تجزیه و تحلیل رشد بهره وری منطقه ای در چین: یک رویکرد فرا مرزی تعمیم یافته از شاخص بهره وری ملکوئیست

This paper analyzes the dynamics of China's productivity for the period 1996–2004 with a newly developed methodology — generalized metafrontier Malmquist productivity index (gMMPI). Implementing the gMMPI, this paper reviews the inequality of the coastal and non-coastal provinces, as well as the latent impact of scale efficiency change (SEC) for China. Using provincial data for the years 1996–2004, the empirical results are as follows. On average, China demonstrates an annual 3.191% productivity change, which is lower than 4.729% for the conventional MPI and accounts for about 26.508% of output growth over the period 1996–2004. Most of this change is propelled by technical progress, while a fraction is driven by the adjustment in production scale, and the efficiency change has an adverse effect. Furthermore, regional inequality is also found in this empirical work, and the productivity change of the coastal region is actually stronger than that of the non-coastal region. This paper also casts some focus on the China Western Development policy. Indeed, we do not find any outstanding achievement from the policy in the sample period, except that the west region sustained its rate of productivity change after 2000. Moreover, the SEC is found to be trivial in the advanced coastal region, but plays an important role in the relatively laggard non-coastal region. The implication of the positive SEC in the non-coastal region means that China's Western Development policy will improve the scale efficiency and the TFP growth of the west region.

Ever since the initiative of an “open door policy” in the late 1970s, China has witnessed continuously spectacular economic growth, at an average rate of 9.8% over the past three decades. While the change in state policy on international investment and trade has been widely recognized to contribute to the economic growth,1 technological progress is alternatively regarded as the critical ingredient for sustained economic growth and catching up. Is China's remarkable growth driven principally by factor accumulation? Is there nothing miraculous about its growth, as in Young's (1995) critique on East Asian growth? These issues have inspired a boom of empirical studies to assess productivity growth in China. One line of research pays attention to examining economy-wide productivity growth in China by adopting total factor productivity (TFP) as a measure of technological change.2 Using the growth accounting approach, Borensztein and Ostry (1996), Hu and Khan (1997), Fleisher and Chen (1997), Ezaki and Sun (1999), Wang and Yao (2003), and Islam, Dai, and Sakamoto (2006) provided assessments of TFP growth in the post-reform period and found that there was a considerable TFP growth rate in China, between 2.41% and 3.90%, during the post-reform period of 1980 to 1995.3 The accuracy and reliability of the growth accounting approach depend heavily on accurate data and on a complicated calculation process regarding the factor price, while Young (2003) and Islam et al. (2006) strongly warned of the problems in China's national income accounts data. In contrast to the growth accounting approach, a branch of studies is emerging that uses the frontier production approach with a more flexible assumption to production behavior to measure TFP growth in China. Wu (2000) first applied the Malmqist index of TFP growth to examine whether China's economic growth is sustainable and found an increasing trend of TFP growth over the period from 1987 to 1995. Chen (2001) found positive average TFP growth during the period from 1992 to 1999, because technology improvement was found to be a larger component for TFP growth. Zheng and Hu (2006) presented considerable average productivity growth for most of the data periods during 1979 to 2001. In their estimate, the TFP growth figure was 5.34% during the 1980s and 2.60% in the 1990s. However, TFP growth slowed significantly between 1995 and 2001, and the weighted average TFP growth rate during that time was only 1.11%. Shiu and Heshmati (2006) used the panel econometrics estimation approach for measuring provinces' TFP growth during 1993 to 2003. They found a spectacular TFP growth rate of nearly 9% across provinces, but they also identified a general trend for TFP growth decline. When using the MPI approach to analyze China's TFP growth, there is nevertheless a serious limitation to a rigorous study — that is, for a long time the regional inequality between the coastal and non-coastal regions has persistently been a problem inside China's economy,4 which implies that the coastal and non-coastal regions have different production frontiers. In light of this discrepancy, we should estimate separate production frontiers for these different regions. Indeed, production units in different regions face different production opportunities that force them to make choices from different sets of feasible input–output combinations. This difference can be attributed to the available stocks of physical, human, and financial capital, economic infrastructure, resource endowments, and any other characteristics of the physical, social, and economic environment in which production takes place (O'Donnell, Rao, & Battese, 2008). Hence, when using the conventional MPI to estimate the frontiers of the two regions separately to obtain the productivity change, the direct cross-sets comparison is restrained, but it requires a construction of some common function that defines the existing technologies. Fortunately for meeting the requirement, in view of the assumption that all producers in different groups (countries, regions, etc.) have potential access to the same technology, Battese, Rao, and O'Donnell (2004) had proposed a framework of metafrontier production function model. The advantage of this framework is that the cross-sets comparison of production efficiencies measured under different frontiers can be conducted on a common basis of potential technology. O'Donnell et al. (2008) subsequently formally utilized distance functions to define and illustrate the framework of the metafrontier production function, while extending the concept of the metafrontier to the domain measuring the total factor productivity; i.e., the metafrontier Malmquist productivity index (MMPI). Therefore the adoption of the MMPI should have more of a methodological advantage than the conventional MPI for investigating China's TFP growth, since the strong assumption regarding all provinces operating under the same frontier and the limitation in cross-sets comparison could be relaxed. Nonetheless, it is also worth noting that the MMPI is still an incomplete measure in nature, since the potential impact of scale efficiency change (SEC) is not taken into account. From the perspective of industrial economics, the technological frontier of a production unit comprises three fractions: increasing return to scale (IRS), constant return to scale (CRS), and decreasing return to scale (DRS). As long as the used technology has variable return to scale, regardless of IRS or DRS, it invariably implies that there is room to improve the average product (productivity) through adjusting the operating scale. As Ray (1998) argued, even a fully efficient production unit operating on the frontier might not necessarily imply an optimal scale of production. Only the input–output combinations of the CRS level can harmonize the point of maximum average product.5Frisch (1965) termed the operating scale corresponding to the CRS as the technically optimal productive scale (TOPS). Therefore, from a static viewpoint, the scale efficiency refers to a ratio of the potential productivity of the current scale on the frontier (i.e., the productivity level without loss of technical efficiency) to the productivity level of the TOPS. Then, from a dynamic viewpoint, the scale efficiency change can be defined as a cross-period adjustment in scale efficiency toward the TOPS (Coelli, Rao, O'Donnell, & Battese, 2005). Indeed, the problem of a productivity index measure ignoring the effect of SEC has been discussed in the literature. Under the conventional MPI framework, the issue is systematically addressed in both non-parametric and parametric contexts.6 Since the MMPI is a productivity measure extended from the MPI that takes the metafrontier as its basis, it should be noted that the imperfection of MPI by ignoring the effect of the SEC, as criticized in the literature, should also be embedded in the MMPI. This study adopts a generalized MMPI framework (gMMPI), therefore further taking into account the effect of SEC in the MMPI. The main purpose of this article is to provide new empirical evidence on China's regional productivity growth. It attempts to contribute in line with the existing empirical literature by providing the following three distinct types of empirical evidence. First, this study develops a generalized MMPI framework (gMMPI), which further takes into account the effect of SEC in the MMPI to reassess China's regional productivity growth during 1996–2004. Second, the study discusses the extent of productivity change contributing to economic growth as well as the components determining the productivity change. In particular, we assess the contribution of SEC in our model to regional economic growth as an attempt to shed light on regional development policy. Third, the regional (coastal and non-coastal) inequality is widely known as a challenge to the Chinese economy. From the viewpoint of productivity change, we assess whether or not the gap has been harmonized in the past decade. By combining analyses of productivity change on both coastal and non-coastal regions, our results may help to explain the regional disparity between the two regions in China and provide policy implications for regional development. In addition, the article also reviews the attainment of the China Western Development policy in terms of change in productivity and its components. After the introduction, the rest of this paper is organized as follows. Section 2 provides a brief illustration of O'Donnell et al.'s (2008) MMPI, while the gMMPI framework is introduced under the parametric context, further taking into account the effect of adjustment in scale efficiency. Section 3 illustrates the data arrangement and model specifications. Section 4 reports on the empirical analyses, including the estimation of production frontiers and the calculations of the gMMPI. Section 5 then provides a brief review of the China Western Development policy. The final section summarizes concluding remarks and policy implications.

This paper aims to reassess the regional productivity growth in China. To avoid the potential limitation of China's statistical data, as noted in the literature, and taking into account the characteristics of regional inequality between coastal and non-coastal regions inside China, a rigorously tailor-made methodology is needed. First, this paper embraces the concept of a metafrontier production function model as a means for accurately assessing the issue. The reason for this is mainly because the coastal and non-coastal regions of China face a potentially common metafrontier regardless of the intuitive, theoretical and empirical angles that would assign them to different technology sets and distinct group frontiers. Here, the underlying background is due to the fact that the economic infrastructure and the characteristics of the production environment in the two regions are widely recognized as heteroskedastic. Nonetheless, from a more long-term perspective, it is possible that the technology gaps might be lessened through designing programs to improve the economic infrastructure and/or to refine the characteristics of the production environment. Furthermore, this paper extends O'Donnell et al.'s (2008) metafrontier Malmquist productivity index (MMPI) approach and then develops a framework of generalized MMPI (gMMPI) for meeting the requirement. We impose the regular conditions of a well-constructed productivity index on the MMPI to rectify the potential deficiency that the MMPI does not have the property of proportionality. The major operation is to use the distance elasticity share of inputs to substitute the distance elasticity of inputs for ensuring the weight of cross-period change in inputs of the productivity measure being one to conform to the condition of homogenous of degree 1. This operation has the advantage of developing the MMPI to become a more theoretically rigorous construction. Further, we induct the revised MMPI mathematically and decompose a component of scale efficiency change from it, in addition to the other two components, technical efficiency change and technical change. The scale efficiency change gauges the extent of cross-periods adjustment of production scales toward the technically optimal productive size. Therefore the gMMPI framework is a relatively theoretically complete and rigorous productivity index measure, which gives us the advantage of being able to assess the regional productivity growth and its inclusions in China while considering the characteristic of regional inequality between the coastal and non-coastal regions inside China. Our empirical analysis shows that on the surface of output growth, the ingredient of productivity gains indeed supports China's striking economic performance. The TFP growth is about 3.191%, which accounts for about 26.508% of annual output growth over the period from 1996 to 2004. Second, from the introduction of the metafrontier-based methodology, the productivity change should comprise three terms: technical efficiency change, technical and scale efficiency changes; while more of the change is propelled by technical progress (3.395%), and a fraction is driven by the adjustment in production scale (0.399%) and the efficiency change has an adverse effect (− 0.569%). We also ask whether the regional inequality in terms of productivity was harmonized in the past decade. We find that the margin of productivity change in the coastal region seems to shrink more even though growth remained high. Relatively, the western region can sustain its growth despite an absolute low figure. The relative productivity slow-down in the provinces of the coastal region may be due to the natural process of convergence, as discussed in Wu (2000). This paper also focuses on the China Western Development policy. Indeed, it can be inferred from this study that the Western Development policy has not significantly manifested a benefit on the dimension of TFP in the sample period of 2000 to 2004. However, our results show that the rate of productivity change is sustained for the western region after 2000. Indeed, it is not unusual to obtain such results, since the productivity growth is a concept utilizing a long-term view and there might not have been enough time yet to demonstrate the attainment of the policy. In its initial stage, the Western Development policy places the most focus on the construction of infrastructure, and it is therefore not easy to find significant achievement in the short term. Finally, an alternative issue introduced from our empirical analysis is also noteworthy. In the past, factor accumulation was usually criticized as having no implication in productivity growth. However, we would like to indicate that it is not necessarily the case for all the development phases. Rather, once the usable technology is matched up, the factor accumulation still contributes to productivity. Perhaps the position of factor accumulation is trivial from the perspective of an advanced economy, but for emerging economies in the initial development stage, the scale adjustment plays an important and non-negligible role on TFP growth. Some key economic policy implications can be taken from the results. While China's productivity growth has slowed since the mid-1990s, it is still a spectacular performance when compared to Asian developing and advanced countries. From the perspective of sustainable growth, how to ease the decreasing trend of productivity growth is a critical issue. China should therefore put more effort into innovative activity as well as into protecting intellectual property rights to promote economic growth. Moreover, as indicated in the above discussion, there are differences in productivity growth and its composition between coastal and non-coastal regions. This implies that the government should formulate a specific development policy for the inland region to reduce the income inequality between coastal and non-coastal regions. Essentially, the “China Western Development” policy is moving in the correct direction by enabling the western regions to have actually experienced good performance in economic growth in the short term. More importantly, as is also echoed in the recent critical literature of Fleisher et al. (2009) concerning China's regional disparity, the government should carry out some policies to promote persistent TFP growth for the inland regions, such as promoting human resources and technological capability.