Chapter 66 Production, Productivity, and Public Investment in East Asian Agriculture

We use multiple measures of agricultural total factor productivity (TFP) change to examine the relationship between agricultural productivity and poverty in developing countries. We employ a stochastic frontier analysis to estimate agricultural TFP changes for 113 countries using output distance function in a multi input multi output framework. We then make alternative groupings of countries to allow for the possibility of different production frontiers for countries with different income level, and we examine the effect of these various measurements of agricultural TFP on poverty reduction. Results from the TFP analysis show that TFP change estimates by income groups differ from those estimated using all countries in a pooled model. This indicates that agricultural technology and production frontiers may differ across countries based on income levels. Preliminary results show that TFP change from the pooled model has significant impact on poverty reduction. However, TFP estimates from different income groups didn’t indicate significant impact on poverty. The relationship between TFP change and poverty is therefore sensitive to the method used to estimate agricultural productivity.

Shenggen FanInternational Food Policy Research Institute and Institute of AgriculturalEconomics of the Chinese Academy of Agricultural SciencesLinxiu ZhangCenter for Chinese Agricultural Policy of the Chinese Academy of SciencesXiaobo ZhangInternational Food Policy Research InstituteI. IntroductionChina is one of the few countries in the developing world that has madeprogress in reducing its total number of poor over the past 25 years.

The impact of climate change and production technology heterogeneity on China's agricultural total factor productivity and production efficiency

Ecological protection approaches are important in achieving sustainable productivity growth in agriculture. Based on an unbalanced panel dataset for 2010–2016, we used stochastic frontier analysis-based Malmquist total factor productivity index to estimate total factor productivity change of Swedish crop production and its components (efficiency change, technical change, scale change). We then examined the effect of ecological protection approaches on total factor productivity change. The empirical results demonstrated that ecological protection approaches such as organic farming, mixed cropping or integrated farming could hamper total factor productivity growth. The results also indicated that average total factor productivity change in the study period was positive and average technical efficiency of the Swedish crop production was 71%. Among the components of total factor productivity change, average scale change was positive. Average technical efficiency change and average technical change were both negative. If technical efficiency and technological progress can be improved, that would increase the positive change in total factor productivity. This suggests that policies on compensation or insurance against productivity loss are required to encourage mainstreaming of ecological protection approaches among farmers.

Agricultural production can be observed to reach 3-4 percent annual growth rates with the exception of drought like shocks in Turkey. In recent years, significant level of investments, qualified input use and more irrigated land led to productivity improvements. However, there are not enough studies on the change in total factor productivity (TFP) or on regional dynamics of the change in TFP in the agricultural sector. Using Turkish statistical classification of territorial units (NUTS-2 classification of the EU), detailed data on economic and social indicators were collected so as to examine the change in regional TFP figures to point out the results and policy priorities. In this context, agricultural output change is determined not only by the labor, capital and other agricultural production inputs but also by the factors that are effective on the change in total factor productivity, that are dealt with in this study. At this point, macroeconomic and institutional factors play an important role in the change in total factor productivity; inflation, exchange rate, economic volatility, share of high-tech exports, rural development supports, human capital. Since data were not available for the structure of the soil at regional level it could not be considered as a factor in estimations. As a result in this study, especially technological progress, human capital and rural development support have significant impacts on the change in total factor productivity whereas changes in the exchange rate have negative effects. An important result here is that the impact of agricultural supports other than rural development supports could not be determined statistically as a cause changing factor productivity.

The present study aims at improving our understanding of the individual contribution of the components of total factor productivity (TFP) change to U.S. agricultural productivity. A novel sequential primal‐dual estimation routine to calculate TFP change is proposed, using a multi‐output input distance function in the first stage, followed by a cost minimization routine in the second stage. TFP change is estimated as the direct sum of the estimates of technical change, technical efficiency change, allocative efficiency change, input price effects, changes in output markup, and changes in returns to scale in each state. The validity of the proposed methodology is supported by the remarkable overlap and high correlation of our annual estimates of TFP change with the USDA's measures of change in TFP by state. Although technical change tends to be the largest contributor to productivity change, it bears a low and statistically insignificant correlation with TFP change on an annual basis, whereas annual changes in the markup effect and returns to scale are highly and significantly correlated with TFP changes. This is the first study to find a slowdown of technical progress in the U.S. farm sector in the 1990s and 2000s, and technical regress during the farm crisis of the 1980s. While technical efficiency shows a positive overall trend, allocative efficiency shows a negative overall trend, and their combined effect (i.e., the overall cost efficiency) slows down TFP growth. The policy recommendations from previous studies on the drivers of TFP should be revised in light of these findings.

Currently, the agricultural growth in developed countries mainly relies on the improvement of productivity, which is also the target for China. Accordingly, the purpose of this study was to describe the spatial-temporal evolution pattern of agricultural productivity, to reveal changes in total factor productivity in 2000-2010, and analyze the impact of these changes in northwestern Sichuan plateau, China. Using data envelopment analysis (DEA) and the Malmquist Index, an in-depth study was conducted on agricultural productivity and changes in total factor productivity of 31 counties in northwestern Sichuan plateau. Results indicated that: (1) geographically, counties with optimal efficiency were mainly located in the north of northwestern Sichuan plateau and those with the lowest efficiency, in the south; (2) relative to pure technical efficiency, scale efficiency was the dominant factor in determining agricultural productivity; (3) the redundancy rate of input factors in 2010 was slightly lower than that in 2000, thereby indicating an improved utilization of input factors to a certain extent and a great potential for further improving such utilization; (4) during the 2000-2010 period, the agricultural total factor productivity had an average annual growth rate of 8.3%, but the growth rates in various regions differed widely; (5) technical progress was the dominant factor promoting the improvement of total factor productivity in agriculture. The disparities in spatial distribution may be due to the differences of natural conditions, former level agricultural productivity between counties. The findings are valuable for the government to make sustainable development policies for agriculture and improving agricultural development in northwestern Sichuan plateau.

In this paper, I examine the levels and trends (Solow type of growth accounting) of Agricultural Total Factor Productivity (TFP) change in Nepal over 41 year. I make use of data drawn from the Food and Agriculture Organization of the United Nations and my study covers the period 1962-2002, which covers from second development plan to ninth development plan period of Nepal. Due to the non availability of reliable data, the study excludes first development plan period. Politically, three major events - Autocratic Panchayat System, Liberal Panchayat System, and Multiparty Democracy - have been considered to see the policy implication on major TFP change during each five year development plan period. The paper derives agricultural growth in five years plans period under Multiparty Democracy period (1990-2001) is due to TFP change while rest of the period growth are posted by use of inputs mainly fertilizers and machinery. It also derives the agricultural growth has greatly varied over 41 years periods.

Purpose: This paper aimed at evaluating changes in agricultural productivity in the group of new (NMS) and old (OMS) member states of the European Union. Design/Methodology/Approach: The analysis covered the years 2007-2016. The calculations made use of unit data from farms participating in FADN (Farm Accountancy Data Network). Surveys were carried out based on the Malmquist productivity index and partial indicators of land, workforce and capital productivity. Findings: The outcome pointed to increased total productivity of agriculture in NMS (9.5%), resulting from positive technological changes and improvement in technical efficiency. A small decrease in productivity was noted in a group of EU-15 countries, which was due to a decrease in technical efficiency. Despite the growth in total productivity, the value of partial productivity indicators in NMS remained at a much lower level than in OMS. Practical Implications: Identification of the determinants of growth in agricultural productivity is the precondition to make up differences occurring between member states in this respect. Originality/Value: This study contributes to reference literature concerning productivity of agriculture for a number of reasons. First, the scope of the survey is extensive, as it covers a group of 27 EU member states split into new and old members. The second aspect of the survey is taking into account both changes in total factor productivity and in productivity of respective production factors. Thirdly, the Malmquist index adopted for the needs of the survey made it possible to identify the sources of change in total factor productivity.

Smallholder maize production in Zambia has been characterised by low productivity despite concerted efforts at improving the situation as is evident in budgetary allocations to programmes such as the Farmer Input Support Programme (FISP). The study assessed if there was a change in total factor productivity (TFP) in smallholder maize production in Southern Province of Zambia between the 2010/11 and 2013/14 agricultural seasons. Using a balanced panel of 778 smallholder farmers, a Stochastic Frontier Analysis was used to estimate the Malmquist Productivity Index (MPI) in measuring the productivity change in maize production. The change in TFP was further decomposed into its components, efficiency change (EC) and technical change (TC) so as to understand more on the change in productivity. It was found that over the period of study, the mean EC was 0.8734, implying that technical efficiency (TE) had declined by 12.7 % with the mean TFP of 0.9401, indicating that over the study period TFP had fallen by 5.99 %. The results further showed that the age of the farmer, education of the farmer, household size, membership to a farmer organization, ownership of cattle, access to credit, and drought stress were significant (ρ<0.05) factors in explaining TFP. In light of the findings, some recommendations were made for policy including the need to facilitate farmers’ access to credit, sensitize farmers on the benefits of belonging to farmer organizations, on ownership of livestock such as cattle and for massive investment in irrigation infrastructure.

This study illustrates and quantifies how overlooking the impact of weather shocks can affect the measurement and decomposition of agricultural total factor productivity (TFP) change. The underlying technology is represented by a flexible input distance function with quasi‐fixed inputs estimated with Bayesian methods. Using agricultural production and weather data for 16 states in the Pacific Region, Central Region, and Southern Plains of the United States, we estimate TFP change as the direct sum of multiple components, including a net weather effect. To assess the role of weather, we conduct a comparative analysis based on two distinct sets of input and output variables. A traditional set of variables that ignore weather variations, and a new set of “weather‐filtered” variables that represent input and output levels that would have been chosen under average weather conditions. From this comparative analysis, we derive biases in the decomposition of TFP growth from the omission of weather shocks. We find that weather shocks accelerated productivity growth in 12 out of 16 states by the equivalent of 11.4% of their group‐average TFP growth, but slowed down productivity by the equivalent of 6.5% of the group‐average TFP growth in the other four states (located in the Northern‐most part of the country). We also find substantial biases in the estimated contribution of technical change, scale effects, technical efficiency change, and output allocation effects to TFP growth (varying in magnitude and direction across regions) when weather effects are excluded from the model. This is the first study to present estimates of those biases based on a counterfactual analysis. One major implication from our study is that the official USDA's measures of TFP change would appear to overestimate the rate of productivity growth in U.S. agriculture stemming from technical change, market forces, agricultural policies, and other nonweather drivers.

The present study concerns the impact of knowledge capital on total factor productivity (TFP) changes in 27 European Union (EU) countries. The TFP analysis covered the years 2009-2013. The study conducted was based on a Malmquist productivity index. The knowledge capital was approximated through investments in research and development in the years 2000 and 2008. Furthermore, the study included external benefits resulting from the R&D activities in other countries. In addition to the knowledge capital, the variables approximating human capital were accepted as determinants of TFP, i.e., the percentage of farm managers with full agricultural training and the percentage of farms managed by holders aged 55. The impact of knowledge and human capital on TFP was determined using a linear regression model. The results of the study indicate that the R&D expenditures incurred in the year 2000 are the stimulants of productivity growth, which confirms the assumption that there is a time lag between R&D and its benefits. Moreover, a positive effect on TFP growth was observed for the variable approximating human capital, i.e., the participation of farmers over the age of 55.

BackgroundAmid the rising demand for healthcare services in Saudi Arabia, there is a need to monitor, evaluate, and improve performance in the delivery of these services. In this regard, the aim of this study was to estimate changes in total factor productivity (TFP) in healthcare services across the health system administrative regions in Saudi Arabia from 2006 to 2018. The contributions of changes in efficiency and technology to the observed changes in TFP were further evaluated.MethodsThe data used for this study were extracted from annual Ministry of Health Statistical Yearbooks for the period of 2006–2018. TFP changes were estimated using the Malmquist Productivity Index, in which technology frontiers were constructed through data envelopment analysis. The changes in TFP were decomposed into changes in technology, changes in pure technical efficiency, and changes in scale efficiency following the Färe-Grosskopf-Norris-Zhang method. As robustness checks, we used bootstrapping to construct intervals and applied alternative decomposition methods. The changes in TFP and its sources were also compared between public and private hospitals.ResultsOver the period from 2006 to 2018, TFP for healthcare services has decreased on average by 5.6% per year solely on account of a technical regress. Public hospitals registered a higher deterioration in productivity (6.0% per year) than private hospitals (4.8% per year).ConclusionUsing the available resources, there is potential for realising gains in productivity of healthcare services by addressing existing technological challenges. Since the decline in TFP is entirely a problem of technical regress, the primary solution should focus on strategies for achieving technical progress (innovation) through investment in more or better machinery, equipment, and structures for healthcare service provision.

Purpose of the Study: Egyptian agriculture suffers from many problems related to the use of available economic resources, the most important of which is lack of optimal utilization of resources, wasteful use of agricultural production inputs, reduced efficiency of irrigation water use, and the fertility of agricultural lands are deteriorating, in addition to increasing rates of encroachments on agricultural lands and shifting it from agricultural use to other non-agricultural uses, which limits the agricultural sector ability to achieve high growth rates, especially with the increasing global production of biofuels from crops that individuals consume as food, including wheat and corn, which constitutes an explicit threat to Egyptian food and national security.
 Objectives: The research aimed to:
 
 Estimate the changes in the sources and components of the total productivity of the factors for the main cereal crops in Egypt in the presence and absence of carbon dioxide emissions,
 Environmental impact assessment of changes in the productivity of these crops.
 
 Methods: The study applied analytical approaches to measure changes in productivity, as parameter analysis methods are used as methods of the aggregate production function, and non-parameterized methods of estimation, in addition to (Malmquist, 1953) which is one of the most important indicators of measurement changes in productivity and relies on a Data Envelopment Analysis (DEA) to measure efficiency and changes in TFP productivity and identify the sources of changes in productivity through changes in technical competence and technological change, as the two most important components of the change in total productivity.
 Results: Wheat Crop: Wheat crop by estimating the change in the different efficiencies of the wheat crop with co2 emissions, it was clear that a decrease in technological change (TC) during the study period, and thus a decrease in the average change in the total factor productivity (TFP), while without co2 emissions effect, the average change in the total factor productivity of (TFPc) indicates an increase in the actual wheat efficiency which is low due to the environmental impact of the emissions.
 Rice Crop: Rice crop by estimating the change in the different efficiencies of the rice crop with co2 emissions, it became clear that a decrease in the average technological change (TC), thus increasing the average change in the total factor productivity of the (TFP), whereas, without co2 emissions, it was found that the average change in the total factor productivity of the (TFPc) for the study areas was higher.
 Summer Maize Crop: It was clear that the average technological change (TC) for the summer maize crop with co2 emissions, decreased during the study period, and therefore a decrease in the average change in the total factor productivity of the (TFP), but without co2, an increase in the annual average of the change in technical efficiency (TEC), and a decrease in the average technological change (TC), i.e. in the use of technology, and an increase in the average change in the total factor productivity (TFPc).

This paper seeks to contribute to the analysis of bank efficiency in the European Union during the aftermath of the international financial crisis that began in 2007, using data envelopment analysis and a sample of 485 banks from all European Union member-states between 2011 and 2017. The results confirm the existence of bank inefficiency, mostly due to inefficient managerial performance and bad combinations of bank inputs and outputs. The existence of bank inefficiency is particularly relevant during this period as European Union countries faced not only financial imbalances but also imbalances in their public budgets. Some were even obliged to request international financial assistance to overcome the deep financial and sovereign crises. Additionally, there was evidence of appropriate scale production and dynamic technological changes during the interval. Moreover, the panel estimates explaining bank total factor productivity changes suggest the choices of banks in terms of fixed assets, profit-before-taxes-to-average-assets ratio and off-balance-sheet-items-to-total-assets ratio contributed positively to productivity changes. The impaired-loans-to-equity ratio and bank interest margins were not in line with the total factor productivity changes of the European Union banking sector.