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Enhancing Poverty Monitoring with Labor Force Surveys

Explore utilizing labor force surveys and market research data for more frequent and timely poverty monitoring, improving policy responses to crises and enhancing poverty measurement methods.

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Enhancing Poverty Monitoring with Labor Force Surveys

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  1. Monitoring Poverty More Regularly: Can labor force surveys and market research data help? Joao Pedro Azevedo LCSPP More Frequent, More Timely & More Comparable Data for Better Results

  2. The need • Food crisis • Environmental shocks • Financial crisis • Policy makers need to understand the consequences of such crisis to design responses More Frequent, More Timely & More Comparable Data for Better Results

  3. How can we handle this? • Econometrics + Assumptions • inFamous Poverty-Growth Elasticity • Imputation methods • Collect new data and/or collect old data in different ways • Continuous household surveys • Listening 2 LAC • Use existing information • Labor force surveys • Market research data • Administrative records (i.e. vital statistics) More Frequent, More Timely & More Comparable Data for Better Results

  4. Two examples of using existing data in a different way • Labor Income Poverty Index (LIPI) • Poverty and Labor Brief http://lacpoverty/ • Prices and Quantities information from Nielsen on food and beverages More Frequent, More Timely & More Comparable Data for Better Results

  5. Labor income Poverty Index More Frequent, More Timely & More Comparable Data for Better Results

  6. Labor Income Poverty Index (LIPI) • How we do it: • Labor Income (70% of total income) – 12 months moving average or 3 months moving average • Poverty line (national + usd/ppp) • Price data (monthly or quarterly) • Index Q1 2007 (base=1) • Mexico March,2010 (CONEVAL) More Frequent, More Timely & More Comparable Data for Better Results

  7. Labor Income Poverty Index (LIPI)(U$ 2.5 a day)Jan 2007 - Jul 2010 More Frequent, More Timely & More Comparable Data for Better Results

  8. MEXICO - Labor Income Poverty Index (LIPI)(U$ 2.5 a day)Q1 2005 – Q4 2010 More Frequent, More Timely & More Comparable Data for Better Results

  9. Testing the validityColombia and Peru • Continuous household survey • Design • 2 random samples • Sample 1 : Poverty incidence (full income) • Sample 2 : LIPI (labor income) More Frequent, More Timely & More Comparable Data for Better Results

  10. Sensitivity to the welfare aggregate in Colombia More Frequent, More Timely & More Comparable Data for Better Results

  11. Sensitivity to the welfare aggregate in Peru More Frequent, More Timely & More Comparable Data for Better Results

  12. Sensitivity to poverty and inequality measures More Frequent, More Timely & More Comparable Data for Better Results

  13. Market Research Data More Frequent, More Timely & More Comparable Data for Better Results

  14. Atlántico: Atlántico, Bolívar, Cesar, Córdoba, Guajira, Magdalena, Sucre Antioquia: Antioquia Oriente: Boyacá, Meta, N. Santander, Santander Cundinamarca: Cundinamarca Centro: Caldas, Huila, Quindío, Risaralda, Tolima Pacífico: Cauca, Nariño, Valle Zona no cubierta 5% población RMS – Nielsen Areas95% Population coverage More Frequent, More Timely & More Comparable Data for Better Results

  15. Categories included: More Frequent, More Timely & More Comparable Data for Better Results

  16. Argentina Brasil Chile Mexico Venezuela CAM Panama Nicaragua Costa Rica Honduras Guatemala El Salvador Information included: Volume sales variation per country Consumption Price variation per Country By Item, Outlet and Geographic location Countries covered in the LAC Region More Frequent, More Timely & More Comparable Data for Better Results

  17. More Frequent, More Timely & More Comparable Data for Better Results

  18. Volume and Price series More Frequent, More Timely & More Comparable Data for Better Results

  19. More Frequent, More Timely & More Comparable Data for Better Results

  20. More Frequent, More Timely & More Comparable Data for Better Results

  21. More Frequent, More Timely & More Comparable Data for Better Results

  22. More Frequent, More Timely & More Comparable Data for Better Results

  23. Applications • Tracking of wellbeing, proxy by consumption • Reweighting of the index to better reflect the consumption patters of the poorer • Better estimation of elasticities and cross-elasticities of substituion • Distributional analysis More Frequent, More Timely & More Comparable Data for Better Results

  24. Final considerations • Labor income explains most of the poverty variation • High correlation between income, expenditure and labor income poverty measures • Highlights the importance to improve income measure in the region • Market research data can help More Frequent, More Timely & More Comparable Data for Better Results

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