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. P L(P) A valid Lorenz curve has to have the following properties: L(0) = 0L(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. P L(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) (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 P L(P) FIGURE 1 LORENZ CURVE (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) (4) (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) (6) (7) GROWTH AND DISTRIBUTION EFFECTS [ Where denotes mean consumption and L denotes the Lorenz curve at time t ] [ Where R is a residual ]

COUNTRY CASE STUDY BANGLADESH [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 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 POVERTY DATA REQUIREMENTS [ For Time 1 and Time 2]

SIMULATIONS USING SimSIP POVERTY RESULTS USING SIMULATOR

COUNTRY CASE STUDY RESULTS USING ACTUAL DATA

SIMULATIONS USING SimSIP POVERTY OTHER RESULTS

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

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.