1. 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

2. 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

3. 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

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

5. Rural-Urban Variation

6. Share

7. Ratio

8. Inequality and Welfare

9. Poverty

10. Sensitivity Analysis

11. Poverty and Inequality

12. 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

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

14. 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)

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

16. 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

17. 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

18. 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

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

20. 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

21. 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

22. 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)

23. 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 (where , the level of significance) from a centre of a standardized normal distribution

24. 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.

25. 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

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

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

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

29. SDG: End poverty in all its forms everywhere

30. 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

31. 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%

32. 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

33. 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

34. 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?

35. 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?

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

37. 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
Handbook on Poverty and Social Analysis: A Working Document by ADB

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