Individual Decision Experiments and Public Policy
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Transcript of 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?
Economists’ answer: be guided by the preferences of the people affected
Elicit VPF (VoSL), VPI, VoLY, QALY to feed into CBA or CUA
Different ‘stated preference’ methods:
Contingent valuation (CV / WTP)
Dichotomous choice
Discrete choice experiments (DCE / SP)
Standard gambles / risk trade-offs etc.
Requirements:
1. Within a method, the value should be independent of the particular parameters of the question
2. Orderings over alternative goods/measures should be consistent across methods
But these requirements are systematically violated
Persistent Practical Problems
UNDERsensitivity to things that SHOULD matter
OVERsensitivity to things that SHOULDN’T matter
Insufficient Sensitivity to Things
That SHOULD Matter
Size of risk reduction or gain in life expectancy
Severity of injury
Period of payment
Other opportunities
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
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
Oversensitivity to
Things That Should Not Matter
1. Variants within a method e.g.
starting point in an iterative procedure
2. Variations between procedures:
e.g. CV vs SG
CV vs SG
CV SGR:Death 0.875 0.233S(4) 0.640
0.151S(12) 0.262X 0.232 0.055W 0.210 0.020
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 …
Experimental studies using lotteries:
familiar consequences
known & comprehensible probabilities
incentive-linked
Money value (certainty equivalent)
vs
choice
vs
probability equivalent
Eliciting CE (CV)
What value of X makes you consider B just as good as A?
25% 75%
A $80 0
B $X
100%
What value of Y makes you consider B just as good as A?
70% 30%
A $24 0
B $Y
100%
Eliciting PE (SG/RTO)
What value of p makes you consider B just as good as A?
70% 30%
A $24 0
B $160 0
p% 1-p%
What value of q makes you consider B just as good as A?
25% 75%
A $80 0
B $160 0
q%
1-q%
Choose whichever of the two you prefer
25% 75%
A $80 0
B $24 0
70% 30%
Classic preference reversal:
First lottery given higher CE
Second lottery preferred in straight choice
Opposite reversal – value the second higher but choose the first – much less often observed
But what if elicit PE rather than CE?
Methods of elicitation
Open-ended
Iterative choice
Dichotomous choice
All produce classic PR asymmetry for CE (CV)
Evidence of opposite asymmetry for PE (SG)
Choice vs
CE CEP > CE$ CEP = CE$ CEP < CE$
Chose P 10 1/3 9 1/3 44 2/3
Chose $ 2/3
2/3 23 1/3
Choice vs
CE CEP > CE$ CEP = CE$ CEP < CE$
Chose P 10 1/3 9 1/3 44 2/3
Chose $ 2/3
2/3 23 1/3
Choice vs
PE PEP > PE$ PE$ = PEP PEP < PE$
Chose P 59 2/3 4 2/3
Chose $ 16 1/3 8 1/3
CE vs PE PEP > PE$ PE$ = PEP PEP < PE$
CEP > CE$ 10 0 1
CEP = CE$ 8 0 2
CEP < CE$ 57 1 10
Exploring reasons & possible ‘solutions’
Imprecision / error
Market discipline
Embed in broader set, rank and infer values
Cut off one head, two more grow …
Set 1
EV
A 0.5 x £25 12.50
B 0.75 x £15 11.25
C 0.6 x £15 9.00
D 0.85 x £10 8.50
E 0.7 x £12 8.40
F 0.9 x £9 8.10
G 0.95 x £8 7.60
H 0.8 x £9 7.20
I 0.4 x £18 7.20
J 0.55 x £13 7.15
Set 1 Set 2
EV EV
A 0.5 x £25 12.50 K 0.25 x £6 1.50
B 0.75 x £15 11.25 L 0.2 x £9 1.80
C 0.6 x £15 9.00 M 0.15 x £15 2.25
D 0.85 x £10 8.50 N 0.1 x £25 2.50
E 0.7 x £12 8.40 P 0.1 x £60 6.00
F 0.9 x £9 8.10 Q 0.15 x £45 6.75
G 0.95 x £8 7.60 R 0.2 x £35 7.00
H 0.8 x £9 7.20 S 0.3 x £25 7.50
I 0.4 x £18 7.20 I 0.4 x £18 7.20
J 0.55 x £13 7.15 J 0.55 x £13 7.15
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
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
Implications for Policy?
If responses are so vulnerable to ‘effects’ how well can they inform policy?
We need toalways build in checksaim to use more than one procedure/varianttry to understand directions of biasmaintain two-way traffic between lab & fieldbe prepared to exercise (explicit) judgment, with data from experiments an important input into those judgments and their justifications
Individual Decision Experiments and Public Policy
Graham Loomes
University of East Anglia, UK