Does human capital raise farm or nonfarm earning more? New insight from a rural Pakistan household panel

This study explores how human capital affects farm household earnings using two tools to refine measurement of human capital effects. First, it employs a two‐sector model to allow the allocation of family labor between farm and nonfarm activities. Second, it accounts for village fixed effects to evaluate whether results from panel data differ meaningfully from a cross‐sectional data analysis with local binary variables. The results show that education has a negligible effect on farm earnings; instead, experience appears to be the principal channel by which human capital affects agricultural performance in a traditional rural setting. Our results also suggest that prior models that fail to separate nonfarm activities spuriously exaggerated the effect of education to the farm sector. In addition, typical cross‐sectional analyses that ignore fixed effects may cause the effects of education on rural household earnings to be significantly overstated. The fact that panel data regressions accounting for village‐level fixed effects found only one instance of education raising earnings—the effect of literacy on nonfarm income—suggests that considerable heterogeneity may have been ignored in cross‐sectional data analyses, especially ones that omitted village‐level effects.     

Effect of off‐farm income on smallholder commercialization: panel evidence from rural households in Ethiopia

This article investigates how off‐farm income affects crop output market participation decisions and marketed surplus of smallholder farmers in Ethiopia. A double‐hurdle model is estimated using three waves of panel data from the Ethiopian Rural Household Survey. Unobserved heterogeneity is controlled for using a correlated random effect procedure and potential endogeneity of explanatory variables using a control function approach. The results show that off‐farm income has no significant influence on household crop output market participation. However, conditional on market participation, additional off‐farm earnings negatively affect the marketed surplus. This indicates that farmers use off‐farm earnings for consumption rather than for investment in agricultural production. Policy measures that promote rural investment may help increase returns to labor for land‐poor households participating in off‐farm work in the process of agricultural commercialization.     

Productivity and efficiency impact of climate change and agroecology on Bangladesh agriculture

The paper estimates the impacts of climate change, agroecological and socio-economic characteristics on agricultural productivity and efficiency changes in Bangladesh agriculture using a rich panel dataset of 17 regions covering a period of 61-years (1948–2008). Results revealed that land has the most dominant role in increasing agricultural production followed by labour and irrigation. The contribution of non-cereal crops (i.e., potatoes, pulses, oilseeds, jute and cash crops) to total production are also significant, ranging from 2 to 8% per annum. An increase in annual-rainfall and long-term-temperature (LTT) significantly enhance production. Production is significantly higher in floodplain agroecologies. However, production efficiency fluctuated sharply and declined overtime. The mean efficiency score of 0.74 implies substantial room to improve production by resource reallocation. Average farm size, crop specialization and investment in R&D significantly improve efficiency whereas increases in annual temperature-variability and LTT significantly reduce efficiency. Efficiency is significantly lower in low-lying floodplain and coastal-plain agroecologies. Policy implications include investments in diversifying cropping portfolio into other cereals (i.e., wheat and maize), research to develop crop varieties suited to changing climatic conditions and specific agroecological regions, and land/tenurial reforms to consolidate farm size to enhance productivity and efficiency of Bangladesh agriculture.     

Simultaneous estimation of work choices and the level of farm activity using panel data

We analyse the simultaneous determination and evolution overtime of work choices and the level of farm activity, using apanel of Israeli farm households. We estimate jointly a multinomialchoice model of work activity and an endogenous switching regressionof farm size, accounting for unobserved heterogeneity and correctingfor simultaneity bias. Our results imply that some family farmstend to expand over time and specialise in farming, whereasother farm households downsize their farming operation and increasetheir engagement in the off-farm labour market. Therefore, thesize distribution of farms is converging towards a bimodal distribution.     

PART TIME FARMING—IMPLICATIONS FOR FARM FAMILY INCOME

The Common Agricultural Policy has been more successful at securing food supplies than at providing adequate incomes for small farmers. Among proposals for resolving the problem, part time farming is a promising option. To date, agricultural policy has at best ignored farms below the ‘full time’ threshold, at worst discriminated against them. The new Agricultural Structures policy embodies a more positive approach, however. Robson's paper calls for better information on farm households with other gainful activities. Statistics from the 1983 Farm Structure Survey provide insight into the nature and extent of part time farming in the European Community. Neglect of part time farming in the UK up to now may have stemmed from its perceived irrelevance for agricultural policy. Now that it is being viewed in a more favourable light, policy makers need to be appraised of the facts. Currently about one third of main agricultural holdings in England and Wales are part time farms in the sense that farmer or spouse combines another paid job with farming. Gasson's paper explores the nature of these other jobs and features of their distribution. Following trends in other developed countries, non-farm activities are becoming increasingly important for UK farming families. Growth in part time farming has obvious implications for income support, widely agreed to be the fundamental objective of agricultural policy in western Europe. Existing farm income measures are not adequate for assessing the extent of the income problem in agriculture. Data from the Inland Revenue's Survey of Personal Incomes and the Wye College part time farming survey fill some of the gaps left in official measurements. Non-farming earnings, pensions and investment income contribute significantly to farm household incomes. A majority of part time farming families in England and Wales make more from other sources than from farming. Although average incomes of part time farmers may exceed those of full timers, Hill's paper identifies a section of part time farming families with inadequate incomes from all sources.     

The effect of nonfarm income on investment in Bulgarian family farming

This article documents a relationship between nonfarm income (primarily earnings and pensions) and agricultural investment in Bulgaria, specifically, expenditures on working capital (variable inputs such as feed, seed, and herbicides) and investment in livestock. Among those with positive spending on farm inputs, the estimated elasticity of these expenditures with respect to nonfarm income is 0.14. Nonfarm income also has an effect on the number of households that purchase farm animals, with an estimated elasticity of 0.35. The use of nonfarm income for farm investment is consistent with the presence of credit constraints, as is the fact that less than one percent of farmers report outstanding debts for agricultural purposes. Yet many farm households take out large unsecured loans for other purposes, primarily to cover consumption expenditures, implying that credit is available, but that farmers prefer not to use borrowed funds to finance agricultural investment. This would suggest that increases in the availability of agricultural credit may have little effect on farm outcomes, whereas increases in nondebt-financed sources of liquidity, such as subsidies or transfers, may better stimulate investment.     

Information Exchange and Distributional Implications of Price Discrimination with Internet Marketing in Agriculture

A price discrimination model is proposed to explain why firms provide extraneous information on Internet sites selling agricultural inputs. Whether an informative site is offered depends on price discrimination potential, which depends on how much farmers reveal heterogeneity by Internet behavior. Price discrimination is greater if information benefits are negatively correlated with farm size (or other characteristics), explaining why extraneous (not product-related) information is offered on Internet sales sites. Price discrimination adversely affects some farmers but may be beneficial on average because it generates free information. Outcomes depend on whether Internet users are aware of price differentials on the basis of clickstream information.     

THE EFFECT OF MINIMUM WAGES ON UK AGRICULTURE

Since the abolition of the Wages Councils in September 1993, agriculture is the only sector in the UK economy covered by any form of minimum wage legislation. However, the recent denunciation of an ILO convention covering minimum wages in agriculture has paved the way for the abolition of the Agricultural Wages Boards. This paper investigates the impact of the system of minimum wages on the level and structure of earnings and employment in agriculture. On wages, our main conclusion is that the minimum wages set by the Agricultural Wages Boards are important determinants of the average level and distribution of earnings in UK agriculture. On employment, our main conclusion is that there is no evidence that minimum wages have reduced the level of employment in agriculture. If anything, our estimates imply a weak positive, though statistically insignificant, impact of minimum wages on employment. This seems to be true when we disaggregate by sex and skill.     

Determinants of off‐farm work and its effects on farm performance: the case of Norwegian grain farmers

Limited attention has been given in the literature to the impact of off‐farm work on farm performance. More knowledge about the determinants of part‐time farming and its effects on farm performance could help policymakers to introduce better targeted rural development policies. The aim in this article is to fill part of the above‐mentioned gaps by analyzing factors that influence the choices of off‐farm work by either the farmer alone or jointly by both the farmer and partner; and simultaneously examining how off‐farm work influences farm performance. These analyses were based on an unbalanced panel data set from Norwegian grain farms during 1991 to 2005. Among the determinants of off‐farm work hours, we found that, in addition to demographic, time trend, and some regional effects, there was a significant negative effect of farm output on farmers' off‐farm work hours. The production function results revealed that off‐farm work had a positive effect on farm output, at first increasing but then decreasing with increase in hours spent in off‐farm work. Furthermore, our analysis revealed no systematic effect of off‐farm work on farm technical efficiency.     

Measuring and causes of inequality in farm sizes in the United States

This article examines the notion of farm size inequality expressed as sales inequality in the United States. The farm size index (FSI) is developed as a measure of farm size inequality. FSIs are calculated for the farming sector in all 50 states and large variation in farm size across the states and over time is determined. The largest FSIs are calculated for a number of Southern, Southwestern, and Pacific states. Increasing FSIs over time are observed in all states as well. The spatial and temporal (between 1987 and 1997) differences in FSIs are explained by running a pooled, cross‐sectional time‐series model. The most influential variables accounting for the differences have to do with the ownership structure, where a larger presence of individual and family farms relative to corporate farms and cooperatives leads to a lower degree of farm size inequality. Also, states and regions having relatively larger number of farms owned by minorities have higher FSIs. Shrinking opportunities in the agricultural sector relative to the rest of the economy, primarily services, are reflected in a declining share of agricultural sector state income in total gross state product (GSP). This in turn leads to an increase in the farming sector's FSI suggesting that only larger, more profitable operations are the likely candidates to pursue farming activities. Finally, grains farming regions have all lower degree of farm size inequality than livestock or fruits and vegetables regions. Profitable grains farming requires relatively large farm size and equipment investments, which leads to a relatively homogeneous structure of grains farms. A larger variation in the size of profitable farm operations is possible in fruits and vegetables and livestock. This leads to the existence of a large number of very small but still sustainable farms, and a relatively small number of large farms that capture most of market sales share.     

An Analysis of U.S. Aggregate Output Response by Farm Size

Past empirical evidence on supply response by size of farm in the U.S.A. provides no clear basis to conclude that supply elasticities vary systematically with farm size. In this paper, the central hypothesis that no systematic relationship exists between production response to price and size of farm is rejected. U.S. farms are disaggregated into nine economic size categories and own-price supply elasticities are measured for per farm and total agricultural output. Empirical results from this study suggest that supply response does vary systematically by farm size, with smaller farms exhibiting greater elasticities than midsized farms.     

The effects of EU agricultural policy changes on farmers' risk attitudes

This analysis utilises a model of production under risk estimatedon Finnish farm-level data to measure farmers' risk attitudesin a changing policy environment. We find evidence of heterogeneousrisk preferences among farmers, as well as notable changes overtime in farmers' degree of risk aversion. This result is dueto the increase in the non-random part of farm income generatedby the policy change after Finland's European Union accession.The analysis confirms the assertion that agricultural policiesthat are decoupled from production do affect input use and cropmix through their effect on farmers' risk attitudes.     

Structural and productivity change in US agriculture, 1950–1982

This paper tests whether structural change in US agriculture is an important channel to TFP growth and evaluates the relative impact of (i) public research and education policies, (ii) private R&D and market forces, and (iii) government farm programs on structural change. We specify a structural econometric model, fit it to US state aggregate data, 1953–1982, and use the associated reduced‐form model to perform counter‐factual policy simulations. The findings include: structural change is a channel to TFP growth in both crop and livestock subsector, i.e. specialization, size, and part‐time farming do impact TFP, holding other variables constant. Public R&D and education have been at least as important as private R&D and market forces for changing livestock specialization, farm size, and farmers’ off‐farm work participation over the study period, but private R&D and market forces have been relatively more important for crop specialization. Changes in farm commodity programs had little impact on farm structure over these study period. Overall, we conclude that if public R&D and education policies had been unchanged at their 1950 values over 1950–1982, major structural changes in US agriculture would have occurred anyway. The forces of private R&D and market forces were at work, including a decline in the price of machinery services and agricultural chemicals, relative to the farm wage.     

Off‐farm labor market decisions and agricultural shocks among rural households in Kenya

Using household level data from rural Kenya, this article explores whether and how farm households respond to unfavorable agricultural production environments, including any ex post adjustments in off‐farm labor supply in response to unexpected weather shocks. While controlling for a wide range of educational, demographic, and other locational factors, we examine how long‐term weather conditions and specific rainfall shocks influence a household's decision to engage in, and their earnings from, the off‐farm labor market. We find that rural households engage in off‐farm work as a long‐term strategy to deal with the effects of anticipated weather conditions on their farming operations. The analysis does not reveal major short‐term adjustments in off‐farm engagement as a result of specific, unexpected rainfall shocks; these households do however rely on remittance income and petty agricultural wage labor under these circumstances. Holding other factors constant, and conditional on participation, households in areas with a more productive local agriculture tend to earn more from off‐farm work especially in the informal/business sector than their counterparts in regions with a less productive agriculture. As expected, a vibrant local economy in the form of public investment increases the probability of off‐farm participation.     

The Measurement of Average Farm Size

Comparisons are regularly drawn between the average sizes of farms in particular countries or regions and in different farm-type categories. Indeed average farm size is often considered to explain (or create expectations of) changes or differences in technical or economic efficiency through time or between countries. There are, however, a number of issues involved in specifying what is meant by a farm and in choosing a suitable measure of farm size. These are examined prior to a discussion of various measures of average size, one of which (the mid-aggregate point) is described in some detail. The measures are illustrated with reference to agricultural holdings in England and in the Member States of the European Union.     

Factors influencing participation in the Nova Scotia Environmental Farm Plan Program

There is growing interest in Nova Scotia's Environmental Farm Plan (NS EFP) program among farmers and policy makers because of several reasons. First, effectiveness of standardized or uniform beneficial management practices in mitigating the negative environmental impacts from agriculture is limited by inherent heterogeneities in agricultural production systems. In addition, there is heightened interest in farmers generating ecological goods and services to society. This study investigates the determinants of participation in the NS EFP program. A discrete choice model of NS EFP participation was applied to a sample of 83 farmers (representing a 31% response rate). To increase relevance of the study to program administrators, the study also examined farmers’ use of various channels and sources of information on farm conservation practices. The most used sources of information on farm conservation practices include a mix of interpersonal sources and government agencies. Although online information (especially those available for free) appears to be gaining popularity in usage, overall, electronic and computer channels of information (especially radio and television) were used less compared with traditional channels of communicating farm conservation information (such as newsletters and agricultural magazines). Regression analysis suggests that farm characteristics (i.e., farm type, farm size, farm income) and farmer capacity variables (i.e., specialized training and knowledge from EFP program information sessions and workshops, and on-farm stewardship demonstrations) were significant determinants of environmental farm planning.     

新农业政策的经济影响分析

本文在介绍了2004年以来我国的新农业政策的内容的基础上,简要分析了新农业政策实施的直接效果,重点分析了新农业政策对我国农业生产、进出口贸易、农产品消费者价格、农业生产者价格的影响,并据此提出了针对性的建议。     

Restructuring of traditional farms and new land relations in Ukraine

This paper reports the results of a 1993/1994 survey of 2500 farm managers, farm employees, and private farmers in Ukraine, highlighting changes at the farm level in response to programs of land reform and farm restructuring. As of early 1994, Ukrainian reform had moved to the first stage, which involves privatization of much of the agricultural land and creation of shareholding farms. Over 60% of agricultural land in Ukraine has been transferred from state to collective ownership; among the collective and state farms surveyed, nearly 75% have reorganized and most of them have allocated land and asset shares to members. The number of independent private farmers in Ukraine exceeds 30 000, but with an average farm size of 20 ha they cultivate less than 2% of farmland. Reform at the farm level in Ukraine has thus begun, but at present is at a very early stage.     

Rural nonfarm income and its impact on agriculture: evidence from Albania

Using data from the 2005 Albania Living Standards Measurement Study (ALSMS05) survey, this article analyzes the overall impact of household nonfarm income-generating activities (RIGA) on agricultural expenditures as well as technical efficiency of rural farm households. We also differentiate the impact for subsistence and commercial farmers, who are in the top 25% of the distribution of value of annual agricultural sales. Our results show that on the whole, Albanian rural households utilize their nonfarm earnings not to invest in time-saving, efficiency-increasing technologies, but to move out of crop production. We derive similar findings when we try to estimate the same relation separately for commercial and subsistence farmers. However, for commercial farmers, we find a positive impact of household nonfarm earnings on livestock expenditures.     

Economic efficiency in farm households: trends, explanatory factors, and estimation methods

Factors explaining differences in economic efficiency between farms are of major interest to owners, managers, and other stakeholders as they strive to improve earnings and improve the chances of firm survival. This study is undertaken to improve our understanding of interfarm differences in, and opportunities to improve, farm household efficiency in utilizing their land, labor, and capital resources to achieve household objectives. The technical, allocative, and scale efficiencies of farm households are estimated using a nonparametric, output-based data envelopment analysis (DEA) of a panel data set from 1993 to 2006. Single and double bootstrapping procedures are used to estimate technical efficiency. Initial technical efficiency assuming variable returns to scale (TEV) is estimated to be 0.83. Using single bootstrapping, the average bias-corrected TEV estimate is 0.70; using double bootstrapping, the TEV estimate is 0.72. Allocative efficiency is estimated to be 0.81. Scale efficiency is estimated to be 0.93. The only factor that is consistently associated with higher technical efficiency across analysis methods and years is larger farm size (as measured by the log of farm income). The significance of other factors changes with analysis methods.