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Factors Affecting Poverty in Rural Bangladesh: an Analysis Using Multilevel Modelling

Md. Farouq Imam
Department of Agricultural Statistics, Bangladesh Agricultural University, Mymensingh-2202, Bangladesh

Mohammad Amirul Islam
Department of Agricultural Statistics, Bangladesh Agricultural University, Mymensingh-2202, Bangladesh

M. J. Hossain
Department of Agricultural Statistics, Bangladesh Agricultural University, Mymensingh-2202, Bangladesh

Abstract/ Executive Summary:

Poverty is a multi-faced problem in the developing world and it is much more complex in rural settings. Hence, policy formulation based on national level studies sometimes fails to find remedies of rural poverty. Thus, the present study aims to identify the determinants of poverty in rural Bangladesh using the nationally representative Household Income and Expenditure Survey (HIES) 2010 data. The HIES follows a hierarchical structure hence, two-level random intercept binary logistic regression models were used to capture the unobserved heterogeneity between communities along with revealing important factors associated with poverty. The analyses found that 32% of the households were absolute poor and 19% were extremely poor in rural Bangladesh. The potential factors having significant association with poverty were found to be age and education of household head, division, household size, household types, number of dependents, per capita income, household own land, access to electricity, amount of cultivable land, engagement in livestock and farm forestry, household non-agricultural assets, number of male earner and number of female earner in the family. Significant community-level variations were observed in the analyses which emphasis the need for special attention on the poor performing communities. Specific policy recommendations have been suggested for the poverty alleviation of rural households in Bangladesh.

Keywords: Poverty, HIES, Multilevel modelling, Community effect, Rural Bangladesh

Location: Department of Agricultural Statistics, Bangladesh Agricultural University, Mymensingh-2202, Bangladesh

Start Date: 00-00-2010

End Date:

Program Area: Socio-economic and Policy

Non-commodity: Constraints

Objectives:

To determine the factors affecting poverty in rural Bangladesh

Methodology:

Data This study is based the data from Household Income and Expenditure Survey (HIES) 2010 conducted by Bangladesh Bureau of Statistics (BBS). A two-stage stratified random sampling technique was used to ensure greater precision. In the first stage, the specific geographic areas (mouza/ward) were considered as primary sampling units (PSUs) within each stratum. In the second stage, 20 households were randomly selected from each PSU covering rural, urban, and statistical municipal areas. A PSU is usually a natural cluster of households. The HIES data are hierarchical due to its formation where households are nested into PSUs, and PSUs into divisions. In the HIES2010, a total of 12240 households were randomly selected from 7 divisions, 64 districts, and 384 sub-districts. In our study, we have used 7840 rural households in Bangladesh to identify the important factors associated with poverty in rural Bangladesh. Measures of Poverty Poverty can be estimated by using a number of approaches. The present study estimates poverty based on Cost of Basic Needs (CBN) method. In CBN method, the poverty line (PL) indicates the average level of per capita expenditure at which persons can meet basic food and non-food needs. However, the upper poverty line (UPL) can be computed as adding the food and upper non-food allowances, while the lower poverty line (LPL) constitutes adding the food and lower non-food allowances (HIES, 2010). In Bangladesh, absolute poverty is defined as the households whose per capita expenditures are below the UPL, whilst hard-core or extreme poverty refers to the households whose per capita expenditures are below the LPL. Determination of household poverty The main goal of this study is to examine the factors related to the response variables (e.g., absolute poor and extreme or hard-core poor). In our study the dependent variables are dichotomous. The categories areas follows: (i) 1 = household is poor if household per capita consumption expenditure is less than UPL; 0 = otherwise (reference category) (ii) 1 = household is extreme poor if household per capita consumption expenditure is less than LPL; 0 = otherwise (reference category). The primary preference of explanatory variables for this study was based on previous other studies on the factors influencing household poverty. The independent variables used in the study are division, age of household’s head (years), age squared of household’s head, household size, household size squared, sex of household’s head, household type, household head’s education, number of dependents, per capita income(BDT), household own land (decimals),access to electricity, amount of cultivable land (decimals), household engaged in livestock, household engaged in farm forestry, household’s nonagricultural assets, number of male earner and number of female earner. Two-level random intercept binary logistic regression model It is very likely that the cluster or community (PSU) effect on the response variable will be present when there is a hierarchical data structure in the survey, for example, HIES 2010. The traditional logistic regression ignoring such cluster effect is inappropriate as the standard errors of regression coefficients are under estimated leading to the significance of a regression coefficient that could be ascribed to likelihood. This may instigate wrong policy formulation. In this context, to overcome this problem a multilevel logistic regression model containing both fixed effects and random effects that attempts to capture the unobserved heterogeneity between clusters is commonly used. The use of appropriate multilevel model provides efficient estimates and the policies devised on the basis of these are reliable. In addition, the significance and extent of the community effect help to find if there is any community that is performing poorly. In the general case, the two-level random intercept binary logistic regression model is the expansion of the single-level binary logistic regression model (for details Goldstein, 2003). Let a binary response variable ij Y be ‘household poverty status’ (= 1if household i in community j is poor, 0 otherwise). The two-level random intercept binary logistic regression model considering household. The two-level random intercept binary logistic regression model is fitted using Stata14.0 software considering only the independent variables found significant in the bi-variate analyses and variables found significant at this stage are kept in the final models. The possibility of multicollinearity and confounding has been explored too. The possible interaction effects were tested and are reported where found. d at level-1 and communities (PSU) at level-2 can be written as follows:

Source of Information: J Bangladesh Agril Univ 16(1): 123–130, 2018 ISSN 1810-3030 (Print) 2408-8684 (Online)

Article Link (Online Journal): doi: 10.3329/jbau.v16i1.36493

Funding Source:

Budget:

Total Budget (Tk.) :

Achievement/Findings:

This study investigated the determinants of poverty among rural households in Bangladesh using a nationally representative HIES 2010 dataset. The cost of basic need (CBN), a widely used measure of poverty, using two poverty lines, namely lower poverty line (LPL) and upper poverty line (UPL) were utilized to assess the incidence of poverty among rural households. The analyses revealed that among households from rural areas, the incidence of absolute and extreme poverty were32% and 19% respectively, which are above the prevalence at urban level. These indicate that the people from rural areas are suffering the most from poverty and require immediate policy intervention.

This study identified several risk factors that were associated with household’s poverty using two-level random intercept binary logistic regression model. In the context of rural Bangladesh, this is the first study which used such relatively innovative approach and is an additional contribution of this paper. The significant determinants of extreme poverty and absolute poverty identified in this study would guide the policy planners to devise important and effective remedial measures. Also, it appears from the study that different interventions are required for different measures of poverty (extreme and absolute). Significant community effects were found in the models for both the measures of poverty. This research recommends that additional specific intervention, besides the national level intervention, be offered for different communities to overcome the problem of poverty. The study also argued that sometimes more can be achieved by addressing only the community level variations.

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