1. Turning statistics into knowledge: use and misuse of indicators and models Data Day Geneva May 18th

2. Modeling: Partial vs General equilibrium The importance of estimation Indices Turning statistics into knowledge2

3. Modeling: Partial vs General equilibrium The importance of estimation Indices Turning statistics into knowledge3

4. Modeling: Partial versus General equilibrium Turning statistics into knowledge4 Definitions Partial equilibrium implies that we only consider a few markets at a time and we do not close the models by including all economic interactions across sectors (e.g., SMART, GSIM in WITS or TRITS at the World Bank). In a general equilibrium setup all markets are simultaneously modeled and interact with each other (e.g., GTAP developed at Purdue University).

5. Why partial equilibrium? Advantages Minimal data requirement. We can take advantage of rich WITS datasets. Crucial if question is about: – Bolivia or Uruguay and not the Rest of South America – Soya exports and not Other cereals – Results of the trade model will feed poverty analysis. Households produce corn or soya, not cereals. Heterogeneity of impacts may be lost in a more aggregate general equilibrium model. Turning statistics into knowledge5

6. Why partial equilibrium? More Advantages Allows analysis of Doha negotiations more accurately: – In the WTO countries negotiate bound tariffs, not applied (tariff overhang in many regions) – Applied and bound tariffs are very different within HS 10 Cereals. General equilibrium approach will miss this. Turning statistics into knowledge6

7. Why partial equilibrium? More Advantages Transparency – Modeling is straightforward and results can be easily explain. No black box. Easy to implement – Excel sheet/SMART/GSIM Solves aggregation bias Turning statistics into knowledge7

8. Adding apples and oranges…. Apples OrangesFruits Pw Pw+Ta Pw+Tf No welfare cost associated with Ta: apples import demand is perfectly inelastic. No tariff on oranges. So no welfare cost associated with fruit protection. Aggregation bias suggests welfare loss = Q P Turning statistics into knowledge8 Pw+ta

9. Why partial equilibrium? Disadvantages One has information only on a pre- determined number of economic variables (partial model of the economy) One may miss important feedbacks – E.g., Labor market constraints. (But if you know they are there you can model them) Can be very sensitive to a few (badly estimated) elasticities. Turning statistics into knowledge9

10. Modeling: Partial vs General equilibrium The importance of estimation Indices Turning statistics into knowledge10

11. The importance of estimation Ex-post One can estimate the impact of a certain policy reform on exports, trade creation, diversion, GDP growth, productivity and with a bit of modeling utility (e.g., gravity equation) Ex-ante One should estimate the critical parameters of the modeling exercise (elasticities, economies of scale, etc..). Otherwise: – Harris (1984) versus Head and Ries (1999) – World Bank (2001) versus Hoekman et al (2004) – GEP(2001) versus common sense Importance of comparing relative and not absolute results Turning statistics into knowledge11

12. But why do simulation results differ? Scenarios are not the same – Full versus partial – Different base years (benchmarks) – Mixing with other reforms (fiscal policy, trade facilitation) Data are not the same – GTAP data is standard, but PTAs, NTBs.. Parameters (elasticities) are not the same Modeling assumptions differ – Perfect versus imperfect competition – Flexible versus rigid labor markets – Endogeneity of TFP to trade openness Turning statistics into knowledge12

13. Modeling: Partial vs General equilibrium The importance of estimation Indices Turning statistics into knowledge13

14. Indices: between analysis and narrative According to statisticians: what cannot be counted does not count, but do indicators try to count what cannot be counted? Composite indices are good for: – Narrative – And advocacy of particular reform/policy – Decision making process if based on policies rather than outcomes, and aggregated using a proper technique. Turning statistics into knowledge14

15. Indices Problems: Modeling versus estimation of weights of different components (or subjective versus objective criteria) Based on theory, not hand-waving (World Banks OTRI versus IMFs old TRI) Rankings and the importance of measurement error (OTRI versus TRI or Doing Business) Turning statistics into knowledge15

16. Concluding remarks Keep it simple and transparent Dont trust your guts: estimate everything you can! Pay attention to measurement error Compare relative policy shocks not absolute numbers Turning statistics into knowledge16