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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.

Ghana's economy is largely agrarian, and the business of agriculture is dominated by smallholder farmers who are predominantly rural dwellers. As a result, efforts to lift rural farming households from poverty have been narrowed to the promotion of agricultural development to the neglect of the rural non-farm sector. However, this is fast changing in the advent of a burgeoning rural nonfarm economy and must engage the attention of policy actors. This study thus assesses the effect of non-farm participation on households' level of commercialization of agricultural crops in Ghana.

The study applies a generalized structural equation model (GSEM) to the Ghana Living Standards Survey round 6 dataset, a stratified and nationally representative random sample of 16,772 households in 1,200 enumeration areas.

This study finds that non-farm participation increases the produce sold to output ratio. It is concluded that non-farm engagement by farmers boosts commercialization in Ghana. Thus, for the Ghanaian and similar contexts, agricultural development interventions that incorporate non-farm activities are more likely to be successful in improving livelihoods.

The study uses only the ratio of sales value to output value definition for commercialization and acknowledges use of multiple definitions could be superior.

Various empirical studies have examined the link between the farm and nonfarm sectors. This paper is original in its approach as it tackles an aspect of the subject that has been understudied, namely, an exploration of nonfarm and farm linkages from the perspective of agricultural commercialization.

This study aims to analyse the consumption inequality between farm and non-farm households in rural Vietnam, using the data from the 2016 Vietnam household living standards survey.

The present paper applies the “recentered influence functions (RIF)” in “Oaxaca-Blinder (OB)” type decomposition as proposed by Firpo et al. (2018) to allow for the flexible distribution of the outcome variables and the non-randomness of non-farm employment that violates the classical linearity assumption.

Non-farm households have significantly higher per capita consumption expenditure than farm households for the entire distribution. The gap in expenditure is large at low percentiles and narrowing with higher percentiles. At 10th percentile, the gap is estimated at 27.1%, but it is decreasing to 11.1% at 90th percentile. Most of the gaps are explained by the differences in the observed characteristics between farm and non-farm households such as ethnicity, education, income, internal transmittances and household composition. Non-farm households are endowed with more productive factors that result in higher per capita consumption expenditure.

Gaps in ethnicity and education are found to be key predictors of the inequality in consumption expenditures between farm and non-farm households, then, government policies that are aimed at increasing access to non-farm employment and education for ethnic minorities and for rural poor households are pathways to improve rural household welfare and hence reduce inequality.

The purpose of this paper is to explore the factors that determine non-farm enterprise revenue and to empirically test the association between access to credit, credit source and firm performance among poor entrepreneurs in rural Bangladesh.

Using a Bangladesh Institute of Development Studies and World Bank survey from over 1,700 households in rural Bangladesh, a panel data model is used to control for unobserved heterogeneity among households and explore the determinants of non-farm revenue.

The findings suggest that village infrastructure and household labor assets have a positive impact on enterprise development. The findings reveal that the use of rural credit as a production input is important in augmenting revenue for the non-farm enterprise, but there are differential effects by credit source.

Because the study uses data from a quasi-experimental survey design, unobserved effects that can bias the results must be controlled for. Also, as credit program impacts can be location-specific, caution in generalizing the results of this study must be exercised.

This study provides evidence on the positive effects of microcredit, family assets and family social capital on economic outcomes and microenterprise growth for poor entrepreneurial households. If enterprise growth is important for development, greater understanding of the determinants of microenterprise performance and the role of credit in the success of microfirms is beneficial for policymakers and the institutions that finance small-scale production.

If it is agreed that entrepreneurship is important in promoting development, self-sufficiency and positive economic outcomes (Yunus, 2007), then credit program design should focus on both the credit needs of the poor and the dynamics inherent in enterprise development for this group of entrepreneurs.

This paper expands the limited literature on the determinants of microenterprise growth and the role of credit in microenterprise development by tracing a positive link between village infrastructure, family demographics and access to credit. The identification of the factors that determine non-farm enterprise revenue is important for policymakers because enterprise growth is perceived as essential for economic development.

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.

This study aims to analyze the factors that determine non-farm enterprise diversification among farm households in Ethiopia. It extends the analysis by examining enterprises and using pooled data, which has the capacity to generate more accurate outcomes. The existing empirical evidence has focused on all non-farm activities, based on single period, single region data. Much of the existing empirical evidence is based on small-scale and location-specific sample surveys that do not demonstrate the characteristics of aggregate populations.

The empirical analysis was conducted using a quantitative method. To cater to the censoring nature of participating in non-farm enterprise activities, a panel data double-hurdle model is used to a representative sample of 3,594 Ethiopian rural households.

The study finds that the age of household head, household size, distance to the market, social capital and access to credit, are determinants for owning one or more non-farm enterprises. The level of income from these enterprises is then determined by the age and education level of the household head, household size, agricultural equipment, distance to markets and access to credit.

This study brings to light factors that influence households to participate in non-farm enterprises and the determining factors for the income level.

Non-farm activities are an important source of household income and a driver of development. This paper provides empirical evidence on factors that determine enterprise ownership using panel data.

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.

The purpose of this paper is to identify the structure of household income and examine the effects of non-farm incomes on the income distribution of farm households in a relatively developed rural area of the Malaysian rice bowl.

The non-farm incomes were disaggregated into different components to determine the contribution of each income source to total household income and overall inequality. The income distribution and decomposition was examined using the Gini decomposition method.

It was found that almost 71 percent of the households in the sample had at least one source of non-farm income. On average, non-farm incomes contributed about 33 percent to total household income. Non-farm wage employment was the dominant source of non-farm income, accounting for almost 26 percent of overall household income. The farm incomes, especially the paddy incomes were found to be the inequality-decreasing income source. The study also confirmed the proposition that the non-farm incomes were the inequality-increasing income source as they contributed up to 35 percent of the overall income inequality.

Previous studies have found that non-farm incomes have different effects on income inequality of rural communities, especially those in the rice granary areas situated in less developed states of Malaysia, where poverty is still a problem. This study is significant because it identifies the effect of certain incomes on the overall income inequality among farm households in the granary areas located in a relatively developed rural area. The studied areas are characterized by an intensive paddy production and a rapid development in business and industrial activities, and hence, providing non-farm employment opportunities to the rural farmers. Therefore, this study shows the income structure and how farm and non-farm incomes affect the overall income distribution of the paddy farmers.

This paper examines the determinants of participation in non-farm activities in rural Sudan. It also investigates whether the factors that influence participation in non-farm activities vary across agriculture sub-sectors.

The study adopts the multinomial logit and probit methods on labour supply participation theory using the Sudanese National Baseline Household Survey (2009). The analysis was applied across job types and agriculture sub-sectors.

The results indicated that educational level, means of transportation, lack of land and access to formal agricultural credit are the most significant factors that push rural farmers to participate in non-farm activities. Surprisingly, the effect of household income was positive and significant, implying that individuals from rich households have higher opportunity to engage in non-farm activities compared to their poor counterparts. The results also revealed some variations in the factors that influence participation in non-farm activities according to the agricultural sub-sectors.

The originality of this article lies in investigating the factors that influence participation in non-farm activities across irrigated and rainfed systems. To the best of our knowledge, this is the first study identifying the determinants of participation in non-farm activities across agriculture sub-sectors. Therefore, the paper fills an important gap in the literature and helps in designing appropriate pro-poor policies to allocate infrastructures across irrigated and rainfed areas in Sudan.

The peer review history for this article is available at https://publons.com/publon/10.1108/IJSE-02-2022-0092.

Off‐farm investment decisions of farm households are analyzed. Farm‐level data for a sample of Kansas farms observed from 1994 through 2000 are utilized. A system of censored dependent variable models is estimated to investigate the factors that influence the composition of farm households’ portfolios. The central question underlying the analysis is whether farm income variability influences off‐farm investment decisions. Previous analyses on the determinants of non‐farm investments have failed to consider the role of income variability. Results of this study indicate that higher farm income fluctuations increase the relevance of non‐farm assets in the farm household portfolio, thus suggesting these assets are used as farm household income risk management tools.