Factor Productivity In China Research Articles

Spatial-Temporal Characteristics of Agriculture Green Total Factor Productivity in China, 1998-2016: Based on More Sophisticated Calculations of Carbon Emissions.

Environmental costs should be taken into account when measuring the achievements of China’s agricultural development, since the long-term extensive development of agriculture has caused huge environmental pollution. This study took agricultural carbon emissions as an undesired output to estimate the agricultural development efficiency in 31 provinces of China from 1998 to 2016, based on the green total factor productivity, as assessed by the slacks-based measure directional distance function and constructing the global Malmquist–Luenberger index. We measured agricultural carbon emissions in terms of five aspects: agricultural materials, rice planting, soil, livestock and poultry farming, and straw burning, and then compared the green total factor productivity index and the total factor productivity index. The study came to the following conclusions: (1) the green technology efficiency change was smaller than the technology efficiency change at first, but the gap between them is narrowing with time, such that the former is now larger than the latter; (2) the green technology efficiency was in a declining state and the green technology progress was increasing, promoting the green total factor productivity growth, from 1998 to 2016; and (3) China’s agricultural green total factor productivity increased by 4.2% annually in the east, 3.4% annually in the central region, and 2.5% annually in the west.

Measuring agricultural total factor productivity in China: pattern and drivers over the period of 1978‐2016

The Chinese agricultural sector has experienced a substantial increase in total output since dramatic reforms were introduced in 1978. This paper uses the index method to measure agricultural total factor productivity (TFP) for China’s crop and livestock industries, based on the gross output model from 1978 to 2016. We construct production accounts for the industries using input‐output relationships for the 26 main agricultural commodities and commodity groups, which account for over 90 per cent of the total agricultural inputs and outputs. The results show that China’s agricultural TFP grew at a rate of approximately 2.4 per cent a year before 2009, which is comparable to the main OECD countries and is double the world average. TFP growth accounts for approximately 40 per cent of output growth, suggesting that input growth was the main driver of output growth in the past. However, average productivity growth slowed down after 2009 though it has gradually recovered since 2012. The slowdown reflects the emerging challenges to existing farm production practices in Chinese agriculture, suggesting the need for further institutional reform.

Dynamic analysis of spatial convergence of green total factor productivity in China’s primary provinces along its Belt and Road Initiative

This paper focuses on China’s primary provinces along its Belt and Road Initiative, measures and analyses the provincial green total factor productivity (GTFP) development by GML index based on SBM...

Impact of Climate Change on Agricultural Total Factor Productivity Based on Spatial Panel Data Model: Evidence from China

To respond to the adverse impact of climate change on agricultural total factor productivity, the question of how to adopt actively appropriate strategies is particularly critical for the stakeholders. However, the previous researchers have paid more attention to investigating the measure methods, regional differences, and determinants of Chinese agricultural total factor productivity, but the possible impact of climate change factors like rainfall, temperature, and evaporation on regional agricultural total factor productivity in China have not yet received the attention that they deserve. Furthermore, more importantly, the study on how to take active measures to reduce and mitigate the negative effects from climate change is relatively small. Therefore, in allusion to the above-mentioned problems, using the data envelopment analysis and building a spatial panel data model embedded with climate change factors, this paper calculated Chinese agricultural total factor productivity and then explored the possible impact of climate change on regional agricultural total factor productivity at a provincial level in China. Results mainly show that the impact of some factors, like annual total precipitation, average temperature in the growing season, and evaporation intensity on regional agricultural total factor productivity, are all very significant and negative, which suggests that the more precipitation, the higher the temperature is, and the higher evaporation intensity would lower agricultural total factor productivity in China. Furthermore, in order to response to mitigate the adverse effects from climate change on agricultural total factor productivity, local governments should continue to increase financial support for the local agricultural economic development, because this action could be beneficial for the related stakeholders in improving agricultural total factor productivity. Summing up, our evidence study would provide an important basic theory basis in terms of increasing agricultural total factor productivity and promoting regional agricultural economic development in China.

Technological factors and total factor productivity in China: Evidence based on a panel threshold model

This study investigates the effects of technological factors, including indigenous research and development (R&D) investments, technology spillovers coming from foreign direct investments, export, and import, on China's total factor productivity (TFP). Using provincial panel data of China, covering 30 provinces over the period 2000–2014, our results confirm that indigenous R&D investments play a leading role in promoting TFP. Linear analysis suggests that, except for export, the technology spillovers through openness are beneficial for TFP growth. However, a further discussion based on a panel threshold model suggests that the different behaviours of these technology spillovers are dependent on the technological absorptive capacity affecting factors, such as human capital and indigenous R&D investments. The human capital will strengthen the spillover effects of each technology spillover. However, R&D intensity initially tends to hamper their spillover effects. Once the R&D intensity exceeds a certain level, the negative spillover effect of export on TFP tends to be alleviated, and the positive spillover effect of foreign direct investment and import on TFP will increase.

Agricultural environmental total factor productivity in China under technological heterogeneity: characteristics and determinants.

With limited resources, growing environment constraints and downward pressure on the economy, increasing agricultural environmental total factor productivity (AETFP) and its contribution to agricultural growth is significant for transforming agricultural development to make it more resource efficient and environment-friendly. This paper considered technological heterogeneity in different regions of China and measured AETFP in 30 provinces from 1997 to 2015 using the Metafrontier Malmquist-Luenberger (MML) productivity index. Multi-dimensional analysis was made on temporal and spatial characteristics, evolution patterns, and influencing factors of AETFP in China. The results showed that: (1) AETFP increased in the Ninth, Tenth, Eleventh, and Twelfth Five-Year Plan periods, with average annual growth rates of 0.76%, 0.88%, 1.17%, and 0.87%, respectively. (2) The average annual growth rate of AETFP in the eastern, central, and western regions decreased successively. The eastern region generally had played a leading role. The central region had a catch-up effect on environmental production technologies from the eastern region, while the western region lacked the catch-up effect. (3) The dynamic evolution of AETFP had prominent features. For the whole nation, the kernel density curve of AETFP continuously moved to the right. The main peak value continuously decreased and the width of the main peak continuously increased. The internal differences of AETFP in the eastern and western regions exhibited an increasing trend, while the internal differences of AETFP in the central region showed little change. (4) There was an inverted U-shaped relationship between agricultural economic growth and AETFP. Both the disaster rate and planting structure had a negative impact on AETFP with varying degrees of significance. Income gaps between urban and rural areas can partially offset the role of urbanization in promoting the growth of AETFP. The greater the income differences between urban and rural areas, the weaker the role of urbanization in promoting the growth of AETFP. These findings can help the government determine policies to change the agricultural development mode and formulate effective measures to improve AETFP.

Interjurisdiction political competition and green total factor productivity in China: An inverted-U relationship

Considering environmental pollution, excessive interjurisdictional competition has an adverse effect on green total factor productivity (GTFP), whereas moderate competition does not. Using a panel dataset of 278 prefectures during the 2004–2013 period, we calculate GTFP via a stochastic frontier model and find an inverse U-shaped relationship between county-level government competition and prefectural GTFP. The relationship is robust to endogeneity and alternative specifications. Mechanisms of the relationship consist of traditional input–output ratio, pollution emissions, and environmental technical progress. The spatial spillover effect of political competition is insignificant, indicating that a political promotion tournament in a prefecture is characterized by internal competition within the prefecture.

Does Foreign Direct Investment Improve Inclusive Green Growth? Empirical Evidence from China

Inclusive green growth is a sustainable development mode in pursuit of economic growth, social equity, and environmental protection. At present, a large number of articles have discussed the impact of foreign direct investment (FDI) on economic growth, green growth, and inclusive growth. However, the research about inclusive green growth is mainly descriptive. This paper constructs China’s inclusive green growth index and analyzes the impact of FDI on inclusive green growth in China. Specifically, by constructing a super efficiency slacks-based measure model (which has two undesirable outputs: income disparity and environmental pollution) to calculate the Inclusive green growth index, this paper compares and analyses the differences and regional characteristics of China’s total factor productivity, inclusive total factor productivity, green total factor productivity, and inclusive green total factor productivity. We find that total factor productivity is decreasing after considering undesirable output, and the traditional total factor productivity is higher than the inclusive green total factor productivity by 0.112; at the regional level, the trend of the total factor productivity is gradually decreasing from east to west, which indicates that there are regional differences in inclusive green growth of China, and there is room for improvement. Meanwhile, we construct a panel vector autoregressive model (PVAR) and use generalized impulse response function and variance decomposition to analyse the influence of FDI on China’s inclusive green total factor productivity. The results show that FDI is beneficial to the promotion of inclusive green total factor productivity in China, and environmental pollution in the FDI process is an important factor hindering the inclusive green total factor productivity.

Structural Bonus of Factor Productivity in China’s High-Tech Industry: A Cross-Sector, Cross-Province, and Cross-Ownership Study

The evolution of Chinese high-tech industry labor force and capital structure is analyzed using a shiftshare technique. Contributions from cross-sector, cross-province and cross-ownership flows of factors to productivity growth were assessed. Cross-sector labor force flow produced “positive structural bonus”, cross-sector capital flow produced “negative structural bonus”, cross-province labor flow produced “negative structural bonus”, cross-province capital flow produced a “positive structural bonus”, crossownership labor and capital flow produced “positive structural bonus.” Implications are decrease intervention in operations, allow free factor movement among sectors, provinces, and ownership; improve capital market and improve labor market to channel skilled workers into the high-tech industry.

Does factor market distortion inhibit the green total factor productivity in China?

Abstract This article measures green total factor productivity (GTFP) to discuss whether factor market distortions inhibit the green development of China's economy. Meanwhile, export and foreign direct investment (FDI) are taken into account based on a threshold model, considering export and FDI are closely related to factor market distortion, environment and the economy. The result shows that the factor market distortion has negative influences on China's GTFP growth, export and FDI's spillover effect significantly. Moreover, factor market distortion, was found to be the threshold of export and FDI's spillover effect, leading to inferior export and FDI, which further inhibit GTFP growth. This article provides a supplement to the issue from the perspective of environment based on the widely confirmed proposition that factor market distortion inhibits TFP growth. According to the results, we suggest that the Chinese government should reduce the control of factor market as much as possible, leaving the factor resources allocation to the market. This will not only promote an innovation-driven development, but also an energy-saving and environment-friendly economy in China. The article provides a new perspective on the influence mechanism of export and FDI on GTFP growth by discussing the impact path of factor distortion, and expands the empirical framework of the research direction as well.

Spatial analysis of change trend and influencing factors of total factor productivity in China’s regional construction industry

ABSTRACT Total factor productivity (TFP) is a measure of long-term economic growth and a comprehensive industry-level productivity measure. There are large gaps in China’s regional construction industry development due to unbalanced regional economy. Based on TFP measurement, this article puts forward a two-hierarchical analysis framework with coefficient of variation, Moran scatterplot and coefficient of convergence to analyse change trend of the construction industry TFP in three major regions in terms of spatial diversity, correlation and convergence. Then, the geographically weighted regression model is utilized to explore the influencing mechanism on the TFP. The results indicate the differences of the regional construction industry TFP are enlarging. There is obvious spatial correlation and heterogeneity in the regional TFP without a relatively stable space pattern. The TFP also exhibits convergence effects among three major regions. The construction industry productivity in all regions is significantly affected by economic environment, industrial organization structure and technological level. Industrial organization structure exerts the various influences on the productivity in different regions.

Research on the Total Factor Productivity in China's Electric Power Industry Based on the GML Model

Research on the Total Factor Productivity in China's Electric Power Industry Based on the GML Model

Effects of local and civil environmental regulation on green total factor productivity in China: A spatial Durbin econometric analysis

This paper employs metafrontier malmquist luenberger index and a spatial Durbin model to investigate the influence of both local and civil environmental regulation and its spatial spillover effect on green total factor productivity in 273 cities of China in 2003–2013. According to city political attribute and regional differences, this article divides the sample city into six categories. The results show that effect of local environmental regulation on green total factor productivity is significantly positive in high political attribute cities (A1 and A2 zone), but negative effects in lower political attribute cities (B2 and B3 zone). Moreover, the ‘race to the top’ results in significantly positive spatial spillover effects from local environmental regulation on green total factor productivity in high political attribute cities (A1 and A2 zone), while the ‘race to the bottom’ causes significantly negative spatial spillover effects in lower political attribute cities (B2 and B3 zone). As for civil environmental regulation, it has positive direct and indirect effects in promoting green total factor productivity. We further find that environmental regulation inhibits the original technological innovation of enterprises, which suggests that government should reduce market intervention, improve enterprise flexibility of market change, and promote enterprises to carry out the original technological innovation. Thus, the government and citizen should make targeted pollution-reduction policies for the green total factor productivity increase.

Impact of Human Capital Inequality on Total Factor Productivity in China

This paper investigates the impact of human capital inequality on Total Factor Productivity (TFP) in China. Approaching this question in dynamic panel data over 18 years and 29 provinces with first-order difference GMM and system GMM estimators reveals: human capital inequality has a significant negative effect on TFP, and high-quality human capital has a significant positive effect on TFP.

Analysis of Total Factor Productivity in China’s Commercial Banks<br/> —Based on Hicks-Moorsteen TFP Indicators

The paper uses Hicks-Moorsteen TFP indicator to measure the changes of total factor productivity in China’s commercial banks from 2010 to 2014 on the basis of big data in BankScope database. By analyzing the sample data and total factor productivity, the paper finds out in-depth motives of the dynamic changes of TFP. At present, domestic commercial banks are at the stage of deepening reforms. Thus, exploring into the motives that drive the efficiency of commercial banks in recent years and affecting factors is of great instructive roles for national supervision institutions’ formulating relevant monitoring policies, improving banks’ efficiency in resource allocation, and launching profound reforms in banks.

Research on Temporal-spatial Evolution of Regional Disparity of Forestry Total Factor Productivity in China

Research on Temporal-spatial Evolution of Regional Disparity of Forestry Total Factor Productivity in China

Effects of knowledge capital on total factor productivity in China: A spatial econometric perspective

The transformation of China into a knowledge based economy is one of the most intensively debated research issues in Economic Geography. The focus of this study is on effects of knowledge capital on manufacturing total factor productivity (TFP) in China through the lens of the regional knowledge capital model (KCM). The objective is to estimate the impact of region-internal and region-external knowledge capital — measured in terms of patents granted by the Chinese patent office — on TFP across Chinese regions. We derive a Spatial Durbin Model (SDM) for empirical testing, using panel data on 29 Chinese regions for the years 1988–2007. The results indeed point to a shift of Chinese productivity growth to a more knowledge based one, statistically confirming the impact of knowledge capital on regional TFP after 1998. Furthermore, this shift is not only based on region-internal knowledge capital, but also on inter-regional knowledge spillovers.

The Quality Promotion of Tax Sources of Private Enterprises in Perspective of Total Factor Productivity in China - Malmquist Index Analysis Based on the DEA

We use the tax survey data of the central treasury, combine with the trajectory and evolution path of China's private economy, introduce a non-parameter DEA method to calculate the Malmquist index and analyze the quality of tax sources and the level of total factor productivity of private enterprises in the post financial crisis era. We find out that the total factor productivities of the three industries have increased in the recent years, nut of different extents. Specifically speaking, there are more efficiency improvement and technical progress in tertiary industry than that in secondary industry, while there is an obvious trend that the secondary industry is shifting to the tertiary industry. At the same time, the status of the first industry has been consolidated and improved.

Testing Stochastic Convergence across Chinese Provinces, 1952–2008

Herrerías M. J. and Ordóñez Monfort J. Testing stochastic convergence across Chinese provinces, 1952–2008, Regional Studies. This paper analyses stochastic convergence across twenty-eight Chinese provinces over the period 1952–2008 using unit root tests that take into account structural breaks and non-linearities. The results suggest a significant degree of convergence in capital intensity, labour productivity and total factor productivity in China's provinces. However, the findings also reveal that provinces located in coastal areas of China have benefited from the economic reforms more than the central and western regions and have created small regional clusters.

Re-estimation of firms' total factor productivity in China's iron and steel industry

Using the firm-level census data, this paper re-estimated the total factor productivity (TFP) of firms in China's iron and steel industry and examined its potential determinants over the period 1998–2007. To deal with the “endogenous input” problem, we used the semi-parametric regression techniques for estimating the firm-level TFP. The results suggest that firms' TFP in China's iron and steel industry has been steadily increasing over time with the key drivers of productivity improvement differing substantially between firms with different characteristics including their size, ownership type and geographical location. Notably, the productivity of small firms is positively related to market share and negatively related to R&D. Large state-owned enterprises' productivity is relatively insensitive to changes in market share and R&D, while the non-state owned enterprises are more likely to obtain their productivity gains through export. Increasing firm size is generally positively correlated to firms' performance in TFP, and it is more so in the less developed Western than the Eastern and Central regions. The findings suggest that different policy instruments targeting firms with different characteristics in the process of restructuring the industry may be desirable.