Trends In Total Factor Productivity Research Articles

Evaluation of the efficacy of implementing Ecosystem-Based Management (EBM) strategies within fisheries across the middle and lower Yangtze River basin lakes in China

To mitigate the decline of fishery resources and the exacerbation of water pollution in Chinese lakes and to enhance the potential and sustainable use of lake fisheries, the Chinese government has actively implemented principles and models of ecosystem-based lake fishery management. This includes promoting ecological aquaculture techniques and the “retiring pens to restore lakes” policy. To assess the effects of ecosystem-based management on Chinese lake fisheries, data from 2007 to 2020 were collected for the Qiandao Lake, Tai Lake, Hongze Lake, Luoma Lake, and Gaobao Lake in the middle and lower reaches of the Yangtze River. The study employed Data Envelopment Analysis (DEA) to assess fishing efficiency through a static input-output efficiency model. Additionally, the Malmquist index and distance functions were used for a dynamic comparison of total factor productivity (TFP) across the five lake management models. The results indicate that trends in technological progress, scale efficiency, and TFP were consistent across the lakes from 2013 to 2020. Qiandao Lake, Tai Lake, and Hongze Lake showed a positive average annual growth rate in TFP, with Qiandao Lake having the highest average annual growth rate of 13.2%. In contrast, Gaobao and Luoma Lakes experienced a negative growth rate in TFP. Furthermore, Qiandao Lake exhibited significantly higher technical efficiency than the other four lakes, effectively achieving an efficient state, followed by Hongze Lake. The study demonstrates that technological progress and variations in scale efficiency are the primary factors driving the increase in TFP in fisheries. Promoting ecosystem-based “fishing for water conservation” aquaculture techniques and restricting aquaculture scale through the “retiring pens to restore lakes” policy contribute to enhancing TFP in ecological fisheries.

The Productivity Slowdown in Advanced Economies: Common Shocks or Common Trends?

This paper reviews advanced‐economy productivity developments in recent decades. We focus primarily on the facts about, and explanations for, the mid‐2000s labor‐productivity slowdown in large European countries and the United States. Slower total factor productivity (TFP) growth was the proximate cause of the slowdown. This conclusion is robust to measurement challenges including the role of intangible assets, rankings of productivity levels, and data revisions. We contrast two main narratives for the stagnating TFP frontier: The shock of the Global Financial Crisis; and a common slowdown in TFP trends. Distinguishing these two empirically is hard, but the pre‐recession timing of the U.S. slowdown suggests an important role for the common‐trend explanation. We also discuss the unusual pattern of labor productivity growth since the start of the Covid‐19 pandemic. Although it is early, there is little evidence so far that the large pandemic shock has changed the slow pre‐pandemic trajectory of labor‐productivity growth.

Total factor productivity of Russian companies: Assessments, trends, and dynamic factors

The article presents the results of the analysis of total factor productivity( TFP) of Russian companies across eight industries — agriculture, extraction, processing, energy, water supply, trade, transportation, and information and communication — during the period of 2012 to 2020. Over this timeframe, there was an observable trend of decreasing TFP among Russian companies cumulatively. Sector-specific analysis reveals an upward TFP trend in agriculture, extraction, and trade sectors, while in information and communication, the metric remained virtually unchanged. Almost across all sectors, except for information and communication, private companies exhibited lower TFP compared to stateowned enterprises (SOEs) with direct government ownership. Furthermore, in the extraction sector, SOEs demonstrated higher TFP than private organizations. However, the TFP of medium and small private companies proved to be higher across all sectors, excluding agriculture and extraction. Additionally, it was found that both private companies and SOEs with direct government ownership lagged behind the productivity of SOEs with indirect government ownership, except for the extraction sector, where these distinctions were negligible

The role of services in India's post-reform economic growth

The services have been the driver of India's overall growth since the onset of economic reforms in the 1990s. The value-added share of services increased from 41 percent in 1990 to 53 percent in 2018. On average, the sector grew at 7.5 percent annually during 1993–2018, contributing to about half of the aggregate economic growth. Using the India KLEMS data, we examine the trends in total factor productivity (TFP) in individual industries within services and their contribution to aggregate TFP growth in the sector during 1993–2018. We find that transport and storage was the highest contributor among various market services industries followed by financial services, while public administration made a relatively high contribution to aggregate non-market services TFP growth. Moreover, we investigate the factors driving TFP growth in the services sector, particularly the role of manufacturing TFP – looking into spillover effect from manufacturing to market services and non-market services. Our results suggest that TFP growth in manufacturing had a significant positive impact on TFP growth in the services sector. Also, we find evidence of significant productivity spillover effect to India's services sector from other countries, particularly the developed countries.

Trends in total factor productivity of sugarcane in Maharashtra: A state level evidence using DEA and SFA approach

Trends in total factor productivity of sugarcane in Maharashtra: A state level evidence using DEA and SFA approach

Productivity and Exports: An Industry-Level Analysis of the Service Sector in India

The large share of the service sector and its status as a net exporter raise concerns about the sustainability of such growth for a developing economy like India. Given the context, the present article attempts to examine the productivity dynamics of the service sector in India at the industry level. We use the India KLEMS dataset to identify the trends in total factor productivity (TFP) growth and the growth in factor inputs. By uniquely combining the India KLEMS dataset with the trade values in the Extended Balance of Payments Services classification, we examine the dynamics between TFP growth and exports of services using panel vector autoregression technique. The trends in factor inputs show that there is capital deepening within services and the growth in employment remained comparatively lower. The results indicate a feedback effect of exports and productivity growth within the service sector. Additionally, we note that the use of more skilled labour that is better equipped to handle new technology also contributes to growth in the service sector, both in terms of productivity and exports.

What is the State of Groundnut Production in India? Uncovering the Productivity and TFP Dynamics of the Major Oilseeds: Evidence from Andhra Pradesh, India

The purpose of this research is to examine the variations in productivity and the trends of Total Factor Productivity (TFP) of the major oilseeds grown in the state of Andhra Pradesh, with a focus on Groundnut. The study utilizes secondary data sources, including the National Statistical Office (NSO) and India Stat, to analyze and interpret the data using descriptive statistics, One-Factor ANOVA analysis, and TFP calculations. The findings of the study indicate that Groundnut has been one of the highest yielding oilseeds in Andhra Pradesh and has exhibited positive trends in TFP over the past two decades, specifically highlighting the years 2009-2010, 2019-2020, and 2020-2021, during which the TFP of Groundnut was particularly high, reflecting an increase in production efficiency. This research provides a comprehensive examination of the productivity of oilseeds in Andhra Pradesh and sheds light on the significance of Groundnut in the state's oilseed production, offering valuable insights into the trends of TFP in Groundnut production and the importance of continuing to monitor and improve efficiency in the sector.

Charting the Course: Total Factor Productivity Trends in Croatia Post-pre-bankruptcy Act

The synthetic control method (SCM) is a valuable tool for unbiased pre-bankruptcy reform analysis in economic policy evaluations. This study utilizes SCM to assess the impact of the Financial Operations and Pre-Bankruptcy Settlement Act (AFOPBS) on Croatia's total factor productivity (TFP). Control units and weights were meticulously chosen to construct a synthetic control for Croatia, creating a counterfactual scenario for the reform's absence. The policy's impact was quantified by comparing TFP growth post-policy between Croatia and its synthetic control. Placebo tests confirmed the results' significance, and further validation was achieved through panel difference-in-differences analysis (PDID). Our findings show that the pre-bankruptcy reform in late 2012 effectively reduced the gap between Croatia and its synthetic control throughout the post-treatment years. However, it had two short-term adverse impacts and a subsequent recovery-like phase. These effects were statistically significant and confirmed by cross-validation. In conclusion, Croatia's pre-bankruptcy reform significantly influenced TFP volatility, highlighting SCM's effectiveness in evaluating economic policies, especially those crucial for economic growth

Trends in total factor productivity in Greece and its determinants during the period 2005-2019

Trends in total factor productivity in Greece and its determinants during the period 2005-2019

Total factor productivity of high coal-consuming industries and provincial coal consumption: Based on the dynamic spatial Durbin model

To promote the coordinated governance of atmospheric pollutants and greenhouse gases (GHGs), and to meet the emission goals of carbon peak and carbon neutrality, China should optimize the spatial pattern of its coal consumption. The present study employs the global Malmquist–Luenberger index to calculate the total factor productivity (TFP) of high coal-consuming industries in different provinces in China. Taking TFP as the independent variable, provincial coal consumption (PCC) as the dependent variable, and urbanization rate (UR) and regional gross domestic product (GDP) as the control variables, we establish a dynamic spatial Durbin model of TFP and PCC. We also use spatial effects decomposition to analyze the spatial influence relation between TFP and PCC. The results show a fluctuating upward trend of TFP in China overall, but the TFP in the eastern region is higher than that in the central and western provinces. The direct effects show a significant U-shaped relation between TFP and PCC, where PCC trends down with an increase in TFP. The indirect effects show an inverted U-shaped relation between local TFP and PCC in neighboring provinces. Finally, according to the estimated results, we propose some policy recommendations for China's government to control coal consumption.

Technology and productivity: a critique of aggregate indicators

Economic analysts have used trends in total factor productivity (TFP) to evaluate the effectiveness with which economies are utilizing advances in technology. However, this measure is problematic on several different dimensions. First, the idea that it is possible to separate out the relative contribution to economic output of labor, capital, and technology requires ignoring their complex interdependence in actual production. Second, since TFP growth has declined in recent decades in all of the developed market societies, there is good reason to believe that the decline is an artifact of the slower rates of economic growth that are linked to austerity policies. Third, reliance on TFP assumes that measures of the gross domestic product are accurately capturing changes in economic output, even as the portion of the labor force producing tangible goods has declined substantially. Finally, there are other indicators that suggest that current rates of technological progress might be as strong or stronger than in earlier decades.

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.

Controlling for the effects of climate on total factor productivity: A case study of Australian farms

Estimates of agricultural Total Factor Productivity (TFP) can be highly sensitive to both short-run climate variability and long-term climate change. This is particularly true in Australia where drought impacts are responsible for most of the annual volatility in official farm TFP statistics. While climate variability can obscure short-term productivity trends, researchers have typically assumed that long run TFP trends are largely unaffected. However, in the presence of global climate change this assumption becomes problematic. For example, in Australia, shifts to higher temperatures and lower winter season rainfall over the last 20–30 years have had a significant negative impact on agricultural productivity. This study presents a framework to account for the effects of climate variability on TFP estimates. In contrast with previous work, this approach applies a reduced form machine learning based model of farm production to generate synthetic (climate-adjusted) farm-level input and output data sets. It therefore has advantages in terms of flexibility—since the synthetic datasets can be combined with any existing TFP estimation framework. In this study, the approach is applied to estimate climate-adjusted TFP indices (TFP under an assumption of constant long-run average climate conditions) for a range of Australian agricultural sectors.

Heterogeneity of Green TFP in China’s Logistics Industry under Environmental Constraints

In the context of China’s supply-side structural reform and the concept of green development, we introduce the energy and environmental factors into the analytical framework of the total factor productivity (TFP) of the logistics industry and use the Global Malmquist–Luenberger index method to analyze the evolution trend and heterogeneity of green TFP in the logistics industry of 30 provinces (cities and districts) in China from 2003 to 2017. The results show that, firstly, the green TFP and traditional TFP of China’s logistics industry are both on the rise and the absence of energy and environmental factors will lead to the overestimation of TFP of the logistics industry. Whether it is green TFP or traditional TFP, the main source of its growth is technical progress. Secondly, there is obvious regional heterogeneity in green TFP of logistics industry. Under the three regional division standards, the average annual growth rate of green TFP is from high to low in the order of eastern, western, and central regions. Under the eight regional classification standards, the eastern coastal economic zone, the southern coastal zone, the northern coastal zone, the northeast region, the middle reaches of the Yellow River, the southwest region, the middle reaches of the Yangtze River, and the northwest region are in order from high to low. Thirdly, there is obvious interprovincial heterogeneity in green TFP of the logistics industry. The highest growth rate of green TFP is in Zhejiang, followed by Jiangsu and Guangdong, and the slowest growth rate is in Chongqing. The technical progress of logistics industry in most provinces contributes more to the growth of green TFP. Fourthly, the differences of green TFP in the three regions of the east, center, and west are shrinking, which may be σ convergence, but the differences among the three regions are expanding. Compared with the existing literature, this paper applies the measurement framework of green TFP to China’s logistics industry and investigates the regional and provincial heterogeneity of green TFP in logistics industry. The conclusions are significant to understand and grasp the heterogeneity of green TFP growth in China’s logistics industry under environmental constraints and how to promote the development of green logistics in China.

Analysis of green total factor productivity trend and its determinants for the countries along silk roads

AbstractIn coping with increasing energy consumption and the consequential environmental pollution, green development is becoming an important part of social development. Since the inauguration of China’s Silk Road Economic Belt Initiative (BRI), economic cooperation between China and other countries along BRI has seen much growth. Therefore, it is of great significance to study the levels of green total factor productivity (GTFP) of the countries in the Silk Roads Economic Belt (B&R) and examine how these levels vary spatially and evolve temporally. In this paper, a panel regression analysis with DEA windows is used to study the spatiotemporal trends of the levels of GTFP. The results are: (1) The B&R countries have seen an increase in their overall levels of GTFP over time; (2) There are regional differences in the levels of GTFP, with higher efficiency in Western Asia and Central and Eastern Europe, and lower efficiency in Southeast and Central Asia; (3) The analysis of GTFP for countries in these regions shows that the efficiencies of most countries have been improving, with that of only a few countries have been decreasing; (4) By using regression analysis, we find that variables such as import, export, industrial structure, and urban affect the B&R countries with growing GTFP.

Research on the green total factor productivity and its influencing factors based on system GMM model

The entropy method is used to calculate pollution comprehensive index reflecting provincial environmental pollution level. On this basis, the Malmquist productivity index is used to study the regional green total factor productivity (GTFP) in China, and the system generalized moment method is used to explore the influencing factors of GTFP. Firstly, the results show that the provinces with lower environmental pollution comprehensive index are mainly in the western and eastern regions, while the provinces with higher pollution comprehensive index are mainly distributed in the central inland areas. Secondly, GTFP shows an N-type upward trend, which is basically consistent with the trend of total factor productivity. The overall GTFP in the east is on the rise, which is promoted by technical efficiency. The GTFP in the central and west has declined, and the main factor restricting their improvement is technological retrogression. The expansion of production scale in the east and west can contribute to the improvement of GTFP. The central region is at a stage where the scale of returns is not economic, and it is more important for promoting technological progress. Lastly, the industrial structure can significantly inhibit the increase of GTFP, however, the energy consumption structure, FDI and pollution control investment have a significant role in improving GTFP.

The Measurement of Total Factor Productivity and Its Spatial Evolution Trend in Four Regions of China

In this paper, we aim to contribute to the growth literature and provide suggestions for improving China's TFP by measuring the Total Factor Productivity (TFP) of 30 provinces (excluding Tibet) in China, and analyzing the spatial evolution trend of Total Factor Productivity (TFP) in China's four major economic regions. The study shows that the TFP in China is not high and shows a downward trend from 2000 to 2015. The scale efficiency is the main driving force for the promotion of TFP in China. In addition, the Total Factor Productivity of each province shows spatial autocorrelation in most years and is relatively backward in the central region, western region and northeastern region of China, where some provinces have relatively large negative effects on the surrounding areas and relatively strong radiation effects. The provinces with high TFP clusters are more in the eastern regions, but the radiation effects are not obvious.

Statistical measurement of total factor productivity under resource and environmental constraints

The existing literature on the measurement of total factor productivity (TFP) only considers capital and labour input, ignoring resource and environmental factors. This paper takes resource and environmental factors into the framework of TFP measurement by constructing the environmental comprehensive indexes. Then the DEA-Malmquist index method is employed to analyze TFP from 1978 to 2016 under resource and environmental constraints. The results show that under resource and environmental constraints, China's TFP (overall) is at a slow rising stage. China's TFP is closely related to macroeconomic fluctuation, macroeconomic regulation, directional control and reform of the economic system—having impact on the changing trend and fluctuation extent of TFP.

Productivity Growth in Organised Manufacturing Sector in India:Evidence from Technology Intensive Classification of Industries

This paper examines the trends in productivity, both labour productivity and Total Factor Productivity (TFP), of organised manufacturing in India. It uses the growth accounting technique based on value added and gross output production framework for assessing the productivity dynamics of 62 industries at the 4-digit level of disaggregation during the period of 1980 to 2007.The study finds a marked shift of industrial composition towards technology and skill intensive production activities. These segments have witnessed considerable growth in labour productivity and total factor productivity, the latter being more pronounced since 2000. Though TFP trend is highly cyclical, there is evidence of a revival in recent period. This suggests that Indian manufacturing has been experiencing sizeable technical change and dynamism of late. However, for a large number of industries, especially the traditional low-technology intensive sectors, the results are sensitive to the method of measuring TFP.

A Decade Trend of Total Factor Productivity of Key State-Owned Forestry Enterprises in China

State-owned forestry enterprises are important elements of the forestry economy in China. The operational efficiency of such enterprises depends on technological progress and other input factors. Total factor productivity (TFP) is an important means to evaluate the efficiency of technical elements. The growth of production efficiency can be classified into efficiency variation and technical variation. The TFP of 135 key state-owned forestry enterprises in the northeast, southwest, and northwest regions of China in 2001–2011 was measured through Malmquist–data envelopment analysis. The technological progress of the state-owned forestry enterprises positively affected TFP variation, but technical efficiency only slightly increased and scale efficiency even negatively affected TFP variation. The average growth rate of TFP in the northwest region is higher than those in the northeast and southwest regions. The Western Development Program of China increasingly contributes to the economic development of western areas. The increasing investment of the government in science and technology accelerates the development of forestry economy in China.