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, PhDBangladesh 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

Achieved

On track

Rural-Urban Variation

5

Total Urban Rural Total Urban Rural Total Urban RuralLow

1% 0.27 0.20 0.31 0.27 0.22 0.31 0.00 -0.02 0.005% 1.72 1.37 1.96 1.72 1.47 1.96 0.00 -0.10 0.00

10% 3.85 3.15 4.37 3.85 3.4 4.36 0.00 -0.25 0.01Quintile

Q1 8.76 7.46 9.86 8.85 8.06 9.93 -0.09 -0.60 -0.07Q2 12.18 11.21 13.38 12.47 11.97 13.63 -0.29 -0.76 -0.25Q3 15.59 15.23 16.72 15.97 15.85 17.00 -0.38 -0.62 -0.28Q4 20.95 21.74 21.53 21.23 22.04 21.73 -0.28 -0.30 -0.20Q5 42.51 44.35 38.51 41.49 42.09 37.70 1.02 2.26 0.81

High10% 27.99 28.96 24.36 26.90 27.03 23.63 1.09 1.93 0.73

5% 18.20 18.16 15.41 17.19 17.24 14.84 1.01 0.92 0.571% 6.09 5.85 5.39 6.01 6.08 5.04 0.08 -0.23 0.35

HIES2005 HIES2010Distribution Change

Share

Interquartile Range

Tails

p25/p10 p50/p25 p75/p50 p90/p75 p75/p25 p90/p10

Total

HIES2005 1.27 1.36 1.46 1.52 1.98 3.85

HIES2010 1.28 1.36 1.44 1.51 1.96 3.79

Urban

HIES2005 1.35 1.47 1.59 1.55 2.33 4.88

HIES2010 1.34 1.41 1.54 1.43 2.17 4.17

Rural

HIES2005 1.24 1.32 1.38 1.40 1.82 3.16

HIES2010 1.26 1.32 1.36 1.38 1.81 3.14

Bottom Half of the Distribution

Upper Half of the Distribution

Ratio

Mean MedianGini

CoefficientCost of

inequalityWelfare

HIES2005

Urban 1,760.0 1,340.4 36.5 642 1118Rural 1,056.7 879.0 28.4 300 757Total 1,230.6 955.0 33.2 409 822HIES2010

Urban 3,467.5 2,726.4 33.8 1173 2294Rural 2,083.4 1,769.5 27.5 574 1510Total 2,447.2 1,944.8 32.1 786 1661Percentage change

Urban 97.0 103.4 -2.6Rural 97.2 101.3 -0.8Total 98.9 103.6 -1.1

Inequality and Welfare

Poverty Headcount

Income Gap Ratio

Poverty Gap

Squared Poverty Gap

HIES2005

Urban 28.4 23.0 6.5 2.1

Rural 43.8 22.4 9.8 3.1

Total 40.0 22.5 9.0 2.9

HIES2010

Urban 21.3 20.2 4.3 1.3

Rural 35.2 20.9 7.4 2.2

Total 31.5 20.8 6.5 2.0

Change

Urban -7.1 -2.8 -2.2 -0.8

Rural -8.7 -1.5 -2.5 -0.9

Total -8.5 -1.7 -2.4 -0.9

Poverty

Poverty Headcount

Rate

Change from the actual (%)

Poverty Headcount

Rate

Change from the actual (%)

Percentage point

Percent

Actual 40.0 0.0 31.5 0.0 -8.5 -21.35% 44.3 10.8 35.8 13.7 -8.5 -19.2

10% 48.3 20.8 39.9 26.7 -8.4 -17.420% 56.3 40.8 47.7 51.4 -8.6 -15.3-5% 35.2 -12.0 26.8 -14.9 -8.4 -23.9

-10% 30.5 -23.8 22.8 -27.6 -7.7 -25.2-20% 20.8 -48.0 15.0 -52.4 -5.8 -27.9

HIES 2005 HIES 2010Change between 2005

and 2010

Sensitivity Analysis

Total Urban Rural Total Urban Rural Total Urban RuralPopulation share 1.00 0.25 0.75 1.00 0.26 0.74 0 4.00 -1.33Consumption share 1.00 0.35 0.65 1.00 0.37 0.63 0 5.71 -3.08Mean consumption 1231 1760 1057 2447 3468 2083 98.78 97.05 97.07Relative mean consumption 1.00 1.43 0.86 1.00 1.42 0.85 0 -0.70 -1.16Consumption inequality 0.332 0.365 0.284 0.321 0.338 0.275 -3.31 -7.40 -3.17Mean consumption of the poor 653 738 635 1237 1445 1192 89.43 95.80 87.72Mean gap of the poor 190 217 183 324 363 315 70.53 67.28 72.13Poverty (threshold) consumption 843 955 818 1561 1808 1507 85.17 89.32 84.23Poverty share 1.00 0.18 0.82 1.00 0.18 0.82 0 0 0Poverty risk 1.00 0.71 1.09 1.00 0.68 1.12 0 -4.23 2.75Poverty incidence 40.0 28.4 43.8 31.5 21.3 35.2 -21.25 -25.00 -19.63

-8.5 -7.1 -8.6Poor population (in crore) 5.53 0.97 4.56 4.67 0.83 3.84 -15.55 -14.43 -15.79

-0.86 -0.14 -0.72Total Population (in crore) 13.83 3.43 10.40 14.80 3.90 10.90 7.01 13.70 4.81

0.97 0.47 0.50

HIES2005 HIES2010 Change

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

X

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

sX

X

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)

X

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

X

2

05.

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

• 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 everywhereTargets Indicators 2.1 Eliminate extreme poverty by 2030 1. Proportion of population under national

extreme poverty line 2.2 Reduce national headcount poverty between 2015 and 2030 by two thirds

1. Proportion of population under national poverty line (urban and rural) 2. Percentage of poverty by using multidimensional poverty index (MPI) 3. Reduce severity index (squared poverty gap ratio)

2.3 Ensure equitable growth 1. Degree of inequality (Gini/Theil) by locality 2. Share of people at bottom 40% in national consumption 3. Share of people at bottom 40% in employment and asset ownership 4. Percentage of poor and vulnerable people under social protection measures 5. Percentage of nutritionally vulnerable poor covered by food and cash based safety nets 6. Growth elasticity of poverty reduction

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

• Publication– 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

Real Data Analysis