#### Transcript Turning statistics into knowledge

Turning statistics into knowledge: use and misuse of indicators and models Data Day Geneva May 18th • Modeling: Partial vs General equilibrium • The importance of estimation • Indices Turning statistics into knowledge 2 • Modeling: Partial vs General equilibrium • The importance of estimation • Indices Turning statistics into knowledge 3 Modeling: Partial versus General equilibrium 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). Turning statistics into knowledge 4 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 knowledge 5 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 knowledge 6 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 knowledge 7 Adding apples and oranges…. P Pw+ta Pw+Ta Pw+Tf Pw Q Apples Oranges Fruits • 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 = Turning statistics into knowledge 8 Why partial equilibrium? Disadvantages • One has information only on a predetermined 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 knowledge 9 • Modeling: Partial vs General equilibrium • The importance of estimation • Indices Turning statistics into knowledge 10 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 knowledge 11 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 knowledge 12 • Modeling: Partial vs General equilibrium • The importance of estimation • Indices Turning statistics into knowledge 13 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 knowledge 14 Indices Problems: • Modeling versus estimation of weights of different components (or subjective versus objective criteria) • Based on theory, not hand-waving (World Bank’s OTRI versus IMF’s old TRI) • Rankings and the importance of measurement error (OTRI versus TRI or Doing Business) Turning statistics into knowledge 15 Concluding remarks • Keep it simple and transparent • Don’t trust your guts: estimate everything you can! • Pay attention to measurement error • Compare relative policy shocks not absolute numbers Turning statistics into knowledge 16