Chapter 72 Total Factor Productivity Growth in Agriculture: The Role of Technological Capital

Purpose – China and India have made significant strides in transforming their agricultural sectors to cut hunger and poverty for the masses through improved agricultural productivity. Given limited land and shift of labor to non-agricultural sector, increasing productivity will continue to be central in agricultural growth in the twenty-first century. The purpose of this paper is to provide comparative analysis of the agricultural total factor productivity (TFP) growth in the two countries. It complements existing literature by examining the evolution and drivers of TFP at disaggregated sub-national level. Richer data allows a deeper understanding of the nature and drivers of TFP growth in the two countries. Design/methodology/approach – This paper applies different analytical framework to address different research questions using data since 1980. China study estimates a parametric output-based distance function using a translog stochastic frontier function. Productivity growth index and its multiple components are calculated using parameters derived from the parametric approach to identify the characteristics of technology such as structural bias. India study first applies data envelopment analysis to estimate the aggregate productivity growth index, technical change (TC), and efficiency change. Next productivity indexes by for traditional crops are estimated using growth accounting framework at state level. Finally, a panel regression links TFP on its determinants. Findings – Several common themes emerge from this comparative study. Faced with similar challenges of limited resources and growing demand, improving productivity is the only way to meet long-term food security. Agriculture sector has performed impressively with annual TFP growth beyond 2 percent in China and between 1 and 2 percent in India since the 1980s. The TFP growth is mainly propelled by technological advance but efficiency had been stagnant or even deteriorated. This study provides a granular picture of within country heterogeneity: fast growth in the North and Northeast part of China, South and East of India. Research limitations/implications – The study suggests some possible policy interventions to improve agricultural productivity, including investment in agricultural R & D to create advanced production technology, effective extension programs and supportive policies to increase efficiency, and diversification from staple crops for sector-wide growth. The India study suggests certain policies may not be contributing much to productivity growth in the long run due to a negative impact on environment. Further studies are needed to expand the productivity analysis to take into consideration of the negative externalities to the society. Data enhancement to account for quality-adjusted inputs could improve the estimation of productivity growth. Originality/value – Each country study reveals certain prospects of the agricultural sector and production technology. China analysis statistically confirms the existence of technical inefficiency and technology progress, suggests the translog form is appropriate to capture the production technology and satisfies conditions stipulated in theoretical models. The results indicate TC does not influence the contribution of output or input to the production process. India study pinpoints the lagging productivity growth of traditional crops, which still derives growth from input expansion. Although different states benefited from different crops, sector-wide productivity gain is primarily the result of diversification to high-value crops and livestock products.

Comment

This study aimed to analyze the trends and causes of productivity growth in the Philippine agriculture by estimating the total factor productivity (TFP) level and growth in the sector using the Tornqvist index number approach. Likewise, the factors causing the movements in TFP over a period of time, and the policy alternatives for increasing productivity growth were identified. The most recent agricultural data set covering 12 administrative regions and years 1974–2004 was used to determine the TFP level and growth. Results showed that output growth in Philippine agriculture was mainly driven by productivity, and minimally by the inputs of production. Productivity gaps were observed among the different regions. Central Luzon was the most productive region, whereas Bicol was the least productive. TFP growth was at its peak in the late 1970s, followed by a deceleration in the 1980s, and resurgence in the 1990s until the early part of the recent decade. The highest TFP growth rate recorded has not been paralleled despite government efforts and initiatives to revive the less dynamic agricultural sector of the Philippines. Across time, revenue growth was also seen to be declining, which may be due to the decrease in growth contribution of output prices (which remained relatively large), and to the decrease in growth contribution of input quantities (which was relatively small). Lastly, output prices contributed substantially to agricultural revenues, and TFP growth accounted mostly for growth in quantities of agricultural outputs. The TFP growth substantiated the importance of infrastructure, rural electrification, and investments in research and development to enhance agricultural productivity. Overall, this study recommends further examination of the role of agricultural output prices in determining farm incomes, and for initiatives to be undertaken to boost agricultural productivity through investments in infrastructure and research and development.

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.

Previous research on agricultural productivity in Africa has focused on conventional Total Factor productivity (TFP) growth rather than Green Total factor productivity (GATFP) growth, thus ignoring the effect of undesirable outputs such as emissions. This has raised concerns about the sustainability of agricultural productivity growth in the continent. The study was designed to examine GATFP growth in agricultural productivity for 49 African nations from 2000 to 2019. We apply the Global Malmquist–Luenberger (GML) Productivity Index, which complies with the sustainable development agenda that promotes greater production of desirable outputs and minimising unwanted outputs. This approach is also compared to Global Malmquist (GM) Productivity Index which ignores unwanted outputs, yielding to conventional TFP growth. We found an average GATFP growth of 0.6% and TFP growth at 0.9% suggesting that the actual agricultural productivity growth is overstated if agricultural emissions are disregarded. Both estimates fell short of the desired annual target of 7% from the Comprehensive African Agriculture Development Programme (CAADP). Regional growth is mostly characterised by high (low) GATFP and TFP except in Southern Africa and East Africa. The two regions represent an ideal situation where GATFP exceeds TFP. At country level growth can be divided into three scenarios: desired growth, where GATFP exceeds TFP; balanced growth with both estimates equivalent; and undesired growth, where TFP exceeds GATFP. Unfortunately, most African nations fall in the last scenario. We conclude that policies must be developed to encourage sustainable agricultural productivity growth in Africa.

Total factor productivity growth (TFPG) is a topic well documented in the economic development literature. The studies related to this topic can be broadly put into three contradicting hypotheses. First, total factor productivity (TFP) is a measure of technological change; secondly, it is associated with the externalities and scale effects. The third group of researchers is sceptical that total factor productivity measures anything useful. Therefore, the paper attempts to categorise the available literature based on their methodological and computational differences and systematically reviews the different factors that have been attributed for the total factor productivity growth. It was found that the studies used a variety of approaches including estimation of rates of shifts in production and cost functions, non-parametric methods and indexing approaches showing the relative importance of different approaches. However, most of these studies are based on highly aggregative data. Studies based on micro level data on various dimensions and determinants total factor productivity growth in agriculture, especially in the countries which have high disparities in soil quality, climate and topographical conditions as well as socio-economic and physical infrastructure across states and agro-climatic zones are obviously scant.

• By 2050, global agricultural demand is projected to grow by 70-100 percent due to population growth, energy demands, and higher incomes in developing countries. Meeting this demand from existing agricultural resources will require raising global agricultural total factor productivity (TFP)1 by a similar level. Maintaining the U.S. contribution to global food supply would also require a similar rise in U.S. agricultural TFP. • TFP growth in U.S. agriculture is predicated on long-term investments in public agricultural research and development (R&D). Productivity growth also springs from agricultural extension, farmer education, rural infrastructure, private agricultural R&D, and technology transfers, but the force of these factors is compounded by public agricultural research. • The rate of TFP growth (and therefore output growth) of U.S. agriculture has averaged about 1.5 percent annually over the past 50 years. Stagnant (inflation-adjusted) funding for public agricultural research since the 1980s may be causing agricultural TFP growth to slow down, although statistical analyses of productivity growth trends are inconclusive. • ERS simulations indicate that if U.S. public agricultural R&D spending remains constant (in nominal terms) until 2050, the annual rate of agricultural TFP growth will fall to under 0.75 percent and U.S. agricultural output will increase by only 40 percent by 2050. Under this scenario, raising output beyond this level would require bringing more land, labor, capital, materials, and other resources into production. • Additional public agricultural R&D spending would raise U.S. agricultural productivity and output growth. Raising R&D spending by 3.73 percent annually (offsetting the historical rate of inflation in research costs) would increase U.S. agricultural output by 73 percent by 2050. Raising R&D spending by 4.73 percent per year (1-percent annual growth in inflation-adjusted spending) would increase output by 83 percent by 2050.

Decomposition of climate-induced productivity growth in Indian agriculture

ABSTRACTIn this paper, we first develop a measure of total factor productivity (TFP) growth and summarize a source‐of‐growth analysis for the manufacturing sector of 48 states. As have others, we find little association between TFP growth differentials and output growth differentials for census regions. At the staterather than the regional level, however, we find a positive association between TFP growth and output growth. We use cross‐sectional data to estimate the determinants of the variation in TFP growth. Two results emerge that are important for regional policy and for understanding national productivity trends. First, state investments in education and in transportation infrastructure may affect TFP growth. Second, energy price increases in the early 1970s had no differential effects on productivity growth across states. We also explore the determinants of manufacturing output growth and find that TFP growth, demand growth, wage growth, wage levels, and state corporate income tax rates are significant.

This article develops new estimates of historical agricultural productivity growth in Jordan. It investigates how public policies such as agricultural research, investment in irrigation capital, and water pricing have contributed to agricultural productivity growth. The Food and Agriculture Organization (FAO) annual time series from 1961 to 2011 of all crops and livestock productions are the primary source for agricultural outputs and inputs used to construct the Törnqvist Index for the case of Jordan. The log-linear form of regression equation was used to examine the relationship between Total Factor Productivity (TFP) growth and different factors affecting TFP growth. The results showed that human capital has positive and direct significant impact on TFP implying that people with longer life expectancy has a significant impact on TFP growth. This article concludes that despite some recent improvement, agricultural productivity growth in Jordan continues to lag behind just about every other region of the world.

This article develops new estimates of historical agricultural productivity growth in Jordan. It investigates how public policies such as agricultural research, investment in irrigation capital, and water pricing have contributed to agricultural productivity growth. The Food and Agriculture Organization (FAO) annual time series from 1961 to 2011 of all crops and livestock productions are the primary source for agricultural outputs and inputs used to construct the Törnqvist Index for the case of Jordan. The log-linear form of regression equation was used to examine the relationship between Total Factor Productivity (TFP) growth and different factors affecting TFP growth. The results showed that human capital has positive and direct significant impact on TFP implying that people with longer life expectancy has a significant impact on TFP growth. This article concludes that despite some recent improvement, agricultural productivity growth in Jordan continues to lag behind just about every other region of the world.

The study estimated the Total Factor Productivity (TFP) growth in the Sri Lankan food crop sector from 1990- 2017 using the Tornqvist-Theil index assuming translog production technology and a competitive market. TFP growth of paddy, maize, potato, big onion, chilli and soybean has increased after 2000. The highest TFP growth is recorded for maize from 2011 -2015 that contributed to about 68% of the maize sector growth. The costless advances in applied technology, managerial efficiency, and industrial organization that brought TFP growth in the food crop sector in Sri Lanka are discussed. The diffusion of new technology to the food crop sector has considerably varied by crop. Although the maize sector benefited through technology spillovers, new technology in terms of new varieties with higher yields, adaptability was slow to diffuse to other food crop sectors. Mechanization has largely substituted labour in many crop sectors due to the rising wage rate. The contract grower system is a credible institutional innovation in the food crop sector. Technological capital is a prerequisite for TFP and cost reduction growth. Hence the long-term commitment to agricultural research and development investments from Sri Lankan governments and aid agencies is required.

This paper applies the Malmquist productivity index method to measure total factor productivity (TFP) growth in Vietnamese agriculture using panel data from 60 provinces in Vietnam during 1985–2000 when Vietnam implemented widespread de-collectivization, trade liberalization, and reformed her agriculture sector. This study indicates that most of the early growth in Vietnamese agriculture during the first reform period 1985–1990 was due to TFP growth in response to incentive reforms. During the second reform period 1990–1995, the growth rate of TFP fell, and Vietnam’s agricultural growth was mainly caused by drastic investment in capital. In the post-reform period (1995–2000), TFP growth increased again, though still much lower than 1985–1990. Overall, the TFP growth rate in the whole period is estimated at 1.96 percent, contributing to 38% of Vietnam’s agricultural growth.

Purpose – This study aims to analyze the total factor productivity (TFP) performance of Chinese counties and cities over the period from 2007 to 2010. Chinese regional and urban–rural TFP performance are investigated by using county-level data, and the impact of the urbanization policy on TFP is discussed. Design/methodology/approach – The data envelopment analysis (DEA)-Malmquist technique and Kumbhakar–Sun’s semi-parametric model are used for TFP change measurement and comparison. The county-level TFP performances are summarized and studied by statistical methods. Their spatial distribution is exhibited in a geographical thematic map. Findings – The county-level analysis proves that China underwent a large-scale TFP decline over the period from 2007 to 2010. Statistically speaking, cities’ TFP growth is more positive than counties’; however, different provinces also have their regional characteristics. In addition, the Chinese Hukou (household registration) institution divides Chinese urbanization into halves, which have the opposite correlation on TFP growth. Research limitations/implications – Because the collection of county-level data is enormous and costly, this study only focuses on a very short period (2007-2010) with estimated data. This TFP change analysis is limited to the short-term phenomenon around the 2008 international financial crisis. Practical implications – This study provides a visual spatial distribution for county-level TFP change in China over the period 2007-2010. Results of the analysis demonstrate that the Chinese Hukou system is among the policy factors that can influence productivity in the course of urbanization. Originality/value – The achievement of the first nationwide county-level TFP change study for economic growth in China is innovative. This study provides a unique perspective for understanding productivity performance at the regional level over the period investigated, which provides invaluable data for investigating the impact of urbanization and the rural–urban gap on TFP growth.

Provincial panel data from the agricultural sector and stochastic frontier production function model were employed to study the total factor productivity (TFP) growth since the 1980s in China. We decomposed the TFP growth into technological progress and technical efficiency changes (efficiency gains) as well as the aggregate agricultural TFP growth into crop-specific subsector's TFP growths. We found that Chinese agriculture experienced significant productivity growth in the last few decades, although the growth rates vary considerably among the subsectors. During this period, the source of productivity growth comes from either technological progress or efficiency gains, not from both of them simultaneously. Particularly since the 1990s, Chinese agriculture experienced a great technological progress and yet a considerable efficiency loss. The differences among sources of productivity growth and among subsectors call for distinct policy responses.