Agricultural Total Factor Productivity Research Articles

Integrating the rent gap into the ground rent theory. Measurement and implications for different models of agriculture

The aim of this paper is to establish a conceptual framework for the rent gap theory in agriculture and to identify potential implications of the rent gap for socio-economic processes occurring in agriculture and rural areas. From an empirical perspective an attempt was made to determine the state and dynamics of the rent gap phenomenon in agriculture in Poland, considering the regional division. The authors used income approach and Capital Asset Pricing Model (CAPM) for estimating the capitalization rate. It was found that rent gap is a permanent phenomenon in Polish agriculture, the dynamics of which largely depends on macroeconomic factors. A widening rent gap may result in the withdrawal of land from farming and a decline in total factor productivity in agriculture. Thus, the implications of the rent gap may relate to food security. Measuring the rent gap can be a step towards determining the quasi-market value of environmental amenities of land.

Agricultural Total Factor Productivity Based on Farmers' Perspective: An Example of CCR, BCC, SBM and Technology Optimization Malmquist-Luenberger Index

Agricultural Total Factor Productivity Based on Farmers' Perspective: An Example of CCR, BCC, SBM and Technology Optimization Malmquist-Luenberger Index

Does internet use promote agricultural green development?Evidence from China

Based on the panel data of CFPS2016, 2018 and 2020 as samples, this paper empirically investigates the green development effect of internet use on farmers from the perspective of agricultural green total factor productivity. The research shows that farmers' internet use has significantly promoted the improvement of agricultural green total factor productivity. The mechanism analysis shows that farmers' internet use promotes the improvement of agricultural green total factor productivity in two ways: land transfer and adoption of agricultural production services. The threshold effect analysis shows that it is only when the level of human capital exceeds the threshold that farmers' internet use can have a positive impact on agricultural total factor productivity. The expansion analysis shows that in the context of urbanisation and marketization development, there is a substitution effect between urban development and internet use by rural households in the eastern region, and a complementarity effect between urban development and internet use by rural households in the central and western regions.

Can Agricultural Industry Integration Reduce the Rural–Urban Income Gap? Evidence from County-Level Data in China

The improvement in urban production efficiency has led to income distribution being skewed towards urban labor, thereby widening the urban–rural income gap. However, integration of the agricultural industry at the county level can accelerate the flow of production factors between industries. Therefore, this study evaluates the degree of agricultural industry integration at the county level using the entropy weight method and explores its impact on the urban–rural income gap, based on sample data from 1122 counties in China spanning from 2014 to 2021. The research findings reveal the following: (1) The fixed model demonstrates that enhancing agricultural industry integration can significantly narrow the urban–rural income gap; (2) The mediating model indicates that this narrowing effect can be achieved by improving the green total factor productivity of agriculture; (3) Regional heterogeneity analysis indicates that the impact of agricultural industry integration is more pronounced in the central region and main crop production areas; (4) The results of the spatial Durbin model demonstrate that agricultural industry integration also exhibits a significant positive spatial spillover effect on neighboring areas. The outcomes of this study contribute to enriching the research on agricultural industry integration for green and low-carbon agricultural development, further promoting the development of county-level agricultural industry integration, and providing valuable insights for other similar countries.

New evidence of the driving forces behind Brazil's agricultural TFP growth—A stochastic frontier analysis with climatic variables and land suitability index

AbstractUsing panel data from 510 Brazilian micro‐regions in three census years (1995, 2006 and 2017), this study presents a productivity decomposition for the Brazilian agricultural sector using stochastic production frontier methods that account for the effects of rainfall, temperature and the land suitability index. We also calculated the total factor productivity (TFP) index and decomposed it into technical efficiency, technological change, scale efficiency and environmental efficiency. This article thus provides a new and more realistic assessment of recent Brazilian agricultural productivity growth. In recent decades, Brazilian agriculture has become widely known for presenting fast productivity growth; however, our results suggest that a lower TFP growth rate than previous estimates (1.96% per annum) and the overall effects of climate change could potentially compromise Brazilian agricultural TFP growth in the long run. Our findings might thus generate insights for agricultural and regional policies to increase efficiency in the sector and promote sustainable agricultural development in Brazil, which will contribute to the sector's competitiveness in international markets, the country's social and economic welfare, and environmental conservancy.

How does the development of rural broadband in China affect agricultural total factor productivity? Evidence from agriculture-related loans

IntroductionThe construction of digital villages is widely acknowledged as a way to achieve the “dual goals” of high quality of the agricultural and rural economy and common prosperity under the digital China strategy. Studies have explored the socio-economic benefits of different aspects of rural digitization, but few have focused on the productivity role of rural broadband development in the context of the urban-rural broadband divide. The purpose of this paper is to explore the relationship between rural broadband development and agricultural total factor productivity (TFP) and the intrinsic mechanism of action, and to provide empirical evidence on the productivity effect of promoting digital transformation in rural agriculture.MethodsUsing panel data from 31 provinces in China from 2011 to 2020, this paper investigates the impact and mechanism of rural broadband development on agricultural TFP from the perspective of agriculture-related loans by setting up a two-way fixed effects model, a mechanism effects model and a threshold effects model.ResultsThe results find that rural broadband development has a significant role in enhancing agricultural TFP. Heterogeneity analysis indicates that the productivity-enhancing effect of rural broadband development is remarkable only in the central region and the region with higher rural disposable income. Mechanism analysis points out that rural broadband development can increase agricultural TFP by influencing the share of farm-related loans. Threshold analysis further reveals that the role of increasing the share of farm-related loans on agricultural TFP is marked only after rural broadband development reaches a certain level.DiscussionThese findings can provide practical guidance for other developing countries in accelerating the digital transformation of villages and optimizing factor allocation to achieve high-quality agricultural development.

A framework for quantification and integration of green development of cultivated land in China: From the perspective of adaptability‐vitality‐resistance

AbstractExploring the green development of cultivated land (GDCL) and solving the problem of “high consumption and high pollution” caused by the traditional model are of great significance for achieving sustainable use of cultivated land and green agricultural growth. However, the existing consensus on action lacks theoretical support and quantitative analysis. To this end, the objective of this study was to develop a new multidimensional framework considering as an integrated capacity to adaptability‐vitality‐resistance (A‐V‐R) assess GDCL according to the essential requirement of green development. The analytical framework and evaluation system proposed in this study are expected to provide empirical support for the green transition of cultivated land use in China or worldwide. This study determines spatiotemporal differentiation and influencing mechanism of GDCL in China from 2000 to 2020. The results demonstrated that the GDCL level of China was low and characterized by a W‐shaped fluctuation. The regional difference in GDCL is greater than that within the regions and the evolution of different types of regions showed significant path dependence and spatiotemporal inertia. The reason behind this phenomenon is that agricultural technology and socioeconomic development factors such as agricultural mechanization level, urban–rural income equity index and extensively influenced GDCL in different regions. The interaction among driving factors forms a complex multi‐resultant force to construct a comprehensive action mechanism at the GDCL level driven by demand, economy, technology, and ecology. Therefore, this study suggests coordinating the relationship between technological progress, technological diffusion, agricultural total factor productivity, and the GDCL path, in order to effectively reduce the internal and external contradictions of GDCL.

Spatio-temporal differentiation on China’s green agricultural total factor productivity guided by digital technology and fuzzy systems

Spatio-temporal differentiation on China’s green agricultural total factor productivity guided by digital technology and fuzzy systems

Retracted: Analysis Method of Agricultural Total Factor Productivity Based on Stochastic Block Model (SBM) and Machine Learning

Retracted: Analysis Method of Agricultural Total Factor Productivity Based on Stochastic Block Model (SBM) and Machine Learning

The nexus of foreign direct investment and agricultural productivity: does absorptive capacity matter?

PurposeThis study aims to examine the complex effects of foreign direct investment (FDI) on China’s agricultural total factor productivity (TFP) from 2005 to 2020. It also explores the role of absorptive capacity as a moderating factor during this period.Design/methodology/approachEmploying provincial panel data from China, this research measures agricultural TFP using the Stochastic Frontier Approach (SFA)-Malmquist method. The impact of FDI on agricultural productivity is further analyzed using a nondynamic panel threshold model.FindingsThe results highlight technological progress as the main driver of agricultural TFP growth in China. Agricultural FDI (AFDI) seems to impede TFP development, whereas nonagricultural FDI (NAFDI) shows a distinct positive spillover effect. The study reveals a threshold in absorptive capacity that affects both the direct and spillover impacts of FDI. Provinces with higher absorptive capacity are less negatively impacted by AFDI and more likely to benefit from FDI spillovers (FDISs).Originality/valueThis study provides new insights into the intricate relationship between FDI, absorptive capacity and agricultural productivity. It underscores the importance of optimizing technological progress and research and development (R&D) to enhance agricultural productivity in China.

Impact of relaxing internal-migration restrictions on agricultural TFP of rural households: evidence from China

ABSTRACT In this paper, we study how a significant relaxation of internal migration restrictions affects the agricultural total factor productivity of rural households, taking advantage of the 2014 Hukou reform in China. This reform completely removed the migration restrictions for cities with an urban population of less than 0.5 million (towns and small cities) but left the migration restrictions in megacities unchanged. We find that the reform has significantly contributed to the off-farm transfer of rural labour and increased the agricultural TFP of rural households. Further mechanistic analysis shows that the reform increased the level of agricultural mechanization of rural households and facilitated the subcontracting of land by low productivity farmers.

Does Land Fragmentation Affect the Effectiveness of Fiscal Subsidies for Agriculture: Evidence from China

Fiscal and land policies are important tools in developing agriculture in China. Understanding how agricultural subsidies and land fragmentation jointly affect agricultural Total Factor Productivity (TFP) is crucial for building a strong agricultural nation. This paper utilizes microdata from fixed observation points in rural China from 2003 to 2017 and employs panel bidirectional fixed-effect models and moderation-effect models to empirically analyze the impact of agricultural subsidies and land fragmentation on agricultural TFP. The research finds: (1) Agricultural subsidies positively affect agricultural TFP, while land fragmentation leads to decreased agricultural TFP. (2) Land fragmentation hinders the positive effects of agricultural subsidies on agricultural TFP. A 1% increase in land fragmentation could lead to approximately a 3% decrease in the enhancement effect of agricultural subsidies, with significant impacts on households in major grain-producing areas and those primarily engaged in agriculture. (3) There is no evidence that reforms in the “three agricultural subsidies” would alter the combined effect of agricultural subsidies and land fragmentation on agricultural TFP. The obstructive role of land fragmentation cannot be mitigated through the “three agricultural subsidies” reform. The study indicates that the incentivizing role of agricultural subsidies has not been fully realized, and land fragmentation remains a key bottleneck in agricultural development. Fiscal support for agriculture should be coupled with effective land reform policies for synergistic efforts.

Rural E-Commerce and Agricultural Carbon Emission Reduction: A Quasi-Natural Experiment from China’s Rural E-Commerce Demonstration County Program Based on 355 Cities in Ten Years

Reducing carbon emissions is of paramount importance to the accomplishment of the 2030 Sustainable Development Goals. The effect of rural e-commerce on agricultural carbon emissions (ACEs) is controversial, and particularly the mechanism behind the effect is unknown. To identify the impact of rural e-commerce on agricultural carbon emissions and its mechanisms, we take advantage of China’s Rural E-Commerce Demonstration County Program (REDCP) as a quasi-natural experiment and use the multi-period difference-in-difference (DID) model to investigate the relationship between rural e-commerce and agricultural carbon emissions. Our data are based on panel data of 355 prefecture-level cities from 2010 to 2019 in China. We identify that rural e-commerce can reduce agricultural carbon emissions by an average of 14.4%, but this effect is not long-lasting. Mechanism analyses suggest that the reduction effect of rural e-commerce on agricultural carbon emissions is mainly due to fostering agricultural economic growth, increasing the share of low-carbon industry, and improving agricultural total factor productivity (TFP). Further heterogeneity analyses demonstrate that rural e-commerce has better carbon emissions reduction performance in eastern cities as well as in non-major grain-producing cities in China.

The spatiotemporal pattern of surface ozone and its impact on agricultural productivity in China.

The slowing of agricultural productivity growth globally over the past two decades has brought a new urgency to detect its drivers and potential solutions. We show that air pollution, particularly surface ozone (O3), is strongly associated with declining agricultural total factor productivity (TFP) in China. We employ machine learning algorithms to generate estimates of high-resolution surface O3 concentrations from 2002 to 2019. Results indicate that China's O3 pollution has intensified over this 18-year period. We coupled these O3 estimates with a statistical model to show that rising O3 pollution during nonwinter seasons has reduced agricultural TFP by 18% over the 2002-2015 period. Agricultural TFP is projected to increase by 60% if surface O3 concentrations were reduced to meet the WHO air quality standards. This productivity gain has the potential to counter expected productivity losses from 2°C warming.

Can development of large scale agricultural business entities improve agricultural total factor productivity in China?: an empirical analysis

Large scale agricultural business entities (hereinafter referred to as “the large scale entities”) are newly sprouted in the process of China’s agricultural transformation from traditional production to modern mode, while the improvement of agricultural total factor productivity is an important driving force to realize agricultural transformation in China. Therefore, it is of great significance to explore whether the former has a promoting effect on the latter. First of all, the effective improvement of agricultural total factor productivity by the large scale entity development is analyzed from the two aspects of scale effect and technology utilization effect, and the effect mechanism is discussed in depth in this paper. Secondly, empirical test was carried out by using the panel data of prefecture-level cities from 2015 to 2020. The benchmark regression results show that the development of large scale entities can significantly improve agricultural total factor productivity. The mechanism test results indicate that the development of large scale entities improves the agricultural total factor productivity by raising the agricultural mechanization level. The results of heterogeneity analysis show that the difference between large scale entities would affect the benchmarking results. Therefore, it is believed through the study as discussed in this paper that devoting greater effort to support the development of large scale entities is an important measure to improve agricultural total factor productivity and promote China’s agricultural transformation, but it is necessary to act according to local conditions based on the difference of entity types in the process.

Driving factors to agriculture total factor productivity and its contribution to just energy transition

This study explores the intricate relationship between agricultural productivity and the pursuit of a just energy transition in China. As the world's largest emitter of greenhouse gases and a nation grappling with environmental degradation and socio-economic disparities, China faces the imperative to transition toward a more sustainable and equitable energy system. This paper investigates the role of agriculture in this transition, recognizing its pivotal position as a significant emitter of greenhouse gases while also being vulnerable to the impacts of climate change. By examining the potential synergies and conflicts between agricultural productivity enhancement and the goals of reducing emissions and promoting social justice, this study aims to shed light on strategies that can effectively align agricultural development with the objectives of a just energy transition. The result indicates substantial internal disparities and strong agglomeration effects (referring to areas with similar agricultural total factor productivity) within China's agricultural total factor productivity distribution. Over the past 17 years (2005–2021), the average agricultural productivity has shown a commendable upward trend, marking a 2.8% growth rate. This increase signifies ongoing efforts and advancements in the agricultural sector, leading to improved productivity levels. Our results also show a significant positive spatial correlation in agricultural total factor productivity, suggesting a tendency for spatial agglomeration. The years 2005 and 2021 stand out as periods marked by significant adverse effects resulting from the interplay of diverse factors on agricultural productivity. By integrating findings from various sectors, this study provides insights into the complex interplay between agriculture and energy transition, emphasizing the need for integrated, holistic approaches that prioritize sustainability, fairness, and resilience in China's pursuit of a just energy future.

Mechanization's impact on agricultural total factor productivity

Mechanization's impact on agricultural total factor productivity

Can rural human capital inputs enhance agricultural total factor productivity? - Based on the dualistic economic structure perspective

<p class="MsoNormal" style="margin-top: 12pt; text-align: justify;"><span lang="EN-US" style="font-family: arial, helvetica, sans-serif;">Human factor inputs are key to promoting agricultural modernisation. Traditional economic theory suggests that improving human capital is the key to boosting agricultural productivity, but can this conclusion hold in China's dualistic economic structure? We use Chinese provincial panel data from 2000-2017 to examine the impact of rural human capital inputs on agricultural total factor productivity. We find that, on the whole, rural human capital inputs have a negative effect on agricultural total factor productivity, and that there is a "rural human capital trap". Mechanism analysis reveals that rural human capital inputs, on the one hand, cause labour loss and reduce the quality of agricultural workers, while on the other hand, they may promote the application of mechanization, which has both positive and negative effects on agricultural total factor productivity. Heterogeneity analysis finds that this negative effect is more pronounced in the central region of China because of the serious loss of rural labour. This study provides new policy insights for further improving the structure of rural education inputs and promoting human capital accumulation in agriculture.</span></p>

The Impact of Agricultural Global Value Chain Participation on Agricultural Total Factor Productivity

Under the condition of opening up, participation in international specialization and global value chains (GVCs) has become the main source for more and more countries to obtain foreign resources and advanced technologies, thereby promoting productivity improvement and technological progress. What are the pathways of agricultural GVC participation that affect agricultural total factor productivity (TFP)? Is the impact of agricultural GVC participation on agricultural TFP consistent across different statuses and modes of agricultural GVC participation? This paper elaborates on the theoretical mechanism of agricultural GVC participation affecting agricultural TFP, and then empirically estimates the impact of different statuses and modes of agricultural GVC participation on agricultural TFP by taking 58 countries as examples. The results show that agricultural GVCs affect agricultural TFP by several direct and indirect pathways. There was a U-shaped relationship between agricultural GVC participation and agricultural TFP, which means that after crossing a certain threshold, the former has a positive impact on the latter. By participating in agricultural GVCs, agricultural TFP in high-income and upper-middle-income countries was significantly improved, while in lower-middle-income countries it was not. Both forward and backward agricultural GVC participation were conducive to improving agricultural TFP in high-income and upper-middle-income countries, but only backward agricultural GVC participation was conducive to improving agricultural TFP in lower-middle-income countries. Therefore, every country needs to actively explore its optimal pathway to participate in agricultural GVCs in order to maximize the participation benefits and promote the improvement in agricultural TFP, simultaneously.

A random walk for agricultural total factor productivity

AbstractGrowth in agricultural total factor productivity (TFP), which explains most of the long‐term growth in U.S. agricultural output, may be slowing. The Economic Research Service (ERS) of the USDA is confident that current levels of below‐average growth will eventually regain the long‐term trend line. Others disagree, arguing instead that due to declining public expenditures on agricultural research, TFP growth experienced a downward and seemingly permanent structural shift about 30 years ago. In this paper, I argue that neither perspective is accurate since agricultural TFP is best modeled as a random walk with drift and thus not governed by a deterministic trend line. When I use a first difference model to accommodate the unit root, I do not find a structural break in the rate of drift. However, I acknowledge that this finding may not be general because I show that my test for a structural break has low power. To add theoretical relevance, I develop a simple model of stochastic innovation and farm technology adoption, and then use simulation results from my model to explain why a random walk for agricultural TFP is a theoretically sound proposition.