Industrial Total Factor Productivity Research Articles

Innovation agglomeration and heterogeneity: a micro-geographic measure

ABSTRACT This study develops two novel agglomeration indexes, combining the feature unbalance agglomeration index family and the distance-based agglomeration index family, to measure innovation agglomeration. We examine the positive effects of innovation agglomeration on the industrial total factor productivity (TFP). We discuss the factors driving the innovation agglomeration at the city level and analyze them in cities of the Yangtze River Delta (YRD). We find proximity to central cities and nearest ports has a positive impact, industrial level has an inverse U-shape impact, investment and commercial culture have positive impacts, and innovation policy has a negative impact. We identify five innovation agglomeration factors and discuss their effects on the theoretical and practical R&D management applications and strategies.

The Impact of Digital Economy on TFP of Industries: Empirical Analysis Based on the Extension of Schumpeterian Model to Complex Economic Systems

The digital economy (DE) is a new driver for enhancing total factor productivity (TFP). Using panel data from 30 provinces in China between 2011 and 2022, this study measures DE and TFP using the entropy-weighted TOPSIS method and the Global Malmquist–Luenberger method and further examines the impact of DE on the TFP of industries. The main findings are as follows: (1) DE can significantly improve TFP, though the extent of improvement varies. DE has the greatest impact on the TFP of the service industry, followed by the manufacturing industry, with the weakest effect on the agricultural industry. (2) The enhancement effect of DE on agriculture and the service industry is more pronounced in the central and western regions, while the improvement effect on manufacturing is more evident in the eastern region. (3) DE has facilitated the improvement of TFP in manufacturing industries such as textiles and special equipment manufacturing, as well as in service industries like wholesale and retail. However, it has not had a significant impact on the TFP of industries such as pharmaceutical manufacturing and real estate. This study has significant theoretical value and policy implications for China and other developing countries in exploring DE and achieving high-quality industrial development.

Can industrial robot utilization drive the total factor productivity of enterprises?

AbstractThis study presents a comprehensive investigation of the impact and mechanism of industrial robot utilization on total factor productivity (TFP) using panel data from A‐share listed companies in China's manufacturing industry. Our findings reveal the significant contribution of industrial robots to enhancing TFP in microenterprises, with this influence persisting over time. Mechanism tests demonstrate that industrial robot utilization effectively reduces labor costs, mitigates financing constraints, and enhances innovation capabilities, thereby improving enterprises' TFP. Additionally, a heterogeneity analysis indicates a more pronounced impact of industrial robot utilization on TFP in labor‐intensive, non‐high‐tech enterprises, as well as in enterprises operating in highly competitive markets. This study deepens our understanding of industrial robot utilization and TFP, which broadens the scope of this field. This has significant practical implications for transforming traditional factors, integrating new technologies, and facilitating digital and intelligent transformations of manufacturing enterprises in emerging economies.

How does digital finance affect the total factor productivity of listed manufacturing companies?

The development of digital finance offers numerous development opportunities, particularly in the accessing of funds for enterprises that traditionally face financial constraints. The economic downturn globally and especially in China requires new policies for stimulating economic growth. The manufacturing industry in particular is apt for this kind of intervention. Although studies have examined these topics, several gaps exist in the literature, specifically in the area of the effect mechanism. Therefore, this study examines the direct relationship between digital finance and the total factor productivity (TFP) of manufacturing enterprises and discusses the mechanism of action between the two from the intermediary and entry/exit perspectives. The findings show that digital finance affects manufacturing TFP positively and significantly. The promotion effect of digital finance is more evident from the enterprise heterogeneity analysis, particularly for small- and medium-sized private and manufacturing enterprises with better growth. The effect mechanism analysis shows that digital finance affects manufacturing TFP positively by reducing financing constraints, improving the level of human capital, and enhancing enterprise risk-taking ability. From the perspective of enterprise entry and exit, the entry of high-productivity enterprises enhances market competition and is an important way that digital finance promotes the improvement of industry TFP.

Advancing Green TFP Calculation: A Novel Spatiotemporal Econometric Solow Residual Method and Its Application to China’s Urban Industrial Sectors

The Solow residual method, traditionally pivotal for calculating total factor productivity (TFP), is typically not applied to green TFP calculations due to its exclusion of undesired outputs. Diverging from traditional approaches and other frontier methodologies such as Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA), this paper integrates undesired outputs and three types of spatial spillover effects into the conventional Solow framework, thereby creating a new spatiotemporal econometric Solow residual method (STE-SRM). Utilizing this novel method, the study computes the industrial green TFPs for 280 Chinese cities from 2003 to 2019, recalculates these TFPs using DEA-SBM and Bayesian SFA for the same cities and periods, and assesses the accuracy of the STE-SRM-derived TFPs through comparative analysis. Additionally, the paper explores the statistical properties of China’s urban industrial green TFPs as derived from the STE-SRM, employing Dagum’s Gini coefficient and spatial convergence analyses. The findings first indicate that by incorporating undesired outputs and spatial spillover into the Solow residual method, green TFPs are computable in alignment with the traditional Solow logic, although the allocation of per capita inputs and undesired outputs hinges on selecting the optimal empirical production function. Second, China’s urban industrial green TFPs, calculated using the STE-SRM with the spatial Durbin model with mixed effects as the optimal model, show that cities like Huangshan, Fangchenggang, and Sanya have notably higher TFPs, whereas Jincheng, Datong, and Taiyuan display lower TFPs. Third, comparisons of China’s urban industrial green TFP calculations reveal that those derived from the STE-SRM demonstrate broader but more concentrated results, while Bayesian SFA results are narrower and less concentrated, and DEA-SBM findings sit between these extremes. Fourth, the study highlights significant spatial heterogeneity in China’s urban industrial green TFPs across different regions—eastern, central, western, and northeast China—with evident sigma convergence across the urban landscape, though absolute beta convergence is significant only in a limited subset of cities and time periods.

Agglomeration economies: different effects on TFP in high-tech and low-tech industries

ABSTRACT We study the impact of agglomeration effects on firms’ total factor productivity (TFP) for industry groups defined by technology intensity. This allows for non-uniform effects on firms depending on their technological level. We find that urban economies have the largest impact on firm productivity in high-technology industries, while they have no effect in low-technology industries. For firms in the latter industries, the diversity of the local economic structure is relevant. Localisation effects have a consistently positive and significant impact on TFP, with the effect increasing with the technology intensity of the industries.

Research on the Impact of Sci-Tech Finance on Industrial TFP

The improvement of industrial Total Factor Productivity (TFP) is the basis for the high-quality development of China’s economy. Sci-tech finance affects industrial TFP through screening and allocation mechanism, governance mechanism, and integration mechanism. Based on provincial panel data from 2009 to 2016, this paper constructs a comprehensive index of regional Sci-tech finance and explores the impact of Sci-tech finance on industrial TFP from two perspectives: differences in financial development levels and transmission paths. The results show that Sci-tech finance has a significant role in promoting the industrial TFP, but this promotion needs to meet certain financial development conditions. In high-level financial development areas, the promotion effect is significant. It is not linear. The effect is a threshold effect with the diminishing marginal efficiency. Sci-tech finance mainly promotes industrial TFP through boosting the industry’s scientific and technological innovation capacity. Policies and channels of Sci-tech finance to support science and technology innovation are put forward.

Import License, Misallocation, and Aggregate Total Factor Productivity in the Chinese Steel Industry*

This paper examines how import license restriction‐induced resource misallocation affects industry aggregate total factor productivity (TFP). We apply a recently proposed consistent estimator to micro‐level data from the Chinese steel industry, considering material price heterogeneity. We back out the potential extent of misallocation, finding the importers' TFP is not significantly higher than that of the nonimporters, and resource misallocation caused by the iron‐ore import license lowers aggregate productivity. Our analysis indicates that removing the trade barrier counterfactually would increase the industrial TFP by 76.8%, on average.

WORKFORCE AGING AND INDUSTRY-LEVEL PRODUCTIVITY

This paper investigates the relationship between the age structure of workforce and industry-level productivities using international panel data. We find that when the growth rate of prime-age workforce (aged between 30 and 49) falls, the growth rate of total factor productivity in industries that are highly dependent on prime-age workers tends to fall relative to that in other industries. We also present a simple general equilibrium model based on industry-level external economies of scale. We show that our empirical findings are consistent with the hypothesis that there are industry-level externalities coming from prime-age workers.

Commodity terms of trade volatility and industry growth

The rising volatility of commodity terms of trade (CTOT) is associated with a high cost of capital and a low credit supply for producers in commodity-dependent countries. In this paper, we examine how volatile CTOT influences various industries’ growth performance based on sector-level panel data for countries specializing in commodity exports. We find robust evidence that CTOT volatility causes a more significant growth loss in manufacturing sectors facing tighter credit constraints. The adverse growth effects operate through lower total factor productivity in industries heavily reliant on external finance for long-term investments and through lower capital accumulation in industries with high liquidity needs for short-term working capital. Our findings offer a complementary explanation for the “resource curse” through the credit constraint channel.

The impact of China’s SO2 emission trading system on industrial total factor productivity: evidence from industrial sub-sectors at the provincial level

Much attention has been devoted to the economic benefits of emission trading systems (ETSs), but research concerning the SO2 ETS in China is scarce. We examine China’s SO2 ETS in 2007 from the perspective of industrial sub-sectors and employ the difference-in-difference-in-difference (DDD) model to test whether the SO2 ETS can promote industrial total factor productivity (TFP). We identify whether it is the average effect or the dynamic effect of the SO2 ETS that can improve industrial TFP. The heterogeneity test results indicate that this policy has diverse influence in different regions and industries, and the effect is better in pollution-intensive industries. We also find that the policy enhances industrial TFP by promoting efficient technological innovation and capital allocation. Our findings provide evidence that supports the Porter Hypothesis and a guide for the industrialization of developing countries. Chinese policymakers should strengthen environmental enforcement, set quotas accordingly, and encourage technological innovation to improve industrial TFP.

Impact of biased technological progress on the total factor productivity of China's manufacturing industry: The driver of sustainable economic growth

Sustainable economic growth relies on an increase in total factor productivity (TFP). To upgrade the core competitiveness and support the sustainable growth and high-quality development of China's economy, ascending TFP in China's manufacturing industry counts. This study analysed the impact of biased technological progress on TFP, which was estimated by using the relevant data of 27 subsectors of China's manufacturing industry from 1980 to 2019. The results revealed that from the early industrialisation period (1980–2000) to the mid-industrialisation period (2001–2012) and then to the middle and late industrialisation period (2013–2019), the mean value of TFP in China's manufacturing industry grew from 0.291 to 0.394 and then to 0.555, embodying a continuously upwards advancing tendency. Technological progress was capital biased from 1981 to 2011 and labour biased during the rest of the sample period. In addition, its direction was heterogeneous among various industries. To some extent, the growth of TFP has been hindered by biased technological progress and factor allocation. The per unit increase in them has led to 1.495 and 1.017 unit decreases in the TFP. Upon examining the factor efficiency and the factor structure, we found that the estimated coefficients of biased technological progress dropped to −1.516 and −1.719, preventing TFP from growing to a greater extent. Consequently, there is room for their optimisation in China's manufacturing industry.

Distribution of Spatial and Temporal Heterogeneity of Green Total-Factor Productivity in the Chinese Manufacturing Industry, and the Influencing Factors

This paper considers GTFP of energy consumption and environmental pollution from a sustainable perspective as a measure of the evolutionary efficiency of manufacturing industries. It uses the super-efficiency SBM model to calculate the GTFP efficiency values of manufacturing industries in 30 Chinese provinces from 2011 to 2019. Moran’s index and the GTWR model were used to study the spatial correlation and impact factors of GTFP. The results found that the following. (1) The overall level of GTFP in China’s manufacturing industry from 2011 to 2019 rose progressively, and the level of GTFP between different regions gradually reduced. (2) The spatial correlation of GTFP in China’s manufacturing industry is significantly positive, with a positive spatial spillover effect. (3) The level of manufacturing GTFP is affected by economic development, investment, and other factors. (4) There is spatiotemporal heterogeneity in the impact factors of manufacturing GTFP. According to empirical research focusing on the goal of sustainable development, it is proposed to increase the use of clean energy and reduce environmental pollution. To carry out green development according to local conditions, the eastern region will strengthen the development of new energy manufacturing and continue to increase investment in innovation, and the central and western regions will strengthen environmental supervision, accelerate industrial transformation, and attract more foreign investment.

Measurement of factor mismatch in industrial enterprises with labor skills heterogeneity

Since 1978, China’s industrial development has made great achievements, but the factor market’s development has lagged behind the commodity market’s development for a long time, leading to a factor mismatch in China’s industry, directly hindering the improvement of China’s industrial total factor productivity (TFP) and the growth of industrial output. It is of great theoretical and practical significance to study the economic efficiency loss caused by the mismatch of industrial factors for the successful transformation and sustainable development of China’s industry. In terms of economic reality, the uneven quality of China’s industrial enterprises’ labor force cannot be ignored; from the perspective of economic theoretical models, ignoring the heterogeneity of labor skills will lead to a wrong estimation of factor mismatch level. From the perspective of labor skill heterogeneity, this study constructs a factor measurement model - a multi-balanced heterogeneous manufacturer model to reveal the efficiency loss caused by the mismatch of labor factors with different skills. The empirical research finds that: first, by comparing the single equilibrium heterogeneous manufacturer model under the perspective of skill homogeneity with the multi-equilibrium heterogeneous manufacturer model under the perspective of labor skill heterogeneity, it can be seen that the former regards all labor forces as homogeneous, overestimates the degree of labor mismatch and then underestimates the potential TFP growth of China’s industrial enterprises; second, the vast majority of factor mismatches of China’s industrial enterprises come from within the industry, and the intra- and inter-industry mismatches of highly skilled labor are very serious.

External technology dependence and manufacturing TFP: Evidence from China

It remains an open question whether the reduction of external technology dependence (ETD) on foreign countries can improve the total factor productivity (TFP) of China's manufacturing industry. This paper uses the data of China's manufacturing industry from 2000 to 2016, to study the impact of ETD on TFP. Empirical results show that a 1% decrease in ETD could induce a 1% increase in TFP. As the TFP gap between China and the frontier countries shrinks, the indigenous innovation gains increasing importance. One can find that the effect of ETD on TFP in technology-intensive industries is stronger than that of other industries. Moreover, this study finds that the influence of ETD on TFP shows a significant threshold effect. Further, this paper uses the ratio of patents granted to represent the innovation capacity, and verifies its moderating role in the effect of ETD on TFP.

How Powerfully and Rapidly Are Developing Countries Catching Up with the Advanced Economies?

The article, based on a number of author’s calculations and models, shows that if in the advanced economies (AEs), despite their preservation of leading positions in modern technologies, due to the loss of their demographic dividend, deindustrialization and contraction in the rate of capital formation, there is a cascade slowdown in the growth rate of per capita GDP and total factor productivity (TFP), the situation in developing countries (DCs) is on the whole more optimistic. Although (a) many DCS are experiencing acute social problems, (b) AEs are pursuing a policy of detainment of the growth of a number of DCs, they on the whole, thanks to the success of China, India, other, predominantly Asian DCs that have implemented pragmatic reforms and export-oriented industrialization, have made significant progress. In the last two decades DCs on the whole outperformed the AEs in terms of capital investment efficiency by 2.5 times, in average growth rates of industrial production and TFP - by five and two times, respectively. Their share in world GDP, which has risen by a factor of 1.5 to 3/5 over the past forty years, may, according to available forecasts, increase to ¾ by the middle of the century. In about three decades, the ratio of economic power of the countries of the East and the South and the countries of the West (in favor of the former) will quite possibly be approximately the same as it was in the planetary economy before the industrial revolution and colonial enslavement of the former by the latter.

China’s Industrial TFPs at the Prefectural Level and the Law of Their Spatial–Temporal Evolution

Calculating China’s industrial total factor productivity (TFP) at the prefectural level comprehensively and accurately is not only an inevitable requirement for China’s industrialization to enter the new development stage of “improving quality and efficiency”, but also a practical need for TFP improvement at the industrial level. Based on the improved Solow residual method with the general nesting spatial model embedded, this paper comprehensively calculated the industrial TFPs of 280 prefectural cities in China from 2003 to 2019, and undertook a detailed analysis of the spatiotemporal evolution law of the calculation results through Dagum’s Gini coefficient and kernel density estimation. Three main conclusions have been drawn in this paper. First, there is an apparent spatial difference among the industrial TFPs of the prefectural cities in China. It is the poorest and has an evident declining trend in northeast China, and best in eastern China, while the development of central and western China is between east and northeast China. Second, the spatial difference level of industrial TFPs of the prefectural cities in China shows a general development trend of firstly falling and then rising. Comparatively speaking, the contribution of intra-group differences is low, while the contribution of inter-group and the intensity of trans-variation are high. Third, the spatiotemporal evolution of China’s industrial TFPs at the prefectural level has the following characteristics: the overall distribution curve moves firstly towards the right and then left, the kernel density at the peak point continuously declines, the distribution ranges are firstly widening and then narrowing, and the tails of the distribution curve are constantly extending. Meanwhile, the distribution figures of the kernel density estimation in different regions show apparent heterogeneity.

The role of productivity and efficiency gains in the sugar-ethanol industry to reduce land expansion for sugarcane fields in Brazil

During the 2000s, the development of flex-fuel automotive vehicles in Brazil and rising international commodities prices allowed the Brazilian sugar-ethanol sector to grow at higher rates and expand area planted to sugarcane relative to levels verified in the 1990s. However, this land expansion increases the pressure on land-use change and management. This paper seeks to estimate the technical efficiency and decompose the total factor productivity (TFP) in the sugar-ethanol industry using a stochastic frontier framework. Primary data was obtained from an unbalanced panel of 90 sugar and ethanol producers for the harvest years from 2012/2013 to 2016/2017, providing 209 valid observations. Results suggest that mills have a small gap to increase their efficiency under the current technology, and the industry's TFP has been primarily impacted by the efficiency effects, on the other hand, SE mills might improve TFP rising their scale of production. Therefore, the results in this paper suggest that small SE mills may benefit from merging with other mills to explore economies of scale to enhance their TFP and strengthen economic competitiveness, contributing to the Brazilian environmental policy agenda.

Industrial Structure Optimization of Wuhan Urban Agglomeration Based on TFP and Industrial Spatial Linkages

As a complex symbiosis, a reasonable industrial structure of each symbiotic unit within an urban agglomeration (UA) is crucial to the sustainable development of the regional economy. In an urban agglomeration (UA), a reasonable industrial structure is crucial to the sustainable development of the regional economy. This paper comprehensively considers the industrial total factor productivity (TFP) and the industrial spatial linkages between cities to adjust the industrial structure. Malmquist index (MI) is introduced to assess the industry performance in this paper to judge the development status of the industry. The calculation method for identifying industrial structure similarity is improved by combining it with industrial spatial linkages, to accurately reflect the degree of industrial structure convergence in the UA and to recognize which cities need industrial adjustment. The results from a case study on Wuhan UA showed that the method proposed in this paper can provide objective and specific suggestions for every industrial sector in each member city of the UA on a regional scale, so that the city can give priority to the developing industry with a certain foundation on the premise of avoiding the low resource allocation efficiency.

Impacts of wind power policies on the total factor productivity of the Chinese wind power industry

Wind power has been treated as a strategic choice to decarbonize the Chinese electricity market, so the Chinese government has introduced various policies to improve the wind power industry's total factor productivity (TFP). However, the impacts of policies and their mixes on TFP are still unclear. In addition, the Chinese wind power industry is supervised and regulated by several agencies, so assessing and comparing the effectiveness of policies from different agencies is also an urgent task. This study evaluates the impacts of different categories of policies and their mixes on the TFP of the Chinese wind power industry based on micro-data from the listed Chinese wind power enterprises from 2006 to 2020. The heterogeneity in policy effectiveness is probed with respect to the policy-issuing agencies. The empirical results indicate that economic, regulatory, and soft policies and their mixes significantly facilitate the TFP. Further analysis suggests that the policies of the National People's Congress and the State Council have no significant effect, while the policies of the State Council's departments can significantly improve the TFP of the wind power industry in China.