Non-farm Income and Consumption Expenditures in Rural Bangladesh: Empirical Evidence from Multilevel Regression Modelling

Abstract

This study examines the effects of non-farm income on household consumption expenditures in rural Bangladesh. A two-stage endogenous treatment effect model is built on data from a nationally representative Household Income Expenditure Survey (HIES) 2010 to control selection bias. The HIES follows a hierarchical data structure because the survey is based on two-stage stratified sampling. A multilevel mixed-effects linear regression model is used to capture the unobserved heterogeneity between clusters (PSUs) along with revealing important factors. Results reveal that non-farm income has a significant positive effect on household's consumption expenditures and non-farm income recipient households spend about 29% more than their counterparts. In addition, higher level of per capita income, education, smaller family size and lower dependency ratio are found to be more effective in increasing consumption expenditures of rural households. Significant cluster-level variations are observed in the analyses. This study recommends that non-farm income generating activities should be encouraged among rural households as this would raise their consumption expenditures and hence, improve welfare and living standards among them. KeywordsNon-farm income • Consumption expenditures • Treatment effect model • Multilevel regression • Rural households • Bangladesh B M. J. Hossain

Consumption inequality between farm and non-farm households in rural Vietnam

“…This is especially true for poorest farm households who are mostly small landholders and do not have enough capital assets to efficiently pursue farming. Empirical studies in Vietnam and other developing countries also confirm the significantly positive impacts of non-farm employment on household wealth, such as Himanshu et al (2013), Hoang et al (2014), Imai et al (2015), Tran and Van Vu (2020) and Hossain and Al-Amin (2019).…”

Can female executives improve employment relations outcomes? Empirical evidence from China with gender implications

“…However, hierarchical data violates the assumption of independence of all observations, which results in the biased standard error of the coefficients computed in OLS regression (Maas and Hox, 2004). It is inappropriate to use traditional OLS regression to analyze hierarchical data, due to the ignored cluster effect and underestimated coefficients' standard errors (Hossain and Al-Amin, 2019). Therefore, we use multilevel regression models to examine the impacts of female executives on employment relations outcomes from the aspects of overtime, labor contracts, promotion opportunities and salary.…”

“…regression to analyze hierarchical data, due to the ignored cluster effect and underestimated coefficients' standard errors (Hossain and Al-Amin, 2019). Therefore, we use multilevel regression models to examine the impacts of female executives on employment relations outcomes from the aspects of overtime, labor contracts, promotion opportunities and salary.…”

Reducing Rural Poverty Through Non-farm Job Creation in India

“…A review of cross-country studies like Nassar and Biltagy ( 2017 ) in Egypt; Awoniyi and Salman ( 2011 ) in Nigeria; Ersado ( 2006 ) in Zimbabwe; Hoang et al ( 2014 ) and Imai et al ( 2015 ) in Vietnam; Owusu et al ( 2011 ) and Zereyesus et al ( 2017 ) in Ghana; Arif et al ( 2000 ) in Pakistan; Woldehanna and Zerfu ( 2002 ) in Ethiopia; and Hossain and Al-Amin ( 2019 ) in Bangladesh has argued that development of non-farm sector has greater potential to reduce poverty in rural areas. But according to Haggblade et al ( 2010 ), although rural non-farm employment is a potential pathway out of poverty for rural poor, it does not happen automatically.…”