PROJECTIONS AND NEEDS FOR TIMELY AND FREQUENT DATA Sergio Olivieri April 20, 2011

Outline Motivation Projections & data needs Main messages

I. Motivation Why do we need projections? What is the main constraint? When a macro-shock hits a country, data analysis is crucial to design alleviation policies Even without a macro-shock, data analysis is relevant to design medium term-development strategies (e.g. PRSP) Different kind of questions What is the main constraint? Lack of “better” data: comprehensive, frequent & timely data What is the alternative? Projections or ex-ante impact evaluations Why is “better” data so important?

II. Projections & data needs Several approaches (*) In terms of data and time O = Overall impacts; D = Distributional impacts Accuracy of projections Data that feeds the estimation model Assumptions of the model Complexity Requirements* Impacts 1. Poverty Output Elasticity Low O 2. Micro-simulation model Medium O + D 3. Top-down approach High

II. Projections & data needs Data requirements: Comprehensiveness Comparison of some basic data requisites by method 1. Poverty Output Elasticity 2. Micro-simulation model 3. Top-down approach: CGE-Micro GDP Y HH Survey N Int’l Remittances Trade Government

II. Projections & data needs Data requirements: Comprehensiveness Overall impacts on poverty and inequality Poverty & Inequality Results – Bangladesh Source: Habib et. al (2010) 2005 2010 Benchmark Crisis Headcount Ratio Poverty-Output Elasticity 40.0 24.4 25.6 Povstat 25.1 26.3 Micro-simulation 24.6 25.8 Gini per capita Expenditure 0.33 0.31 0.32

II. Projections & data needs Data requirements: Comprehensiveness Distributional impacts: Growth Incidence Curves Complex models: Micro-simulation or Top-down Source: Habib et. al (2010 (a)) and Habib, et. al (2010(b)) Philippines Bangladesh Important distributional impacts but significant heterogeneity across areas and countries: Urban-rural differences in losses highest in Philippines Losses distributed evenly across the distribution in Philippines, skewed towards the better off in BD (due to key role of remittances); skewed towards the poor in MX BD is the only country were the GIC cross, meaning some part of the ruiral distribution lose more than the corresponding urban group—i.e. relatively richer rural households suffer big losses

II. Projections & data needs Data requirements: Comprehensiveness Distributional impacts: Characteristics of crisis-vulnerable Complex models: Micro-simulation or Top-down % of crisis-vulnerable household heads who are low-skilled (0-9 yrs of education)

II. Projections & data needs Data requirements: Timely & Frequent data Less timely & frequent data  less accurate projections Elasticity of employment to output by sector: Mexico Source: Own estimations based on INEGI (2008-09) and SEDESOL (2010) 2009 Based on actual data of GDP and ENOE 2009 More inelastic during the financial crisis except “Other Industries” 2010-11 Longer-term elasticity estimated over 2003-08 data Agriculture Manufacturing Other Industries Services 2009 -0.021 0.222 2.091 0.067 2010-11 -0.711 0.316 0.986 0.949

III. Main messages Why is “better” data so important for projections? It is important because: In terms of Comprehensiveness: Complexity of technique  answer question In terms of Timeliness & Frequency: Quality and accuracy of projections