Total Factor Productivity Measures Research Articles

A new approach for estimating trade elasticities and measuring the productivity effects associated with trade

Although there is an extensive literature on the measurement of total factor productivity (TFP) and its decomposition, the benefits of international trade for aggregate TFP have been less frequently studied. Combining the theoretical trade models and the so-called proxy-variable approach, this paper provides a new method for estimating trade elasticities based on a production model where trade elasticities and technological parameters are estimated simultaneously. Our proposed model is used to analyse the main determinants of the degrees of openness (embeddedness) of countries and sectors. Using data from the World Input-Output Database, we find that the pecuniary productivity gains attributable to embeddedness tend, on average, to offset its effect on sectors’ underlying productivity. We also use the model to measure the different factors affecting productivity changes due to the Covid-19 pandemic. We find that the trade-related consequences are important in explaining the contraction in countries’ productions.

Research on the Measurement of Gold Green Total Factor Productivity in the Yangtze River Economic Belt under the Background of Green Finance

With the continuous growth of the economy, the contradiction between finance and the environment has become increasingly prominent, and green finance has attracted much attention due to its sustainable development function. This study integrates green financial efficiency into green total factor productivity based on the two-stage super efficiency unexpected SBM model, and constructs the financial green total factor productivity of the Yangtze River Economic Belt from 2013 to 2021. The convergence model is used to verify the convergence of productivity development. The research results indicate that, in terms of time dimension, the efficiency of green finance in the Yangtze River Economic Belt is relatively stable, the level of financial ecological performance first decreases and then increases, and the financial feedback ecological performance remains at a high level for a long time. The overall trend of financial green total factors is increasing, and financial green technology innovation is an important driving factor for the increase; In terms of convergence testing, no significant alpha convergence was found in the upper, middle, and lower reaches of the Yangtze River Economic Belt, and the regional differences did not significantly decrease. However, there was overall beta convergence, indicating a "catch-up effect" from low productivity provinces to high productivity provinces.

Unraveling the Productivity Paradox: Evidence for Germany

Abstract Despite massive digitization efforts, the German economy has experienced a marked slowdown in its productivity growth. This paper empirically analyzes three prominent explanations for this development. First, using a novel quarterly utilization-adjusted total factor productivity measure for the German economy, we find that the slowdown in U.S. productivity growth since the mid-2000s had only a negligible impact on the German productivity trend. Second, the structural shift towards services in the German economy explains a sizeable share of the weaker aggregate productivity gains. This transformation process is associated with a strong labor market performance. And third, employing a novel identification procedure, we show that technological progress in the German information and communication technology (ICT) producing sector stimulates both, aggregate output and employment growth. Its effect on aggregate productivity is, however, small.

Evaluation of regional comprehensive development efficiency under low-carbon policy: based on optimized DDF-GML combined with unsupervised clustering method

In the study of urban development, it is very important to evaluate the influence of production factors reasonably and efficiently for the region to achieve efficient development. The principal aim of this investigation is to amalgamate the conventional measurement model characterized by robust interpretability with the non-parametric model characterized by limited interpretability, thereby enhancing the precision of research outcomes. Towards this objective, the study employs an optimized directional distance function integrated with a global Malmquist–Luenberger index to formulate a comprehensive total factor productivity measurement framework. In elucidating the homogeneous attributes of regions, departing from prior methodologies reliant on manual or direct algorithmic partitioning, this paper employs the K-means clustering algorithm for index discernment, abstracting the concept of K-means clustering centroids to encapsulate regional homogeneity, thereby delineating results through the visualization of regional development potential maps and the evolution of centroid-based clustering trend maps. The findings of the investigation illuminate common patterns of change across disparate regions, proposing a strategy for leveraging regional resource endowments towards a cohesive framework, thereby transcending constraints imposed by production efficiency limitations. Amidst the backdrop of the COVID-19 pandemic, this study draws upon provincial-level data spanning from 2000 to 2018 in China. The conclusive analytical outcomes underscore the pivotal role of energy factors in regional development efficiency, particularly within high-potential development regions, followed by the capital and labor factors. Concurrently, the study discerns a discernible hierarchical pattern among areas of development potential, which exhibits correlation with factor mobility dynamics.

Economic and Environmental Decomposition of Luenberger-Hicks-Moorsteen Total Factor Productivity Indicator: Empirical Analysis of Chinese Textile Firms With a Focus on Reporting Infeasibilities and Questioning Convexity

We discuss an environmental Luenberger–Hicks–Moorsteen (LHM) total factor productivity (TFP) indicator and its decomposition that incorporates a negative externality into the measurement of economic performance. Special cases of a generalized environmental directional distance function are involved in the definition of this LHM indicator and its proposed decomposition. We also seek to test whether changes in the convexity assumption provoke differences in the TFP measures. We apply two specifications of the by-production nonparametric environmental technology to implement this LHM TFP. This LHM TFP indicator decomposes into three terms representing technical change, technical inefficiency change, and scale inefficiency change. The changes in the environmental TFP for China's textile industry is then estimated for the period from 2001 to 2010. We report infeasibilities and we show the differences of the proposed framework for the decomposition of the LHM indicator depending on the convexity assumption. The results suggest there has been an increase in the TFP of China's textile industry: the amount depends on the convexity or not of the technology. The environmental performance is poorer than the economic one. Moreover, contradictions between convex and nonconvex LHM indicators for individual observations appear for a substantial part of the sample.

What is the degree of high-quality development of oil–gas resource-based cities in China: based on a new total factor productivity measurement method

What is the degree of high-quality development of oil–gas resource-based cities in China: based on a new total factor productivity measurement method

The impact of financing constraints on total factors productivity : evidence from property law reform in China

ABSTRACT This paper examines the effect of financing constraints on total factor productivity (TFP) using China’s Property Law reform as a natural experiment, employing a difference-in-difference (DID) model. The study reveals that easing financing constraints significantly enhances TFP. These results hold through parallel trend tests, placebo time checks, treatment group randomization, and regression using an alternate TFP measurement. Quantitatively, alleviating the financing constraint by one standard deviation can boost TFP by 26.5% of its standard deviation. It is noted that state-owned enterprises are more responsive to property rights protection than private and foreign enterprises.

A productivity indicator for adaptation to climate change

This study draws on economic index theory to construct a new indicator for adaptation to changing environmental conditions, most notably climate change, which may shift the production technology over time. Such environmental shifts are largely exogenous to firm decision making, for instance investments in research and development, which may also lead to technology change. Few existing measures of total factor productivity (TFP) make this distinction, between exogenous environmental shifts and shifts due to firm decision making or innovation. We introduce a nonparametric Luenberger productivity indicator for adaptation, which allows for decomposition of standard technology and efficiency change measures into both environmental and production components. We apply this framework to agricultural production in the US Mississippi River Basin for recent decades, working with USDA Census of Agriculture data at the county level and key climate conditions. We also match the production and climate data to estimates of Nitrogen loading over time, to incorporate water quality into the adaptation indicator. Our results indicate sustained overall productivity growth, for both agricultural production and nitrogen loading reductions, driven by technology gains outweighing efficiency losses. Decomposing further to the adaptation component, our results indicate modest overall adaptation gains, driven by both adaptation efficiency and technology gains.

Analysis of productivity performance of real estate and construction firms in Indonesia

Sustained total factor productivity (TFP) growth of firms is essential to achieve sustained supply growth, which is necessary to solve the housing shortage and affordability problems. Existing productivity literature focuses on large firms whilst smaller firms make a significant contribution to the total supply. This paper investigates the productivity of 363 Indonesian real estate construction firms of medium and large sizes. We estimate Färe-Primont TFP measures under the meta-frontier framework, accounting for differences in the overall technologies under which each group of firms operates. Results deliver several important findings. First, large firms have higher productivity levels than medium firms, but average productivity levels are very low. Second, the productivity growth of large and medium firms is negative, which contributes to the growth of the housing shortage in Indonesia from 2012 to 2016. Third, in case of easier transfer or spill-over of production technologies between two firm groups, firms could enhance further their efficiency and productivity. Fourth, those factors related to branch numbers, legal structure and experience of doing business have positive correlations with the productivity performance of firms in both large and medium groups. Additionally, medium firms could gain higher productivity by diversifying their income. Importantly, our study shows evidence to support that government subsidies have a positive influence on the productivity level of medium firms.

Total factor productivity gap between the “New” and “Old” Europe: an industry-level perspective

ABSTRACT The main goal of this article is to investigate the source of productivity growth for the Central and Eastern Europe (CEE) countries, at the industry level, based on the Schumpeterian creative destruction mechanisms. Our study provides valuable insights into productivity drivers for the CEE countries, which are vital for the policy makers. To measure productivity, we calculate the Total factor Productivity (TFP) measure based on the raw labour, at the industry level. There is a substantial productivity gap at the industry level between the CEE and core European countries (EU14). The result of econometric analysis suggests that the further a country lies behind the frontier, the higher is the rate of TFP growth, i.e. the convergence process.

Visual Analysis of Green Total Factor Productivity Research in China based on CiteSpace

In order to achieve high-quality economic development and ecological civilization construction, we must pay attention to the continuous improvement of green total factor productivity. In this context, it is particularly important to systematically review the literature in order to better grasp the current status and development trend of green total factor productivity research. Based on this, we use 596 CSSCI papers in the database of China Knowledge Network from 2009 to 2023 as a sample, and use CiteSpace software to visualize and analyze the "green total factor productivity". The results show that the number of papers has increased significantly in general, and the core group of authors has taken shape, but the publishing institutions are independent of each other and have not yet formed cooperation. The research stage can be roughly divided into three stages: budding stage, exploratory stage and rapid growth stage, and the research hotspots focus on the measurement and influence factors of green total factor productivity.

Research on the Measurement and Spatial Spillover Effects of Green Total Factor Productivity in Western Regions

Green total factor productivity emphasizes the progress of technology and the improvement of factor input efficiency. The western region is an important ecological barrier in China, and more attention should be paid to green development. Firstly, the paper selects the panel data of 12 provinces in western China from 2007 to 2020, and measures their green total factor productivity and its decomposition factors using the EBM model and GML index. Based on this, the paper selects control variables such as industrial structure, government behavior, economic development level, foreign investment and scientific research investment, and uses the spatial Durbin model to study the spillover effect of green total factor productivity in western China, it was found that the driving force of green total factor productivity in the western region to promote the economy mainly comes from the efficiency of green technology. Foreign investment has a positive effect on it, while government behavior is opposite to the level of economic development, and the industrial structure has a positive spillover effect on it, while the level of economic development is opposite. Therefore, suggestions have been put forward to improve the level of green technology, promote industrial structure upgrading, and focus on the quality of foreign investment introduction, in order to provide reference for the development of green total factor productivity in the western region.

Value Co-creation through IoT: An Investigation of the Synergies between Market and Government Policies in the Equipment Manufacturing Industry of Shandong

Currently, many equipment manufacturing enterprises in China lack effective management integration. While research on total factor productivity measurement and influencing factors exists, there is a lack of systematic research from the perspectives of different stakeholders, both inside and outside the enterprise market and government. To address this gap, this study examines the differences in demands for integrated management among enterprises of different ownership and sizes. Using the theory of value cocreation and Internet of Things technology, 273 enterprises in 16 cities in Shandong Province were sampled. The study also compares the impact of market fiscal and tax incentives on enterprise resource allocation and innovation performance, as well as the differences between "tangible and intangible" means of the market and government. The Internet of Things technology is utilized to build integrated management systems and verify the mechanisms of market incentives and government policies. Results reveal that market incentive policies have a differential effect on enterprises of different ownership and sizes, and the total factor productivity of sample enterprises is significantly and positively correlated with the strength of market incentive policy. Moreover, there are noticeable differences in the impact of government subsidy policies on enterprises of different sizes. Small and medium-sized enterprises do not respond significantly to fluctuations in government subsidy policies, while large enterprises are more sensitive to such fluctuations.

Measurement of Green Total Factor Productivity in China's Regional Manufacturing Industry—Taking the Yangtze River Delta Region as an Example

Measurement of Green Total Factor Productivity in China's Regional Manufacturing Industry—Taking the Yangtze River Delta Region as an Example

Re-Measurement of Agriculture Green Total Factor Productivity in China from a Carbon Sink Perspective

Accurate measurement of agricultural total factor productivity (AGTFP) is crucial to measure the level of sustainable agricultural development, and agricultural carbon sink is an important element to leverage the development of green transformation. Few studies have incorporated agricultural carbon sink into the measurement framework of AGTFP, and the evolutionary dynamics and related spatial effects of Chinese AGTFP from the perspective of carbon sinks are unclear. On this basis, the paper used a provincial-level agricultural panel data set of China from 2000 to 2019 to measure the provincial indicators of agricultural carbon sinks, CO2 emissions and agricultural non-point source pollution. Then, we incorporated these environmental factors into the measurement framework of AGTFP and used the SBM-DEA model to calculate the Chinese AGTFP from the perspective of carbon sinks. We further analyzed the spatial and temporal divergence and convergence of AGTFP in China using Moran’I and spatial econometric models. We found that after measuring AGTFP, including agricultural carbon sinks, 28 out of 30 Chinese provinces showed an increased trend, but the development gap between regions was obvious. The spatial econometric model showed a significantly positive spatial correlation between the AGTFP of each province and did not have absolute α-convergence and absolute β-convergence characteristics. After adding the control variables of resource endowment of each province, it showed conditional β-convergence characteristics, and the spatial spillover effect of China’s AGTFP was increasing. Finally, the paper proposed policy recommendations for the sustainable and coordinated development of China’s agricultural regions in response to the research findings.

Measuring environmentally sustainable growth in agriculture across the EU countries based on Solow residual-based MFP

Scientific publications on environmentally sustainable or green growth in agriculture are increasingly becoming more important but empirical research is scarce. In empirical studies, the most commonly accepted way to assess environmentally sustainable or green growth in agriculture is based on measures of total factor productivity (TFP) or multifactor productivity (MFP) growth. Both measures are important for analytical and monitoring tools that can help to better understand the factors affecting output growth as well as the determinants of changes in production factors (labour and produced capital) in agriculture. Growth of TFP or MFP is achieved through the application of technologies and advanced production practices that result in higher output from the same amount, or lower inputs (labour and produced capital).Conventional TFP and MFP are not suitable for the assessment of environmentally sustainable growth in agriculture because both indicators do not include environmental variables such as environmental pollution and natural capital. There is a lack of comparative empirical studies between EU countries. This study focuses on the problem of measuring environmentally sustainable growth in agriculture. The aim of this study is twofold: firstly, to develop a framework for the assessment of environmentally sustainable growth in agriculture, based on information collected in public databases; and secondly, to empirically analyse environmentally sustainable growth in agriculture in EU countries over the long period. The environmentally adjusted multifactor productivity (EAMFP) growth measure was applied to assess environmentally sustainable growth in agriculture of the EU’s countries. For analysis, the environmental pollution of agricultural production was expressed as net GHG emissions, and natural capital was expressed as the quality-adjusted agricultural land area.The research was conducted using literature overview, decomposition technique and cluster analysis method. The 28 EU countries (including the United Kingdom, which was a member of the EU until January 1, 2020) were included in the empirical analysis. The analysis covered the period between 2005 and 2019 and a five-year average annual change rates (2005-2009 and 2015-2019 respectively) were used to compare the environmentally sustainable growth in agriculture between the beginning and the end of the considered period, as is common in most agricultural growth studies.The findings show that pollution-adjusted GDP growth in agriculture was achieved in less than a five of the EU countries at the beginning of the considered period, and in most of the EU countries at the end of the considered period. In most of the EU countries, the environmentally sustainable growth in agriculture was mainly determined by technological progress, while the slow change in environmental pollution (net GHG emissions) did not have a significant contribution to agricultural growth in all EU countries. Following the hierarchical clustering method, three significantly different clusters of the EU countries were identified in terms of gross added value growth and technological progress in agriculture of EU countries in the context of environmentally sustainable growth.

A new Malmquist-type green total factor productivity measure: An application to China

China's GDP has shown a swift rate of increase over the past forty years, and rapid economic development has also brought serious environmental problems. This research is based on green total factor productivity (GTFP) combined with the sound characteristics of the Malmquist and Luenberger indices, and by defining technical efficiency in terms of the orientation-distance function, a new model of the green Malmquist productivity index is recommended. GTFP is extended to the framework of the Malmquist index. We also break down this new Malmquist productivity index (MPI) into the three factors of efficiency changes, pure technology changes, and scale efficiency changes. The empirical application studies 30 Chinese provinces during the period between 2007 and 2017 as the tested sample. The estimated result shows that GTFP in China is on the rise, but the GTFP growth rate is decreasing, mainly due to a slowdown in the rate of technical progress. Furthermore, the empirical study also finds a significant difference among the eastern, central, and western regions of China.

Alternative capital asset depreciation rates for U.S. capital and total factor productivity measures

The U.S. Bureau of Economic Analysis (BEA) and the U.S. Bureau of Labor Statistics (BLS) use estimates of depreciation rates for structures and equipment to construct estimates of capital stock from data on capital investments. The depreciation rates are based on research by Frank C. Wykoff and Charles R. Hulten from the 1980s. More recent studies by Statistics Canada, from 2007 and 2015, use Canadian data on used asset transactions from Canada’s Annual Capital and Repair Expenditures Survey of establishments. They found faster depreciation rates, especially for structures. Sheharyar Bokhari and David Geltner’s 2019 study of U.S. used asset prices also found faster depreciation rates for structures. To illustrate the potential effects of implementing these estimates from newer studies, we created a concordance to match Canadian to U.S. asset categories. We reestimated BEA capital stock measures and the BLS capital and total factor productivity (TFP) measures using depreciation rates based on the Canadian Annual Capital and Repair Expenditures Survey. Using these faster depreciation rates results in substantially lower estimates of net capital stocks and higher estimates of depreciation in BEA accounts but has minimal effects on growth rates of TFP in the BLS accounts.

Measurement of Green Total Factor Productivity and Its Spatial Convergence Test on the Pig-Breeding Industry in China

The pig-breeding industry is one of the pillar industries of China’s agriculture. Improving the green total factor productivity of pig breeding is the basis for ensuring the stable supply of pork, and is also the key to the green transformation of the pig industry. The existing studies about the green total factor productivity of pig breeding lack an analysis of regional coordination and the spillover of spatial technology efficiency at the macro level, and most studies focus on the impact of agricultural production’s environment pollution and other undesirable outputs. Based on the input–output index system of the pig-breeding industry’s green production, the DDF directional distance function model and the Malmquist–Luenberger (ML) productivity index were combined to measure the green total factor productivity of the pig-breeding industry. Moran’s I-Theil index model was used to measure and reveal the technical efficiency differences among the dominant regions of the pig-breeding industry in China and the σ-convergence test was adopted to reveal the convergence trend of green total factor productivity. The results showed that: (1) The growth level of green total factor productivity of pig breeding in China was generally low from 2006 to 2018, and there were obvious regional and scale differences. (2) The green total factor productivity of pig breeding in each province had spatial autocorrelation; that is, there was technology spillover. From 2006 to 2018, with the advance of time, a pattern of gradual evolution from low-level equilibrium to high-level imbalance was formed. (3) Through the convergence test, the convergence trend of large and medium-scale development between different regions fluctuated, while the convergence trend of small-scale development between different regions was not obvious. Therefore, it is necessary to increase investment in technological innovation, promote the large-scale and standardized development of the pig-breeding industry, and strengthen the promotion of technology in producing areas with advantages in pig breeding.

Measurement of China's Green Total Factor Productivity Introducing Human Capital Composition.

In the face of resource and environmental problems caused by extensive economic development, China has put forward a green development strategy. Scientific measurement and analysis of green total factor productivity (GTFP) is of great significance for achieving high-quality economic development. By introducing the human capital composition, including education, health, scientific research, and training, this paper study adopts the Slack Based Measure-Global Malmquist-Luenberger (SBM-GML) index to re-measure the GTFP and its decomposition of 30 provinces, municipalities, and autonomous regions in China from 2000 to 2019. The results show that: (1) China’s GTFP has a fluctuating growth trend, with an average annual growth rate of 2.31%. (2) In terms of its decomposition, technical progress is the main force driving GTFP growth, with a contribution rate of 1.59%; the improvement of technical efficiency is a secondary driving force, with a contribution rate of 0.71%. (3) The measurement results of GTFP, considering the human capital composition, are generally higher than those without consideration, and the GTFP growth under the two modes shows a trend of “high in the east and low in the west”. The conclusions have enlightening significance for improving GTFP and the growth potential of the economy in China.