3 country application of Alberini/Krupnick survey instrument – Methodology and Results Alistair Hunt and Anna Alberini University of Bath & University of Maryland For UK Defra Workshop

Theoretical basis for valuation of Mortality risk changes

Life Cycle model at age j, max expected utility over remaining life time:

Definition of VSL Ambiguous net effect of age j on VSL j

Study Features Survey-based; UK, France, Italy Directly values mortality risk changes Uses framework methodology developed in N.America Targets age group 40+ Computer-based; self-administered; voice- over

Methodology Adaptation Testing comprised: –10 one-to-one 1-2 hour in-depth interviews –3 1-hour focus groups (8 participants) And aimed to clarify linguistic and comprehension issues whilst retaining comparability with N.American instrument –In UK, 330 people surveyed: recruited in 30-mile radius around Bath, SW England, using specialist recruitment company

Sample size and experiment design for the three-country study. UKItalyFrance No Locale of the StudyBath*Venice, Genoa, Milan and Turin Strasbourg Experimental DesignWave 1 Wave 1 and wave 2 * recruited within 35 Km of Bath. Random digit dialing, in-street recruiting and snowballing Eligible and contacted: Cooperative: 355. Finally attended: 330.

Structure of Survey Instrument 5 sections –Personal information –Introduction to probability concepts –Causes of death; risk-mitigating behaviours and associated costs –WTP for risk reductions –Debriefing and socio-demographic questions

Introduction to probability concepts

Causes of death; risk-mitigating behaviours and associated costs

WTP for risk reductions Dichotomous-choice approach with two follow-up questions and final open-ended question Respondents are asked to value: – a 5 in 1000 risk reduction spread over the next 10 years, with effect immediately; – a 1 in 1000 risk reduction spread over the next 10 years, with effect immediately and; –a reduction of 5 in 1000 over the ten years from age 70.

Initial and follow-up bids in the UK study. (£) Initial bidBid if response to first payment question is no Bid if response to the first payment question is yes

Debriefing questions understanding of idea of ‘chance’ accept specific baseline? specific product in mind? Yes – what kind of product? Doubts about product? Yes – influence WTP? Did you think you would suffer any side-effects? Did you consider whether you could afford payments? Think of other benefits?Yes - to yourself, others, for you living longer, improved health Yes – influence WTP? – raise/lower? Other people On WTP 70 did you consider whether – would live to age 70? Or your health at age 70? Household Income

Health Status data Gathered from application of short-form (SF 36) questions within survey instrument –Series of questions relating to respondent’s current and historic physical and mental health status

Results of Survey application in EU

Descriptive Statistics of the Respondents’ Socio- demographics. Sample averages or percentages for selected variables UKItalyFrance Age Male49.39%48.63%47.29% Income in EUR Mean Median 40,096 38,690 40,115 25,000 32,186 32,012 Education (years of schooling)

Health status of the respondent Elicited using three sets of questions: -- direct question: “Compared to other people your age, how would you rate your health?” (Excellent, very good, good, fair, poor) -- direct questions about specific illnesses: “Has a health care professional ever diagnosed you to have…” (list of cardiovascular and respiratory illnesses) -- Short Form 36 questions about general health and functionality

. Health status of the respondents Percentages of the sample with specified conditions UKItalyFrance Rates own health as good or excellent relative to others same age High blood pressure Any chronic cardiovascular disease (CARDIO) Any chronic respiratory illness (LUNGS) Cancer (CANC) High blood pressure or other cardiovascular illness, or chronic respiratory illness, or stroke (CHRONIC)

Percent of the sample who have various problems with risk comprehension Based on complete samples UKItalyFrance A. Wrong answer in the probability quiz B. Confirms wrong answer in the probability quiz C. Probability choice qn: - prefers person - higher risk - indifferent D. Confirms wrong answer in probability choice question A and C (FLAG1=1)

Responses to starting bid values

Responses to immediate & future risk reductions

Percentage of respondents with WTP = 0 Risk reductionSample sizePercentage respondents with zero WTP 5 in 1000 over the next 10 years (immediate) in 1000 over the next 10 years (immediate ) in 1000 between ages 70 and * * = only respondents up to age 60 were asked to value the future risk reduction

Statistical Model of WTP

UK Study: Annual WTP Figures Immediate 5 in 1000 Risk Reduction In Euro (s.e.) In £ (s.e.) Implied annual VSL Mean WTP 672 (86.02) 460 (60.27) € million or £ million Median WTP 354 (34.23) 242 (23.89) € million or £ million *cleaned data (FLAG1=1 deleted); n=322

Internal validity of the WTP responses

Pooled data interval-data regressions for WTP. Immediate 5 in 1000 risk reduction. CoefficientSt. error Intercept5.8024** Household income (thou. Euro)0.0098** Age Age Age 70 or older Male Education Chronic resp or cardio illness visited ER < 5 years – cardio/ resp * Has or had had cancer France dummy0.8636** Italy dummy0.6705** Weibull Shape parameter (  ) Respondents with FLAG=1 excluded. * = significant at the 5% level; ** = significant at the 1% level.

Pooled Data: Annual WTP Figures Immediate 5 in 1000 Risk Reduction In Euro In £ Implied annual VSL Mean WTP € million or £ million Median WTP € million or £ million

Summary of results UK sample is very small: no statistically significant association between WTP and age or health. Pool data to increase sample size, but account for different cultural factors and sampling procedures through country dummies Age is not significant associated with WTP, although the oldest respondents tend to have lower WTP Of the health status dummies, dummy for hospital admission or ER visit in the last 5 years is strongly associated with WTP Income is significantly associated with WTP Gender and education not important

Relating WTP with predictions from epidemiological studies

Regressions of WTP on proportional risk reduction (5 in 1000 immediate risk reduction) (cleaned data) coefficientStandard error Intercept6.3047** France dummy0.7788** Italy dummy0.4400* Proportional risk reduction (=5 / baseline risk) * Weibull shape parameter (  ) **0.0816

Relating WTP with predictions from epidemiological studies study values redns in risks  VSL but can couch in terms of  in remaining life expectancy (or loss/gain of days/months of life spread over the population) Rabl (2001) derives  in remaining L.E. associated with 5 in 1000 risk change over next 10 years –averages 1.23 months (37 days) for our sample.

Derived VOLYs

Latency

2 Step estimation of discount rate Immediate 5 in 1000 Risk Reduction → predict WTP 70,70 Regress log WTP j,70 on log WTP 70,70 (coefficient restricted to 1); log ρ j,70 (coefficient restricted to 1) -Δ=j-70 → coefficient is δ

RESULTS UK δ≈ 10% France δ≈ 5% Italy δ≈ 6%