Search results

The purpose of this study is to determine the impact of off-farm work on food security in rural Bangladesh. We use rural household-level data and a nonparametric propensity score matching (PSM) estimator. Matching estimators are used in observational data to address the potential selection biases caused by nonrandom allocation of the treatment. Monthly food-consumption data and household income and expenditure surveys from rural Bangladesh for 2013–2014 are used in this chapter. We found that rural Bangladeshi households participating in nonfarm income-generating activities, especially in higher return nonfarm employment, enjoy higher levels of per-capita food expenditures and diet diversity – two of the measures of food security. In particular, we find that rural households increased diet diversity in cereals, fruits and vegetables, and meats. Finally, our estimates reveal that rural households participating in off-farm work increased per-capita food consumption by about Taka 1,576, on average, and increased per-capita expenditures on milk and milk products (Taka 212), and fruits and vegetables (Taka 235) significantly. Policy makers should design and implement policies that create off-farm livelihood activities. These nonfarm activities would help smallholder farm families to diversify, to supplement their income, and to continue their agricultural operations as well as increase food security.

The purpose of this paper is to examine the impact of off-farm employment on agricultural fixed assets among households in rural China.

The authors drew on panel data from two rounds of household-level surveys of more than 2,000 households in rural China. The two surveys were conducted in 2008 and 2012 in five provinces. The authors used instrumental-variable Tobit model to test whether the current value of agricultural fixed assets differ between households with different levels off-farm employment.

The authors observe that off-farm employment has a negative effect on the current value of agricultural fixed assets at the household level in rural China.

The authors believe that the results will contribute positively to the assessment of the effect of off-farm employment on the investment in agricultural fixed assets at the household level in the context of China.

This study aims at exploring the choices of livelihood strategies amongst the rural youth and how these choices relate to food security and income poverty.

The study used data from the 2016/17 wave of Integrated Household Living Condition Survey, with a sample size of 1,050 rural youths. Statistical and econometrics methods including descriptive statistics and the Multinomial Endogenous Treatment Effect (METE) model were used to analyse the data.

Livelihood choices were grouped into five categories, namely agriculture, non-farm wage employment, agriculture plus non-farm wage, agriculture plus self-employment and agriculture plus non-farm wage plus self-employment. The estimates from METE indicate that the youths' choice of non-farm wage, agriculture plus non-farm wage and agriculture plus self-employment contributes substantially to household food security improvement and poverty reduction. These findings show that agriculture is necessary but not a sufficient livelihood strategy to sustain the rural youth's contribution to youth's household welfare. The rural youth will pursue agriculture as a reliable source of livelihood not only for food self-sufficiency, but also for ensuring adequate return to labour.

This paper extends single choice analysis to multiple choices impact analysis, which has the advantage of accounting for selection bias due to both observed and unobserved heterogeneity. This paper assesses the differential impact of the choice of single as well as multiple livelihood strategies.

The determinants of income of rural and urban farm households, with emphasis on the role of off-farm employment by farm household members and of farm size, are examined using data from the 2016 Agricultural Resource Management Survey (ARMS) and quantile regression procedure. The implemented quantile regression technique is extended to allow for the decomposition of the income gap between the two groups of farm households. Findings indicate, regardless of the location of the farm, a positive and significant impact of a previous year's participation in off-farm work by household members on the distribution of current household income. Having operated a larger-sized farm in the previous year is shown with a similar effect in the upper range of the income distribution for urban households and with a comparable impact but across the whole income distribution for rural farm households.

Data from the 2016 ARMS are used in conjunction with quantile regression in order for decomposition of the income gap between the two groups of farm households.

Findings show that urban farm households who in a previous year have participated in off-farm work and operated larger-sized farms tend to earn higher incomes. Results further indicate higher rates of return to education for “urban” farm households in comparison to “rural” farm households, particularly for those with a college education and beyond who are at the lower portion of the income distribution.

To the extent that the ARMS is an annual cross-sectional data, the temporal impacts of factors that potentially may influence the incomes of farm households in urban and rural areas cannot be measured.

Findings from this research indirectly support previous published research where higher earnings by urban US population were documented in comparison to rural population and where earnings tend to rise as a result of participation in off-farm work and in expanding the size of the farming operation; this is in addition to the procurement of higher education.

The results of a higher rate of return to education for “urban” farm households in comparison to “rural” farm households have important policy implications for policymakers.

This is the first paper in the agricultural economic literature that implements a method of assessing the rural–urban divide across all of the quantiles of income distribution.

Doubling farming households’ income through occupational diversification to the non-farm sector has been advocated to be of paramount importance in an agrarian economy such as India. The purpose of this paper is to analyse the effects of non-farm activities on rural household incomes in four different Indian states by using a propensity score matching technique and developing an endogenous switching model.

The research is based on secondary data taken from four quinquennial rounds of employment and unemployment surveys conducted by the National Sample Survey Organization.

The matching results indicate a maximum monthly rise in per capita income of Rs. 60 in Gujarat and a minimum increase of Rs. 18 in Rajasthan among rural households employed in the non-farm sector as compared to the farm sector. The findings confirm that rural non-farm structural diversification cannot be viewed as a blueprint for increasing rural household incomes in different states. Further, it suggests the need to segmenting the different states on the basis of agricultural development for increasing rural incomes.

The study argues that Indian states with a strongly developed farm sector i.e. Gujarat and Punjab are not ideally suited to undergo structural changes in their economic pursuit. The estimates suggest that the transition of rural households from farm to non-farm-sector activities is a very weak strategy in agriculturally developed states of Gujarat and Punjab, whereas non-farm diversification becomes a pivotal strategy for increasing rural household incomes in less agriculturally developed states such as Rajasthan and Uttar Pradesh. A contrasting point that arises from these evidence is that although diversification to the non-farm sector leads to higher income, but the resultant figures are very scanty.

The present study contributes to the existing literature by providing evidence and policy implications on rural non-farm diversification in India and its impact on the rural household income. The study can help the policymakers in framing policies aiming at increasing the income of the rural household through the structural transition of the rural economy.

The purpose of this paper is to evaluate the effects of access to formal credit on rural household technical efficiency in China.

Based on the rural household survey data in Weifang city, Shandong province in northern China, the authors apply recent developed bootstrapped DEA approach to investigate rural technical efficiency at the household level under the consideration of off-farm activities. Rural households are then identified as credit constrained and classified as supply-side and demand-side credit constraints by applying direct elicitation method. Finally, the authors apply a tobit regression to examine the effects of credit constraints on household technical efficiency.

Rural households in China not only suffer supply-side credit constraints, but also demand-side credit constraints resulted from the transaction costs and risk rationing. The tobit regression discloses that demand-side credit constraints impose significant negative impacts on household technical efficiency.

The authors clarify the definition of credit constraints and classify the credit constraints into supply-side and demand-side credit constraints. The results of this paper have significant policy implications for rural finance policies in China.

We analyze the food security implications of off-farm labor reallocation decisions of rural farm households in transitional Albania. We accomplish this by examining local and nonlocal off-farm incomes for at-home food consumption expenditures.

An instrumental variable approach is employed to correct for endogeneity and censorship biases of off-farm income variables in a two-stage estimation of the food consumption expenditures.

We find that local off-farm income exerts a positive and significant effect on per capita food consumption expenditures of farm households, while private remittances from nonlocal off-farm income has the opposite effect on food consumption expenditures. In terms of regional heterogeneity, we discover that the mountain region spends significantly less on annual per capita food consumption compared to the central region. This confirms anecdotal evidence that food and nutrition insecurity in rural Albania is predominant in the mountain region.

Our findings suggest the need for policy makers to promote a development agenda that enables farm households to exploit the synergies among the various income-generating activities in the rural economy. This spectrum of income-generating activities forms complex livelihood strategies adopted by rural farm households to improve and maintain their food security.

We distinguish between local and nonlocal sources of off-farm income. Knowing which off-farm income source(s) has the largest impact on household welfare through improved food security status should be of interest to policy makers.

The purpose of this paper is to identify the determinants of China’s rural households’ non-farm participation. The authors pay special attention to the effect of potential income differential on this participation.

The data used in this study come from a household survey conducted in Hubei Province. The authors estimate participation equation and income equation, respectively, then introduce potential income differential simulated in participation equation to examine its effect on non-farm participation.

Potential income differential serves as the major pull factor that favors non-farm participation. Education, proximity to a city and specialized commercial farming are crucial in helping rural households to participate in non-farm production; while the land shortage or the labor surplus act as the push factor in non-farm participation. Better quality of land reduces the household’s propensity to participate in non-farm activities. Moreover, the income gap between households that participate in non-farm activities and pure farmers is mainly determined by the differences in household characteristics.

The authors use the method of “switching regression and structural probit” to examine the impact of potential income differential on non-farm participation, and simulate the response of the participation probability to the change of potential income differential. The authors also analyze the sources of income gap between non-farm and farm households using Oaxaca decomposition.

Nonfarm small businesses are an integral part of the US economy, and access to credit is crucial to their success. In rural America, a significant proportion of these businesses are owned by farm households. The purpose of this research is to compare farm households that operate a nonfarm business to other farm households as well as to rural and urban households operating a small business; and identify key factors that differentiate these businesses in their access to credit.

The paper uses a unique data set to draw comparisons between farm households (from Agricultural Resource Management Survey data) and rural and urban small businesses (from Survey of Small Business Finances data). Each of these data sets asks similar financial, demographic, and access to credit questions. Combining these data provide a unique way to analyze the financial health of farm households that operate nonfarm businesses.

The paper finds that farm households with a nonfarm business tend to have more household income and assets than other rural and urban small businesses and farm households without a nonfarm business. However, rural small business owners as well as farmers were able to access credit more freely than their urban counterparts.

Many studies have looked at the farmer's decision to work or invest off the farm. However, no study has considered the impact of owning a nonfarm business on the financial health and creditworthiness of a farm household.

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.