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The objective of this study is to investigate the simultaneity between farm couples’ decisions on labor allocation and production efficiency. Using an unbalanced panel data set of Norwegian farm households (1989–2008), we estimate off-farm labor supply of married farm couples and farm efficiency in a three-equation system of jointly determined endogenous variables. We address the issue of latent heterogeneity between households. We solve the problem by two-stage OLS and GLS estimation where state dependence is accounted for in the reduced form equations. We compare the results against simpler model specifications where we suppress censoring of off-farm labor hours and endogeneity of regressors, respectively. In the reduced form specification, a considerably large number of parameters are statistically significant. Davidson–McKinnon test of exogeneity confirms that both operator and spouse's off-farm labor supply should be treated as endogenous in estimating farming efficiency. The parameter estimates seem robust across model specifications. Off-farm labor supply of farm operators and spouses is jointly determined. Off-farm work by farm operator and spouses positively affects farming efficiency. Farming efficiency increases with operator's age, farm size, agricultural subsidises, and share of current investment to total farm capital stock.

The purpose of this paper is to examine the income strategies adopted by rural households in Ghana and analyzes the determinants of households’ choice of income portfolio.

A multinomial logit approach is employed by the paper to investigate the determinants of various income strategies adopted by households in rural Ghana.

Results indicate that household characteristics, location and infrastructure all play a role in explaining the adoption of income strategies other than a purely on-farm strategy by households. Education is a key determinant of income strategies involving non-farm wage employment, while access to credit and electricity play important roles in non-farm self-employment income strategies.

The findings of the paper call for a promotion of off-farm income opportunities to complement farm incomes and to enhance access of rural households to these sources of income.

The paper models rural household income portfolios into mutually exclusive categories which enables the application of the multinomial logit approach. The paper deviates from mainstream rural income diversification literature that has focussed on assessing the determinants of income shares.

This paper aims to analyze the rates of returns on education in China and in the process raises issues relevant to the management of China’s system of education. In the ongoing great transformation period of China, the rising rates of returns on education may have been indicators reflecting China’s social progressiveness. However, very little research efforts have been devoted to the study of the impacts of such factors as geographical regions and genders, etc. The authors hope to fill these gaps in the literature.

The China Health and Nutrition Survey (CHNS) database is used for this study (University of North Carolina). The longitudinal nature of the data sets covering 1989, 1991, 1993, 1997, 2000, 2004, 2006, 2009 and 2011 provides a good basis for comparative analyses. The theory is grounded upon the Mincer equations through which econometric estimates are then made.

Disparities in returns on education are found between genders and across geographical regions. The regression results show that the women’s returns on education are consistently higher than those of men. However, the scales of such gender differences differ between the rural and urban areas: smaller for rural area and larger, more significantly so for urban. Additionally, we have found that the rates of returns on education in China have risen significantly over these years, and these increases have been largely due to the effects of institutional reforms. The urban-rural gap in their degrees of market orientation has contributed to the differences in their rates of returns on education. The analyses also suggest that foreign direct investment inflows, international trade and the increasing competitiveness from private enterprises render human capital more valuable to urban businesses. In case of the rural areas, a lack of incentive system tends to have contributed to the lower rates of returns on education.

The authors have presented evidence on the trends in the rates of returns on education during China’s critical transition period. Analyses of the possible reasons behind the differential rates of returns are provided. These findings are helpful for the government to shape their policies towards education. For instance, the government should give more emphasis to vocational schooling due to their significantly higher rates of returns.

The aim of this study was to evaluate determinants of four diversification practises by cocoa smallholders in West Sulawesi, Indonesia: (1) growing other crops, (2) keeping livestock, (3) off-farm work for wages (4) off-farm self-employment, and the impact of diversification on welfare of community members.

Household interviews (n = 116) conducted in two subdistricts (Anreapi and Mapilli) of Polewali-Mandar District, West Sulawesi, provided quantitative data on household characteristics, crop and livestock production, income sources, expenditure and credit access. Two villages per subdistrict were included in the study, each producing cocoa as the main crop but differing in their proximity to a market town. Logistic regression was applied to identify determinants of diversification by households. Multiple linear regression (MLR) models evaluated the impact of diversification practices and other explanatory variables on two proxies of welfare (or household wealth): per capita value of durable assets (household assets other than land or livestock) and per capita expenditure for each household.

Mean per capita cocoa production in the sample was low (51 kg dry beans/annum). The mean dependency ratio (proportion of household occupants age <18 and >64) was 35%, with an average of five occupants per household. Household heads were predominantly male (95%), averaging 46 yo and 7 years of formal education. Most households (72%) depended on loans, but only 24% accessed formal loans. Significant determinants of diversification practices were access to formal credit for self-employment and subdistrict for livestock, with Mapilli subdistrict households more likely to keep livestock. Household predictors in the MLR accounted for 28% variation of the dependent, per capita value of durable goods. Off-farm self-employment and raising livestock significantly improved welfare, but growing other crops or off-farm work for wages had little effect. Other household variables demonstrated to have significant positive effects on welfare were education of the household head, proximity to a market town and land area per household.

The study was restricted to a relatively small sample size (n = 116). Studies including panel data or larger numbers of households could enable the identification of further determinants of diversification.

The study demonstrates that diversification has the potential to improve rural livelihoods, but that obstacles, especially formal credit access, may deter poorer households from diversifying their income sources.

Programs and policies that facilitate access to formal finance by smallholders could encourage diversification into small business and improve livelihoods in cocoa-dependent communities.

In the light of the decline in cocoa farm productivity in West Sulawesi, the study demonstrates the potential benefits, as well as limitations, of income diversification by smallholders.

The purpose of this paper is to assess the development of China's rural labor markets and the identification of the important factors that affect rural labor's off-farm employment and migration.

Based on a set of long-term panel data, this paper makes a clear judgment on the trend of rural labor transfer. High-quality survey design makes it possible to examine the development of the rural labor market from multiple dimensions. Adding household fixed effects to the empirical model alleviates endogenous problems.

The authors find that the increasing trend toward off-farm employment, which is dominated by migration, has continued. There are some other important findings: (1). young male workers dominated off-farm employment, but the gap between groups continues to narrow; (2). the structure of employment is a good response to the economic transformation and (3). the quality of off-farm laborers, especially in terms of human capital, has also enhanced significantly and has continued to support off-farm employment and migration. These findings all indicate that the China's rural labor markets have been constantly improving in recent years, although there is still segmentation.

It is the first paper that uses a nationally representative survey data to address the development of rural labor market in the 21st century. With the help of a long-term panel data structure and by controlling the household-level fixed-effect, the authors obtained a deeper and more robust conclusion. Specifically, this article finds that whether it is for the off-farm transfer or the migration, the influence of labor age, gender, human capital and marital status is gradually weakening.

The purpose of this paper is to explore the potential causes of the reduction of cotton cultivation in Shandong Province of China from the perspective of smallholders and notably examine the role of off-farm employment.

This paper extends an integrated behavioral model to analyze the relationship between off-farm employment and cotton cultivation by taking into account farmers’ risk attitudes. A household survey data of 144 Bt cotton farmers in six villages in Linqing County, Shandong Province conducted in 2012 and 2013 is used. A simultaneous equations model is established and further estimated by using three-stage least squares method.

Although the introduction of Bt cotton has promoted the increase in cotton acreage in China from 1999 to 2007, the planting area of cotton has been decreasing since 2007. The results show the significant correlations among risk attitude, off-farm employment, and cotton cultivation. The planting area of cotton is positively correlated with farmers’ willingness to take risk but negatively associated with off-farm employment of family members. The findings imply that the rapid emergence of off-farm labor markets is a major reason for the reduction of cotton acreage in Shandong Province. In the context of the more opportunities of off-farm employment in China, cotton acreage is expected to decrease further.

The findings provide a reasonable explanation for the reduction of cotton cultivation in Shandong. This analysis contributes to a better understanding of the relationships among individual risk attitude, off-farm employment, and agricultural behavior, thereby adding to the literature about the application of the integrated behavioral model.

The purpose of this paper is to critically review the literature to assess the relevance of the S‐shaped model of family labor supply for industrialized countries.

Studies use a wide variety of methodologies and therefore are not readily comparable, but instead they cover a wide range of relevant factors such as historical trends, fringe benefits and home mortgages, ethnic differences, farm labor, low‐income households, child care, the impact of welfare benefits, and the problem of the measurement of work hours.

In spite of welfare systems that blur somewhat the predicted income effect at lower wage levels (forward falling segment primarily for women), this model appears to still bear some relevance for these countries, in particular in the face of declining real wages. Families have generally moved up higher along that curve, with less differentiated gender roles, women's stronger labor force attachment, and assortative mating of educated women.

The model is mostly relevant for LDCs and has far‐reaching practical consequences, while the review highlights the complexity of labor supply in industrialized countries.

The purpose of this paper is to examine the factors affecting the relative variability in farm and off‐farm income for Canadian farm operators.

Variability of farm and off‐farm income is analyzed using a dataset of 17,000 farm operators from 2001 to 2006. Relative ranking of the coefficients of variation (CV) for farm and off‐farm income are compared across farm types and are regressed against factors conditioning the variations.

Greater reliance on farm income results in lower (greater) relative variability in farm (off‐farm) income. Larger commercial operations experience larger farm income volatility because they are less risk averse or they can manage more risk. Diversification and off‐farm employment appear to be risk management strategies for commercial operations.

Government payments have a small, positive effect on farm and off‐farm income variability, indicating this support leads farmers to take on more risky activities and/or reduce the use of self‐insurance activities. Results could also be due to the lag between the time of the income reduction and the time in which the aid is received. Further research is necessary to decipher the effects of government support on farm decisions.

The results on relative variation in the farm and off‐farm income across farm type raises questions about whether government programs should target specific operations.

While income variation remains a focus of public policy, factors affecting its variability are not well‐understood. Studies have examined the level of farm income and the decision to participate in off‐farm employment but none has examined the variance in both income sources.

For many farm families and operators across the OECD countries, off‐farm income has become a major determinant of their well‐being. The purpose of this paper is to investigate the potential role of off‐farm employment as a risk management tool among farm operators.

A two‐part model is applied to a longitudinal farm‐level data set for about 20,000 Canadian farms, from 2001 to 2006, in order to estimate the relationship between farm income risk and the decision to participate in the off‐farm labor market and the level of off‐farm employment income.

The variability of farm market revenue is found to be positively related to the likelihood of off‐farm work and the level of off‐farm employment income, in particular for operators of relatively large farms. Hence, farm operators' production decisions appear to be conditioned on an income portfolio that includes a substantial amount of off‐farm income for all sizes of farms.

These results reinforce the need to consider the portfolio effect induced by the integration of farm resources within the non‐farm sector. This is particularly relevant to risk management farm policies that have typically considered decisions made in the agricultural sector in isolation.

This paper uses a true farm‐level panel data set to investigate the relationship between farm income risk and off‐farm work. The size of the data set also allows the robustness of the results across farm typologies and size to be tested. This study contributes to the understanding of structural changes in the farm sector, and their potential implications for both rural and agricultural policies.

The purpose of this paper is to analyze the effect of the different components of off-farm income on multi-dimensional poverty. Furthermore, the study aims to measure multi-dimensional poverty and also identify the determinants of multi-dimensional poverty in Nigeria. The paper reveals the different contributions of the dimensions of education, health and living standard.

The study focuses on rural farm households in Nigeria. Data are obtained from the Nigeria General Household Survey, 2013. The survey covers both urban and rural areas of the 36 states of Nigeria. Owing to the interest of this study in the rural farm household’s sub-sector, a nationally representative sample of 836 rural farm households are selected for the study after the data merging process. Rural farm households in this paper earn 50 percent of their total income from crop and livestock production. The paper employs the Multi-dimensional Poverty Index (MPI) to measure multi-dimensional poverty across the six different geographical zones of Nigeria. The probit regression model is used to estimate and analyze the effect of off-farm income components on multi-dimensional poverty and also to identify the determinants of multi-dimensional poverty.

The results of the study show that among the off-farm income components, the non-farm wage income and non-farm self-employment income have negative association with multi-dimensional poverty. Findings show that multi-dimensional poverty is high in Nigeria with deprivations in health contributing the most. Northern Regions have a higher estimate. Results reveal that sex, age, number of adults, formal credit access, access to extension services and location characteristics are key determinants of multi-dimensional poverty. The MPI for Nigeria averaged 47 percent. Across regions, deprivation in the health dimension contributes about 44 percent to multi-dimensional poverty. Deprivation in living standards contributes 40.5 percent, while deprivation in education contributes 15.5 percent to multi-dimensional poverty.

Due to the nature of the data used, the health indicators (nutrition and child mortality) are absent but proxies are used instead. Future research could introduce gender dimensions.

Improving the involvement of rural farm households in non-farm self-employment sector could improve their livelihoods and prevent migration to urban centers, especially among the youths.

Improving the quality of health, education and living standards will lead to lower poverty levels in Nigeria. Farmers can best reduce their multi-dimensional poverty by engaging in more off-farm jobs.

This paper provides information to policy makers on the effect of different components of income from the off-farm sector on multi-dimensional poverty alongside with the determinants of multi-dimensional poverty at a national level for the rural farm households. By using MPI, the contribution of the different dimensions used in computing the MPI across the six geographical regions within the country is revealed. This provides policy makers with more information for development purposes.