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ICT Tools for Poverty Monitoring Introduction to SimSIP

ICT Tools for Poverty Monitoring Introduction to SimSIP

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ICT Tools for Poverty Monitoring Introduction to SimSIP

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  1. ICT Tools for Poverty MonitoringIntroduction 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) • 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 !!!

  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 …. • Today’s 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) >= 0 L’’(P) >= 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 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 L(P) (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 > 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

  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.