Lecture 9 Life and other anomalies

Life and other non-mon. benefits In RIA we need to monetize benefits; but benefits in case of externalities come in terms of non monetary benefits, notably reduction of certain risks; for ex risk of daeth of injuries Ned to have a monetary value for those Are those measurable? How do we measure them?

Life Might seem imoral to give a value to life but infact it is not what we actually do. Suppose you face a 1/100,000 risk of death by accident tonight. I ask you: how much are you willing to pay to reduce risk to 1/200,000? You are going to anwer definitely a certain finite sum. Implicitly you are defining a value for your own life which is: V= S/(1/100,000) Where S is the sum you stated and the denomin. Is the risk reduction you bought Point is unless you are willing to pay an infinitwe sum there is always a value you implicitly put on life. Not on a specific life or the certain loss of a life. It’s a statistical life.

Willingness to Pay Approach Allows valuing non-market goods Consistent with microeconomics (consumer theory) Other methods are human capital and direct cost approaches.

Estimation of Willingness to Pay Revealed preference approaches Household production analysis, for example purchase of smoke detectors Stated preferences, contingent valuation analysis via surveys Experimental analysis: combination/hybrid of the above

Alternative approaches Lifetime Income (LI); often used by insurances- how to evaluate a retired person/one may work more to save his life/leisure has a value; Variants: LI net of own consumption- what he contributes to others (family and society at large); Variants: taxes (what one contributes to society) All these are easy to calculate, but neglect important part of value of life

Variations There are well established differences in peoples’ evaluations of risk reductions- hence life; Rich people demand more risk reduction (elasticity to income being ) There are significant differences also across different occupational choices; Should we use those differences when setting standards? Seems unfair to spend more on one type of worker than another but think about trade-offs in costs that workers themselves have to shoulder Ultimately there’s a case for differentiation when risks are well understood- choices are rational

Variations Might want to take into account a specific type of variations in RIA, concerning future generations; We discount future benefits but we know that future generations will be richer and thus be more willing to pay for benefits so should increase the future Benefits by the (elasticity to income)x(growth rate), before discounting.

Labour market model Firms in different sectors face a trade off between reducing risk in work and increasing salaries. Of course progressive reduction in risk cost more and more in terms of salaries; Given technology each sector offers a certain efficient trade off between the two and workers can choose their preferred risk/salary combination; From these one can infer the trade-off that workers are ready to make (hence price willing to pay) for risk. There’s a huge variation among countries- seems to depend primarily on income variations (1-9 millions)

Estimation We can estimate the value of life from wage and job risk data: Annual Earnings = constant + b*Annual death risk + personal characteristics+ job characteristics Annual Death Risk B measures the value to life Heterogeneity in same country is due to different samples/preferences of target groups

How to use the estimate take the table below. It tells us how much is the cst of different US regulation per statistical life saved/normalized life (adj. for number of years)

Surveys In some cases indirect pricing is difficult (value of a leisure park)- may use a survey Contingent valuation. We can ask in different ways for ex. Simulatiing an ascending auction. But main problem is that the good is virtual so no payment can be imposed. Can lead to bias. Also revealed prefern. can be causal as no payment involved Surveys should be used as an exploratory tools

Hedonic Preferences WTP can be detected by examining how the price for a traded good varies with the amount of intangible characteristic it contains. You can use method if you have differentiated market goods. See how the variation of price of the tangible good varies with the intangible characteristic, obtain marginal willingness to pay for intangible, then see who levels of intangible are related to marginal willingness to pay and income level to get demand for intangible. It does not capture non-use value.

Household production function Look at quantity demanded of market goods that are complements or substitutes to or directly produce the intangible in order to infer the demand for the intangible. Travel cost models: cost of travel is a proxy for the price Averting behaviour models: cost of item is a proxy for willingness to pay for intangible: for example smoke detectors

Travel Cost (Clawson and Knetsch) For a given recreational site, divide the surrounding area into zones, each zone representing a given level of cost to travel to site. Ask visitors to tell you where they came from (which zone). Calculate the number of visits per zone. Find cost/visit by zone, where c=mileage*cost/mile+opportunity cost of time*time it takes to travel to site. An increase in cost is equivalent to an increase in price 6. Plot cost/visit against visits per population. Plot cost against number of visits.

Smoke detectors and Value of Life (Dardis) Purchases are voluntary (. Start by assuming marginal WTP for a change in risk of death=cost of device that changes risk. Cost=change in probability of death X value of life (assuming that injury = $0). Cost estimates: Smoke detectors cost $52 in 1974, batteries cost $7 per year, detectors last 10 years, discount rate = 10%: annualised cost = $21.37.

Ctnd Probability of death from a fire = 6492/ households Probability of reducing death if detector works = Probability of detector working = 0.8. Together risk decreases by if you have a smoke detector. Value of life=cost/change in probability of death = 21.37/ = $676,266

Cost effectiveness and scoring May compare different regulatory options with CE Analysis that is ofr ex. How many life saved divide by cost; Alternatively (if may types of benefits) convert them on a single scale with a scoring procedure (for ex. 1 life is worth 20 serious injuries) and do as above

Risk-risk analysis Do economically unsatisactory proposal diminish welfare in some other way? Wge perfomring RIA we should not overlook side effects on risk For example a regulation requiring recall of all cars to repairs for some reason delivers some additional traffic that may result in aggregate in one statistical death and other externalities; Regulatory Costs (maybe 4% of GDP) could be used for health for ex reducing other risks; Reduction of resources in general may cause casualties. Viscusi (1994) develops a model for calculating the amount of regulatory expenditure that cuases adverse effects on poorest that determine a statis. Death. (50 m.)