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

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) M&E 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 !!!

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.

“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.

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 ….

Today’s presentation Poverty Module in some detail Basics of Evaluation Module

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

THE 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.

A

B

45° Line

Lorenzcurve

P

L(P)

A valid Lorenz curve has to have the following properties:L(0) = 0 L(1) = 1 L’(0) >= 0 L’’(P) >= 0 with P in [0,1]

FIGURE 1

LORENZ CURVE

THE 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.

A

B

45° Line

Lorenzcurve

P

L(P)

2( )L P P P

( ) (1 )L P P P P

FIGURE 1

LORENZ CURVE

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)

z

dyyfz

yz0

)(

01 '( )

HL P dPz

(2)

CALCULATING POVERTY AND INEQUALITY USING THE LORENZ CURVE

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 A

B

45° Line

Lorenzcurve

P

L(P)

FIGURE 1

LORENZ CURVE

1

01 2 ( )G L P dP (3)

DECOMPOSITION IN CHANGES IN POVERTY

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)

iP iwi ii

P w P (4)

2 1 1 2 1 1 2 1( ) ( )t t t t t t t tu u u r r rP P w P P w P P

1, ,2 1 2 1 2 1( ) ( )( )tu r u rt t t t t t

i i i i i i ii iw w P w w P P

(5)

[ Where u denotes urban and r denotes rural ]

INTRA AND INTER SECTORAL EFFECTS

DECOMPOSITION IN CHANGES IN POVERTY

Changes in poverty can be decomposed into growth and inequality effects (Datt and Ravillon, 1992)

( , )t t tP P L (6)

2 2 1 1 2 1 1 1( , ) ( , ) [ ( , ) ( , )]t t t t t t t tP L P L P L P L 1 2 1 1[ ( , ) ( , )]t t t tP L P L R

(7)

GROWTH AND DISTRIBUTION EFFECTS

[ Where denotes mean consumption and L denotes theLorenz curve at time t ]

[ Where R is a residual ]

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.

SIMULATIONS USING SimSIP POVERTYDATA REQUIREMENTS [ For Time 1 and Time 2]

Percent of Population per interval

Mean income or Expenditures

Percent of Population per interval

Mean income or Expenditures

Percent of Population per interval

Mean income or Expenditures

Percent of Population per interval

Mean income or Expenditures

Percent of Population per interval

Mean income or Expenditures

Percent of Population per interval

Mean income or Expenditures

5.89 239.27 10.69 232.54 11.29 230.58 12.24 225.21 7.26 242.59 10.00 232.547.41 310.99 10.44 311.08 10.31 311.93 8.31 308.05 10.20 309.53 10.00 311.086.82 361.19 10.53 360.60 11.02 360.81 10.79 359.22 8.61 360.44 10.00 360.607.91 406.56 10.36 407.25 10.69 407.15 7.67 406.69 9.73 407.90 10.00 407.258.50 453.17 10.25 453.97 10.87 453.73 12.00 453.26 8.21 454.62 10.00 453.979.27 507.23 10.12 507.71 10.50 507.90 8.93 506.29 9.33 506.73 10.00 507.717.59 567.06 10.40 566.67 9.88 566.36 10.57 571.77 9.31 565.17 10.00 566.67

11.25 633.19 9.80 637.92 9.88 537.47 10.12 639.66 10.10 638.84 10.00 637.9212.91 755.57 9.51 751.25 9.06 749.21 8.37 755.24 12.21 753.82 10.00 751.2522.45 1320.92 7.89 1207.88 6.50 1147.30 10.99 1191.23 15.03 1270.34 10.00 1207.88

z1(mod) z2(ex) z1 z2 z1 z2 z1 z2 z1 z2 z1 z2

535.00 399.00 524.00 451.00 526.00 446.00 526.00 446.00 526.00 446.00 526.00 446.00

Populations SharesUrban Rural Group 1 Group 2 Group 3 National Urban Rural Group 1 Group 2 Group 3

280.63 0.14 0.86 0.58 0.09 0.32

337.38

382.64

430.37

478.60

536.92

597.02

679.21

850.23

9755.58

Group 3 National

Intervals

Urban Rural Group 1 Group 2

SIMULATIONS USING SimSIP POVERTYRESULTS USING SIMULATOR

Period 1 Period 2 Period 1 Period 2 Period 1 Period 2 Period 1 Period 2 Period 1 Period 2 Period 1 Period 2Mod. PovertyHeadcount 45.37% 36.12% 61.20% 52.61% 66.93% 54.67% 58.90% 49.74% 52.31% 41.94% 58.86% 49.50%Pov. gap 12.26% 10.08% 17.50% 14.06% 18.55% 14.91% 17.61% 12.86% 14.49% 11.17% 16.82% 13.37%Sq. pov. gap 4.56% 3.74% 6.87% 5.02% 7.32% 5.39% 7.33% 4.48% 5.40% 4.00% 6.59% 4.82%Ext. PovertyHeadcount 23.09% 18.64% 46.62% 37.58% 48.38% 37.43% 43.84% 32.77% 38.35% 27.99% 43.37% 33.56%Pov. gap 4.69% 3.63% 11.57% 8.22% 11.46% 8.09% 11.51% 6.65% 8.93% 5.98% 10.62% 7.21%Sq. pov. gap 1.44% 0.98% 4.18% 2.51% 4.29% 2.43% 4.49% 1.93% 2.91% 1.79% 3.78% 2.17%Social WelfareMean (t2 value) 903 1149 692 786 656 766 717 883 793 989 713 859

Gini v=2 30.94% 37.03% 24.94% 27.13% 22.91% 26.61% 26.99% 32.00% 28.44% 33.92% 25.97% 30.67%

Mean*(1-Gini) 623 724 519 572 506 562 524 600 568 653 528 595Growth and Inequality DecompositionGrowth Impact Mod Ext Mod Ext Mod Ext Mod Ext Mod Ext Mod ExtHeadcount -18.69% -13.54% -12.30% -12.16% -17.45% -17.31% -18.90% -17.51% -17.85% -16.63% -16.62% -15.77%Pov. Gap -6.55% -3.11% -5.10% -3.93% -6.74% -4.76% -7.42% -5.44% -6.88% -5.00% -6.59% -4.83%Sq. Pov Gap -2.76% -1.00% -2.34% -1.57% -2.88% -1.73% -3.41% -2.20% -3.01% -1.84% -2.97% -1.87%Inequality ImpactHeadcount 5.62% 8.76% 2.11% 2.10% 0.61% 2.74% 7.30% 6.86% 4.91% 4.73% 4.45% 4.58%Pov. Gap 5.06% 3.61% 1.81% 0.82% 3.05% 1.87% 3.81% 1.78% 4.07% 2.73% 3.59% 2.07%Sq. Pov Gap 3.05% 1.46% 0.77% 0.08% 1.54% 0.35% 1.50% 0.18% 2.40% 1.32% 1.86% 0.71%ResidualHeadcount -3.81% -0.33% -1.60% -1.02% -4.58% -3.61% -2.43% 0.42% -2.56% -1.54% -2.82% -1.38%Pov. Gap 0.69% 0.56% 0.15% 0.25% -0.05% 0.47% 1.13% 1.19% 0.51% 0.68% 0.45% 0.65%Sq. Pov Gap 0.53% 0.00% 0.28% 0.18% 0.58% 0.49% 0.95% 0.54% 0.79% 0.60% 0.67% 0.46%

Urban Rural Group 1 Group 2 Group 3 National

COUNTRY CASE STUDYRESULTS USING ACTUAL DATA

COUNTRY CASE STUDYRESULTS USING ACTUAL DATA

SIMULATIONS USING SimSIP POVERTYOTHER RESULTS

Geographic Headcount Pov. Gap Sq. Pov. GapUrban Moderate -1.33% -0.31% -0.12%

Extreme -0.64% -0.15% -0.07%Rural Moderate -7.36% -2.95% -1.59%

Extreme -7.74% -2.87% -1.43%Migration Moderate -0.90% -0.30% -0.13%

Extreme -1.34% -0.39% -0.16%Residual Moderate 0.23% 0.11% 0.06%

Extreme -0.09% 0.00% 0.03%

Total Moderate -9.35% -3.45% -1.77%

Extreme -9.81% -3.41% -1.62%

OccupationGroup 1 Moderate -7.16% -2.13% -1.12%

Extreme -6.40% -1.97% -1.09%Group 2 Moderate -0.84% -0.44% -0.26%

Extreme -1.02% -0.45% -0.23%Group 3 Moderate -3.36% -1.08% -0.45%

Extreme -3.36% -0.96% -0.36%Sector shiftModerate -0.14% 0.02% 0.03%

Extreme -0.05% 0.04% 0.03%Residual Moderate 2.16% 0.18% 0.04%

Extreme 1.02% -0.08% 0.04%

Total Moderate -9.35% -3.45% -1.77%Extreme -9.81% -3.41% -1.62%

Changes in Poverty

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

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)

Example of statistics provided: GIE

GIE = 1 Distributed like income/con sumption GIE > 1 Increase in inequality at the margin GIE < 1 Decrease in in equality at the margin GIE > 0 Positive correlation with

income/consumption GIE = 0 No correlation with income/consumption GIE < 0 Negative correlation with

income/consumption Impact on inequality:

Marginal Change in Gini = Income Share * (GIE – 1) Smallest GIEs indicate most redistributive

programs

Key results for the GIEs

Table 12: Decomposition by source of Gini for per capita income, Bangladesh, 2001 VGD Program VGF Program GR Program FFE Program Sec. School

Stipends GIE using unit-level data -.657 -.510 -.115 -.561 0.625 GIE using SimSIP Evaluation -.666 -.525 -0.095 -.582 0.664 Source: Authors’ estimation using Bangladesh 2001 HES.

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.