Dan Farber’s wide-ranging article, Inequality and Regulation,1 makes two core arguments. First, Farber argues that using a uniform value of statistical life (VSL) in cost–benefit analysis is justified not only as a pragmatic compromise but also as a matter of first principles. In particular, he argues that a uniform VSL can be based on a theory of equality that he calls harm egalitarianism, which holds that individuals have equal entitlements to protection against harm. Second, Farber, looking for ways to address environmental justice concerns through regulations, argues that “a heightened focus on differences in exposure and vulnerability [in the cost–benefit analysis of regulations] offers the most promising path forward for environmental justice to expand protection for the goals of environmental justice.”2 I will address these claims in reverse order.3I start with Farber’s claim about the ability of cost–benefit analysis to address problems of environmental justice (EJ). The claim is that many disadvantaged communities—by which he means both communities that are predominantly racial minorities and communities that are lower-income or poor—face higher exposures to pollution and have higher levels of vulnerability to pollution. If cost–benefit analysis paid careful attention to those differences in exposures and vulnerabilities, the resulting regulations would address many of the concerns raised by the EJ movement while avoiding potentially illegal explicit reference to race. As noted, he states the claim as follows: “a heightened focus on differences in exposure and vulnerability offers the most promising path forward for environmental justice to expand protection for the goals of environmental justice.”We might paraphrase Farber as saying that the best way to promote EJ in the regulatory sphere is good old cost–benefit analysis (CBA), but done well by taking into account data on exposures and vulnerability to pollution. That is, I do not think Farber is advocating for a change in CBA, but rather better CBA that pays careful attention to the facts in disadvantaged communities around the country. If correct, the claim illustrates one of the great virtues of CBA, namely that it provides a transparent, publicly announced formula for evaluating regulations. If the government is not following its own criteria, it is easy for harmed groups to call the government to account.I want to explore this in more detail. My conclusion will be that while I wish Farber’s claim were true, the results of taking into account local differences are likely much more ambiguous than Farber suggests and may even be counter to the goals of the EJ movement.Farber’s factual claims on exposure and vulnerability are consistent with my reading of the literature.4 It is important, however, to keep exposure and vulnerability separate because the empirical support and implications for regulatory policy for the two are not the same. Vulnerability is the extent to which a given exposure causes harm. It is marginal harm, the slope of the damages function. Because cost–benefit analysis should set marginal harm equal to the marginal cost of care, higher vulnerability immediately translates into a different level of regulatory stringency. That is, even if the exposure to a pollutant is the same, a community with higher vulnerability will face more harm. As a result, a regulation might require lower levels of exposure in that community.In contrast, exposure is about the level of harm, not the sensitivity to harm. Higher levels of exposure do not automatically change the optimum that cost–benefit analysis would reach. If all communities have the same vulnerability—that is, the same marginal harm for a given exposure—a regulation might set uniform exposure levels for all communities. Communities with higher initial exposure levels would see greater reductions, but the final result would be similar exposures across all communities. While this result may satisfy EJ, it is not the result we would see with different vulnerabilities. Because differences in exposure and differences in vulnerability can lead to entirely different regulatory results, it is important to keep the two separate.5Most of the empirical literature examining differences in pollution across different communities focuses on differences in exposures, not vulnerability. The early work by the United Church of Christ6 and Robert Bullard7 documented higher levels of exposure in minority or poor communities. These findings generated a substantial literature addressing the connection between pollution and race, poverty, and other markers of disadvantage. Although there is some variation in findings, particularly when controls and multiple regression analysis are used,8 for the most part studies have confirmed the results of that early work.For example, a 2011 study examined about 11 million births in five states between 1989 and 2003, comparing the characteristics of mothers within two kilometers of Superfund and Toxic Release Inventory sites to those living outside that radius.9 It found that black mothers are 0.7 and 5.3 percentage points more likely than others to live near a Superfund and Toxic Release Inventory site, respectively. Another study looked at exposure to PM2.5, finding that black, Asian, and Hispanic communities (measured at the zip code level) have higher exposures than white communities.10The most recent estimates are able to use satellite data combined with machine learning techniques to estimate PM2.5 exposure in grids as small as 1 kilometer (making up about 8.6 million grid cells in the United States). These studies are able to link this grid-level data to individuals through census and tax records, allowing a highly detailed tracing of exposures to health or other outcomes. One recent study using this approach found that black households tend to live in the most polluted areas nationally but that the black–white gap in mean pollution exposure has closed by about two-thirds since 2000.11 As Janet Currie, one of the leading researchers in this area, summarizes, “minorities and people of lower socioeconomic status are more likely to be exposed to potentially harmful pollutants for reasons that cannot be explained by their broad geographical distribution, education, or other observable characteristics.”12A related, but distinct, question is how much these disparities in exposure affect health and other outcomes. Our understanding of the effects of exposure to different pollutants remains imperfect,13 but it appears that exposure to PM2.5 even in utero can have long-term deleterious effects.14 For example, prenatal exposure to PM2.5 appears to reduce the rates of upward mobility at age 30 and has been estimated to explain 17% to 26% of the black–white earnings gap.15 The effects may last their lifetimes and indirectly even to their children (the grandchildren of the exposed mothers).16 The more researchers look at the effects of exposure to many pollutants and the better they are able to trace and identify the effects, the worse things look.There is far less evidence on vulnerability. An early and seminal study found that black infants respond more negatively than white infants to increases in total suspended particulates.17 Another study looked at dose–response differences between black and white people to traffic-related air pollution, finding larger effects on the former than the latter.18 Exposure to carbon monoxide appears to have a greater effect on poor children than on others, even when attempting to account for greater exposures.19 Plainly, the research provides some evidence that differences in vulnerabilities exist.Nevertheless, a recent survey on differences in vulnerabilities concluded that “there is much less evidence that … differences in health-related dose–response functions translate into differences in marginal damages or welfare.”20 The underlying problem is that it is not easy to eliminate possible sources of heterogeneity. For example, suppose we observe households in different communities responding differently to a change in exposure to a pollutant. This might mean different vulnerabilities, but it might also be explained by different levels of exposure and a common nonlinear dose–response function. In addition, differences in vulnerability could be due to non-pollution-related factors such as less access to healthcare.21 These would be true differences in vulnerability, but the appropriate response might lie in addressing the cause of the difference in vulnerability rather than requiring different pollution exposures based on geography.In short, my review of the literature is roughly consistent with Farber’s, if perhaps somewhat more skeptical. To the best of our current knowledge, there are significant disparities in exposure to pollution, and these disparities generate long-term harms, including to health and earnings. Some of these effects are large, and addressing them is a matter of utmost urgency. Moreover, background conditions, including but not limited to prior exposure to pollution, seem to lead to greater vulnerability to pollution, in the sense that the slope of the damage function from pollution is steeper for minority and other disadvantaged communities than for others. The evidence on this issue is limited, though.The implications for the stringency of regulations and the ability of cost–benefit analysis to address EJ concerns, however, is more ambiguous than Farber indicates. Incorporating information about disparities in pollution exposures and vulnerabilities into cost–benefit analysis may affect disadvantaged communities in surprising ways and, in particular, may not lead to greater regulatory stringency or more emissions reductions in those communities. In fact, focusing on local community differences may lead to less regulatory stringency, rather than more, in affected communities. Whether that approach promotes the goals of environmental justice depends on what, precisely, those goals are.To understand the effects of taking the differences in exposures and vulnerabilities of local communities into account, let us assume that we are crafting an idealized regulation of a pollutant. The regulation sets the marginal cost of care equal to the marginal harm from a pollutant.22 Moreover, the regulation can take local exposures and vulnerabilities into account if appropriate, which means that it can be spatially differentiated, requiring different levels of pollution in different locations. The question is whether this regulation advances the goals of environmental justice.As noted, differences in exposures and vulnerabilities enter into the cost–benefit analysis for this hypothetical regulation differently. Differences in vulnerabilities mean that marginal harm varies by location. This means that the regulation should be geographically differentiated to take differences in vulnerabilities into account. All else being equal, the regulation should be more stringent in the locations where marginal harm is higher.Differences in exposures do not generate that same effect because differences in exposure do not mean there are differences in marginal harm. If the marginal harm from pollution is the same for different locations, all else being equal, regulations should set the same standard in each location. Regulations should not necessarily take geographic differences in exposures into account.In either case, the regulation may seem to satisfy the goals of the EJ movement. In the first case, the vulnerable would be exposed to lower levels of pollution to account for the differences they face. Even when there are merely different levels of exposure, the regulation may still address EJ concerns. By imposing a uniform standard across communities, communities with higher preexisting exposures to the pollutant would see larger reductions in pollution. Moreover, uniform standards would mean treating all communities equally.This analysis, however, considered only exposures and vulnerabilities, holding all else equal. All else may not be equal. Just as vulnerabilities or exposures may differ across communities, so may other factors that go into CBA. In particular, CBA sets the marginal costs of care equal to marginal harm, and just as vulnerabilities and exposures may vary by community, so may the marginal costs of care. In addition, to determine whether the goals of EJ are advanced, we need to take into account the consequences of the regulation, including who benefits. Once differences in costs and benefits are taken into account, the resulting regulations may do little to advance the cause of environmental justice. It is entirely possible that an optimal regulation would be less stringent where people are more vulnerable or where people face greater exposure to pollution. Moreover, it is entirely possible that an optimal regulation may not benefit disadvantaged communities even if it reduces pollution in those communities. That is, taking local geographic variation into account may not produce results that EJ advocates would promote.To see this in more detail, start with costs of care. We know far less about the distribution of the costs of care than we do the harms from pollution. To understand the distribution of the costs of care, we need to know the incidence of the costs, both by income and geographic location. This is hard to estimate.To illustrate, suppose that a regulation requires a facility to reduce emissions of a pollutant. The owners of the facility—that is, the capital providers—might bear the costs because they have to pay for equipment that prevents the emissions. On the other hand, workers at the facility might bear the costs because the facility, now more expensive to operate, might fire workers, lower wages, or shut down. Or the customers of the facility might bear the costs because it raises prices. These possibilities will have quite different effects on the local community. If, for example, the facility reduces employment or lowers wages, the local community might face high costs, while if capital bears the cost of the regulation, the local community might not bear any costs whatsoever. The particular effects will likely vary by location and by regulation.The problem of incidence is most widely studied in the context of pollution taxes, such as carbon taxes. Pollution taxes tend to be regressive because the poor spend a relatively greater fraction of their income on energy or goods produced with energy, and energy production is a major source of pollution. Because pollution taxes raise revenue, however, the distributional effects can vary widely depending on how the revenue is spent. The geographic incidence of pollution taxes is less studied. One study of carbon taxation found that the key factor is the geographic variation in electricity consumption and the carbon intensity of electricity.23 With some exceptions, states with larger losses from electricity price increases tend to be poorer.The conventional wisdom for environmental regulations is similar.24 Lower-income households bear a greater portion of the costs because they spend a larger fraction of their income on energy-intensive goods and are more often employed in energy-related industries.The effects, however, can be subtle and difficult to measure. For example, one study looked at whether renters or owners bear the costs of regulations reducing suspended particulate matter (PM10) under the Clean Air Act.25 It found that renters bear a substantial fraction of the costs in the form of increased rent. Another study, looking at the same regulation, however, found the reverse because local regulators targeted the dirtiest areas to clean up first and that pattern tended to favor low-income households.26Estimating who benefits from a regulation may, if anything, be even more complex. That disadvantaged communities tend to have greater exposure and vulnerability to pollution not mean that they are the communities that benefit when pollution is To understand who benefits from pollution, we have to understand there are differences in exposures and vulnerability in the first are a of different for EJ is an in were in the with the of black people in the even the for a One the able to the there that the local and the people the ability to such is For example, one study looked at the of and taxes. for multiple such as the costs with the it found that not have an effect on the level of these Another study looked at how or whether community can affect regulatory It found that communities are able to to more to in their it found that this to poor and Hispanic communities but not black communities. On the other hand, there are on black communities, of of community and It is possible that the studies are not to the or problems by the EJ community. 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On this we need much more work that Farber’s is possible to the is to an of the all the is and there is but we know that disadvantaged communities face We need to rather than all are I do not however, that such a would Farber’s The problem is not just one of It is that we do not even know the of the effects from many possible we we should at have some that the people we to will be better other is that equal for all individuals are appropriate not merely as an or as a to but as a matter of first principles. To a great the is because use equal and there is to change it is understanding the if for this issue of has been well in the so I will keep my that CBA the value of a statistical life to estimate the benefits of by a These benefits are often through such as the for taking at individuals will a higher for taking which means that their will be higher than the for poor one may question the in I will take them as as I assume Farber we might call when the benefits of a If the regulation benefits poor the benefit will be lower than if it benefits The government not do it equal for all for example, the This is often justified as a to or to Farber argues that in equal are as a matter of first is based on an to this case, equality of calls this harm egalitarianism, that harms should be the same by regulators of the of the The is that the government equal for all That when treating harms the same of which bears the I the pragmatic reasons for using equal I do not think the can be justified on first or at Farber’s do not I take Farber’s to be to poor or lower-income CBA the poor less than the and it is this that The however, makes reference to whether the individuals Farber to benefit are better If one to a of one should at some to whether the Farber he any reference to of the people he to makes this is for the is to to the life or health of an with the that a particular would have been to spend that particular people are to pay to reduce to not how much of a cost a regulation should in to reduce their is Farber not claim people are that they from or that there is some other for own about their and not claim the empirical estimates of or the of are not claim that there is a to or some other benefit that individuals do not take into account when their Even if one is not a even if one has a equality in some reference to the of the people one to Farber such an he would have found that equal often low-income households worse In particular, using equal means we may often require low-income households to pay more for than they wish to To illustrate, using an from recent work by a regulation reducing from where the costs of the regulation are forward to If poor households have a VSL million but the regulation that they have a VSL of they are to that are more expensive than to benefits they would not if While they they cannot other that they to those benefits. That is, we are those households to expensive but when they would to somewhat less and use the on other things even in other to do this because this promotes In fact, it is a and the the they want while the poor are to other goods to with they do not households in this would us that while they the good they are better our fact, the is even worse than equal can worse not just the are easy to To illustrate, suppose there are equal of and poor and the have a VSL of the a VSL of a regulation that will for the poor at a cost of million statistical The regulation costs the a but not them at all because they are taking the If the government were to use the it would the regulation because it at a cost of million when are at the regulation in makes the poor worse million of benefits at a cost of and the worse benefits at some this regulation to be but if we use equal we would it should be from these the key issue in understanding the effects of using equal is the incidence of the costs and benefits of the If the costs of the regulation are forward so that they on the or if the benefits do not to the it is likely that the regulation will poor households worse The problem is similar to the problem We need to understand the of regulations to the if we this we would that most of the using equal the poor because of how the incidence of the costs and benefits is, of to this is the case, and Farber not it suppose this were true such that harm is in those it to It would still not using equal while equal on the poor better we would need to that to other means of If there are better of the same we would not want to use equal particular, by far the most important we use for to the poor is the That is, even if we could the empirical that equal the poor rather than we would have to that it is a better Farber, I far the use of taxes and but there has been much on this and I on this issue for other but problem if we were to harm as an is that we would have to of that we to Farber’s of harm varies in its In the of he the to he states that harms should be the same by In the however, he it much more individuals have equal entitlements to protection against harm. a in the of the he the approach in the is hard to see a good for the to regulators and not it to the of government and to all of It is a of that to all people to have the harms they face is about the to regulators as to those it on in this because a not to to to is a there is to the to areas where to in the the to all harms, however, is based on their own not on For example, when about the between in the and people use their own with the poor using a lower VSL than the is, how are to less or they would not if they a higher would seem to require us to all these and require people to as if they equal there is a way to the to government policy it to just regulations, a of government much more The would be when we through the not when we on our it to each other to each other The problem with this approach is that it depends on a prior of when to through the and that prior may not the equality that harm We to for all of most entirely to the are that harm egalitarianism, which means that the of the Farber sets a broad have equal entitlements to protection against not to it in any way to the he there is a but the claim would require to the way our that I most would as noted, the may the people it to worse us consequences and suppose that we are to an of Even the are two One is by recent work by analysis is and so I it but he that equality cannot between CBA using equal and CBA that differentiated by which is well is that equality are because they need a prior to the we are and we are that prior we need the equality For example, perhaps we should harm with we might call all individuals of race, or other by taking their into account, including their as by their It individuals equal on that affect a higher VSL on individuals than they have It is a harm to their because it that their about their own life are could of for when individuals information or have problems the information for one or given the of and these should be those or reasons for however, requires policy to use the including and of the people they is of a I do not think we should policy on these of rather than by reference to the of the If we are to a equality on however, we have to that particular equality as to the of other is I that would have to into an analysis of the I cannot see the of harm some of whether it makes the people it to better and some understanding of when it and Farber It is entirely possible that the will those it to worse because it their even if those are well I can see using equal for pragmatic reasons or but Farber has not that so is as a matter of first principles.
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David A. Weisbach (2023) studied this question.
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