Dipankar Roy, PhD Bangladesh Bureau of Statistics dr.droy69@gmail.com Urban poverty, limitations of income measures and an introduction to multidimensional poverty index GED-ERG Training Workshop on Measuring Poverty 11 May 2015 Dipankar Roy, PhD Bangladesh Bureau of Statistics dr.droy69@gmail.com

Introduction Household Income and Expenditure Survey (HIES) is the main national survey instrument available to generate household-level data for analysis and monitoring the level and extent of poverty, living standards and income distribution HIES provides a high quality household level data to monitor the progress of five year plan, perspective plan and MDGs particularly goal 1 and a base for post 2015 development agenda i.e., SDGs HIES is the standalone survey to support for sustainable development through providing sustainable statistics

Poverty Reduction Bangladesh has experienced considerable poverty reduction especially since 2000 Poverty incidence which was as high as 48.9 percent in 2000, declined to 40.0 percent in 2005 and further declined to 31.5 percent in 2010 Bangladesh is on track of achieving MDG 1 on poverty incidence and has already achieved the poverty gap target

MDG 1: poverty incidence, poverty gap, and share On track Achieved

Rural-Urban Variation

Share

Ratio

Inequality and Welfare

Poverty

Sensitivity Analysis

Poverty and Inequality

Poverty at different levels Poverty at national level Regional poverty By locality By Division Poverty at finer level Zila, upazila, union Poverty at sub-groups level Age, sex, occupation

Source and Dimension HIES Poverty Mapping Exercise Small Area Estimation (SAE) technique ELL method Proxy Means Test Formulae Wealth quintile Multidimensional Poverty Index

Methods Direct Calorie Intake (DCI) method In DCI method, only calorie intake is considered. There are three types of poverty Absolute poverty: the threshold is <=2122 k. calorie Hardcore poverty: the threshold is <=1805 k. calorie Ultra poverty: the threshold is <=1600 k. calorie Food Energy Intake (FEI) method Cost of Basic Needs (CBN) method Lower poverty line (extreme poverty) Upper poverty line (poverty)

Urban poverty Urban poverty can be characterized by Migration Urbanization Urban agglomeration Informal sector Income variations

Urban poverty Urban poverty statistics may be misreported Sample size: coefficient of variation Allocation Stratification Explicit stratification Implicit stratification Master sample/IMPS Subset Non response

Statistical inference A major goal of data analysis sample mean (or proportion) to estimate the corresponding parameters in the respective population Statistical inference about a population NOT for sample Statistical inference is the act of generalizing from a sample to a population with calculated degree of certainty

Uncertainity Household surveys are based on samples, but interest is in the underlying population Hence, sampling errors are needed, especially when comparing poverty estimates between two groups or two time periods because these errors affect the confidence with which we can claim that poverty is higher in region A rather than region B, or in year 1 compared with year 2

Sampling error Sampling error Standard error Relative standard error Margin of error Confidence interval

Sampling distribution If we had the opportunity to take repeated samples from the same population, samples means ( s) would vary from sample to sample and form a sampling distribution means (SDM) The SDM is used to help us understand the random behavior of a sample mean

Sampling distribution A way to quantify this variability is by determining the standard deviation of the SDM. This standard deviation is called the standard error of the mean (SEM) Large sample sizes produce that closely cluster around the true value of μ. When individual values have standard deviation σ, sample mean based on n has deviation (error) σ / √n SEM= σ / √n

Sampling distribution Sample mean is the point estimator of population mean μ The distance between them can be gauzed by accuracy To gain insight into its precision, we surround the point estimate with a margin of error-that is called confidence interval (CI) The lower end of the confidence interval is the lower confidence limit (LCL) and the upper end is the upper confidence limit (UCL)

Sampling distribution The length of the confidence interval (UCL – LCL) quantifies the precision of the estimate A 95% confidence interval for μ is given by ± 1.96 SEM; where SEM = σ / √n and MOE= 1.96 SEM Here 1.96 represents the critical value corresponding to an area 1- (where , the level of significance) from a centre of a standardized normal distribution

Complex sample design There are three essential features of complex sample designs: Weights, where some sampled observations represent more members of the population than do others, Two-stage sampling, where Primary Sampling Units (PSU) are first selected and then certain households within those PSUs are surveyed, and Stratification of the sample.

Limitations of income measures Income-based measures may have limitations They are only an indirect indicator of poverty; they do not measure need and deprivation They do not take into account financial resources other than income, or other financial deductions such as debt They do not take into account non-income-based resources, such as the level of service provision

Causes of poverty Causes of poverty Demographic dynamics Economic dynamics Environmental dynamics Social dynamics Political dynamics

Socio-economic impacts of poverty Education Health Environment Growth and employment Food security Participation

Challenges Major challenges in poverty eradication Access to basic services Financial resources Institutional capacity and political will Climate change

SDG: End poverty in all its forms everywhere

Multidimensional Poverty Index The Multidimensional Poverty Index (MPI) is a new measure of poverty taking into account a range of factors –education, health and standard of living- rather than just income Income-based poverty measures such as “under a dollar-a-day” (actually $1.25 since 2005) don’t capture issues of access to, for example, good healthcare or education The MPI information is more actionable than income-based poverty

Multidimensional Poverty Index There are ten poverty indicators, grouped into three dimensions covering education, health and standard of living Poverty intensity is measured by a weighted count of the indicators that affect the household. The weighting is 1/6 for each of the two indicators in the education and health dimensions and 1/18 for each of the six indicators in standard of living A household’s inhabitants are considered MPI poor if their poverty intensity is greater than 30%

Multidimensional Poverty Index The MPI identifies multiple deprivations at the household and individual level in health, education and standard of living Each person in a given household is classified as poor or non-poor depending on the number of deprivations his or her household experiences This data are then aggregated into the national measure of poverty

Multidimensional Poverty Index The MPI reflects both the prevalence of multidimensional deprivation, and its intensity—how many deprivations people experience at the same time The MPI builds on recent advances in theory and data to present the first global measure of its kind, and offers a valuable complement to income-based poverty measures

Multidimensional Poverty Index Dimension One: Health [weighting 1/3 of total] Indicators: Child Mortality: has any child in the family died? Nutrition: Are any adult or children in the family malnourished? Dimension Two: Education [weighting 1/3 of total] Indicators: Years of Schooling: Have all household members completed 5 years of schooling? Child Enrollment:  Were any school-aged children out of school in years 1 to 8?

Multidimensional Poverty Index Dimension Three: Standard of Living [weighting 1/3 of total] Indicators: Electricity: Does the household lack electricity? Drinking water: Does the household fail to meet MDG targets, or is it more than 30 minutes walk to water Sanitation: Does the home fail to meet MDG targets, or is the toilet shared? Flooring: Is the floor of the home dirt, sand, or dung? Cooking Fuel: Is cooking fueled by wood, charcoal, or dung? Assets: Does the household own only one or none of the following: radio, TV, telephone, bike, motorbike?

Relevant publications and software Bangladesh Poverty Assessment Report Child Equity Atlas BDHS report MICS report HIES report Software ADePT DASP

References: Reading materials The Multidimensional Poverty Index (MPI) by Maria Emma Santos and Sabina Alkire Multidimensional Poverty Measures: New Potential by Sabina Alkire Counting and Multidimensional Poverty by Sabina Alkire and James Foster http://www.ophi.org.uk/ http://www.ophi.org.uk/multidimensional-poverty-index/mpi-2014-2015/ Handbook on Poverty and Social Analysis: A Working Document by ADB

Exercise on poverty and mpi Real Data Analysis STATA commands Exercise on poverty and mpi