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Individual Decision Experiments and Public Policy

Individual Decision Experiments and Public Policy. Graham Loomes University of East Anglia, UK. The Value of Health & Safety. When measures affect risks to length and/or quality of life, how do we balance those effects against other costs and benefits?

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Individual Decision Experiments and Public Policy

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  1. Individual Decision Experiments and Public Policy Graham Loomes University of East Anglia, UK

  2. The Value of Health & Safety When measures affect risks to length and/or quality of life, how do we balance those effects against other costs and benefits? Economists’ answer: be guided by the preferences of the people affected Elicit VPF (VoSL), VPI, VoLY, QALY to feed into CBA or CUA

  3. Different ‘stated preference’ methods: Contingent valuation (CV / WTP) Dichotomous choice Discrete choice experiments (DCE / SP) Standard gambles / risk trade-offs etc.

  4. Requirements: • Within a method, the value should be independent of the particular parameters of the question • Orderings over alternative goods/measures should be consistent across methods But these requirements are systematically violated

  5. Persistent Practical Problems UNDERsensitivity to things that SHOULD matter OVERsensitivity to things that SHOULDN’T matter

  6. Insufficient Sensitivity to Things That SHOULD Matter Size of risk reduction or gain in life expectancy Severity of injury Period of payment Other opportunities

  7. Why Insensitivity Matters One safety measure reduces risk by 1 in 100,000 Another reduces the risk by 3 in 100,000 3 out of 10 said they’d pay the same for both Another 4 would only pay up to twice as much Extrapolating to 1 million people: £98m to prevent 10 deaths: VPF = £9.8m £138m to prevent 30 deaths: VPF = £4.6m

  8. Also for extra months of life: DEFRA Air Pollution study 1 month 3 months 6 months 60.15 67.72 80.87 (25) (30) (40) Implied VoLY 27,630 9,430 6,040 Also: severity of injury/illness; period of payment; other opportunities

  9. Oversensitivity to Things That Should Not Matter • Variants within a method e.g. starting point in an iterative procedure 2. Variations between procedures: e.g. CV vs SG

  10. CV vs SG CV SG R:Death 0.875 0.233 S(4) 0.640 0.151 S(12) 0.262 X 0.232 0.055 W 0.210 0.020

  11. Other Possible Approaches Dichotomous choice: market-like; but ‘yea-saying’? DCE: infer from simple choices; but can these be TOO simple and subject to ‘effects’? Ranking: extra complexity – and effects of its own? Study problems and properties experimentally …

  12. Experimental studies using lotteries: familiar consequences known & comprehensible probabilities incentive-linked Money value (certainty equivalent) vs choice vs probability equivalent

  13. Eliciting CE (CV) What value of X makes you consider B just as good as A?

  14. What value of Y makes you consider B just as good as A?

  15. Eliciting PE (SG/RTO) What value of p makes you consider B just as good as A?

  16. What value of q makes you consider B just as good as A?

  17. Choose whichever of the two you prefer

  18. Classic preference reversal: First lottery given higher CE Second lottery preferred in straight choice Opposite reversal – value the second higher but choose the first – much less often observed But what if elicit PE rather than CE?

  19. Methods of elicitation Open-ended Iterative choice Dichotomous choice All produce classic PR asymmetry for CE (CV) Evidence of opposite asymmetry for PE (SG)

  20. Exploring reasons & possible ‘solutions’ Imprecision / error Market discipline Embed in broader set, rank and infer values Cut off one head, two more grow …

  21. Inferred values for I and J Set 1 Set 2 I 5.80 8.64 J 5.82 8.40 Set 2 > Set 1 Set 2 = Set 1 Set 2 < Set 1 I 121 16 17 J 120 28 6

  22. But if control for other items in Sets, PR goes Even so, ordering from ranking diverges from ordering from pairwise choices Especially in area where choice ‘anomalies’ have inspired ‘alternative’ theories But rankings still don’t conform with standard theory

  23. Implications for Policy? If responses are so vulnerable to ‘effects’ how well can they inform policy? We need to always build in checks aim to use more than one procedure/variant try to understand directions of bias maintain two-way traffic between lab & field be prepared to exercise (explicit) judgment, with data from experiments an important input into those judgments and their justifications

  24. Individual Decision Experiments and Public Policy Graham Loomes University of East Anglia, UK

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