Differences In Total Factor Productivity Research Articles

Examining the impact of environmental centralization on enterprise total factor productivity through a quasi-natural experiment conducted in China

This paper explores the effects and influence mechanisms of environmental centralization on enterprise technological progress and productivity in China. Taking the reform of vertical environmental governance (VEG) as a quasi-natural experiment, this paper compares the differences in total factor productivity (TFP) of enterprises in environmental centralization regions and environmental decentralization regions by adopting the staggered difference-in-differences (DID) method. The empirical results show that: (1) The average TFP of enterprises in the environmental centralization areas is 0.0598 higher than that in the environmental decentralization areas, and this average effect increases with the extension of the reform duration. (2) Environmental centralization strengthens government intervention in environmental issues. The improvement in the intensity of environmental regulation and the willingness of firm green innovation are the intermediate causes of the improvement in enterprise TFP. (3) VEG’s effect is heterogeneous regarding regional development, industry type, and enterprise characteristics. This study has empirical implications for further refining the fiscal system to leverage the role of public finance on environmental governance and enterprise development.

Global Value Chain, GVC、biased technological change and reginal total factor productivity differences

We discuss the link between global value chains (GVC) and the widening differences in total factor productivity (TFP) across regions. Firstly, the empirical evidence from China indicates that differences in technological bias may be a vital factor in TFP differences. Further, we test the connection between GVC and regional TFP differences, providing a new explanation from the perspective of technological bias. Due to different speeds of factors mobility, GVC can intensify regional factor cost disparities, thereby expanding technological bias differences. As a result, it impedes regional TFP convergence. Finally, we have tested the above mechanism using data from China.

On the productivity advantage of cities

Abstract Agglomeration externalities are the key factor explaining the existence of cities and their size. However, while the various micro foundations of agglomeration externalities stress the importance of total factor productivity (TFP), the empirical evidence on agglomeration externalities rests on measures obtained using firm revenue or value-added as a measure of firm output: revenue-based TFP (TFP-R). This article uses data on French manufacturing firms’ revenue, quantity, and prices to estimate TFP and TFP-R and decompose the latter into various elements. Our analysis suggests that the revenue productivity advantage of denser areas is mainly driven by higher prices charged rather than differences in TFP. At the same time, firms in denser areas are able to sell higher quantities and generate higher revenues, despite higher prices. These and other results we document suggest that firms in denser areas are able to charge higher prices because they sell higher demand/quality products. In this respect, two key elements related to this capacity to sell more at higher prices are the degree of vertical differentiation and trade cost, with products characterized by higher vertical differentiation and/or lower trade costs featuring stronger patterns and driving overall results.

Forestry Subsidies, Forestry Regulatory Policies, and Total Factor Productivity in Forestry—Plot-Scale Micro-Survey Data from A Heterogeneous Forest Types Perspective

Enhancing the total factor productivity in forestry is an important part of deepening the reform of the collective forest rights system. Based on the survey data of 295 forest plots in 12 towns of Liuyang City, Hunan Province, China, the study utilized a three-stage DEA model to assess the total factor productivity of forestry at the plot level. The empirical study employs Tobit and fractional regression models to investigate the effects and differences of forestry subsidies and forestry regulatory policies on the heterogeneous total factor productivity of different types of forests. The study found that: (1) the mean value of plot-scale forestry total factor productivity is 0.127, and there are obvious differences in total factor productivity among timber forests, economic forests, and mixed forests; and (2) afforestation subsidies and nurturing subsidies significantly positively influence high-level TFP. Ecological benefit compensation positively affects high-level TFP, but is not significant at any level of TFP. Forestry regulatory policies negatively impact high-level TFP, but are not significant at any level of TFP. This paper puts forward countermeasure suggestions to improve forestry subsidy policies, optimize forestry regulatory policies, and improve forestry total factor productivity from the perspective of heterogeneous forest types.

Differences in Total Factor Productivity and the Pattern of International Trade

In this work, we develop a trade model that explains the pattern of trade between countries based on differences in total factor productivity (TFP) while also accounting for differences in relative factor endowments. The novelty stems from the introduction of production functions derived by combining the Ricardian and Heckscher–Ohlin–Samuelson (H-O-S) theories, with TFP differences serving as the basis of comparative advantage. To this end, a testable hypothesis is derived. For the empirical measurement of the TFP in each industry and country, a constant elasticity of substitution (CES)-type production function was employed, and the TFP was calculated as the Solow residual from the production function’s fixed term. To offer a better understanding, the model was tested for the bilateral trade between Germany and Russia, and Germany and the Czech Republic. It was found that TFP differences can be used as a basis for explaining comparative advantages and, consequently, the bilateral pattern of trade between two countries.

Total factor productivity and outsourcing: the case of Vietnamese small and medium sized enterprises

PurposeThe paper aims to investigate the difference in total factor productivity (TFP) among those firms with and without outsourcing in a developing country like Vietnam. Also, it explores the effect of outsourcing activities on total factor productivity with a specified concentration on the Vietnamese small and medium-sized enterprises (SMEs).Design/methodology/approachThe panel data set of SMEs used in this study was originated from biannual surveys conducted under the collaboration between educational organizations and government agencies: Stockholm School of Economics (SSE), Department of Economics – the University of Copenhagen, the Institution of Labor Studies and Social Affairs (ILSSA) in the Ministry of Labor, Invalids and Social Affairs (MOLISA). In this study, the model is developed based on the production function in accordance with the model of Girma and Görg (2004). The firms’ TFP is the difference between the actual and the predicted output as with the approach by Levinsohn and Petrin (2003).FindingsThis study finds out that firms with outsourcing have higher total factor productivity than those without outsourcing activities. In addition, the more firms spend on outsourcing, the higher total factor productivity they can gain. Outsourcing to SMEs in a developing country can significantly increase its TFP by means of either maintaining core competencies or searching external resources in conducting some internal activities.Originality/valueAlthough outsourcing has been widely applied by large firms, the research studying its impact on productivity at firm level is limited. Especially, this study can shed light on the impact for the case of SMEs in a developing economy.

Accounting for the duality of the Italian economy

After 162 years of political unification, Italy still displays large regional economic differences. In 2019, the per capita GDP of Lombardia was 39,700 euros, but Calabria's per capita GDP was only 17,300 euros. We build a two-region, two-sector model of the Italian economy to measure the wedges that could account for the differences in aggregate variables between the North and the South. We find that the largest driver of the regional disparity in per capita output is the difference in total factor productivity, followed by fiscal redistribution. These two factors, together, account for more than 70 percent of the output disparity between the North and the South.

Spatial growth theory: Optimality and spatial heterogeneity

Spatiotemporal dynamics are introduced in a standard Ramsey model of optimal growth in which capital moves toward locations where the marginal productivity of capital is relatively higher and initiate a study of the effects of nonlinear capital transport in terms of a linearized analysis around steady state solution solutions.The potential spatial heterogeneity of optimal growth, as seen from the point of view of an optimizing social planner, is examined. Our results suggest that for a high utility discount rate, the spatial capital flows induce the emergence of optimal spatial patterns, while for a low utility discount, a flat earth steady state is socially optimal. Furthermore, when spatial heterogeneities exist due to total factor productivity differences across locations, we identify conditions under which the spatial capital flows could intensify or weaken spatial inequalities.

Investigating the Reasons for the Difference in Total Factor Productivity Between East and West Asia with Emphasis on Institutions, Human Capital and Relative Backwardness

Investigating the Reasons for the Difference in Total Factor Productivity Between East and West Asia with Emphasis on Institutions, Human Capital and Relative Backwardness

Within-Firm Productivity Dispersion: Estimates and Implications

This paper develops a flexible recipe for identifying unobserved input allocations, as well as quantity- and revenue-based total factor productivity (TFP) across product lines, for multiproduct producers, using demand- and supply-side information. Applying variants of this recipe to a panel of plants manufacturing machinery in India from 2000–2007 yields sizable within-plant TFP differences. Removing an average plant’s lowest-performing product increases the unweighted average of plant-level revenue-based productivity by 10%–65%. A 1 standard deviation decline in product-level revenue-based TFP generates around a 6 percentage point increase in the probability of dropping that product.

Trust or bust: Growth effects of knowledge, human and social capital revisited

• Trust appears to be one of the main drivers of productivity growth across EU subnational regions. • Our estimations suggest the presence of gravity effects emanating from social and human capital in which some regions lose against others nearby that have more abundant social and human capital. • Productivity gains prompted by social capital appear to diminish over time. Social capital consists of trust, collective values, and shared norms. It helps society to work efficiently and, hence, promotes knowledge accumulation and growth. This paper identifies the contribution of social capital to differences in total factor productivity among European subnational regions, but, unlike other studies, it focuses on the effects of interregional differences in social capital. To do so, the paper employs a spatial Durbin model of a social-capital extended Cobb-Douglas production function. Our results suggest that transfers of knowledge might be secondary to the presence of collectivism and human capital in knowledge-generating regions. Although knowledge can cross boundaries, an absence of skills and trust limits local growth capacity. Our estimations also suggest the presence of gravity effects emanating from social and human capital in which some regions lose against others nearby that have more abundant social and human capital.

Dynamics of total factor productivity growth: An empirical analysis of Indian commercial banks

This paper investigates the total factor productivity (TFP) change scenario among Indian commercial banks during 1998–2017. We measure the change in the Malmquist TFP index and decompose this into technological, efficiency, and scale change components using data envelopment analysis (DEA). Further, the bootstrap-based Simar and Wilson (2007) procedure is applied to correct the bias from the conventional DEA estimates. We also explain the variations in TFP estimates using a set of bank-specific and macroeconomic variables with a system-generalised method of moments and bias-corrected fixed-effects models. Our results show an improvement in TFP among Indian commercial banks– with growth relatively higher during the pre-crisis than in the post-crisis period. The productivity gains are driven mainly by technological progress rather than efficiency improvements. Amongst different ownership categories, foreign-owned banks achieve the highest overall productivity gains, followed by the domestic privately owned and state-owned banks. Finally, our TFP regression results show that return on assets, diversification, GDP per capita, Herfindahl–Hirschman Index, and inflation are the main variables responsible for driving these total factor productivity differences.

A Comprehensive Approach to Measuring the Multidimensional Productivity Index: A Reiteration of Global Productivity Convergence

The undeniable significance of production has prompted experts to explore further the competitive productivity of various nations across the globe. Despite the importance of global productivity competitiveness, prior studies have not included a comprehensive assessment of the multidimensional productivity index (MPI). Therefore, this study aims to achieve two objectives. First, it extends the scope of prior studies by integrating capital as an input alongside labor and energy consumption, based on 50 factors under 11 indices (including democracy, global competitiveness, and innovation index). Second, global competitive productivity convergence is reaffirmed and expanded. This study employed secondary panel data from 2007 to 2018, and 60,000 data points were obtained from 100 nations. The results reveal that the USA is the most productive country, followed by China, India, and Japan in the context of global competitive productivity. Regional productivity scores show that Asia has a superior productivity rank compared to Europe. However, Africa is performing worse than average. Unlike earlier studies, this study shows that macroeconomic, innovation and infrastructural variables mainly determine the MPI score. The main finding of this study is that there is no statistically significant difference in total factor productivity (TFP) among the developed, developing, and least developed countries. Also, there is no significant influence of regions or alliances on TFP across the countries, confirming the global convergence in competitive productivity. The novelty of this study is that certain statistical evidence accurately portrays global competitiveness in terms of productivity.

Comparison of Total Factor Productivity of Rice in China and Japan

Because rice is one of China’s staple foods, studying the total factor productivity (TFP) of rice is of great importance for China’s food security. There are many similarities between rice production in China and Japan. Japan has achieved an effective supply of high-quality rice under the constraints of insufficient production resources and limited environmental capacity. In this paper, we use the DEA Malmquist index method to comparatively analyze the production efficiency of the rice industry in China and Japan, as well as its trends and changes. The contribution of each decomposition index is analyzed by using grey correlation, and kernel density estimation is used to analyze the dynamic evolution of rice productivity in both countries. The empirical results show that rice TFP in Japan is higher than that in China. Technological progress is an important driver of TFP and is the main reason for the difference in rice TFP between the two countries. The concentration of rice TFP distribution in China is decreasing, and regional differences are increasing, whereas in Japan, the opposite trend is observed, with the proportion of areas of high TFP increasing in both countries.

Measurement and Difference Analysis of Total Factor Productivity of Strategic Emerging Enterprises in China

Improving the total factor productivity of strategic emerging enterprises is of great significance for promoting the optimization and upgrading of the industrial structure and achieving high-quality economic development. Based on the data of 2,760 strategic emerging enterprises of China a-share listed companies from 2014 to 2016, this study uses Levinsohn and Pertrin(LP) method to measure the total factor productivity of China's strategic emerging enterprises, and analyzes regional differences in total factor productivity of strategic emerging enterprises. The results showed that during the sample study period, the total factor productivity of China's strategic emerging enterprises decreases first and then increases. From the perspective of different regions, the total factor productivity of strategic emerging enterprises in the four regions showed a "stepwise distribution", the total factor productivity of strategic emerging enterprises in the East, Central and Western regions showed a downward trend, and the total factor productivity of strategic emerging enterprises in the central region showed a downward trend and then an upward trend. From the perspective of regional heterogeneity, Beijing, Shanghai, Guangdong, Shandong, Jiangsu and other provinces have higher total factor productivity of strategic emerging enterprises. However, the total factor productivity of strategic emerging enterprises in Xinjiang, Gansu, Guangxi, Yunnan, Shaanxi and other provinces and cities is low. In order to promote the total factor productivity of strategic emerging enterprises, we can increase R&D investment, link financing constraint and pay attention to regional development differences.

Unpacking Total Factor Productivity on Dairy Farms Using Empirical Evidence

This study examines the farm-level factors that influence differences in total factor productivity (TFP) on dairy farms. To this end, a fixed-effects regression approach is applied to panel data for dairy farms obtained from the Farm Accountancy Data Network for Northern Ireland over the period of 2005 to 2016. The findings are largely consistent with existing empirical evidence, showing that herd size, milk yield, stocking density, and share of hired labour have a positive and statistically significant impact on TFP, while labour input per cow, purchased feed input per cow, and share of direct payments in total farm output have a negative and statistically significant impact. The more complex relationships, namely age, education, and investment, have been unpacked using interaction terms and nonlinear approximation. The impact of age is negative, and the drag on productivity grows as age increases. Capital investment and education both have a positive impact on farm-level TFP, as well as on their interaction. Policy recommendations on strategies and best practices to help dairy farms tackle productivity constraints are suggested.

Green TFP Heterogeneity in the Ports of China's Pilot Free Trade Zone under Environmental Constraints.

In the context of China’s Pilot Free Trade Zone (FTZ), ports have a new opportunity to realize high-quality development. Based on the analysis of the current situation of pollutant emissions from ports in China’s Pilot Free Trade Zones (FTZs), this paper introduces environmental factors into the analysis framework of the total factor productivity (TFP) of ports in China’s FTZs, and uses the Global Malmquist–Luenberger index method to analyze the evolution trend and heterogeneity of green TFP in 28 ports of China’s 19 FTZs from 2011 to 2017. The results show that firstly, the emissions of sulfur dioxide (), nitrogen oxides () and other pollutants in China’s FTZs have been decreasing year by year. Secondly, both the green TFP and the traditional TFP of the ports in FTZs are on the rise. The absence of environmental factors leads to the underestimation of the TFP of ports. For the green TFP, the main source of its growth is technological progress. Thirdly, there is obvious port heterogeneity in the green TFP of FTZ ports. Nanjing Port has the highest green TFP growth rate, with an average annual growth rate of 21.95%. Ningbo Port, which ranks 14th, has an average annual growth rate of 5.46%. Fuzhou Port, which is rated last, has negative growth. Fourthly, there is also obvious types and regional heterogeneity in the green TFP of FTZ ports. When categorized by type, the average annual growth rate of green TFP in inland ports is significantly higher than that of coastal ports. When categorized by region, the descending order of the average annual growth rate of green TFP is the western region, the eastern region and the central region. Fifthly, the green TFP differences among the eastern, central, and western regions, as well as between inland ports and coastal ports, are shrinking. Moreover, the green TFP differences within inland ports and coastal ports and within central ports and eastern ports are also shrinking, implying there may be σ convergence. The conclusions of this paper have important implications for the scientific understanding of the heterogeneity of green TFP growth in ports in China’s FTZs, and how to promote the green development of ports in China’s FTZs under environmental constraints.

Fiscal decentralization and interregional Capital misallocation: evidence from China

Misallocation of factors of production has been recently viewed as a promising explanation accounting for the large difference in total factor productivity (TFP) across countries. This article differs from previous studies by concentrating on interregional capital misallocation and by focusing on the role of fiscal decentralization in shaping misallocation. Using a city level panel data set, we measure intra-provincial and inter-city capital misallocation in China over 2003–2018. The empirical results based on provincial level panel data suggest that fiscal decentralization can lower inter-city capital misallocation while revenue decentralization performs better than expenditure decentralization. We further find that this positive effect is more significant and much larger when it is the market rather than government intervention that is driving the flow of capital. The results are robust to subsample regressions, IV estimations, spatial autoregressions and alternative measurement of interregional misallocation.

Total factor productivity of Chinese industrial firms: evidence from 2007 to 2017

ABSTRACT Accurately measuring industrial total factor productivity (TFP) is considered vital to a country’s economic system. However, in China, the measurement of industrial TFP has been difficult owing to a lack of firm-level data. This study is the first to measure TFP of Chinese industrial enterprises from 2007 to 2017 and makes a rich heterogeneity analysis based on the National Taxation Survey Database (NTSD). Results show that the weighted TFP rises from 3.65 in 2007 to 4.69 in 2017, with an average growth rate of 2.58%. There are obvious differences in industrial TFP among different enterprise sizes, industries, ownership types, export types, and regions, and the growth of TFP tends to have a converge trend. By assessing the ‘Productivity Paradox’ in China, we also find that Chinese exporting manufacturing enterprises are more productive than non-exporting ones.

The importance of spatial differences in total factor productivity: New Zealand, 2001–16

ABSTRACT Using firm-level panel data and estimating production functions for 37 industries, covering the 2001–16 period, this paper finds that firms in the Wellington region of New Zealand are on average about twice as productive as those in the rest of the South Island (which has the lowest average productivity). As to whether ‘place’ effects are the major explanation for such spatial differences or if ‘firm mix’ is more important, this study finds that agglomeration plays only a minor role in determining firm-level productivity levels, while the importance of spatial factors in accounting for the differential between productivity in Wellington and other areas was generally very small.