still related to the reference production set. Different base technologies may affect GSBPI values, but these have little impact on the relative ranking of GSBPI values among all DMU_S, and the positive or negative symbol of GSBPI values, which has no disruptive effect on the main research findings in most cases.

Murty and Kumar (2002) clarify the weak disposability by proposing a distance function approach to measure the cost of environmentally sustainable industrial development in India, where the shadow prices reflect the trade-off between desirable and undesirable outputs. It will be better if the approach in Murty and Kumar (2002) can be incorporated into the GSBPI. However, the output distance function, being on the comprehensive level in Murty and Kumar (2002), cannot be easily extended to the factor-level distance functions, making it difficult to propose the GSBPI for individual inputs and outputs. Moreover, the estimation of shadow prices requires the observed prices of desirable outputs, whereas reliable data are hard to obtain for our study. Therefore, here we adopt the traditional expression of weak disposability of undesirable outputs as shown in Eqs. (4) and (9).

The productivity indicators measure the relative productivity change of production points between $t$ and $t + 1$ periods. The L indicator is defined on two consecutive contemporaneous benchmark technologies as:$L^{s} \left( {x^{t} ,y^{t} ,b^{t} ;x^{t + 1} ,y^{t + 1} ,b^{t + 1} } \right) = D^{s} \left( {x^{t} ,y^{t} ,b^{t} } \right) - D^{s} \left( {x^{t + 1} ,y^{t + 1} ,b^{t + 1} } \right)$, where $s = t, t + 1$, and $D^{s} \left( {x^{t} ,y^{t} ,b^{t} } \right)$ and $D^{s} \left( {x^{t + 1} ,y^{t + 1} ,b^{t + 1} } \right)$ are directional distance functions. $L^{t} \left( {x^{t} ,y^{t} ,b^{t} ;x^{t + 1} ,y^{t + 1} ,b^{t + 1} } \right)$ and $L^{t + 1} \left( {x^{t} ,y^{t} ,b^{t} ;x^{t + 1} ,y^{t + 1} ,b^{t + 1} } \right)$ are defined on t period technology and $t + 1$ period technology, respectively. Since $L^{t} \left( {x^{t} ,y^{t} ,b^{t} ;x^{t + 1} ,y^{t + 1} ,b^{t + 1} } \right) \ne L^{t + 1} \left( {x^{t} ,y^{t} ,b^{t} ;x^{t + 1} ,y^{t + 1} ,b^{t + 1} } \right)$, the L indicator is typically the arithmetic average of $L^{t} \left( {x^{t} ,y^{t} ,b^{t} ;x^{t + 1} ,y^{t + 1} ,b^{t + 1} } \right)$ and $L^{t + 1} \left( {x^{t} ,y^{t} ,b^{t} ;x^{t + 1} ,y^{t + 1} ,b^{t + 1} } \right)$ as follows:

$$L\left( {x^{t} ,y^{t} ,b^{t} ;x^{t + 1} ,y^{t + 1} ,b^{t + 1} } \right) = \frac{1}{2}\left\{ {\left[ {D^{t} \left( {x^{t} ,y^{t} ,b^{t} } \right) - D^{t} \left( {x^{t + 1} ,y^{t + 1} ,b^{t + 1} } \right)} \right] + \left[ {D^{t + 1} \left( {x^{t} ,y^{t} ,b^{t} } \right) - D^{t + 1} \left( {x^{t + 1} ,y^{t + 1} ,b^{t + 1} } \right)} \right]} \right\}$$

Being different from the L indicator based on varying benchmarks, there is only one global benchmark technology, such that GSBPI can be expressed as:

$$\begin{aligned} & L^{G} \left( {x^{t} ,y^{t} ,b^{t} ;x^{t + 1} ,y^{t + 1} ,b^{t + 1} } \right) \\ & \quad = \frac{1}{2}\left\{ {\left[ {D^{G} \left( {x^{t} ,y^{t} ,b^{t} } \right) - D^{G} \left( {x^{t + 1} ,y^{t + 1} ,b^{t + 1} } \right)} \right] + \left[ {D^{G} \left( {x^{t} ,y^{t} ,b^{t} } \right) - D^{G} \left( {x^{t + 1} ,y^{t + 1} ,b^{t + 1} } \right)} \right]} \right\} \\ & \quad = D^{G} \left( {x^{t} ,y^{t} ,b^{t} } \right) - D^{G} \left( {x^{t + 1} ,y^{t + 1} ,b^{t + 1} } \right) \\ \end{aligned}$$

Chongqing was a prefecture-level city in Sichuan before 1997, but has been upgraded to a municipality in 1997. Data for Chongqing and Sichuan after 1997 are combined to maintain statistical consistency with that of the period 1995–1997.

The Heihe–Tengchong Line, which extends from Heihe in the Heilongjiang Province to Tengchong in the Yunan Province in China, marks a striking difference in the distribution of China’s population and economic development. It can also be called the “Hu Line,” since it was proposed by Huanyong Hu, who was a famous Chinese demographer. The area southeast of the Hu Line accounts for approximately 30% of China's total land area, but includes over 90% of China's total population, whereas the area northwest of the Hu Line accounts for over 60% of the total land area, but only includes less than 10% of the total population.

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Titel: Disaggregate productivity growth sources of regional industries in China
verfasst von: Lan-Bing Li
Cong-Cong Zhang
Jin-Li Hu
Ching-Ren Chiu
Publikationsdatum: 04.11.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Empirical Economics / Ausgabe 3/2021
Print ISSN: 0377-7332
Elektronische ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-019-01792-4

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