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

2. Outline
Motivation
Projections & data needs
Main messages

3. 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?

4. 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

5. 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

6. 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

7. 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

8. 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)

9. 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 ( ) and SEDESOL (2010)
2009
Based on actual data of GDP and ENOE 2009
More inelastic during the financial crisis except “Other Industries”
Longer-term elasticity estimated over data
Agriculture
Manufacturing
Other Industries
Services
2009
-0.021
0.222
2.091
0.067
-0.711
0.316
0.986
0.949

10. 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