Size-productivity Relationship Research Articles

Revisiting the size–productivity relationship with imperfect measures of production and plot size

AbstractMonitoring smallholder agricultural productivity growth, one of the targets of the Sustainable Development Goals, rests on accurate measures of crop production and land area. Existing methods and protocols for measuring smallholder production and plot size are prone to various sources and forms of mismeasurement. Inaccuracies in production and land area measurement are likely to distort descriptive and predictive inferences. We examine the sensitivity of empirical assessments of the relationship between agricultural productivity and land area to alternative measurement protocols. We implement six production and six land area measurement protocols, and show that most of these protocols differ systematically in their accuracy. We find that an apparent inverse size–productivity relationship in our data is fully explained by measurement error in both production and plot size. Moreover, we show that some of the previously used “gold standard” measures are themselves prone to nonclassical measurement error, and hence can generate spurious inverse size–productivity findings. Our results also show that slight improvements in the precision of objective measures significantly reduce the inferential bias associated with the size–productivity relationship.

Allocative Efficiency of Cropshare Cultivation: Interpreting the Empirical Evidence

Because of the simultaneity problems raised by supervision problems, and the low opportunity cost of labour which may induce an inverse size-productivity relationship in agriculture, tests requiring a comparison of the productivity of farmers or farms of different tenure groups tend to be biased. Even a comparison of the performance of farmers of different tenure groups, though of the same size, may not necessarily yield unbiased results. The paired t-test, which involves comparing the performance of the owner· tenants on their own and cropshare land, has been found to be the most satisfactory method of comparing the effiCiency of crop share tenancy with that of owner cultivation. The study also suggests some disciplining influence imparted by a better choice of tenant and threat of eviction, preventing the tenants from wasting the rental land.

Agricultural labor markets and the inverse plot size–productivity relationship: Evidence from China's apple growers

The size–productivity relationship and the reasons for the relationship had puzzled researchers and policymakers for decades. As rural internal labor markets had gradually developed in China, influencing mechanisms of agricultural labor markets on land size–productivity relationship were worth being deeply analyzed. Two main contributions to the existing literature were that: (1) owing to the labor-intensive production characteristics, better-developed rural internal labor markets, and fewer measure errors in self-reported plot size and production, apple growers were used to analyze the plot size–productivity relationship and the reasons for the relationship in rural China for the first time; (2) the household fixed-effect model was used to remove the impacts of household-level unobservable variables, and positive impact of labor input on agricultural productivity was analyzed. The empirical results from 1163 plot-level data indicated that: (1) when labor input was not included, the inverse plot size–productivity relationship was robust for apple growers; (2) agricultural labor input was a key reason for the inverse plot size–agricultural productivity relationship; (3) transaction costs of participating in agricultural labor markets influenced the inverse plot size–productivity relationship by changing the amount of labor input. This line of study was conducive to improving the efficiency of rural internal labor markets and increasing agricultural productivity.

Are small farms really more productive than large farms?

This paper shows that using yields may not be informative of the relationship between farm size and productivity in the context of small-scale farming. This occurs because, in addition to productivity, yields pick up size-dependent market distortions and decreasing returns to scale. As a result, a positive relationship between farm productivity and land size may turn negative when using yields. We illustrate the empirical relevance of this issue with microdata from Uganda and show similar findings for Peru, Tanzania, and Bangladesh. In addition, we show that the dispersion in both measures of productivity across farms of similar size is so large that it renders farm size an ineffective indicator for policy targeting. Our findings stress the need to revisit the empirical evidence on the farm size-productivity relationship and its policy implications.

A revisit of farm size and productivity: Empirical evidence from a wide range of farm sizes in Nigeria.

The relationship between farm size and productivity has been studied extensively in the agricultural and development economics literature. However, most of the documented evidence in sub-Saharan Africa (SSA) is based on samples of small-scale farms operating 5ha or less, with very little evidence assessing this relationship over a wider range of farm sizes. This omission is especially important considering the rapid expansion of medium-scale farms in much of Africa. This study examines the farm size-productivity relationship over a range of farms between zero and 40ha in Nigeria. It also tests whether there is heterogeneity in productivity within medium-scale farms depending on how they came into being. Using four measures of productivity, empirical estimates reveal a U-shaped relationship where the IR holds between zero and about 22ha, turning positive afterwards. Moreover, when medium-scale farms are distinguished between those who were actively engaged as small-scale farmers and stepped up/expanded their scale of operation and those who were primarily in non-farm employment and later stepped into medium-scale farming, the turning point for farmers who stepped up into medium-scale farming is at 11ha, in contrast to 22ha for those who stepped in. Further evidence suggests heterogeneity in productivity within medium-scale farms depending on whether the owner-operators stepped up or stepped into medium-scale farming. These findings imply that policies facilitating smallholders' ability to expand the scale of their activities could contribute substantially to growth in farm productivity, agricultural commercialization and increase in food security in Nigeria, although in most areas only a small proportion of smallholder households are in a position to do this.

Farm Size, Tenancy, and Productivity: An Overview

The present study intends to investigate the relationship between farm size and productivity. The objectives of the study are: to investigate the relationship between farm size and productivity, to suggest some policy implications. The study is based on secondary data. Data were collected from different published and unpublished documents. The main findings of the study are: the small farms have the higher productivity of land than the larger ones, there exists the inverse farm size productivity relationship, few studies showed that although there exists an inverse relationship between these two this inverse relationship got weakened or even disappeared in the regions adopting new technology, some cases there also exists the positive relationship between farm size and productivity, the output level of owner cultivator is likely to be higher than the share-cropper. Few researchers pointed out the higher productivity of sharecroppers than the owner cultivators. In view of the above findings, the following policy measures are suggested: emphasis should be given to farm-related research, the assistance of small farmers in order to form associations for enhancing production, absorbing credit, and adopting farm technologies.

Exploring firm-level and sectoral variation in total factor productivity (TFP)

PurposeThe objective of the study is to identify and explore factors affecting the productivity of companies in the Czech Republic with a focus on the role of firm size, firm age, indebtedness and long-term negative equity, efficiency of assets usage, liquidity, legal form, location and sector affiliation.Design/methodology/approachThe study utilizes a large unbalanced panel dataset of 91,257 firms (548,998 observations in total) covering the period 2000–2019. The dependent variable, i.e. total factor productivity (TFP), reflecting the overall firm productivity, was estimated by ordinary least squares (OLS) regression. The main findings were obtained through the estimation of two econometric models explaining the effects of factors on firm-level TFP. First, the OLS regressions together with Nomenclature of Territorial Units for Statistics (NUTS) 3 regions, year dummies and robust standard errors were estimated. Second, as a robustness check, the very same model was estimated with the random effects (RE) generalized least squares (GLS) method.FindingsThe analysis has shown a statistically significant U-shaped relationship (with the turning point of 38, resp. 36 years) between firm age and the overall TFP among the Czech enterprises. The authors provide two key findings in terms of a firm size-productivity relationship. Firms with fewer employees, often officially registered as self-employed individuals/freelancers, report higher levels of productivity. Nevertheless, when it comes to firm property (assets), the authors find a positive relationship between firm size and TFP. A high proportion of debts in the capital structure of analysed companies, or even negative equity, has been negatively associated with TFP levels.Research limitations/implicationsMore research is needed in the deeper exploration of sectoral and regional determinants of firm TFP, as both regional and sectoral heterogeneity were observed in the study. The authors propose the employment of a multi-level modelling approach, including a range of continuous variables and investigation of their role in shaping firm-level productivity.Practical implicationsConcerning the results, managers should be mindful of optimal capital structure principles due to the negative impact of a high level of debts on the productivity level. High indebtedness means high-interest payments drawing earnings off, which may be, especially in the long term, a hindrance to investments. The entrepreneurship and small- and medium-sized enterprise policies may be targeted at the soft policy actions, including advisory services and counselling on business development or risk and on the provision of financial capital allowing firms to strive for growth-oriented projects.Originality/valueTo the best of the authors' knowledge, this is the first attempt to provide insight into the firm-level productivity determinants, based on the large dataset covering enterprises across the whole economy over the long term, representing the structure of the country's entrepreneurial activity.

Land Measurement Bias: Comparisons from Global Positioning System, Self-Reports, and Remote Sensing Data

We investigate the reliability of land measurement modes on non-classical measurement error and empirical relationships. In our multi-country survey experiment, we find significant differences between GPS and remotely sensed data only in Viet Nam, where plot sizes are small relative to the other countries. The magnitude of farmers’ self-reporting bias relative to GPS measures is nonlinear, with the largest magnitude of self-reporting bias of 130% of a standard deviation (2.2-hectare bias) in the Lao People’s Democratic Republic relative to Viet Nam, which has 13.3% of a standard deviation (.008-hectare bias). In all countries except Viet Nam, the inverse land size–productivity relationship is upwardly biased for lower land area self-reported measures relative to GPS measures. In Viet Nam, the intensive margin of organic fertilizer use is negatively biased (30.4 percentage points) by self-reported measurement error. We conclude by considering sources of measurement error in implementation and costs.

Factor Market Activity and the Inverse Farm Size-Productivity Relationship in Tanzania

Although the inverse farm size-productivity relationship (IR) is sometimes used to motivate arguments in favour of smallholder-led agricultural development, it remains unclear what drives this relationship. It may be attributed to market imperfections that compel small farms to use land more intensively than large farms. Using a three-wave longitudinal household survey from Tanzania, we examine whether the intensity of the IR is related to local factor market activity for land, labour, credit, and animal and machine traction. The IR holds in Tanzania when family labour is either not counted or valued at its shadow cost, though it disappears when family labour is valued at the prevailing local agricultural wage rate. Moreover, the IR is significantly weakened in regions with relatively active agricultural factor markets, such as for land and mechanization services. This suggests that the IR is at least partly driven by imperfections in rural factor markets. As household participation in agricultural factor markets continues to rise, the IR may be expected to weaken or even reverse.

Close to the edge: High productivity at plot peripheries and the inverse size-productivity relationship

Smaller farms and plots appear more productive per hectare than larger ones in most developing country data. Using unique plot-level panel data from Uganda, with agricultural plots matched over a decade using geospatial location, we estimate the size-productivity relationship using variation in plot size over time under plot fixed effects, as well as variation in plot size within a farm, under household-period fixed effects. Like many other authors, we find that the observed inverse relationship arises at the plot, not farm, level, and that observable plot characteristics such as soil quality cannot explain the relationship. However, the plot perimeter/area ratio explains most of the inverse plot size-productivity relationship, reflecting an edge effect widely acknowledged in the agronomy literature wherein productivity is highest around the periphery of plots. We present suggestive evidence consistent with behavioral and biophysical mechanisms underpinning the edge effect. • The inverse size-productivity relationship arises at the plot, not farm, level in rural Uganda. • The relationship is not explained by measurement error in land size nor oft-omitted variables. • The plot perimeter/area ratio explains most of the inverse relationship. • This likely reflects an agronomic edge effect wherein productivity is highest at plot peripheries.

Revisiting farm size-productivity relationship: New empirical evidence from Ethiopia

ABSTRACTAre small farms more productive? With this question in mind, this study revisits the farm size–productivity relationship and explores potential explanations using a unique plot-level data from predominantly wheat producers in Ethiopia. Overall, we find that small plots are more productive than large plots. We next test the conventional explanations hypothesised in the literature – labour market imperfection related to costly monitoring of hired workers and omitted variable bias related to soil quality – and find that neither of them essentially explains the inverse relationship. More importantly, we account for agricultural intensification and found no relationship between plot size and productivity. This suggests that the inverse relationship posited in the literature could simply arise from neglecting the impact of agricultural intensification.

Correlated non-classical measurement errors, ‘Second best’ policy inference, and the inverse size-productivity relationship in agriculture

We show that non-classical measurement errors (NCME) on both sides of a regression can bias the parameter estimate of interest in either direction. Furthermore, if these NCME are correlated, correcting for either one alone can aggravate bias relative to ignoring mismeasurement in both variables, a ‘second best’ result with implications for a broad class of economic phenomena of policy interest. We then use a unique Ethiopian dataset of matched farmer self-reported and precise ground-based measures for both plot size and agricultural output to re-investigate the long-debated relationship between plot size and crop productivity. Both self-reported variables contain substantial NCME that are negatively correlated with the true variable values, and positively correlated with one another, consistent with prior studies. Eliminating both sources of NCME eliminates the estimated inverse size-productivity relationship. But correcting neither variable generates a parameter estimate not statistically significantly different from that generated using two improved measures, while correcting for just one source of NCME significantly aggravates the bias in the parameter estimate. Numerical simulations demonstrate that over a relatively large parameter space, expensive collection of objective measures of only one variable or correcting only one variable's NCME may be inadvisable when NCME are large and correlated. This has practical implications for survey design as well as for estimation using existing survey data.

The Relationship between Farm Size and Productivity in Agriculture: Evidence from Maize Production in Northern China

AbstractThe relationship between farm size and productivity has long been a topic of debate in development economics. Using farm‐level panel data from 2003 to 2013, we investigate the relationship between maize yield and farm size in Northern China. After controlling for farm‐specific characteristics, we restore a mild U‐shaped relationship between maize yield and cropping area from the apparent inverse U‐shaped curve. This suggests that an inverse farm size–productivity relationship persists for most small‐sized farms. Further analyses demonstrate that farmer input choice between labor and capital is likely to smooth the non‐linear farm size–productivity relationship, with capital use being more likely to affect the farm size–productivity relationship at a larger scale. The findings imply that subsidizing farmers to rent land without helping them become better‐equipped could result in resource misallocation towards larger farms using less‐efficient labor‐intensive technologies.

Can Labor-Market Imperfections Explain Changes in the Inverse Farm Size–Productivity Relationship? Longitudinal Evidence from Rural India

A large national farm panel from India covering a quarter century (1982, 1999, and 2008) is used to show that the inverse farm size-yield relationship weakened significantly over time, despite an increase in the dispersion of farm sizes. Key reasons are substitution of capital for labor in response to nonagricultural labor demand. Family labor was more efficient than hired labor in 1982-99, but not in 1999–2008. In line with labor market imperfections as a key factor, separability of labor supply and demand decisions cannot be rejected in the second period, except in villages with very low nonagricultural labor demand.

Too small to be beautiful? The farm size and productivity relationship in Bangladesh

This paper examines the agricultural productivity-farm size relationship in the context of Bangladesh. Features of Bangladesh’s agriculture help overcome several limitations in testing the inverse farm-size productivity relationship in other developing country settings. A Stochastic Production Frontier (SPF) model is applied using data from three rounds of a household panel survey to simultaneously estimate the production frontier and the technical inefficiency functions. The ‘correlated random effects’ approach is used to control for unobserved heterogeneous household effects. Methodologically, the results suggest that SPF models that ignore the inefficiency function are likely mis-specified, and may result in misleading conclusions on the farm size-productivity relationship. Empirically, the findings confirm that the farm size and productivity relationship is negative, but with the inverse relationship diminishing over time. Total factor productivity growth, driven by technical change, is found to have been robust across the sample. Across farm size groups, the relatively larger farmers experienced faster technical change, which helped them to catch up and narrow the productivity gap with the smaller farmers.

Land Measurement Bias: Comparisons from Global Positioning System, Self-Reports, and Satellite Data

Agricultural statistics derived from remote sensing data have been used primarily to compare land use information and changes over time. Nonclassical measurement error from farmer self-reports has been well documented in the survey design literature primarily in comparison to plots measured using Global Positioning System (GPS). In this paper, we investigate the reliability of remotely sensed satellite data on nonrandom measurement error and on agricultural relationships such as the inverse land size–productivity relationship and input demand functions. In our comparison of four Asian countries, we find significant differences between GPS and remotely sensed data only in Viet Nam, where plot sizes are small relative to the other countries. The magnitude of farmers’ self-reporting bias relative to GPS measures is nonlinear and varies across countries, with the largest magnitude of self-reporting bias of 130% of a standard deviation (2.2-hectare bias) in the Lao People’s Democratic Republic relative to Viet Nam, which has 13.3% of a standard deviation (.008-hectare bias). In all countries except Viet Nam, the inverse land size–productivity relationship is upwardly biased for lower land area self-reported measures relative to GPS measures. In Viet Nam, the intensive margin of organic fertilizer use is negatively biased by self-reported measurement error by 30.4 percentage points. As remotely sensed data becomes publicly available, it may become a less expensive alternative to link to survey data than rely on GPS measurement.

The Inverse Farm Size Productivity Relationship: Some New Evidence From Sub-Sahara African Countries

The Inverse Farm Size Productivity Relationship: Some New Evidence From Sub-Sahara African Countries

Rising real wages, mechanization and growing advantage of large farms: Evidence from Indonesia

This paper examines dynamic patterns of land use, capital investments and wages in agriculture using farm panel data from Indonesia. The empirical analysis shows that with an increase in real wages that prevailed in both agricultural and non-agricultural sectors in rural areas, relatively larger farmers increased the size of operational farm land by renting in land as well as used more hired-in machines through machine rental and/or service providers, which induces the substitution of labor by machines. Machines and land are complementary and, consistently, the inverse farm size-productivity relationship tends to be reversed among relatively large holders. The results support growing (diminishing) advantage of large (small) farms in the presence of rapidly rising real wages and have food security policy implications to many Asian countries where real wages are rapidly rising and small farms are predominant.

Is small still beautiful? A comparative study of rice farm size and productivity in China and India

Purpose– The farm size-productivity relationship has long been the subject of debate among development economists. Few studies address this issue for China, and those that do only with outdated data sets poorly representing the current situation after the past decade of rapid change, which includes the rapid development of land rental markets, village labor out-migration and use of farm machines. Meanwhile, many studies have researched this relationship for Indian, which is undergoing similar changes except for the development of active land rental markets. The purpose of this paper is to measure the farm size-productivity relationship under the situations of rapid transformation in China and India.Design/methodology/approach– Based on the data of 325 Jiangxi and 400 Allahabad rice farmers in 2011, the survey covered multiple plots of each household in one/multiple growing season(s). The authors use the production function approach and the yield approach, and control for farmland quality, imperfect factor markets, and farm size measurement error, to identify the farm size-productivity relationship.Findings– The regressions show that land yields increase with plot size both by season and over the year in China. This may be one of the reasons that farm sizes are growing in some areas. In India, however, the inverse farm size-productivity relationship is observed by the study, despite recent changes. Moreover, land yields increase with farm machine use in both China and India. This result contributes to the debate over whether mechanization improves yields or just expands the land frontier.Originality/value– The paper empirically estimates the farm size-productivity relationship under rapid agrarian transformation in both China and India based on a unique data set collected by the authors in a detailed primary survey. The paper considers measurement error in the analysis, which adds values to this type of analysis.

Is There a Farm Size–Productivity Relationship in African Agriculture? Evidence from Rwanda

Whether the negative relationship between farm size and crop productivity that is confirmed in a large global literature holds in Africa is of considerable policy relevance. Plot-level data from Rwanda point toward constant returns to scale and a strong negative relationship between farm size and crop output per hectare that is robust across specifications and emerges also if profits with family labor valued at shadow wages are used but disappears if family labor is valued at market rates. In Rwanda, labor market imperfections, rather than other unobserved factors, seem to be a key reason for the inverse farm-size productivity relationship. <i>(JEL O13, Q15)</i>