Value of a New Zealander's life

Ages back, I'd run the usual income elasticity of the value of a statistical life drill to get a reckon on what Viscusi might say about the value of a statistical life in New Zealand. It looked like about $5m was the right figure.

But, time passes. And Viscusi last year put up an actual figure. The updated figure for America is now $9.6m (where the figure I keep in my head is $7m) and the value for New Zealand is now $6.885m (where the figure I'd kept in my head was $5m). They also use a better income elasticity measure. I'd been using the 0.5 figure that had come out of American work; they use 1.0 because it's more elastic in the international sample.

And so let us all update. VSL for New Zealand is about $6.9m. Conduct yourself accordingly. You're worth it.

And thanks to the student in my public economics course at Vic who wanted the Viscusi work I'd been talking about. That prompted me to find the much fresher estimates. I'll have to let the kids all know in next week's lecture that they're all more valuable than I'd previously thought.

The international VSL figures range from $45,000 to $18.3 million. Remember that when folks push for international harmonisation of safety rules and the like.

The Ministry of Transport survey-derived, inflation-adjusted VSL measures are getting increasingly out of date. I think it's pretty second-order whether the VSL figure used is $4.2m or the new figure since the main gains are using a standard number across policy areas, but it's probably getting to be time to update it.

Update: Ryan Murphy points me to some robustness worries on the VSL figures. Ionnidis, Stanley and Doucouliagos hit on power problems across a range of economic studies, and hit VSL here:

To understand the practical consequences of accounting for power in economic research better, it might be instructive to consider some specific areas. For example, of the 1,474 reported estimates of the employment elasticity of a US minimum wage increase (Doucouliagos and Stanley, 2009), 96% are underpowered and the median power is 8.5%. The weighted average elasticity of the 60 adequately powered estimates is -0.0113, less than one-tenth (6.5%) of the reported average (-0.19) across all of these 1,474 estimates. As a second example, consider the 39 estimates of the value of a statistical life (Doucouliagos et al., 2012), 74% are underpowered. The WAAP estimate of the 10 adequately powered studies is $1.47 million compared to the simple average of $9.5 million across all 39. Of the 110 reported price elasticities of residential water demand (Dalhuisen et al., 2003), 84% are underpowered. The weighted average elasticity of the 10 estimates that have adequate power is -0.1025, while the average across all 110 is -0.378. This means that for these three research areas, 94%, 84.5% and 72.9%, or more, of the average reported effects are likely to be biased.

Their critique is not particular to the areas here quoted. They looked at over 6700 studies across a range of areas and found substantial problems with underpowered studies.

Update 2: Another excellent econometrician, very well versed in metastudy work and who I trust, warns me not to rely on the power estimates in the above-cited metastudy. So I can go back to relying more heavily on the Viscusi numbers.

Micromorts

When public stupidity is the constraint, you can either rail against the stupidity, or route around it. And Tim Harford finds a nice hack around public repugnance at putting a value on human life.

Instead of talking about the value of a statistical life, which still puts people off, talk about micromorts: a one-in-a-million chance of dying. If the value of a statistical life is $7 million, a micromort costs about $7. In New Zealand, the Ministry of Transport uses a $4.2 million figure, so a micromort here costs $4.20.

Here's Harford:

Sir David Spiegelhalter, my favourite risk communication expert, reckons that going under general anaesthetic is 10 micromorts. Travelling 28 miles on a motorbike is four micromorts; cycling the same distance is just over one micromort. The National Health Service in the UK uses analysis that prices a microlife at around £1.70; the UK Department for Transport will spend £1.60 to prevent a micromort. In a world where life-and-death trade-offs must be made, and should be faced squarely, this is a less horrible way to think about it all. A human life is a special thing; a microlife, not so much.

As Ronald Howard, the decision analysis expert who invented the micromort, put it back in 1984: “Although this change is cosmetic only, we should remember the size of the cosmetic industry.”

It's also a more accurate way of framing things. Since reasonable VSL measures derive from willingness-to-pay measures for risk reductions around small risks, we're really talking in millimort or micromort space to begin with. The scaling up makes things easier for economists; scaling down makes it instead politically palatable.

Downturns and life expectancy

I don't buy it. GNXP points to a study claiming that the Great Depression increased life expectancy.

Recent events highlight the importance of examining the impact of economic downturns on population health. The Great Depression of the 1930s was the most important economic downturn in the U.S. in the twentieth century. We used historical life expectancy and mortality data to examine associations of economic growth with population health for the period 1920-1940. We conducted descriptive analyses of trends and examined associations between annual changes in health indicators and annual changes in economic activity using correlations and regression models. Population health did not decline and indeed generally improved during the 4 years of the Great Depression, 1930-1933, with mortality decreasing for almost all ages, and life expectancy increasing by several years in males, females, whites, and nonwhites. For most age groups, mortality tended to peak during years of strong economic expansion (such as 1923, 1926, 1929, and 1936-). In contrast, the recessions of 1921, 1930-1933, and 1938 coincided with declines in mortality and gains in life expectancy. The only exception was suicide mortality which increased during the Great Depression, but accounted for less than 2% of deaths. Correlation and regression analyses confirmed a significant negative effect of economic expansions on health gains. The evolution of population health during the years 1920–1940 confirms the counterintuitive hypothesis that, as in other historical periods and market economies, population health tends to evolve better during recessions than in expansions.

It sounds to me like what's going on is that the downturn makes folks poorer and consequently more willing to take on risk for little compensation. When the economy ticks up a bit, folks jump into those riskier jobs and mortality goes up. But it isn't the recovery that did it; rather, it's the effect of the prior increase in poverty affecting folks' willingness to accept risk.

At least that's my first cut explanation. It's at least consistent with the Viscusi and Aldy finding that the income elasticity of the value of a statistical life is about 0.5 to 0.6. Recall that Viscusi's numbers all come from revealed willingness to pay to accept risk: as folks get richer, they require higher compensation for a given risk. If the first study were right, elasticity would be negative. I don't buy it.