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

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

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

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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).

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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

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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

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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

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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

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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

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

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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

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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

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

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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

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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

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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

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