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Small Area Estimation of Poverty in Rural Bangladesh

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

Mohammad Amirul Islam
Department of Agricultural Statistics, Bangladesh Agricultural University, Mymensingh.

Md. Akhtarul Alam*
Department of Agricultural Statistics, Bangladesh Agricultural University, Mymensingh.

Md. Jamal Hossain
Department of Agricultural Statistics, Bangladesh Agricultural University, Mymensingh.

Sumonkanti Das
Department of Statistics, Shahjalal University of Science and Technology, Sylhet

Abstract/ Executive Summary:

Poverty is a complex phenomenon and most of the developing countries are struggling to overcome t he problem. Small area estimation offers help to allocate resources efficiently to address poverty at lower administrative level. This study used data from Census 2011 and Household Income and Expenditure Survey (HIES)-2010. Using ELL and M-Quantile methods, this study identified Rangpur division as the poorest one where Kurigram is the poorest district. Finally, considering both upper and lower poverty lines this study identified the poverty estimates at upazila level of Rangpur division using ELL and M-Quantile methods. The analyses found that 32% of the households were absolute poor and 19% were extremely poor in rural Bangladesh. Among the upazilas under Rangpur division Rajarhat, Ulipur, Char Rajibpur, Phulbari, Chilmari, Kurigram Sadar, Nageshwari, and Fulchhari Upazilas have been identified as the poorest upazilas.

Keywords: Small area, Poverty, ELL, M-Quantile methods

Location: Rangpur division

Start Date:

End Date:

Program Area: Socio-economic and Policy

Non-commodity: Evaluation

Objectives:

More specifically, in the study we have used a novel linear M-quantile model (Chambers and Tzavidis, 2007) for poverty estimation at small area levels and provided a poverty map for rural Bangladesh. The poverty maps provide a graphical summary of areas, which are suffering from a relatively high deprivation. The main purpose in producing such maps is to aid the planning of social interventions.

Methodology:

This study was based on secondary data, e.g., Household Income and Expenditure Survey (HIES)-2010 and Census-2011. It can be noted that though HIES 2016 report has been published, the complete set of data has not been released yet. Household Income and Expenditure Survey (HIES) 2010 conducted by Bangladesh Bureau of Statistics (BBS) was a two-stage stratified random sample. In the HIES 2010, a total of 12240 households were randomly selected from 7 divisions, 64 districts, and 384 sub-districts. In this study, we have used 7840 rural households in Bangladesh to identify the important factors associated with poverty in rural Bangladesh. This study used 5% Census-2011 data for small area estimation practice collected from BBS. The census was a nationwide operation counting all the population of the country. In order to conduct the Population and Housing Census 2011 efficiently, the unit of enumeration was considered as the Enumeration Area (EA) constituted with around one hundred households. Usually, Population and Housing Census collects a wide range of data on household and individual characteristics, including employment, housing conditions, educational attainment, sources of drinking water, access to sanitation, electricity, etc. However, as a global practice, population census does not include consumption and income data, which are supplemented from HIES for small area estimation practices. Like the HIES 2010 data, similar variables were created from census data. In addition, data checking has been done for consistency and reliability.

Source of Information: Bangladesh Journal of Agricultural Economics, XL 1&2 (2019): 1-16

Article Link (Online Journal):

Funding Source:

1.

Budget:

Total Budget (Tk.) :

Achievement/Findings:

The MQ estimates of poverty incidence, gap and severity (Upper poverty line) for Kurigram district were 68.71%, 20.09% and 7.70% respectively. The MQ estimates suggest that Panchagarh district has the lowest poverty incidence, gap and severity. Upazila wise estimates of poverty incidence were presented in maps. Upazilas suffering most from poverty considering both upper and lower poverty lines have been confirmed by both ELL and MQ methods. Some upazilas of Kurigram and Gaibanda districts are performing badly. Both ELL and MQ estimates identified more or less the same list of upazilas to be extremely poor. Appropriate interventions should be taken to address these areas taking the environmental hazards and other socio-economic aspects into account. Among the upazilas under Rangpur division Rajarhat, Ulipur, Char Rajibpur, Phulbari, Chilmari, Kurigram Sadar, Nageshwari, and Fulchhari Upazilas have been identified as the poorest upazilas. These upazilas have underlying causes, which may be somewhat different from other upazilas, for such poor condition. Specific interventions should be taken to address the poverty issue in the region.

Knowledge Management: Journal