Explaining agricultural productivity growth: an international perspective

This article presents multi-output, multi-input total factor productivity (TFP) growth rates in agriculture for 88 countries over the 1970–2001 period, estimated with both stochastic frontier analysis (SFA) and the more commonly employed data envelopment analysis (DEA). We find results with SFA to be more plausible than with DEA, and use them to analyze trends across countries and the determinants of TFP growth in developing countries. The central finding is that policy and institutional variables, including public agricultural expenditure and proagricultural price policy reforms, are significant correlates of TFP growth. The most significant geographic correlate of TFP growth is distance to the nearest OECD country.     

供给侧结构改革背景下的西南地区农业全要素生产率分析

［目的］探究在制度改革背景下的农业生产力发展水平和地区差异,有利于全面了解农业发展状况,为后期农业政策的制定、调整以及农业发展提供理论基础。［方法］文章利用DEA-Malmquist指数法对我国西南地区5个省（市）农业全要素生产率（TFP）水平变化发展趋势进行分析,并在此基础上对不同地区农业TFP差异变化和发展趋势进行预测。［结果］（1）Malmquist指数分析结果表明,我国西南地区农业TFP呈缓慢增长的趋势。通过对不同地区农业TFP结果进行比较,四川省农业TFP有先逐渐上升后下降的趋势,而技术进步是影响四川省农业TFP增长的原因;（2）重庆市农业TFP也是呈现波动式缓慢增加的趋势,年平均增长率为354%; 贵州省农业TFP近20年来增长速度最快,主要影响因素为技术进步; 云南和广西农业TFP也均呈缓慢增长的趋势,技术效率和技术进步在不同时期对农业TFP的影响有所差异。（3）收敛性分析结果表明,我国西南地区农业TFP差异有减小的趋势,并且不同地区农业TFP水平具有向各自稳定状态发展的可能性。［结论］我国西南地区农业TFP增长与技术进步具有较高的同步性,因此,制约我国西南地区农业全要素生产率增长的关键因素是农业科技进步。在供给侧结构性改革背景下,必须以市场需求为科研导向,加大科技创新投入力度,促进我国西南地区农业的可持续发展。     

Is there a slowdown in agricultural productivity growth in South America?

This article estimates agricultural productivity growth in 10 South American countries in 1969–2009 with the objective of investigating if the slowdown being measured in other countries is present in the region. Results show that productivity growth accounts for half of the three‐fold increase in agricultural output during this period and that performance is sensitive to R&D investments in the sector. The slowdown found for the 1990s to 2000s in the U.S. and some European economies does not seem to be present yet in South America. The region's total factor productivity (TFP) growth rate increased steadily from 1.07% during the 1970s to 2.29% during the 2000s. Given lags in adoption and the adaptive nature of innovations in these economies, we have yet to see the potential effects in South American agriculture of decreases in R&D in advanced economies.     

Aid to Agriculture, Growth and Poverty Reduction

Agriculture and rural growth promotion show a recent 'comeback' in development cooperation, but action on the ground so far is not sufficient. After years of neglect, policy makers have recognized that poverty reduction in many low income countries can only be achieved if development efforts are clearly focused on the sector which employs most of the poor, and the space where most of the poor live. The importance of agricultural growth was amply demonstrated during the economic transformation of Asia. Forty years ago, Asia was a continent of widespread poverty. Today, most Asian countries are experiencing significant growth and poverty reduction. Rapid growth in productivity in the small-farm sector helped drive this process. Sub-Saharan Africa, however, failed to achieve rapid agricultural growth and remains mired in poverty and hunger. If Africa is to halve poverty by 2015 in accordance with the Millennium Development Goals (MDG), agriculture will need to maintain an annual growth rate of 6 per cent between 2000 and 2015. China's experience from 1978 to 1984 shows such growth is possible. Achieving the desired rapid rates of growth in Africa will require coherent policies by governments and donors, a substantial investment of public resources in rural infrastructure and access to agricultural technology, and significant improvement in national governance.     

Is a slowdown in agricultural productivity growth contributing to the rise in commodity prices?

A slowdown in the rate of agricultural productivity growth is thought by many observers to be contributing to the recent rise in agricultural prices. In this article I decompose sources of output growth in global agriculture into aggregate input and total factor productivity (TFP) components and examine whether productivity growth slowed substantially in the years leading up to the recent rise in commodity prices. Contrary to widely held perceptions, I find no evidence of a general slowdown in sector‐wide agricultural TFP, at least through 2006. If anything, the growth rate in agricultural TFP accelerated in recent decades. However, the results do show a slowdown in the growth of agricultural investment. Accelerating TFP growth largely offset decelerating input growth to keep the real output of global agriculture growing at about 2% per year since the 1960s. Regionally, however, agricultural productivity performance has been uneven. These findings have important implications for the appropriate supply‐side policy response to the current agricultural price crisis.     

China's regional agricultural productivity growth in 1985–2007: A multilateral comparison1

In this study, we estimate total factor productivity (TFP) growth as well as multilateral TFP index for 25 contiguous China provinces over the 1985–2007 period. Agricultural output growth for each province was decomposed into TFP growth and input growth, where input growth was further disaggregated into contributions from growth of labor, capital, land, and intermediate goods. Over the study period, TFP growth contributed 2.7 percentage points to output growth annually, which was slightly higher than the input growth contribution of 2.4 percentage points per annum. On average, the annual rate of productivity growth peaked during 1996–2000, at 5.1%. It slowed in 2000–2005 to a rate of 3.2% per annum and declined in the most recent years (2005–2007) to ?3.7%. Differences in productivity among regions persisted over the entire period. The tendency toward faster TFP growth in relatively well‐off coastal regions may imply a widening of regional inequality.     

Livestock in China: Commodity-Specific Total Factor Productivity Decomposition Using New Panel Data

Studies of total factor productivity (TFP) in livestock production are rare, but when available provide useful information especially in the context of developing countries such as China where livestock is becoming more important in the domestic agricultural economy. We estimate TFP for four major livestock products in China employing the stochastic frontier approach, and decompose productivity growth into its technical efficiency (TE) and technical progress components. Efforts are made to adjust and augment the available livestock statistics. The results show that growth in TFP and its components varied between the 1980s and the 1990s as well as over production structures. While there is evidence of considerable technical innovation in China's livestock sector, TE improvement has been relatively slow.     

Measuring agricultural total factor productivity in China: pattern and drivers over the period of 1978‐2016

The Chinese agricultural sector has experienced a substantial increase in total output since dramatic reforms were introduced in 1978. This paper uses the index method to measure agricultural total factor productivity (TFP) for China’s crop and livestock industries, based on the gross output model from 1978 to 2016. We construct production accounts for the industries using input‐output relationships for the 26 main agricultural commodities and commodity groups, which account for over 90 per cent of the total agricultural inputs and outputs. The results show that China’s agricultural TFP grew at a rate of approximately 2.4 per cent a year before 2009, which is comparable to the main OECD countries and is double the world average. TFP growth accounts for approximately 40 per cent of output growth, suggesting that input growth was the main driver of output growth in the past. However, average productivity growth slowed down after 2009 though it has gradually recovered since 2012. The slowdown reflects the emerging challenges to existing farm production practices in Chinese agriculture, suggesting the need for further institutional reform.     

Publicly-funded UK agricultural R&D and 'social' total factor productivity

Most studies concerned with measuring the rate of return to publicly‐funded agricultural R&D investment have found high returns, suggesting under‐investment, and calls for increased expenditure have been common. However, the evaluation of returns tends to measure the effect of research expenditure against growth in total factor productivity (TFP), based on market inputs and outputs. When compared against growing public unease over the environmental effects of pursuing agricultural productivity growth, TFP indices become a misleading measure of growth. This paper integrates some non‐market components into the TFP index. The costs of two specific externalities of agricultural production, namely fertiliser and pesticide pollution, are integrated in a TFP index constructed for the period 1948–1995. This adjusted, or ‘social’, TFP index is measured against UK public R&D expenditures. The rates of return to agricultural R&D are reduced by using the ‘social’ as opposed to the traditional TFP index. Whilst both remain at justifiable levels, previous studies appear to have over‐estimated the effect of agricultural R&D expenditures. Furthermore, with changes in policy towards more socially acceptable but non‐productivity enhancing outcomes, such as animal welfare, rural diversification and organic farming, the future framework for analysing returns to agricultural R&D should not be so dependent on productivity growth as an indicator of research effectiveness.     

Assessing Total Factor Productivity Growth in Sub‐Saharan African Agriculture

The importance of the agricultural sector in the economic development process is well known. Improvements in agricultural productivity are often found to spill into other areas of a developing economy, potentially improving the standards of living of urban and rural workers alike. Given the importance of this sector, accurate measures of total factor productivity (TFP) across countries can be helpful in identifying conditions, institutions or policies that promote agricultural development. In this article, we estimate TFP growth in agriculture for a panel of 39 sub‐Saharan African countries from 1961 to 2007. We also develop a set of development outcome measures theoretically consistent with strong agricultural performance to serve as external validation of our results. We find that three estimation methods (stochastic frontier, generalised maximum entropy, and Bayesian efficiency) generate relative rankings that are consistent with the development outcome measures, providing external validation of the methods. However, the data envelopment analysis approach performs poorly in this regard.     

Productivity of Agricultural Labour and Land: An International Comparison

This paper examines partial agricultural land and labour productivity in 1975 and 1980, for different world regions. The results suggest that land and labour productivity are higher in developed countries relative to developing countries. However, agricultural labour productivity differences are more marked than those for agricultural land productivity. The productivity values for 1975 and 1980 indicate a widening of productivity differences, more so in the case of agricultural labour than land. The paper also proposes an alternative approach to estimating agricultural land and labour productivity. This approach, which regresses agricultural labour productivity on a given level of agricultural land productivity, suggests a narrowing of agricultural land productivity differences, relative to the initial approach, across Africa, Asia and Europe during the 1975-1980 period. A brief discussion of the agricultural development policy implications of the results concludes the paper.     

Technology catch-up in agriculture among advanced economies

We examine whether countries with low initial levels of agricultural total factor productivity (TFP) tend to ‘catch up’ with the technology leaders. We first compare relative levels of agricultural TFP, capital services and labour input levels in agriculture for 17 OECD countries between 1973 and 2011. Then we apply (conditional) convergence analysis to the panel data to examine the speed of convergence and test whether the convergence is transitory or permanent by analysing TFP changes over the business cycle. Capital intensities, quality improvement of capital, factors such as human capital spillovers, and certain agricultural policies are conditioning variables. We examine how differences in relative capital intensities affect agricultural productivity convergence over the business cycle. We find evidence that the speed of convergence increases during periods of contraction in economic activity.     

Agricultural competitiveness: The case of the United States and major EU countries

Growth in the agricultural GDP of four major European countries is compared with US agricultural growth for the period 1974–1993. The agricultural sector's relative prices are taken into account along with economy-wide factor market adjustments. For Denmark, France, Germany and the UK, the effects of declining real prices and changes in input levels on growth in agricultural GDP are relatively small. Total Factor Productivity (TFP) growth appears to be the major contributor to European agricultural GDP growth. In comparison, TFP is the major source of growth in US agricultural GDP, but its rate of growth is lower than the European countries. In contrast, the declining real prices for US agriculture had a relatively large effect on its GDP. However, in recent years, the effects of declining real prices and declining rates of growth in TFP on European agriculture are relatively large. In the longer-run, the relative competitiveness of US agriculture is largely dependent on its ability to sustain and increase growth in TFP.     

中国农业全要素生产率的动态演进及其影响因素分析

[目的]通过测算中国农业全要素生产率,揭示其动态演进趋势及影响因素,从而为推动我国农业供给侧结构性改革,实现农业现代化提供科学的决策依据。[方法]文章使用SBM-Global Malmqusit生产率指数法测算了中国农业全要素生产率,在此基础上利用Kernel核密度估计方法刻画了中国农业TFP增长的动态演进,并通过建立面板数据模型对影响中国农业TFP增长的诸多因素进行了分析。[结果]中国农业全要素生产率增长的主要源泉是技术进步,而技术效率的下降是阻碍其增长的主要原因;核密度估计表明各省份之间的农业相对全要素生产率水平差距不断扩大,技术效率普遍恶化,而技术进步增长速度显著;农业金融发展水平、工业化水平、农业科技水平对中国农业TFP都产生了显著的促进作用,而农业产业结构调整、对外开放水平对中国农业TFP则产生了阻碍作用,农村人力资本则对农业TFP产生的正效应不显著。[结论]提高农业全要素生产率,实现农业现代化.各省份应加强农业科技创新,深化农业科技体制改革,完善农村金融服务体系,推动工业化和农业现代化深度融合,培育新型职业农民等措施。     

Productivity growth and convergence in crop, ruminant, and nonruminant production: measurement and forecasts

Projections of future productivity growth rates in agriculture are an essential input for a great variety of tasks, ranging from development of an outlook for global commodity markets to the analysis of interactions between land use, deforestation, and ecological diversity. Yet solid projections for these variables have proven elusive—particularly on a global basis. This is due, in no small part, to the difficulty of measuring historical total factor productivity growth. Consequently, most productivity projections are based on partial factor productivity measures that can be quite misleading. The purpose of this work is to provide worldwide forecasts of agricultural productivity growth till the year 2040 based on the latest time series evidence on total factor productivity growth for crops, ruminants, and nonruminant livestock. The results suggest that most regions in the sample are likely to experience larger productivity gains in livestock than in crops. Within livestock, the nonruminant sector is expected to continue to be more dynamic than the ruminant sector. Given the rapid rates of productivity growth observed recently, nonruminant and crop productivity in developing countries may be converging to the productivity levels of developed countries. For ruminants, the results show that productivity levels in developing countries are likely to be diverging from those in developed countries.     

Productivity growth in Indian agriculture: is there evidence of convergence across states?

This paper explores the question of convergence in total factor productivity (TFP) in agriculture across fourteen major agricultural states of India. Using a Törnqvist–Theil index for TFP growth for the period 1973–1993, we find no evidence to support convergence to a single TFP level (σ‐convergence). After grouping the various states on the basis of their productivity performance, we find that the high‐performing states show a gradual movement towards the trend, whereas the low‐performing states generally show more volatility. Testing for long‐run convergence in levels of agricultural productivity, we find evidence of conditional beta‐convergence after controlling for state‐specific factors and idiosyncratic year‐specific volatility. The results are robust to alternative specifications of tests of unit root in panel data developed recently.     

COINTEGRATION,AND CAUSALITY: EXPLORING THE RELATIONSHIP BETWEEN AGRICULTURAL AND PRODUCTIVITY

This study investigates the causal relationships between total factor productivity (TFP) and explanatory variables, such as public sector agricultural extension, farmer education, private sector patents and the weather. Cointegration and Granger causality tests are applied to the UK data which were used by Hallam (1990). Unlike Hallam, we find that there is a relationship between research spending and productivity. The same methodology is applied to new data for ten EC countries and the USA. In all cases there is evidence of a long run relationship between TFP and. Pooling the data for the ten EC countries and the USA, and then testing for causality shows that expenditures are Granger prior to TFP and that TFP is also Granger prior to expenditures. This result agrees with Pardey and Craig's (1989) US study.     

Measuring and decomposing agricultural productivity and profitability change*

Profitability change can be decomposed into the product of a total factor productivity (TFP) index and an index measuring changes in relative prices. Many TFP indexes can be further decomposed into measures of technical change, technical efficiency change, scale efficiency change and mix efficiency change. The class of indexes that can be decomposed in this way includes the Fisher, Törnqvist and Hicks–Moorsteen TFP indexes but not the Malmquist TFP index of Caves, Christensen and Diewert (1982). This paper develops data envelopment analysis methodology for computing and decomposing the Hicks–Moorsteen index. The empirical feasibility of the methodology is demonstrated using country‐level agricultural data covering the period 1970–2001. The paper explains why relatively small countries tend to be the most productive, and why favourable movements in relative prices tend to simultaneously increase net returns and decrease productivity. Australia appears to have experienced this relative price effect since at least 1970. Thus, if Australia is a price‐taker in output and input markets, Australian agricultural policy‐makers should not be overly concerned about the estimated 15 per cent decline in agricultural productivity that has taken place over the last three decades.     

Research and productivity in Thai agriculture

This paper examines the impact that publicly funded agricultural research has on productivity in crop production within Thailand. It tests empirically the two hypotheses that, first, publicly funded research and development (R&D) in crop production is a significant determinant of total factor productivity (TFP) in the crop sector and, second, that its social rate of return is high. The statistical analysis applies error correction methods to national level time series data for Thailand, covering the period 1970–2006. Emphasis is given to public research in crop production, where most publicly funded agricultural R&D has occurred. The role of international research spillovers and other possible determinants of TFP are also taken into account. The results demonstrate that public investment in research has a positive and significant impact on TFP. International research spillovers have also contributed to TFP. The results support the finding of earlier studies that returns on public research investment have been high. This result holds even after controlling for possible sources of upward biases present in most such studies, due to the omission of alternative determinants of measured TFP. The findings raise a concern over declining public expenditure on crop research, in Thailand and many other developing countries.     

Convergence and divergence in regional agricultural productivity growth: evidence from Italian regions, 1951–2002

This article analyzes long‐term agricultural Total Factor Productivity (TFP) growth at regional level by testing its time‐series properties and identifying factors associated with divergence as opposed to convergence. The empirical application concerns Italian regions over the 1951–2002 time period. TFP growth decomposition ultimately attributes the observed productivity growth performance to these contrasting (convergence vs. divergence) forces. We find that technological spillovers are the key convergence force regardless of how the spillover effects are computed. At the same time, forces favoring convergence are almost offset by divergence forces (mainly scale or learning effects). This decomposition may explain the persistence of TFP growth rate differences in Italian agriculture, and could be applicable elsewhere.