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ICT Tools for Poverty Monitoring

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Information & Communications Technologies (ICT) Tools in Poverty Monitoring ... Vulnerable Group Feeding (VGF) and Gratuitous Relief (GR) are the main programs ... – PowerPoint PPT presentation

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Title: ICT Tools for Poverty Monitoring


1
ICT Tools for Poverty Monitoring Introduction
to SimSIP
REGIONAL CONFERENCE ON POVERTY MONITORING IN
ASIA
THEMATIC SESSION 4 Information Communications
Technologies (ICT) Tools in Poverty Monitoring
2
ICT Tools for Poverty Monitoring
  • Faster, cheaper, better analysis
  • Use for PRSPs and development strategies
  • Setting of targets (e.g., growth path and
    poverty)
  • Costing of targets (e.g., education, health)
  • ME of targets (e.g., cross-country comparable
    data bases)
  • Governance and transparency (e.g., e-databases)
  • Key challenges
  • Choosing the right tool for each question
  • Understanding the limits/weaknesses of each tool
  • Ensuring replicable results
  • Training stakeholders (empowerment through
    information)
  • Simplicity rules for policy impact !!!

3
Examples of ICT Tools
  • Easier Statistics Econometrics
  • Ado files in Stata (e.g., propensity score
    matching)
  • Poverty mapping routines in SAS
  • Easy-to-use Excel-based tools examples at the
    World Bank
  • SimSIP
  • PAMS (macro consistency framework hh data)
  • PovStat (similar to SimSIP Poverty, but with unit
    level data)
  • Other easy to use tools
  • DAD (Laval University)
  • Other tools
  • Data mining
  • Comparable survey data bases indicators
  • Etc.

4
Easier Statistics Econometrics
  • Example of ado files in stata
  • Propensity score matching
  • Inequality estimation and decompositions
  • Poverty estimation and decomposition
  • Robust poverty comparisons
  • Example for poverty mapping
  • SAS program to handle large data sets (Lanjouw et
    al.)
  • Many applications
  • Basic poverty maps based on census-survey data
  • Estimation of poverty for small survey population
    (e.g., disabled in Uganda)
  • Health maps (infant mortality, malnutrition)
  • Decentralized policy making stools
  • Etc.

5
Excel-based tools the case of SimSIP
  • SimSIP Modules
  • Poverty
  • Evaluation
  • Determinants of Poverty
  • Education targets costing (also health, others)
  • Debt sustainability
  • Indirect taxation and welfare
  • Pension reform
  • Subsidy analysis (utilities)
  • Other modules in development .
  • Todays presentation
  • Poverty Module in some detail
  • Basics of Evaluation Module

6
SimSIP Poverty
  • The Tool The Lorenz Curve
  • Calculating Poverty and Inequality using the
    Lorenz Curve
  • The FGT class of poverty measures
  • The Gini Coefficient
  • Decomposition of changes in poverty
  • Growth and distribution effects
  • Intra and Inter sectoral effects
  • Country case study Bangladesh
  • Context
  • Simulations using SimSIP poverty

7
THE LORENZ CURVE
FIGURE 1 LORENZ CURVE
  • The Lorenz curve maps out the cumulative income
    distribution as a function of the cumulative
    population distribution.
  • L represents the cumulative income distribution,
    and P the cumulative population distribution.
  • L(P) represents L of the income accruing to the
    bottom P of the population, where income per
    capita is ordered from lowest to highest.

L(P)
P
A valid Lorenz curve has to have the following
properties L(0) 0 L(1) 1 L(0) gt 0
L(P) gt 0 with P in 0,1
8
THE LORENZ CURVE
FIGURE 1 LORENZ CURVE
  • The Lorenz curve can be estimated using group
    data (e.g. data by decile)
  • The General Quadratic (GQ) Lorenz Curve.
  • The Beta Lorenz Curve.
  • Data Requirements
  • Percentage of the Population by Interval
  • Mean welfare indicator (i.e. income or
    expenditure per capita) within interval.

L(P)
P
9
CALCULATING POVERTY AND INEQUALITY USING THE
LORENZ CURVE
  • FGT CLASS OF POVERTY MEASURES
  • (Foster, Greer, and Thorbecke, 1984)
  • In terms the Welfare Distribution
  • In terms the Lorenz Curve

(1)

(2)
10
CALCULATING POVERTY AND INEQUALITY USING THE
LORENZ CURVE
FIGURE 1 LORENZ CURVE
L(P)
  • INEQUALITY
  • THE GINI COEFFICIENT (G)
  • G A / (A B)
  • A 1/2 B
  • G 1 2B
  • where B is the integral of the
  • Lorenz curve


(3)
P
11
DECOMPOSITION IN CHANGES IN POVERTY
INTRA AND INTER SECTORAL EFFECTS
  • FGT poverty measures have additive properties.
  • Denoting the poverty measures and population
    shares of the sub-groups by and we
    have
  • Sector Decomposition (Ravillon and Huppy, 1991)

(4)

(5)
Where u denotes urban and r denotes rural
12
DECOMPOSITION IN CHANGES IN POVERTY
GROWTH AND DISTRIBUTION EFFECTS
  • Changes in poverty can be decomposed into growth
    and inequality effects (Datt and Ravillon, 1992)

(6)

Where denotes mean consumption and L
denotes the Lorenz curve at time t
(7)
Where R is a residual
13
COUNTRY CASE STUDYBANGLADESH 1991/92 2000
  • The country enjoyed high levels of economic
    growth during the 1990s.
  • 2.4 percent annual growth in mean per capita
    expenditure.
  • Poverty and extreme poverty in Bangladesh
    significantly decreased between 1991/92 and 2000.
  • By 9 and 10 percent respectively.
  • Poverty is concentrated in urban areas.
  • 80 percent of the poor live in the countryside.
  • The country experienced high mobility from rural
    to urban areas.
  • Urban population shift from 14 to 20 percent.
  • Expenditure inequality deteriorated
  • The Gini Coefficient increased by approximately 5
    to 6 percentage points.

14
SIMULATIONS USING SimSIP POVERTYDATA
REQUIREMENTS For Time 1 and Time 2
15
SIMULATIONS USING SimSIP POVERTYRESULTS USING
SIMULATOR
16
COUNTRY CASE STUDYRESULTS USING ACTUAL DATA
17
COUNTRY CASE STUDYRESULTS USING ACTUAL DATA
18
SIMULATIONS USING SimSIP POVERTYOTHER RESULTS
19
SimSIP Evaluation
  • The Tool Still the Lorenz Curve
  • Calculating Poverty and Inequality using the
    Lorenz Curve
  • The FGT class of poverty measures
  • The Gini Coefficient
  • Impact of changes in income/consumption sources
  • Impact on poverty various statistics
  • Impact on inequality Gini Income Elasticity
  • Country case study Bangladesh
  • Context VGD, VGR, GR, FFE, Secondary stipend
  • Simulations using SimSIP Evaluation

20
Main transfer programs in Bangladesh
  • Vulnerable Group Feeding (VGF) and Gratuitous
    Relief (GR) are the main programs used by the
    government to provide emergency, short-term
    relief to disaster victims.
  • Food-for-Work (FFW) and Test Relief (TR) are
    counter-cyclical workfare programs that provide
    the rural poor with employment opportunities
    during the lean seasons.
  • Vulnerable Group Development (VGD) has evolved
    from providing relief to increasing self-reliance
    by tying food transfers to a package of
    development services NGOs working in
    partnership with government provide poor rural
    women with skill, literacy, and numeric training
    credit and savings mobilization and health and
    nutrition education.
  • Food-for-Education (FFE) aims to remove economic
    barriers to primary school enrollment by the poor
    (in-kind stipend links monthly food transfers to
    poor households to primary school enrollment of
    children)

21
Example of statistics provided GIE
  • GIE 1 ? Distributed like income/con sumption
  • GIE gt 1 ? Increase in inequality at the margin
  • GIE lt 1 ? Decrease in in equality at the margin
  • GIE gt 0 ? Positive correlation with
    income/consumption
  • GIE 0 ? No correlation with income/consumption
  • GIE lt 0 ? Negative correlation with
    income/consumption
  • Impact on inequality
  • Marginal Change in Gini Income Share (GIE
    1)
  • Smallest GIEs indicate most redistributive
    programs

22
Key results for the GIEs
23
CONCLUSIONS
  • Poverty indicators using group data give a fairly
    good approximation of reality.
  • Results using SimSIP give a good overall picture
    of poverty and inequality trends
  • Urbanization in Bangladesh contributed to
    approximately 1.34 percent in poverty reduction.
  • Poverty is concentrated in rural areas. The
    incidence is 16 percent higher in rural areas.
  • The decrease in rural poverty has significantly
    reduced overall poverty (7.26 percent out of the
    9.35 percent reduction in national poverty is due
    to poverty reduction in rural areas)
  • Poverty has been reduced during the 90s mainly
    through growth effects and has been negatively
    affected by distributional effects
  • Inequality has increased significantly during the
    90s, specially in urban areas and within the
    manufacturing sector.
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