Peter Dorman writes:
Still interested in Viscusi and his value of statistical life after all these years? I can finally release this paper, since the launch just took place.
The article in question is called “Risk without reward: The myth of wage compensation for hazardous work,” by Peter Dorman and Les Boden, and goes as follows:
A small but dedicated group of economists, legal theorists, and political thinkers has promoted the argument that little if any labor market regulation is required to ensure the proper level of protection for occupational safety and health (OSH), because workers are fully compensated by higher wages for the risks they face on the job and that markets alone are sufficient to ensure this outcome. In this paper, we argue that such a sanguine perspective is at odds with the history of OSH regulation and the most plausible theories of how labor markets and employment relations actually function. . . .
In the English-speaking world, OSH regulation dates to the Middle Ages. Modern policy frameworks, such as the Occupational Safety and Health Act in the United States, are based on the presumption of employer responsibility, which in turn rests on the recognition that employers generally hold a preponderance of power vis-à-vis their workforce such that public intervention serves a countervailing purpose. Arrayed against this presumption, however, has been the classical liberal view that worker and employer self-interest, embodied in mutually agreed employment contracts, is a sufficient basis for setting wages and working conditions and ought not be overridden by public action—a position we dub the “freedom of contract” view. This position broadly corresponds to the Lochner-era stance of the U.S. Supreme Court and today characterizes a group of economists, led by W. Kip Viscusi, associated with the value-of-statistical-life (VSL) literature. . . .
Following Viscusi, such researchers employ regression models in which a worker’s wage, typically its natural logarithm, is a function of the worker’s demographic characteristics (age, education, experience, marital status, gender) and the risk of occupational fatality they face. Using census or similar surveys for nonrisk variables and average fatal accident rates by industry and occupation for risk, these researchers estimate the effect of the risk variable on wages, which they interpret as the money workers are willing to accept in return for a unit increase in risk. This exercise provides the basis for VSL calculations, and it is also used to argue that OSH regulation is unnecessary since workers are already compensated for differences in risk.
This methodology is highly unreliable, however, for a number of reasons . . . Given these issues, it is striking that hazardous working conditions are the only job characteristic for which there is a literature claiming to find wage compensation. . . .
This can be seen as an update of Dorman’s classic 1996 book, “Markets and Mortality: Economics, Dangerous Work, and the Value of Human Life.” It must be incredibly frustrating for Dorman to have shot down that literature so many years ago but still see it keep popping up. Kinda like how I feel about that horrible Banzhaf index or the claim that the probability of a decisive vote is 10^-92 or whatever, or those terrible regression discontinuity analyses, or . . .
Dorman adds some context:
The one inside story that may interest you is that, when the paper went out for review, every economist who looked at it said we had it backwards: the wage compensation for risk is underestimated by Viscusi and his confreres, because of missing explanatory variables on worker productivity. We have only limited information on workers’ personal attributes, they argued, so some of the wage difference between safe and dangerous jobs that should be recognized as compensatory is instead slurped up by lumping together lower- and higher-tiered employment. According to this, if we had more variables at the individual level we would find that workers get even more implicit hazard pay. Given what a stretch it is a priori to suspect that hazard pay is widespread and large—enough to motivate employers to make jobs safe on their own initiative—it’s remarkable that this is said to be the main bias.
Of course, as we point out in the paper, and as I think I had already demonstrated way back in the 90s, missing variables on the employer and industry side impose the opposite bias: wage differences are being assigned to risk that would otherwise be attributed to things like capital-labor ratios, concentration ratios (monopoly), etc. In the intervening years the evidence for these employer-level effects has only grown stronger, a major reason why antitrust is a hot topic for Biden after decades in the shadows.
Anyway, if you have time I’d be interested in your reactions. Can the value-of-statistical-life literature really be as shoddy as I think it is?
I don’t know enough about the literature to even try to answer that last question!
When I bring up the value of statistical life in class, I’ll point out that the most dangerous jobs pay very low, and high-paying jobs are usually very safe. Any regression of salary vs. risk will start with a strong negative coefficient, and the first job of any analysis will be to bring that coefficient positive. At that point, you have to decide what else to include in the model to get a coefficient that you want. Hard for me to see this working out.
This has a “workflow” or comparison-of-models angle, as the results can best be understood within a web of possible models that could be fit to the data, rather than focusing on a single fitted model, as is conventionally done in economics or statistics.
As to why the literature ended up so bad: it seems to be a perfect storm of economic/political motivations along with some standard misunderstandings about causal inference in econometrics.
Yes, and it’s fair that poor people may live on a toxic dump site because they choose to accept the hazards because they can pay a low rent. Therefore there is no economic point in cleaning up the site.
Bah! Analyses like these that ignore major non-economic forces are pure baloney. And those that try to convert some of the non-economic forces into equivalent monetary terms seem to omit the statistical uncertainties and biases in those conversions. If these errors were to be propagated correctly through the entire calculation (if that were even possible), the uncertainties would be much greater than these analysts allow themselves to be aware of.
That point about uncertainties is true in most cases. One of the advantages of Bayesian analysis is that it uses probability for all forms of uncertainty so you can finally get error bars wide enough to account for all the things you don’t know, not just the random sampling components. Of course this is only an advantage to people who care about truth in uncertainty. Most people love to understate their uncertainty, it pays well too.
A quick google shows that the medium annual income of a coal miner is $53905 and $105630 for a economist. This income differential seems to compensate for the negative health effects of academic pulmonary fibrosis, a.k.a. profs’ lung. Loretta Lynn and others produced moving testimonials to being an academic’s daughter.
I’ve never heard of “academic pulmonary fibrosis”. Does the etiology have anything to with a career-long pattern of blowing hot air and/or breath-taking unethical behavior?
This is being actively investigated. There is a strong correlational pattern between the two, but as you well know, correlation does not imply causation.
If risk and compensation were correlated, every billionaire’s commute to work would look like the opening sequence in Raiders of the Lost Ark.
My prior is that if you averaged in enormous outliers like Gates or Bezos, you’d find more of a negative correlation between risk and compensation than a positive correlation.
Not to diminish the importance of the work or the importance of checking assumptions against evidence, but personally would be inclined to file this under the “research confirms blindingly obvious common sense” category.
I think the correlation would still hold, even if you left out the billionaires. There’s a lot of office workers making six figures for not-very-risky jobs, and a lot of coal miners, industrial workers, etc. making much less for much riskier jobs.
Adebe, I agree with the gist of your point, but interestingly, the “office worker” versus “coal miner” comparison isn’t what it used to be. Today the average office worker makes $41k, while the average coal miner makes $70k.
I meant Adede! apologies.
It’s been a while since I’ve read any risk compensation papers, but the idea is usually conditional on various things, particularly education or “skill” level of employees. So you wouldn’t be comparing Bezos (at Amazon) and Jim Bob (at concrete finishing). You’d be comparing Jim Bob (at concrete finishing) with Billy Bob (at coal mining), Cleetus (at oil drilling), and James (on the crab fishing boat).
JFA –
Interesting.
If so, is there a meaningful absolute measure of “skill,” comparing say the amount of skill it takes to fish as compared to make corporate decisions? Or if not, how is it that the categories of skill are created to differentiate by type? How are concrete finishing, oil drilling, and crab finishing differentiated on a skill basis from corporate-decision making? What are the criteria?
Joshua – like I said it’s been a while since I read the papers. Sometimes education is used as a proxy for skill (though that has obvious problems), but I’d like to step back from that to at least recognize that these skill gaps do exist and suggest a broader definition (that might be hard to proxy): whether a lot or a few people can actually perform the job. I think it’s important to recognize that while every job requires some skill, we can also eyeball what positions require greater or lesser mastery in order to complete the task. I’d submit that as long as one is relatively able-bodied (we’re not talking athlete level physical prowess), concrete finishing is low skill relative to corporate decision making (I’ve done both concrete finishing and corporate decision making) because just about anyone can walk onto a job site and within a couple of months (tops) be performing at a pretty high level, whereas corporate decision making is actually very hard and most people couldn’t walk off the street and perform the task well (even after a few years).
If you don’t think we can meaningfully compare skill requirements for corporate decision making to concrete finishing, then I can see why critiquing billionaire and office worker pay in the context of risk compensation make sense, but I don’t think it makes any sense. I would also note that the economists studying this would never say that risk of bodily harm explains all (or even most) of the variation in wages across the economy.
Side note: The average concrete finisher in MD makes $47k/year. The average fast food worker makes $26k/year. Having worked in both industries, I’d say they are of relatively equivalent “skill” requirements. I don’t think all that variation is coming from risk of bodily harm, but I can’t imagine none of the variation is explained by risk to bodily harm either. Whether the current VSL literature takes account of it appropriately, I don’t know… I haven’t read Dorman’s critique. But a lot of the comments here imply that people are skeptical that any risk compensation actually exist. That doesn’t track with my experience doing many jobs of differing skill and risk levels. Heck, that’s why the military has combat pay.
JFA, thanks for a reasonable perspective. Some similar comparisons might be Underwater Welder on oil rigs vs boilermaker making pressure vessels vs production shop welder making lawn furniture. Sure there’s some considerable skill variation as well but I gotta guess the differences in income among these is also partly explained by risk premiums.
JFA –
I get your overall point, and I don’t dismiss that there is some general merit to it, but I am not as convinced as you seem to be in some categorical fashion.
> but I’d like to step back from that to at least recognize that these skill gaps do exist and suggest a broader definition (that might be hard to proxy): whether a lot or a few people can actually perform the job. I think it’s important to recognize that while every job requires some skill, we can also eyeball what positions require greater or lesser mastery in order to complete the task.
Maybe. But I wonder if you’re using a rather binary framework for something that’s not so binary in reality.
> I’d submit that as long as one is relatively able-bodied (we’re not talking athlete level physical prowess), concrete finishing is low skill relative to corporate decision making (I’ve done both concrete finishing and corporate decision making) because just about anyone can walk onto a job site and within a couple of months (tops) be performing at a pretty high level, whereas corporate decision making is actually very hard and most people couldn’t walk off the street and perform the task well (even after a few years).
I haven’t worked in concrete finishing (although I’ve done it a few times), but I did work as a carpenter (and in construction) for more than a decade and while I’d say carpentry employs perhaps a wider range or more varied set of skills than concrete finishing, I’m not sure I’d say it requires more skills in some absolute sense. But regardless, my guess (based on wider experience in other similar domains) is that the skill of a concrete finisher would span a very wide range. Some could work at it for years and only approach the skill level of others. We could consider speed or a number of relevant variables that might come in to play with regard to conditions or materials. I think of masons I’ve watched constructing a dry wall, where they could quickly isolate a rock from a large pile of rocks and place it in a spot where it would fit perfectly considering attributes along many different dimensions (color, shape, overall size, compatibility of the edges in relation to the surrounding rocks, etc.). One person might be able to develop a roughly similar skill in a relatively short period of time but only able to achieve an equivalent skill after years of experience, and perhaps never at all.
On the other hand, high level corporate decision-makers make mistakes all the time, despite many years of experience. There’s a reason why there’s such a concept as the Peter Principle. Even if I don’t think that it applies as some uniform rule of how the world works, I do think it has some merit as a conceptual framework. Consider this presentation from an Ig Nobel prize winner who presents a model that organizations do better on average if they promote people at random:
https://www.youtube.com/watch?v=9481TGp7k7M
https://www.csh.ac.at/andrea-rapisarda-wins-the-ig-nobel-prize-for-economics-for-the-second-time/
Or consider what’s mentioned in these pages often, that presumably highly skilled and highly educated financial advisors don’t on average do better than index funds.
> If you don’t think we can meaningfully compare skill requirements for corporate decision making to concrete finishing, then I can see why critiquing billionaire and office worker pay in the context of risk compensation make sense, but I don’t think it makes any sense.
It’s not that I’m saying we can’t meaningfully make the comparison, it’s that I’m saying making a meaningful comparison is probably pretty hard to do.
Because I’m an impatient sort, I’m gonna repost with only one link to see if it gets through.
JFA –
I get your overall point, and I don’t dismiss that there is some general merit to it, but I am not as convinced as you seem to be in some categorical fashion.
> but I’d like to step back from that to at least recognize that these skill gaps do exist and suggest a broader definition (that might be hard to proxy): whether a lot or a few people can actually perform the job. I think it’s important to recognize that while every job requires some skill, we can also eyeball what positions require greater or lesser mastery in order to complete the task.
Maybe. But I wonder if you’re using a rather binary framework for something that’s not so binary in reality.
> I’d submit that as long as one is relatively able-bodied (we’re not talking athlete level physical prowess), concrete finishing is low skill relative to corporate decision making (I’ve done both concrete finishing and corporate decision making) because just about anyone can walk onto a job site and within a couple of months (tops) be performing at a pretty high level, whereas corporate decision making is actually very hard and most people couldn’t walk off the street and perform the task well (even after a few years).
I haven’t worked in concrete finishing (although I’ve done it a few times), but I did work as a carpenter (and in construction) for more than a decade and while I’d say carpentry employs perhaps a wider range or more varied set of skills than concrete finishing, I’m not sure I’d say it requires more skills in some absolute sense. But regardless, my guess (based on wider experience in other similar domains) is that the skill of a concrete finisher would span a very wide range. Some could work at it for years and only approach the skill level of others. We could consider speed or a number of relevant variables that might come in to play with regard to conditions or materials. I think of masons I’ve watched constructing a dry wall, where they could quickly isolate a rock from a large pile of rocks and place it in a spot where it would fit perfectly considering attributes along many different dimensions (color, shape, overall size, compatibility of the edges in relation to the surrounding rocks, etc.). One person might be able to develop a roughly similar skill in a relatively short period of time but only able to achieve an equivalent skill after years of experience, and perhaps never at all.
On the other hand, high level corporate decision-makers make mistakes all the time, despite many years of experience. There’s a reason why there’s such a concept as the Peter Principle. Even if I don’t think that it applies as some uniform rule of how the world works, I do think it has some merit as a conceptual framework. Consider this presentation from an Ig Nobel prize winner who presents a model that organizations do better on average if they promote people at random:
https://www.youtube.com/watch?v=9481TGp7k7M
Or consider what’s mentioned in these pages often, that presumably highly skilled and highly educated financial advisors don’t on average do better than index funds.
> If you don’t think we can meaningfully compare skill requirements for corporate decision making to concrete finishing, then I can see why critiquing billionaire and office worker pay in the context of risk compensation make sense, but I don’t think it makes any sense.
It’s not that I’m saying we can’t meaningfully make the comparison, it’s that I’m saying making a meaningful comparison is probably pretty hard to do.
Here was the second link:
https://www.csh.ac.at/andrea-rapisarda-wins-the-ig-nobel-prize-for-economics-for-the-second-time/
To me, skill level differences really manifest themselves analytically. As someone who grew up in a rural area with non-college education parents, I feel that I have navigated thru the skill spectrum. Nothing convinced me more of this when, as a lackluster STEM grad from a lackluster college who thought I would get an MBA because why not, I was shocked at how much better my performance was relative to other students with undergrad business degrees from better name-brand schools. And my performance was much, much better relative to students with degrees such as journalism and fine arts who were also from much, much better name schools. And I wasn’t even trying to be a model student, just that STEM degrees confer a skill set that improves performance overall in many disciplines. And the engineering students I’ve met have all said that law school was a walk in the park relative to their engineering undergrad.
I am frustrated by those who dismiss “skill” differences because of a lack of awareness. They have never navigated the skill spectrum, just have always been in one spot, and are surrounded by those at the extreme rhs of the skill distribution, so they do not see these differences.
Malcolm:
Interesting. I don’t think this comment of yours is particularly relevant to the discussion on risk compensation, but more generally, yeah, I know what you mean about quantitative skills being relevant in less quantitative areas, while the reverse is less true.
On one hand, your point is obvious: consider the whole STEM education movement, with the idea being that serious math should be for all students, not just the subset who find it easy from the beginning.
On the other hand, lots of people don’t study math, physics, etc. An undergraduate business degree kinda sounds almost like STEM (it’s “business,” not something frivolous like psychology or basket weaving, right?), but it won’t necessarily give you much in the way of quantitative skills.
The other thing that your comment makes me think about is the distinction between “skill” and “ability.” Yes, your ability to do something can improve or decline, but I have the general impression that people think of “ability” in a field as an inherent characteristic of a person and “skill” as something that can be learned. And “ability” is prized. But skill is important too! Especially because, once you have skill A, that can help you learn skill B. That is, “skill” includes certain latent aspects that might be associated with “ability.”
Regarding your last paragraph: there, I disagree. I teach STEM at an elite university, but even there I see lots of skill differences in various key areas, including the all-important skills of being able to map a model of the world into a set of mathematical expressions or lines of computer code. At every level there are lots of differences which become super-clear if you ask people to actually do something. Indeed, that’s why we teach this stuff, to improve people’s skills! Once you think about skills being variable over time, it’s clear they can also vary across person or scenario. This is related to some things that Jason Collins and I wrote a few years ago about the hot hand.
Andrew
I do believe in the value of quantitative education – and, on this blog, that position is widely shared. But I do wonder if the “value” of quantitative skills for varied disciplines is more of a statement of how those subjects are taught than anything intrinsic to the subject matter. Philosophers can be rigorous thinkers and may not have any traditional quantitative training (though logic is likely to be part of their curriculum). Similarly with sociology. The fact (here I’ll assert it as such) that a quantitative background makes many of these non-quantitative fields appear relatively easy in comparison may have more to do with how they are taught and assessed than whether those fields are really any “easier.”
Having taught business students for many years, I can certainly agree that those with quantitative backgrounds generally excel compared with those without. At the same time, I’ve been impressed by working with smart lawyers, sociologists, and philosophers who had little quantitative training. But they possessed a rigor in thinking and language that set them apart. When we generalize about these subjects, I think we may be observing features of our education system rather than features of the subjects themselves.
The flip side of “Risk without reward” is “Reward without risk”— an apt characterization of statistical approaches to causality.
Thanks for posting this, Andrew. I’d urge readers to look at the linked paper for more juicy details. A number of themes that come up on this site over and over also appear in the paper:
taking measurement seriously: Essentially no effort has been expended by the VSL crowd to investigate how well the proxy measures of on-the-job risk reflect the true differentials, yet they use data pertaining to a relatively small subset of occupational fatalities and ignore the actual attributions for fatal outcomes given in the data.
fixation on summary statistics: The VSL literature translates a regression coefficient as “the” willingness to pay for an incremental reduction of risk and doesn’t begin to consider whether the might be variations in the wage-risk relationship across jobs, employers, industries etc.
the garden of forking paths: Rather than treating model uncertainty as an aspect of the problem to be confronted directly, models are pushed and pulled to get the “right” risk coefficient.
Cass Sunstein: OK, the paper doesn’t bring him up, but those who know this literature will be aware that Sunstein and Viscusi coauthored a number of papers, and that the VSL “results” play a visible role in Sunstein’s case for relying on “objectively measured” preferences to discipline political processes.
There’s other stuff for people interested in labor markets, life and health valuation, and the like.
It’s hard to know where to start with this one it’s so far off the mark.
First, why should we necessarily expect that workers are compensated more for “dangerous” work if all other things are equal, when all other things are never equal? In other words, you can’t compare loggers to asset managers based on the danger of their job, because the number of differences between them is immense. Aside from education and the direct risk, a major difference is the cost of living and the availability of different types of work – in other words, there are no loggers in New York and few if any asset managers in Butte MT.
Second, why should we expect the oil industry to pay laborers more than the software industry pays laborers when the oil industry averages about 8% margin vs. >20% margin in the software industry? That is, aside from the fact that software has no laborers? A given industry can’t compensate workers for risk except to the extent that in makes enough money to cover that compensation. Tragically most high risk work is also in low margin businesses.
I’m not sure what to make of several points in the opening paragraphs of the paper. IT’s not clear how the constitute equal comparisons or even any comparison at all.
Fore example:
“Many high-risk workers are also poorly paid. For example, the 1.5 million nursing assistants, whose injury rates are over twice the workforce average, have median annual pay that barely exceeds the poverty level for a family of four…”
So what? No doubt, the injury rate of nursing assistants is sure to be much higher than that of coders or insurance salesman! No doubt nursing assistants require more education than trench diggers (who probably earn similar wages), but I hardly imagine them dying on the job frequently (as trench diggers do, when trenches collapse). This is statement meaningless without context on at least the relative seriousness of the injuries and skill required for the job – not to mention what the public is willing to pay for “nursing assistant” services.
Another example:
“Black workers, have elevated injury risk because they are over represented in relatively hazardous occupations. This is the case even for workers of the same age, education, and sex as their white counterparts. Structural racism is a likely cause of these disparities.”
Aside from the fact that age and sex are mostly proxies for education and experience, there are ***dozens*** of other factors that impact wages. There is no mention of geographic control or minimum wage, even though black populations are highest in the south where minimum wages – and the cost of living – are low. (which brings up the additional issue of adjusting for local cost of living).
Here are other factors employers consider implicitly or explicitly when hiring / firing and offering pay, especially in low skill work:
– employment history (e.g., how and why you left previous jobs)
– experience (time doing certain job)
– credit scores (it’s common now to check credit scores)
– driving record (many low wage jobs require driver’s license with an insurable record)
– marital status
– kids at home / not at home
– criminal history
Also:
– businesses in high crime areas are less profitable and likely pay less
– there are different min wage structures for different work and different employers *even in the same city*: in Seattle large corps have to pay higher min wage and NGOs get their own special lower min wage.
– Wages vary regionally depending on cost of living
– employers can’t pay more than they make and most dangerous jobs are in low margin businesses
So the claim that inequality of wages based on age, sex and education is evidence of “structural racism” is just flat out bunk.
So, on the one hand, I’m pretty sure Peter is right in saying that wages don’t compensate for danger. I’m also pretty sure he’s wrong to think that they should compensate for danger, since most dangerous jobs are in low margin businesses. And I’m absolutely sure that if he’s comparing across the entire US on age, sex and education alone, he’s going to meaningless results, since this is only a small sampling of the factors that influence wages and hiring, particularly in low-skill work.
Chipmunk –
> I’m also pretty sure he’s wrong to think that they should compensate for danger, since most dangerous jobs are in low margin businesses.
As really does seem to be a consistent pattern – here you conflate (your) opinion with fact.
“Should” is not a matter of right or wrong but a matter of opinion. The way you reach your assessment of “wrong” is by starting with your own value system as to what are the “right” and “wrong” criteria for compensation, and then working backwards.
It’s kind of remakable how easily you assume your worldview regarding the morality and ethics of economics are just objective truths.
And this –
> since this is only a small sampling of the factors that influence wages and hiring,
It will be interesting too see if Peter has a response. But egardless, id say it’s important to consider whether age, sex, education, etc. are independent of those other influences you think need to be included on the analysis. Your comments reads to me as if you’re considering them independent with no interaction effect.
Well, maybe the people who have dangerous low margin business should charge more, and we should be willing to pay more.
When I was young I worked in commercial fishing, which is one one the more dangerous kinds of work. I made reasonable money, but the real attraction was the feeling of doing authentic work that produces something tangible. Loggers I know feel the same, and I gather that miners do, too.
Closer to the main point, I remember a controversy about the effect of height on earnings.. As I recall, some professor of business found that height was a strong predictor of the starting salaries of his school’s graduates, all else equal. Then, there was an answering study by Case and Paxson (NBER Working Paper 12466) that argued that tall people were smarter, so there was no discrimination based on height. The argument was not straightforward: here is the abstract:
“It has long been recognized that taller adults hold jobs of higher status and, on average, earn more
than other workers. A large number of hypotheses have been put forward to explain the association
between height and earnings. In developed countries, researchers have emphasized factors such as
self esteem, social dominance, and discrimination. In this paper, we offer a simpler explanation: On
average, taller people earn more because they are smarter. As early as age 3 — before schooling has
had a chance to play a role — and throughout childhood, taller children perform significantly better
on cognitive tests. The correlation between height in childhood and adulthood is approximately 0.7
for both men and women, so that tall children are much more likely to become tall adults. As adults,
taller individuals are more likely to select into higher paying occupations that require more advanced
verbal and numerical skills and greater intelligence, for which they earn handsome returns. Using
four data sets from the US and the UK, we find that the height premium in adult earnings can be
explained by childhood scores on cognitive tests. Furthermore, we show that taller adults select into
occupations that have higher cognitive skill requirements and lower physical skill demands.”
Anne Case
> Well, maybe the people who have dangerous low margin business should charge more, and we should be willing to pay more.
Indeed, it could be reasonably said that our friend Chipmunk is arguing in favor of a trade-off, of worker safety for keeping profitability high, by keeping prices low. The potential tradeoffs for profit are endless. Pollution for profits? Why not? Only virtue signaling libruls could possibly object.
It’s difficult to know how to reply to what seems to be a vehement criticism when the basis for it is essentially the basis for the argument ostensibly being criticized. We say there are a myriad of factors that influence risk and wages, separately and together. Many aren’t measurable, at least currently, and some are but the VSL folks don’t seem interesting in measuring and incorporating them. They treat a single coefficient in a single model applied to observational data, with few controls and no consideration for heterogeneous effects, as measuring “the” willingness to pay by the marginal worker for a marginal improvement in safety. If I understand correctly, you are saying the whole idea of regulating job safety through wage compensation is crazy because of so many diverse and interacting factors. Well, yeah.
We think regulation of some sort (which we don’t go into) is ethically mandatory to make jobs much safer than they currently are, and use covid as an example. (The paper was written early in the pandemic.) We also think, to the extent jobs can’t be made safer, those asked to bear the extra risk should be compensated. Does chipmunk disagree?
Incidentally, and this applies to many of the comments here, it is worth noting that the wage-risk literature we criticize usually considers only subsamples of the labor force working in manual jobs or some approximation to that. We briefly touch on this issue in the paper, noting that promotion paths often cross such sample inclusion criteria, and that endogenizing them in this way obscures an important point: promotion hardly ever goes from safer to more dangerous jobs. If workers were fully compensated for risk this wouldn’t be such a stark pattern.
Finally, a brief note about racism. We attribute differential injury and fatality rates by race to unequal sorting across jobs. Really, what else can it be? By invoking structural racism, all we’re doing is to say that this sorting draws on racial disparities at several points in society — in education, in residential location, in job search institutions, etc. Their cumulative effect is what social scientists mean, or at least should mean, by “structural racism”.
There is a certain amount of “learn to weld” -> “learn to weld on more dangerous environments” -> “learn to weld on remote and highly dangerous environments” career path. I follow the reddit r/welding and see some people doing it. But lets face it, there are relatively few diving welders, and relatively few pressure vessel welders compared to all welders. I think it’s more about motivation, some of which is money but also some of which is certain personalities enjoying the challenge / thrill. A lot of welders are also highly internally motivated by desire to do the craft at a high level of performance. One thing that’s clear is that there’s probably no compensation for the level of danger from exposure to toxic fumes etc in the lower levels of the profession. The r/welding people are CONSTANTLY telling people to wear respirators and report business owners to OSHA for welding stainless with zero ventilation and the like (hexavalent chromium is **terrible** for you, causing cancer and neurological damage).
Dangers that build up through time are usually way under-compensated if at all. Stuff like toluene poisoning in automotive painters, or hexavalent chromium in welders, or other organ damaging chemical exposures.
> We attribute differential injury and fatality rates by race to unequal sorting across jobs. Really, what else can it be?
One can imagine lots of things that could result in people of similar age, sex and education level taking different jobs. Like what jobs are available in their region or whether they can just stay in mom’s basement playing videogames. On the other hand, one can also pretend that everything is structural racism and those are just examples of the totality of ways in which societies foster [racial] discrimination, via mutually reinforcing [inequitable] systems…(e.g., in housing, education, employment, earnings, benefits, credit, media, health care, criminal justice, etc.) that in turn reinforce discriminatory beliefs, values, and distribution of resources, reflected in history, culture, and interconnected institutions.
Peter: has anyone ever run a “movers” design regression to calculate VSL? using panel data, follow the same worker as she transitions from a low risk job to a high risk job? the individual fixed effect should hold the workers’ productivity and risk aversion constant.
As far as I know, there was one such study in the 1980s, which I discussed in my book Markets and Mortality. I’d have to go back for the details (it’s been a while), but I recall there were some issues. Still, it’s a logical approach.
I think the potential advantage of longitudinal data of this sort is not in controlling for worker-level characteristics, which are in any case endogenous (workers’ productivity traits are acquired in large part through the jobs they hold), but from the possibility that, if voluntary, job transitions might control for some of the “luck” aspects of labor market outcomes.
It’s interesting you focus on unmeasured worker characteristics — see my initial comment to Andrew!
“When I bring up the value of statistical life in class, I’ll point out that the most dangerous jobs pay very low, and high-paying jobs are usually very safe.”
You are very likely confusing your students, Andrew. The argument is not “dangerous jobs will be higher paid than less dangerous jobs,” it’s *holding all else equal* a riskier job will in equilibrium pay more. That said, it is not the case that the “most dangerous jobs pay very low” — those jobs are in industries like logging, fishing, and construction, and tend to pay well given the education requirements.
Generally, the fact that it’s not easy to measure risk compensation doesn’t mean the concept is wrong, nor certainly that it should be dismissed out of hand. And the stronger econometric efforts to make these measurements attempt to address many of the issues brought up here, e.g., https://law.vanderbilt.edu/files/archive/307_The-Value-of-Statistical-Life_Evidence-from-Panel-Data.pdf
Chris:
In your comment, you point to one of the papers by Viscusi and collaborators. I agree with you and Viscusi that in theory there should be some risk compensation: if someone has the choice between two otherwise identical jobs but job A is more risky than job B, then we would expect the person to demand more compensation for job A.
That theoretical reasoning is fine. However, as Dorman has pointed out, there are two big challenges with applying that theoretical reasoning to the real world. One problem is that the “choice between two otherwise identical jobs” thing does not seem to be happening very often, which puts a heavy burden on the statistical modeling that is done with observational data. The second problem is that there are enough options in these models that a lot of it seems to be an exercise in working hard to get what seems to be a reasonable number: not negative, and also not too small or too large when positive. I was persuaded by Dorman that the best way to understand the work of Viscusi etc. is that they are coming up with data-based analyses that give them numbers consistent with their theoretical models. Which is not nothing, but it’s not so much either.
Finally, I mentioned the raw correlation because for causal inference I always suggest starting with the raw comparison. That’s the starting point. As I wrote in my above post, “Any regression of salary vs. risk will start with a strong negative coefficient, and the first job of any analysis will be to bring that coefficient positive.” In general, it’s hard to estimate a small effect from observational data when you first have to adjust for much larger effects in your data. It’s not necessarily impossible, but it’s a challenge, not always do-able even if people really want to be able to do it.
As to whether I’m confusing my students . . . that’s possible! There’s often a delicate balance in teaching social science between positivity and criticism. On one hand, we want to teach successes and show students examples where our methods work well and solve problems. On the other hand, we don’t want them to fool themselves. To consider a method that’s come up a bit in this space in recent years, regression discontinuity analysis can work well or it can be a disaster, and I guess there are also possibilities in between (although it usually seems to be one extreme or the other!). To only show the positive or negative would be a mistake, but we don’t always have time in class to do both, so we often only show the positive . . . I dunno. I try my best, but, yeah, sometimes it seems that confusing the students is a necessary step in leading them to deeper understanding!
In many workplaces hazard pay is a premium paid on salary.
Example (sample clause in Collective Agreement): Dangerous conditions shall be defined as working at heights more than 25 feet above ground on towers [..etc..], working under a helicopter …] Employees requires to work under such conditions shall receive hazard pay of 7.5 percent in four hour increments so worked.
Thus, for example, a carpenter normally making $30/hr will make an extra $9 per four hour shift ($2.25 per hour) or less for working at heights.
Another example: Employees assigned to and working directly in a hazardous situation as declared by management, shall be paid six dollars ($6.00) per hour for all hours actually worked in a hazardous situation in addition to their base hourly rate.
In such settings, the worth of hazard pay is not Jim Bob versus Jeff Bezos or even Jim Bob versus Cletus. Jim Bob has to be compared to JB’s cohort in JB’s workplace. Cletus may have a powerful union that has negotiated significant hazard pay clauses, but Jim Bob may have accepted a job where management defines what is hazardous.
I understand that much of this discussion addresses compensation for hazardous work more generally, just wanted to make the point that in such settings hazard pay is marginal.
Yes, unionized workers *do* tend to get hazard pay. That’s not only a function of collective bargaining, but also the interaction effects between unionization and OSH enforcement, about which David Weil has written quite a bit.
What I found in my own wage-risk work was that risk premia tended to be positive for unionized workers but negligible or even negative for the nonunionized. Of course, the vast majority of workers in this country are not represented by unions. (But that comes with the giant caveat that I was using the same risk measure as Viscusi, and it’s pretty dubious.)
Dorman: ” Can the value-of-statistical-life literature really be as shoddy as I think it is?”
AG:”As to why the literature ended up so bad: it seems to be a perfect storm of economic/political motivations along with some standard misunderstandings about casual inference in econometrics.”.
No. Just tossing outliers. Before analysis..
Perhals Peter may have a clue?
Has anyone ever seen the raw original question / answers / VSL / QALY data and reviewed survey and the surveyed?
When I think of shoddy research, my thoughts go not so much to the researcher — lord knows, there are many reasons why people would push and pull data until they get the results they want — but to all the peers who ought to be gatekeeping: the coauthors brought in to handle side issues, the reviewers, and the larger community that evaluates professional work and awards recognition. In Viscusi’s case, what is remarkable is that he has won just about every honor an economist can receive short of a Nobel prize: he was a vice president of the AEA, an endowed chair at Harvard (which he gave up), and his work has swept the table in government agencies. How do we explain this?
I agree with Andrew that one element is the demand for a VSL to plug into cost-benefit analysis. (DALY’s, concocted by Chris Murray who has had his share of criticism on this site, work for cost-effectiveness analysis but not the CBA’s that are held in higher esteem.) He seems to have found the sweet spot for the actual number, too: high enough to satisfy most pro-regulation people, low enough to attract funding from right wing think tanks. That’s an achievement! The theoretical model is a simple application of benchmark competitive market theory, which satisfies economists who like that sort of thing. Yes and yes and yes, but still I wonder why obviously flawed econometrics has waltzed through several decades with only the slightest whiffs of criticism. You tell me.
Peter:
I think that part of the problem here is that there’s no true number for the estimate to be compared to, so if a researcher can give an estimate that makes the right people happy, then that estimate can stand. It can’t just be that, though. I think there’s no way this could all stay afloat without the authors sincerely believing in what they’re doing. That’s where the theory comes in. From all theoretical principles, the number has to be positive, so then it’s just a matter of fine tuning.
A perhaps relevant comparison point is . . . do you remember, nearly 20 years ago, some public health researchers did a survey in Iraq to estimate excess deaths after the war had started? The paper was published in the medical journal Lancet, so it was sometimes referred to as “the Lancet study.” Anyway, the estimates got some attention and some criticism, and it became this big political thing. Eventually it became clear that the survey had lots of problems—see discussion here—and one of my take-home points here was that lots and lots of surveys are done sloppily, and the writeups for surveys typically don’t get around to giving lots of detail on how the sampling was actually done. So it wasn’t that this Iraq study was uniquely bad; it just got lots of attention.
Similarly, the statistical/econometric approach used by Viscusi et al. is not uniquely bad—it’s the standard method for estimating elasticity using observational data. It just has the problem that it fits all too comfortably within economic theory. It looks so close to what’s in the textbook that people turn off their skepticism.
And there’s the political angle. That bad Iraq paper was criticized by a bunch of political conservatives who didn’t like the message that the U.S. invasion of Iraq was a bad thing. The result was that non-conservatives were inclined to not take the criticisms seriously, to take them as empty and politically motivated. From the other direction, this might be what’s happened with Viscusi etc.: Their numbers were criticized by political liberals, people supportive of labor unions, and then it was all too easy for them to not take the criticisms seriously, to take them as empty and politically motivated.
> From all theoretical principles, the number has to be positive, so then it’s just a matter of fine tuning.
Here’s my theory:
People with significant agency (via political or other means) are less likely on a relative scale to feel pressure to earn money by engaging in activities that risk physical harm, and/or have a means to ensure higher compensation if they decide to do so.
People with relatively little agency feel proportionately more pressure to earn money by engaging in activities that risk physical harm, and/or don’t have the power to ensure they’re compensated more in proportion to the increased exposure to risk.
In contrast to statistics, coming up with theories is easy!
Your point about the similarity of VSL to other elasticity-estimating (hedonic) research is well taken. The first estimate of willingness to pay for elements of a marketed good, or at least the first one to get a lot of attention, was about cars in the 1950s. I once invited the county assessor to a stats class to explain how his office models housing prices, and it turned out there was a standard hedonic model used around the country. It’s probably true that the usefulness and relative credibility of these models increased the receptiveness of economists to what Viscusi was doing.
There are two large remaining problems, however. First, the contexts in which hedonic estimation works pretty well are those in which people are *already* taking these factors into account. Car buyers read up on the specs. Home buyers consult their checkboxes about number of bathrooms, square footage, the reputation of the local school system. Because of this, we have decent measures of these things. If workers routinely examined the safety of jobs before taking them in a similar way, no doubt there would be data sources serving this demand. But they don’t and there aren’t. So instead Viscusi had to concoct an ad hoc measure of safety which turns out to have massive error. I don’t fault him for the error, actually, but for not facing up to it.
Second, the commodity qualities successfully priced by hedonic methods are well-defined and largely homogeneous. Sure, one bathroom may be prettier than another or have a layout the prospective buyer really likes, but it’s not a stretch to say, for pricing purposes, the number of bathrooms is a meaningful quantity. “Risk” isn’t like that. It’s multi-dimensional and elusive (risk of disease especially, where etiology can be obscure). And it’s embedded in a market, labor, which is notoriously shot through with frictions and a fair amount of randomness, not to mention discrimination and strategic interaction. (Employment is a repeated, not a one-off, game.) It’s just so much less amenable to methods that work well elsewhere.
There isn’t anything particularly profound about these two points. In a healthy scientific community they would be recognized, and VSL studies would get critical scrutiny, no matter what Viscusi thinks about his own work. That’s what concerns me the most.
Peter:
You write, “In a healthy scientific community . . . [value of statistical life] studies would get critical scrutiny . . .”
But those studies do get critical scrutiny, by you and others!
I’m guessing, though, that the criticisms too often get dismissed as “political.” Meanwhile, we live in a world where Cass Sunstein gets treated like an intellectual while making asinine claims about each death sentence saving 18 lives.
> …some standard misunderstandings about casual inference in econometrics.
Maybe the root of the problem is that inference in economics is so casual…
Typo fixed; thanks.