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Economic Models and Experiments

Economic Models and Experiments. Francesco Guala Università degli Studi di Milano. A common complaint:. Economists spend too much time and effort constructing and analysing mathematical models Such models are unrealistic, idealized, inadequate representations of real economies

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Economic Models and Experiments

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  1. Economic Models and Experiments Francesco Guala Università degli Studi di Milano

  2. A common complaint: Economists spend too much time and effort constructing and analysing mathematical models Such models are unrealistic, idealized, inadequate representations of real economies Modelling is a source of bias, leads to mistaken explanations and misleading policy advice

  3. “As I see it, the economics profession went astray because economists, as a group, mistook beauty, clad in impressive-looking mathematics, for truth.” “the central cause of the profession’s failure was the desire for an all-encompassing, intellectually elegant approach that also gave economists a chance to show off their mathematical prowess.” (Krugman 2009)

  4. «To put it bluntly, the discipline of economics has yet to get over its childish passion for mathematics and for purely theoretical and often highly ideological speculation, at the expense of historical research and collaboration with the other social sciences. Economists are all too often preoccupied with petty mathematical problems of interest only to themselves. This obsession with mathematics is an easy way of acquiring the appearance of scientificity without having to answer the far more complex questions posed by the world we live in.» (Piketty 2014: 32)

  5. How modelling works: • Imagine (or build) a system that is in some ways ‘similar’ to the target (real-world) • Describe its characteristics (assumptions) • Manipulate an aspect of the model (e.g. change some assumption) • Derive conclusions (describe other characteristics of the model that were not obvious initially) • Apply them (to a target)

  6. Model vs world: “The automobile market is used as a finger exercise to illustrate and develop these thoughts. It should be emphasized that this market is chosen for its concreteness and ease in understanding rather than for its importance or realism.” (Akerlof 1970: 489)

  7. Suppose there are 4 kinds of cars: • New and used • Good and bad (“lemons”) Owners know their quality, buyers do not (asymmetry of information) There must be just one price for all cars What is it?

  8. Suppose that Quality = x (0 ≤ x ≤ 2) Buyers value cars more than sellers U1 = Σxi U2 = (3/2) Σ xi μ = average quality

  9. What price? μ=p/2 p 0 2 Ifμ=p/2, then p=2μ i.e. p>3μ/2 But then, D=0 !!

  10. Surprising conclusion: • In Akerlof’s market, there is no trade: bad cars drive good cars out of the market • These are the ‘economic costs of dishonesty’ • But in the real world there is a market of used cars! • So what does Akerlof’s model explain? Does it explain anything at all??

  11. 1. Quality is uniformly distributed 2. Utility is linear in the number of cars 3. Traders do not make mistakes 4. There are just 4 kinds of cars 5. Quality can be measured on a continuous scale from 0 to 2. 6. There are just two types of traders 7. All cars belong to group 1 traders 8. Group-1 traders know the quality of each car 9. Group-2 traders don’t know the quality of each car 10. Group-2 traders know exactly the average quality of cars 11. Group-2 traders derive 1.5 times utility from enjoying a car than group-1 traders 12. Prices are known by every trader …

  12. 14. All traders maximise their own utility (there’s no altruism in the model) 15. Utility is a function of the quality of the car only (no ‘pure joy’ of buying a car) 16. Demand depends on quality and price only 17. Supply depends on price only 18. There are no counteracting institutions (no trademarks, guarantees, etc.) … -> Conclusion: no trade (or less trade)

  13. Problem: • Models are useful only if they are simpler than their targets • Simplifying assumptions mis-describe the target • So the model gives a false account (or is a false analogue) of the target

  14. The ‘explanation paradox’ • Economic models are false • Economic models are nevertheless explanatory • Only true accounts can explain (Reiss, Philosophy of Economics, Chapter 7, p. 127)

  15. Solution 1: • Economic models are false • Economic models are explanatory • Only true accounts can explain A good model need not be true: It must be ‘credible’ Bob Sugden

  16. Problem: Credibility is not a substitute for truth “Why has John ordered a salad?” 1. Beause he’s vegetarian 2. Because he’s on a diet 1. is ‘good for me’, 2. is ‘good for you’ (given our background beliefs) But we want to know which one is right!

  17. Solution 2: • Economic models are false • Economic models are explanatory • Only true accounts can explain A good model must be useful for prediction. Explanation and truth are unnecessary. Julian Reiss Milton Friedman

  18. Problem: prediction is not a reliable guide for intervention • For example: cumulative rainfall predict prices (Hendry) • Why? They are highly correlated • But global warming does not cause deflation (probably)

  19. Solution 3: • Economic models are false • Economic models are explanatory • Only true accounts can explain Dan Hausman Nancy Cartwright Uskali Maki Good models must tell true causal stories

  20. For example: Akerlof (1970) 1. Shows that market exchange takes place with symmetric information 2. Shows that it disappears with asymmetric information The falsity in the model is functional to make a causal truth emerge clearly “The example of used cars captures the essence of the problem” (p. 489)

  21. But how do we know that the causal story is true?

  22. ‘Supers’ equilibrium p=300 ‘Regulars’ equilibrium p=165 Lynch et al (1986) “Product Quality, Consumer Information and ‘Lemons’ in Experimental Markets”

  23.  “Numerous institutions arise to counteract the effects of quality uncertainty. One obvious institution is guarantees […] to ensure the buyer of some normal expected quality. […] the risk is borne by the seller rather than by the buyer. A second example of an institution which counteracts the effects of quality uncertainty is the brand-name good. Brand names not only indicate quality but also give the consumer a means of retaliation if the quality does not meet expectations. For the consumer will then curtail future purchases. […] Chains – such as hotel chains or restaurant chains – are similar to brand names. […] Licensing practices also reduce the quality of uncertainty. For instance, there is the licensing of doctors, lawyers, and barbers.” (Akerlof 1970, pp. 499-500)

  24. Introduce warranties

  25. Objection: it is true ... of what? • A laboratory market is not a real market • In real markets there aren’t only 6 sellers and 8 buyers • There aren’t only ‘Supers’ and ‘Regulars’ • The warranties are not perfectly enforceable (etc etc) So, are we back to square one?

  26. NO! Important distinction: Internal vs External Validity Internally valid = captures the true causal story in the experimental setting Externally valid = holds also in other circumstances

  27. There is usually a trade-off: the more confident we are about IV, the less confident about EV But this is true always, in all science For example: genetics, medical experiments, nuclear physics, etc.

  28. Distinguish pure science from application • There’s no reason to believe that scientific knowledge is always easily applicable

  29. For example: penalty kicks Palacios-Huerta, I. (2003) “Professionals Play Minimax”, Review of Economic Studies 70: 395-415.

  30. The deepest thought ever: “After all, it is not our stupidity which hampers us, but chiefly our lack of information, and when one has to make do with bad guesses in lieu of information the success cannot be great. But there is a significant difference between the natural sciences and the social sciences in this respect: experts in the natural sciences usually do not try to do what they know they cannot do; and nobody expects them to do it. They would never undertake to predict the number of fatalities in a train wreck that might happen under certain conditions during the next year. They do not even predict next year’s explosions and epidemics, floods and mountain slides, earthquakes and water pollution. Social scientists, for some strange reason, are expected to foretell the future and they feel badly if they fail.” (Machlup 1961: 14) 30

  31. francesco.guala@unimi.it users.unimi.it/guala

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