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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.

This paper aims to study the different factors that determine the performance or success of small-scale, non-farm enterprises in Lesotho. Evidence shows that small-scale enterprises in developing countries are confronted with different challenges and problems that make them less viable. As a result, the capacity of small-scale, non-farm enterprises in employment creation, income generation and providing the means of livelihood to the poor people is not significant. In Lesotho, many people who are retrenched from the South African mines are absorbed in small-scale, non-farm enterprises to make a living. However, small-scale enterprises are faced with different challenges. The research findings suggest that factors leading to success/performance of rural non-farm enterprises in Lesotho include gender of the entrepreneur, age of the entrepreneur, ability of the entrepreneur to establish wider social networks, large population/market, availability of communication networks and infrastructure, participation of enterprises in the international market and costs of doing business and competition. In this regard, the paper makes policy recommendations that can be used to improve performance/success of small-scale, non-farm enterprises.

This research uses both qualitative and quantitative research methods to analyse data.

The main finding of the research is that foreign competition hinders the success of non-farm enterprises in Lesotho. The research findings further reveal that enterprises owned by women make the highest turnover compared to those owned by men.

This study brings in different factors that can ensure or hinder success/performance of small-scale, rural non-enterprises.

The research paper is of value in that it is the first study in Lesotho that considers different factors that determine business success in relation to employment creation, turnover and profitability.

This paper examines household and community characteristics that influence the entry of rural households into non-farm entrepreneurship and investigates the various factors that influence the market exit of non-farm enterprises (NFEs).

The authors use data from three rounds (2011/12, 2013/14 and 2015/16) of the World Bank’s Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA). The authors employ panel logit and multilevel logit models to examine the probability of opening one or more enterprises and the enterprise exit rates.

Results indicate that the likelihood of starting a NFE is positively associated with primary education attainment, access to credit, experiencing idiosyncratic shocks and availability of formal financial institutions. Age, higher education attainment and rising farm input prices constrain entry into non-farm entrepreneurship. The enterprise exit rate is negatively associated with small-town residence, wealth, access to tar/gravel roads and cellphone communication.

Policymakers and administrators should strive to address the challenges that communities face in transportation, communication and financial services. Policies aimed at stabilizing prices and increasing access to mobile communication, primary education and road infrastructure could help expand the rural non-farm sector.

Previous studies primarily examined the determinants of participation in NFEs at a given time using cross-sectional data. The current study uses panel data to study the dynamics of NFE ownership by investigating households’ decisions to enter into or exit from the sector.

The peer review history for this article is available at https://publons.com/publon/10.1108/IJSE-09-2022-0611

Recent research into enterprise performance has focussed on the importance of firm proximity to total productivity. Using spatial correlation of firm performance as a proxy for knowledge transfers and diffusion, the purpose of this paper is to examine the evidence for these spatial effects in non-farm enterprise performance in Uganda, across space and time.

The author uses data from the geo-referenced Uganda National Panel Survey from 2010 to 2012, and employs explicit spatial techniques in the analysis of rural non-farm enterprise performance. Spatial autocorrelation of firm performance are used as proxies for knowledge transfers and information flows among enterprises across space and over time.

The study finds evidence of spatial spillover effects across space and time in Uganda. This implies that, as existing studies of developed countries have found, social infrastructure and firm proximity contribute significantly to the performance of rural economies, through information exchange and knowledge transfers.

Given the communal nature of rural households in the African setting, knowledge exchange and transfers among neighbouring firms should be encouraged as studies have found they have strong effects on business performance. Additionally, business “leaders” could also be useful in disseminating useful new technologies and applications to neighbouring enterprises in order to boost performance and productivity.

There should be better targeting of policy interventions to clusters of particularly needy enterprises.

To the best of the author’s knowledge, this is the first time that spatio-temporal effects of business performance have been explored. While spatial analyses of business performance have been carried out in developed countries, studies using explicit spatial techniques in the developing country setting have been conspicuously absent.

The aim of this paper is to address a local separability character partly identified by non‐farm participation behaviors in the context of multiple market imperfections.

The paper develops a model to analyze agricultural household's non‐farm participation based on heterogeneous asset endowments. The model is applied to recent data from Zhouzhi, a mountainous county in rural western China.

The paper shows that human capital, social capital and other capital assets have significant but different effects on the agricultural household's participation in non‐farm activities, and they help to break down non‐farm labor constraints. Nonseparability holds only for those households unable to participate in non‐farm activities due to poor asset endowments.

The agricultural household model developed in this paper and its application in China provide insights into theory and empirical analysis of agricultural households' behavior and rural development.

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.

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.

Informal associations are typical features of farm and non-farm ventures especially within rural communities. Owing to the informality of these associations, members of the groups usually evolve strategies to cope with different kinds of economic and social shocks such as the COVID-19 pandemic or unexpected economic recessions. Accordingly, entrepreneurship and non-farm business development in rural areas require massive finance input, which this group largely lacks owing to agrarian activities which is the main source of revenue. Therefore, to inform rural development policies, this chapter draws on the interrelationships that exist between finance options (including formal, informal and social networks) and small business development. Using the World Bank Living Standards Measurement Survey – Integrated Surveys on Agriculture (LSMS-ISA), the analytics identifies informal financing and social networks as leading alternatives to formal financing option in rural businesses. Therefore, we suggest that the government institutions recognise and formalise informal finance systems. This will not only aid access to government interventions and programmes, but foster collaborations with existing formal institutions and investors for sustainable rural business financing.

The purpose of this paper is to assess the impacts of the 2008 economic crisis on the change in income inequality among farm households using the micro‐level data. The paper aims to discuss these issues.

Using a national household survey data in Taiwan in 2007 and 2009, the paper applies the Gini decomposition method for income inequality.

Results of a decomposition method indicate that the Gini coefficients decrease for farm households, and that the effects are more pronounced among the full‐time farms. The reduction in the overall income inequality is due to the decreased farm income and off‐farm wage salaries, as well as the increased government payments.

Although these findings are interesting and useful, some caveats still remain. First, the analysis can only reveal the short‐term effect of the economic crisis. The long‐term effect could be different. In addition, the paper does not further address whether the impacts may differ for households with different socio‐demographic characteristics of the household members and household characteristics. More detailed information on each household member is required to address this issue. This issue is beyond the scope of the current study and is left for further investigation.

Numerous studies have examined the impact of 2008 economic crisis on the general economy, however, little is known about the effect among farmers. This study is among the first to assess the impacts of financial crisis on income inequality between farm and nonfarm households.

This chapter uses the Ghana Living Standards Survey (GLSS) 7 datasets to investigate and examine the effect of rural non-farm diversification and its implications on agricultural (tree-crop) farming sector inequalities and sustainable development in Ghana. Applying a Gini-decomposition method and/or technique within a quantitative approach, the study outcome indicates the average non-farm income thus, increased income inequality among tree-crop smallholder rural livelihoods and households. Income diversification by farm households has gained the attention of governments, policy makers, and researchers because of its commonness and contribution to socio-economic development especially in developing countries. Aggregationally, non-farm self-employment reduced income inequality, and non-farm wage employment income led to an increase in income inequality. Increased rate of educational enrollment and achievement is the most important variable of non-farm income inequality. Government effort at expanding tree-crop acreages and improve yields have to degree achieved its intended policy implementation, increased rate of educational achievement could undermine the socio-economic policy cohesion and sustainable development of rural livelihood, communities, and national economy. Tree crop policies should take account of the spatial distribution of tree-crop commodity production and in particular, the implication and effect of rural non-farm diversification on agricultural sector inequalities.