The Mighty Waves of Regulatory Reform: Regulatory Budgets and the Future of Cost-Benefit Analysis

In the past 70 or 80 years, there have been three “waves” of reforms to the process of creating and managing U.S. federal and state regulations. The first wave began in 1946 with the passage of the federal Administrative Procedure Act, after which states went on to pass and formalize their own administrative procedures. The second wave began decades later in the mid-1970s, ushering in the era of cost-benefit analysis reforms for regulations. This article focuses on the third wave of regulatory reforms that appears to be sweeping the nation and includes a prediction that the next wave may include a return to some unsettled issues from the past

The Mighty Waves of Regulatory Reform: Regulatory Budgets and the Future of Cost-Benefit Analysis

In the past 70 or 80 years, there have been three “waves” of reforms to the process of creating and managing U.S. federal and state regulations. The first wave began in 1946 with the passage of the federal Administrative Procedure Act, after which states went on to pass and formalize their own administrative procedures. The second wave began decades later in the mid-1970s, ushering in the era of cost-benefit analysis reforms for regulations. This article focuses on the third wave of regulatory reforms that appears to be sweeping the nation and includes a prediction that the next wave may include a return to some unsettled issues from the past

Make Benefit-Cost Analysis Meaningful

In 1947, economist Tjalling Koopmans wrote a review of the book Measuring Business Cycles, written by his fellow economists Arthur Burns and Wesley Mitchell. The title of Koopmans’s review, Measurement Without Theory, referenced the fact that Burns and Mitchell had collected and summarized impressive amounts of macroeconomic data and described in detail the business cycle fluctuations they observed, but they had not drawn sufficiently on economic theory to explain the observed regularities in their data. By omitting theory that might otherwise give their data context and meaning, Burns and Mitchell’s contribution was cast into doubt. In more recent years, there is another area where theory has taken a backseat to empirical measurement: namely, the theory behind benefit-cost analysis (BCA). BCA intends to predict how regulations and other public policies impact society for better or worse. Conspicuously missing from BCA, however, is the necessary theory connecting whatever BCA is measuring to the well-being of actual people. Debates over the relative merits of theory versus empirical measurement are not new to academic inquiry. In the late 19th century, for example, a debate took place in the German-speaking world over the importance of theory as it pertains to economics. On one side of the Methodenstreit, as the debate came to be known, was the so-called German historical school, led by Gustav Schmoller. Schmoller cautioned against fundamental laws in economics, arguing that historical context and culture are always changing and that outcomes will vary by time and by place. Theory can be developed, he accepted, but careful measurement comes first. Only after extensive data collection and analysis can hard conclusions be drawn, or so members of the historical school believed. A group of economists in Austria, led by Carl Menger, viewed matters differently. They saw an important role for theorizing in the social sciences, believing that a scholar can construct an idealized model economy and, through careful reasoning, arrive at certain fundamental truths about its workings. Insights can then be extended to the messy and complicated world around us, with its nearly unlimited and imperfect data that can often be contradictory and misleading. Furthermore, any analysis of data involves theory—theory is impossible to escape—because understanding what data represent is critical to their interpretation. If we fast forward to today, we see similar debates playing out in economics. When measuring the effects of the minimum wage, some commentators act almost as if measurement is all that matters, as if “Econ 101” supply and demand theory, which would otherwise indicate that artificially high wages mean fewer jobs, can be cast aside. When an empirical study finds no employment effects, they conclude that those who reason based on the theory of supply and demand must be ideologues. But are they? Or are our data and measurement techniques too imperfect to detect the law of downward sloping demand in a fast-paced world? These kind of issues are also at play in BCA. Open up a textbook and it will likely tell you that the welfare measure underlying BCA is economic efficiency, which relates to maximizing a broad conception of society’s overall wealth. But BCA, at least as it is produced in the government and in countless academic studies, is not measuring efficiency. This is true for at least two reasons: First, BCA does not properly account for the opportunity cost of capital, detailing how capital would be employed with and without a government policy change. Second, BCA applies weights to consumption based on when the consumption occurs in time. With respect to the opportunity cost of capital, economists have understood, since the early 1960s, that the proper way to account for the opportunity cost of capital in analysis is by using a shadow price, which is a factor by which all capital benefits and costs are converted to equivalent units of consumption. Sometimes this conversion device is called a marginal cost of funds factor. Whatever the name used, without such a conversion, comparing one dollar of capital to one dollar of consumption is comparing apples to oranges. The government tries to address this issue by using a discount rate, but its approach is almost always inappropriate. Units of consumption and units of capital are all discounted at the same rate, as if they are growing over time at the same rate. But consumption benefits dissipate quickly, while the returns to capital can increase with time as some are reinvested. Treating these different benefits as if they are the same gives too much weight to consumption—capital increases social wealth by more. Through discounting, the analyst also applies a set of weights to consumption streams based on who receives them and when. If John receives a consumption benefit worth one dollar today, this benefit receives more weight than if Sally receives an equivalent benefit next year. But is Sally’s consumption really so different from John’s? Even if it is, a government analyst is ill-equipped to distinguish how this experience varies across people. From the standpoint of economic efficiency, a dollar’s worth of consumption should always receive the same weight (adjusting for inflation, of course). Equal weighting along these lines is standard within a time period but for some reason it becomes controversial across time. Analysts must be careful not to attribute characteristics of individuals—like time preference, diminishing marginal utility, or risk aversion—to society as a whole, an error known as a fallacy of composition. Ironically, Koopmans himself, through his influential work on time preference, likely contributed to this tendency of analysts to blur individual and social characteristics. BCA has been a fundamental part of the regulatory process in the U.S. federal government since the early 1980s. Executive Order 12,866, which governs the U.S. regulatory analysis and review process has just enjoyed its 25th anniversary. The government’s benefit-cost watchdog, the Office of Information and Regulatory Affairs, has existed for nearly 40 years. And yet, after all these years, what exactly is BCA measuring? If not efficiency, then what? Without a clear welfare measure, BCA is like a rudderless boat adrift at sea. It can be a useful tool, but to be truly useful in practice, first BCA has to measure something meaningful in theory

The Irrationality of Market Failure Theory

On January 27, President Joe Biden signed an executive memorandum on “restoring trust in government through scientific integrity and evidence-based policymaking.” The memo included an easy-to-miss provision about incorporating behavioral science insights into regulatory policy. This provision could play an important role in the Biden Administration’s regulatory program, so it is worth investigating further. The typical economic rationale for government regulation of the economy is market failure. Underlying the market failure concept is the idea that, because of certain frictions known as transaction costs, mutually beneficial exchanges that would make society better off—gains from trade—fail to take place. These transaction costs are present in situations involving externalities, asymmetric information, monopoly power, and the provision of public goods. In these situations, neoclassical economic theory holds that government intervention can potentially be a corrective force to improve people’s welfare. Some government agencies and academics, however, have begun justifying policy interventions based on “behavioral market failures.” These failures extend the “neoclassical market failures” mentioned above to include instances of suboptimal decision-making by the public. Behavioral market failures occur because of various cognitive biases we have as individuals—which create mental transaction costs, if you will, that result in poor decisions that reduce our own welfare. Many of the biases identified by behavioral economists, who study the intersection of economics and psychology, relate to situations involving tradeoffs that occur over time. The idea is that our own decisions can be problematic if they involve up-front costs that may deter us now, but produce streams of benefits in the future (or vice versa—produce benefits upfront and costs later). Not surprisingly, these issues are often associated with self-indulgent behavior or self-control problems. Ironically, textbook presentations of market failure theory suffer from their own form of present bias. Economists tend to explain market failures in a static context without considering the effects of time. One way they do this is by failing to distinguish social benefits and costs that are consumed from those effects that are invested and grow in value with time. Even with a problem such as global warming—known to have long-run intergenerational consequences—standard economic analysis tends to evaluate only how negative externalities stemming from climate change impact the present generation’s well-being. To correct market failures, it can be worthwhile to override people’s choices. This is not to say that consumers are better off by having had their choices overridden, something behavioral economists often assert. Rather, all regulations override choice to some extent and thus impose costs. That alone is not a sufficient reason to preclude regulation. The key, however, is to account for all the impacts, not just those that occur in the present. Consider the hypothetical example of a regulation for an appliance that would save $1 million by reducing energy use. Faced with a requirement that their appliances conserve energy, manufacturers might remove a feature for which current consumers would have collectively been willing to pay$2 million because the appliance is more user-friendly with the feature. From the standpoint of the appliance’s purchasers, this is a bad deal. If there are no other benefits, they value the lost product quality more than the financial savings from lower utility bills. They would be made worse off by this regulation. But consider the same regulation from the standpoint of future consumers. If even a small fraction of the million dollars in financial savings was invested and reinvested continually, it would grow into far more than $1 million or even$2 million in the future, owing to compounding. The short-run reduction in consumer well-being as a result of lower-functioning appliances seems trivial from this long-run perspective because it is a fleeting loss. Meanwhile, the compounding gains of capital accumulation have much further-reaching consequences. If future consumers could participate in present-day markets, they might be willing to compensate current consumers to accept the lower-functioning appliance. In our example, they might be willing to pay consumers $2.1 million to accept some functionality impairment in their appliances. If such compensation took place, everyone would be made better off by the regulation without making anyone else worse off, a situation known as a Pareto improvement. Consumers today would have a worse device but would also have extra money that more than compensates them for the lower-functioning device. Furthermore, consumers in the future would benefit from increased investment that boosted economic growth and raised their incomes. Even without compensation, the winners in the future gain more than the losers in the present lose, a situation known as a Kaldor-Hicks improvement—or a potential Pareto improvement—a principle which underlies cost-benefit analysis. In this example, time creates transaction costs. Similar to how externalities, asymmetric information, and even poor decision-making result from physical or mental transaction costs, time creates a market failure by preventing mutually beneficial exchanges from occurring. The difference is that unlike with traditional market failures, harmed third parties are not yet alive or old enough to lobby on behalf of their own interests. These intertemporal market corrections have an interesting characteristic: They can reverse the policy implications of the static market failures described in economics textbooks. Say that a polluting power plant reduces air quality in a city. Residents, if they could organize, might be willing to pay the plant to reduce its emissions. But organizing involves incurring high transaction costs, so the exchange likely will not take place. This situation seems like a case for government regulation. Usually, the economic analysis stops there. But there is still the future to consider. If the pollution in our example only reduces short-term consumer well-being, for example because it only has nonmarket impacts with returns that cannot be reinvested, but regulation comes at the expense of capital accumulation and future economic growth, then people in the future might be willing to compensate present citizens to accept current levels of pollution. Well-being cannot be invested like money, so future social welfare is unlikely to be improved by regulating the power plant in this case—unless the pollution also lowers productivity and market production, causing society to forgo compounding returns to capital. It may well be that, in this case, the efficient outcome is for the power plant to produce more energy—not less—and by extension, to increase present pollution somewhat. One could also envision a different situation where future citizens pay to prevent catastrophic outcomes or pollution that causes long-term harm to the economy. The point is that standard cost-benefit analysis fails to recognize these future improvements or reductions in allocative efficiency, as it focuses exclusively on static outcomes in the present, not dynamic ones across time. The task for economists, then, is to think about policy impacts in a dynamic way. President Biden’s recent memo on scientific integrity foreshadows increased use of behavioral science to justify regulations. The irony, though, is that the same regulators and academics who presume to be able to correct the irrational decisions of others suffer from their own form of present bias, at least when it comes to cost-benefit analysis and the market failure theory that supports it. This irony prompts an obvious question: Who is it that is irrational? Is it consumers—or is it economists

The Myopic Short-Termism of the Value of a Statistical Life

I appreciate that Professor W. Kip Viscusi took the time to respond to my recent essay in The Regulatory Review, titled “Rethinking the Value of a Statistical Life.” In general, I would invite Viscusi, as well as anyone else who is interested, to read the entire paper on the topic of the value of life on which my essay draws. It provides more in-depth discussion of the issues raised in my essay and also addresses some of those concerns raised in Viscusi’s response. Among other things, I explain why a variant of the cost of death approach commonly used prior to the value of a statistical life’s (VSL) widespread adoption is vastly superior to the VSL along a wide range of ethical, economic, and also practical dimensions. For the purposes of this response, however, I want to focus on the primary area of disagreement between Viscusi and myself, which I believe relates to the treatment of future generations in analysis. To be perfectly blunt, I think their preferences matter as much as our own; Viscusi does not appear to agree. Viscusi also makes several inaccurate claims in his response to my essay and correcting them should provide further clarity. First, Viscusi states that the VSL represents “society’s willingness to pay for the mortality risk reduction.” This is objectively not true. In fact, the VSL is an estimate of what some people in the current generation are willing to pay for mortality risk reduction. The present generation and society as a whole (which would presumably include future generations, as well as those who have been born but are not in a position to make economic decisions yet) are two different things. It may well be true that senior citizens today, or any other group for that matter, are willing to pay something for risk reduction that roughly corresponds with the VSL value. What Viscusi misses is that people in the future would also be willing to pay something for present-day policies that will eventually benefit them, or to avoid present-day policies that will harm them. A more universal measure of willingness to pay should therefore account for their preferences too. But future generations obviously cannot trade in our markets. When resources are exhausted in wasteful ways, it may well be in line with present preferences. Much like cashing in a well-performing stock to go on a decadent vacation, there is an immediate benefit. But in exchange for this ephemeral gain, there is an unseen future cost to consider as well: how the stock would have performed had it been held on to. And although spending some of society’s present-day wealth to cover the cost of regulation is inevitable to a certain degree, we should strive to understand the opportunity cost for our children. Viscusi is correct when he states that policy-making “should be restricted to situations where there are demonstrable, consequential negative externalities directly generated by individuals’ own consumption decisions.” The decisions made by present consumers in the marketplace do impose such externalities on their successors. The VSL is estimated from these market conditions, which is precisely why the VSL misrepresents society’s willingness to pay for risk reduction—because it is built upon a foundation of market failure. For similar reasons, Viscusi is also factually incorrect when he says that using the VSL “enables policies to be guided by the preferences of the citizenry who are affected.” People in the future are very much affected, but their willingness to pay to prevent or encourage the unseen effects described above goes overlooked. The implicit model that Viscusi is using holds that the preferences of present citizens should be adhered to irrespective of any external costs imposed on future generations. In this sense, the present generation is like a dictator that gets whatever it wants. If we took that to the extreme, and the present generation wanted to consume 100 percent of society’s wealth and leave behind nothing for the next generation—essentially forcing civilization to start over—should the government work to fulfill those desires through coercive regulation? After all, it would be consistent with the preferences of current citizens. The fact that cost-benefit analysis could, in theory, produce such a horrifying recommendation—and the fact that it makes less-extreme versions of this recommendation a routine practice—should give economists everywhere pause and lead them to reconsider how analysis is presently conducted. Viscusi is correct that the problems I have identified have broader implications than just how lives are valued in cost-benefit analysis. But this does not justify ignoring the VSL’s problems. The VSL provides the basis for some of the largest benefit estimates in regulatory impact analysis. If problems with the value of life are addressed, perhaps other troubling aspects of cost-benefit analysis will be addressed too. As I said in my earlier essay, the VSL directs policymakers to “give people what they want now!” But “right now” is not all that matters. Those who care about fostering a civilization for the long term should think carefully about what it would mean to follow the VSL to its logical conclusion

Make Benefit-Cost Analysis Meaningful

In 1947, economist Tjalling Koopmans wrote a review of the book Measuring Business Cycles, written by his fellow economists Arthur Burns and Wesley Mitchell. The title of Koopmans’s review, Measurement Without Theory, referenced the fact that Burns and Mitchell had collected and summarized impressive amounts of macroeconomic data and described in detail the business cycle fluctuations they observed, but they had not drawn sufficiently on economic theory to explain the observed regularities in their data. By omitting theory that might otherwise give their data context and meaning, Burns and Mitchell’s contribution was cast into doubt. In more recent years, there is another area where theory has taken a backseat to empirical measurement: namely, the theory behind benefit-cost analysis (BCA). BCA intends to predict how regulations and other public policies impact society for better or worse. Conspicuously missing from BCA, however, is the necessary theory connecting whatever BCA is measuring to the well-being of actual people. Debates over the relative merits of theory versus empirical measurement are not new to academic inquiry. In the late 19th century, for example, a debate took place in the German-speaking world over the importance of theory as it pertains to economics. On one side of the Methodenstreit, as the debate came to be known, was the so-called German historical school, led by Gustav Schmoller. Schmoller cautioned against fundamental laws in economics, arguing that historical context and culture are always changing and that outcomes will vary by time and by place. Theory can be developed, he accepted, but careful measurement comes first. Only after extensive data collection and analysis can hard conclusions be drawn, or so members of the historical school believed. A group of economists in Austria, led by Carl Menger, viewed matters differently. They saw an important role for theorizing in the social sciences, believing that a scholar can construct an idealized model economy and, through careful reasoning, arrive at certain fundamental truths about its workings. Insights can then be extended to the messy and complicated world around us, with its nearly unlimited and imperfect data that can often be contradictory and misleading. Furthermore, any analysis of data involves theory—theory is impossible to escape—because understanding what data represent is critical to their interpretation. If we fast forward to today, we see similar debates playing out in economics. When measuring the effects of the minimum wage, some commentators act almost as if measurement is all that matters, as if “Econ 101” supply and demand theory, which would otherwise indicate that artificially high wages mean fewer jobs, can be cast aside. When an empirical study finds no employment effects, they conclude that those who reason based on the theory of supply and demand must be ideologues. But are they? Or are our data and measurement techniques too imperfect to detect the law of downward sloping demand in a fast-paced world? These kind of issues are also at play in BCA. Open up a textbook and it will likely tell you that the welfare measure underlying BCA is economic efficiency, which relates to maximizing a broad conception of society’s overall wealth. But BCA, at least as it is produced in the government and in countless academic studies, is not measuring efficiency. This is true for at least two reasons: First, BCA does not properly account for the opportunity cost of capital, detailing how capital would be employed with and without a government policy change. Second, BCA applies weights to consumption based on when the consumption occurs in time. With respect to the opportunity cost of capital, economists have understood, since the early 1960s, that the proper way to account for the opportunity cost of capital in analysis is by using a shadow price, which is a factor by which all capital benefits and costs are converted to equivalent units of consumption. Sometimes this conversion device is called a marginal cost of funds factor. Whatever the name used, without such a conversion, comparing one dollar of capital to one dollar of consumption is comparing apples to oranges. The government tries to address this issue by using a discount rate, but its approach is almost always inappropriate. Units of consumption and units of capital are all discounted at the same rate, as if they are growing over time at the same rate. But consumption benefits dissipate quickly, while the returns to capital can increase with time as some are reinvested. Treating these different benefits as if they are the same gives too much weight to consumption—capital increases social wealth by more. Through discounting, the analyst also applies a set of weights to consumption streams based on who receives them and when. If John receives a consumption benefit worth one dollar today, this benefit receives more weight than if Sally receives an equivalent benefit next year. But is Sally’s consumption really so different from John’s? Even if it is, a government analyst is ill-equipped to distinguish how this experience varies across people. From the standpoint of economic efficiency, a dollar’s worth of consumption should always receive the same weight (adjusting for inflation, of course). Equal weighting along these lines is standard within a time period but for some reason it becomes controversial across time. Analysts must be careful not to attribute characteristics of individuals—like time preference, diminishing marginal utility, or risk aversion—to society as a whole, an error known as a fallacy of composition. Ironically, Koopmans himself, through his influential work on time preference, likely contributed to this tendency of analysts to blur individual and social characteristics. BCA has been a fundamental part of the regulatory process in the U.S. federal government since the early 1980s. Executive Order 12,866, which governs the U.S. regulatory analysis and review process has just enjoyed its 25th anniversary. The government’s benefit-cost watchdog, the Office of Information and Regulatory Affairs, has existed for nearly 40 years. And yet, after all these years, what exactly is BCA measuring? If not efficiency, then what? Without a clear welfare measure, BCA is like a rudderless boat adrift at sea. It can be a useful tool, but to be truly useful in practice, first BCA has to measure something meaningful in theory

The Value of a Statistical Life is Not Efficient

A life cannot be invested in an account, but the returns to capital investment that are created or displaced by government policies can. If compounding investment returns were properly accounted for in cost-benefit analysis (for example, using shadow prices rather than improperly using a social discount rate for this purpose), it is easy to see why many regulations could cost well in excess of $100 million-per-life saved at some point in the future. The simple reason is the power of compound interest. This$100 million figure happens to be the counterproductive cost-effectiveness threshold identified in W. Kip Viscusi’s (and my own) research. Although opportunity cost may be an underutilized concept at federal regulatory agencies (and in academic research), this does not mean “no valid economic theory” supports its consideration in cost-benefit analysis. The value of a statistical life (VSL) treats compounding investment returns as equivalent to fleeting consumption. As such, reliance on it will often lead to excessive risk reduction in the present. That may well be in line with present preferences, but it is inefficient and can come at a cost of unnecessary loss of life in the future. Ultimately, analysis should present a transparent accounting of the tradeoffs between present and future interests. Present use of the VSL stands in the way of that objective

Putting the Horse Before the Cart in Cost-Benefit Analysis

Once again, I appreciate Professor Viscusi responding to my essay. I believe he has a decent grasp of my position. I would, however, like to point to several areas of misunderstanding that remain and make one important point for clarification. First, the position I am advocating for is perfectly consistent with basing benefit values on the willingness to pay (WTP) of citizens. I simply choose to take the WTP of present and future people into account, rather than to consider some generations and not others or to weight the WTP of people in an unequal way. The value of a statistical life (VSL) is based on an assumption that marginal WTP values observed in the marketplace are efficient. That assumption is untenable; although consumers and workers may optimize their own utility across their lifetimes, they are not optimizing utility across generations. The VSL gives inefficient recommendations as a result (as do many other estimates of policy benefits, as Viscusi correctly notes), and market failures are exacerbated when policy is guided by the VSL. Furthermore, I disagree with Viscusi’s contention that this is a recipe for more-dangerous workplaces or “polluted neighborhoods with hazardous waste exposures.” Viscusi’s own research demonstrates why a cost-of-death approach to valuing lives would likely reduce risk, while basing policy on the VSL will increase it over time. Regulatory policy directed by the VSL may well reduce risk temporarily—assuming, of course, that regulations are well-designed enough to work. But it generally does so at the expense of capital investment and growth, which means fewer resources to devote to health, safety, and the environment in the future. We enjoy short-run benefits while our successors have fewer resources to address those risks most pertinent to their own lives. We should not limit ourselves to a choice between their safety and our own, however. Policy can aim to increase efficiency over the long term and make people better off today according to their own values. Systematically overriding consumer choices is not required, but we do need to think carefully about what kinds of institutional arrangements produce such a balancing of present and future interests. Finally, I would like to take a moment to explain why Viscusi is wrong that “benefit-cost analyses of regulatory policies recognize the preferences of both current and future generations without shortchanging either group.” I believe Viscusi makes this claim because he misunderstands the role of the discount rate in cost-benefit analysis (CBA). He has stated in several places that he views the social discount rate as representing something like an underlying rate of productivity growth in the economy. I can hardly criticize him for taking this view. Indeed, at one time I did too. It is true that the underlying productivity growth rate he describes is represented by a discount rate in the special case of CBA, in which all benefits and costs are financial. In that special case, all benefits and costs are growing in value at the same rate. But in more general cases where some of the benefits and costs of policy are nonpecuniary—which is the case with regulations that address mortality risks—this productivity growth rate is represented by a shadow price, not a discount rate. Why? Because benefits and costs grow or depreciate at different rates, and these differences need to be accounted for separately. This may seem like a minor technical detail, but it is not. The discount rate for social regulations diminishes the value of benefits and costs that occur in the future. That is its role. Furthermore, the productivity of capital that Viscusi describes is consistently not accounted for in the regulatory impact analyses for social regulations, because no shadow price is applied to the value of capital investment. For these two reasons, I do not share Viscusi’s confidence that CBA treats all generations equitably. The lives of future citizens receive less weight in analysis than do our own, and compounding returns from induced or displaced capital investment go systematically overlooked. An additional advantage of my approach is that it is entirely consistent with the Kaldor-Hicks framework that underlies cost-benefit analysis. That criterion requires that benefits and costs receive the same weight irrespective of who receives them. The unequal treatment of benefits and costs Viscusi endorses does not meet this requirement of distributional insensitivity, and therefore is inconsistent with CBA measuring efficiency. Some tradeoffs we face may make us uncomfortable. I believe that is a key reason for the VSL’s popularity. It obscures uncomfortable truths, while reliably reaching fashionable policy conclusions. But we are economists. It is our job to face reality head on. That means settling on a meaningful measure of welfare for CBA that makes tradeoffs transparent, and then letting the chips fall where they may with respect to the policy implications. It is time to put economic science back in its proper order

The Value of a Statistical Life is Not Efficient

A life cannot be invested in an account, but the returns to capital investment that are created or displaced by government policies can. If compounding investment returns were properly accounted for in cost-benefit analysis (for example, using shadow prices rather than improperly using a social discount rate for this purpose), it is easy to see why many regulations could cost well in excess of $100 million-per-life saved at some point in the future. The simple reason is the power of compound interest. This$100 million figure happens to be the counterproductive cost-effectiveness threshold identified in W. Kip Viscusi’s (and my own) research. Although opportunity cost may be an underutilized concept at federal regulatory agencies (and in academic research), this does not mean “no valid economic theory” supports its consideration in cost-benefit analysis. The value of a statistical life (VSL) treats compounding investment returns as equivalent to fleeting consumption. As such, reliance on it will often lead to excessive risk reduction in the present. That may well be in line with present preferences, but it is inefficient and can come at a cost of unnecessary loss of life in the future. Ultimately, analysis should present a transparent accounting of the tradeoffs between present and future interests. Present use of the VSL stands in the way of that objective

The Irrationality of Market Failure Theory

On January 27, President Joe Biden signed an executive memorandum on “restoring trust in government through scientific integrity and evidence-based policymaking.” The memo included an easy-to-miss provision about incorporating behavioral science insights into regulatory policy. This provision could play an important role in the Biden Administration’s regulatory program, so it is worth investigating further. The typical economic rationale for government regulation of the economy is market failure. Underlying the market failure concept is the idea that, because of certain frictions known as transaction costs, mutually beneficial exchanges that would make society better off—gains from trade—fail to take place. These transaction costs are present in situations involving externalities, asymmetric information, monopoly power, and the provision of public goods. In these situations, neoclassical economic theory holds that government intervention can potentially be a corrective force to improve people’s welfare. Some government agencies and academics, however, have begun justifying policy interventions based on “behavioral market failures.” These failures extend the “neoclassical market failures” mentioned above to include instances of suboptimal decision-making by the public. Behavioral market failures occur because of various cognitive biases we have as individuals—which create mental transaction costs, if you will, that result in poor decisions that reduce our own welfare. Many of the biases identified by behavioral economists, who study the intersection of economics and psychology, relate to situations involving tradeoffs that occur over time. The idea is that our own decisions can be problematic if they involve up-front costs that may deter us now, but produce streams of benefits in the future (or vice versa—produce benefits upfront and costs later). Not surprisingly, these issues are often associated with self-indulgent behavior or self-control problems. Ironically, textbook presentations of market failure theory suffer from their own form of present bias. Economists tend to explain market failures in a static context without considering the effects of time. One way they do this is by failing to distinguish social benefits and costs that are consumed from those effects that are invested and grow in value with time. Even with a problem such as global warming—known to have long-run intergenerational consequences—standard economic analysis tends to evaluate only how negative externalities stemming from climate change impact the present generation’s well-being. To correct market failures, it can be worthwhile to override people’s choices. This is not to say that consumers are better off by having had their choices overridden, something behavioral economists often assert. Rather, all regulations override choice to some extent and thus impose costs. That alone is not a sufficient reason to preclude regulation. The key, however, is to account for all the impacts, not just those that occur in the present. Consider the hypothetical example of a regulation for an appliance that would save $1 million by reducing energy use. Faced with a requirement that their appliances conserve energy, manufacturers might remove a feature for which current consumers would have collectively been willing to pay$2 million because the appliance is more user-friendly with the feature. From the standpoint of the appliance’s purchasers, this is a bad deal. If there are no other benefits, they value the lost product quality more than the financial savings from lower utility bills. They would be made worse off by this regulation. But consider the same regulation from the standpoint of future consumers. If even a small fraction of the million dollars in financial savings was invested and reinvested continually, it would grow into far more than $1 million or even$2 million in the future, owing to compounding. The short-run reduction in consumer well-being as a result of lower-functioning appliances seems trivial from this long-run perspective because it is a fleeting loss. Meanwhile, the compounding gains of capital accumulation have much further-reaching consequences. If future consumers could participate in present-day markets, they might be willing to compensate current consumers to accept the lower-functioning appliance. In our example, they might be willing to pay consumers $2.1 million to accept some functionality impairment in their appliances. If such compensation took place, everyone would be made better off by the regulation without making anyone else worse off, a situation known as a Pareto improvement. Consumers today would have a worse device but would also have extra money that more than compensates them for the lower-functioning device. Furthermore, consumers in the future would benefit from increased investment that boosted economic growth and raised their incomes. Even without compensation, the winners in the future gain more than the losers in the present lose, a situation known as a Kaldor-Hicks improvement—or a potential Pareto improvement—a principle which underlies cost-benefit analysis. In this example, time creates transaction costs. Similar to how externalities, asymmetric information, and even poor decision-making result from physical or mental transaction costs, time creates a market failure by preventing mutually beneficial exchanges from occurring. The difference is that unlike with traditional market failures, harmed third parties are not yet alive or old enough to lobby on behalf of their own interests. These intertemporal market corrections have an interesting characteristic: They can reverse the policy implications of the static market failures described in economics textbooks. Say that a polluting power plant reduces air quality in a city. Residents, if they could organize, might be willing to pay the plant to reduce its emissions. But organizing involves incurring high transaction costs, so the exchange likely will not take place. This situation seems like a case for government regulation. Usually, the economic analysis stops there. But there is still the future to consider. If the pollution in our example only reduces short-term consumer well-being, for example because it only has nonmarket impacts with returns that cannot be reinvested, but regulation comes at the expense of capital accumulation and future economic growth, then people in the future might be willing to compensate present citizens to accept current levels of pollution. Well-being cannot be invested like money, so future social welfare is unlikely to be improved by regulating the power plant in this case—unless the pollution also lowers productivity and market production, causing society to forgo compounding returns to capital. It may well be that, in this case, the efficient outcome is for the power plant to produce more energy—not less—and by extension, to increase present pollution somewhat. One could also envision a different situation where future citizens pay to prevent catastrophic outcomes or pollution that causes long-term harm to the economy. The point is that standard cost-benefit analysis fails to recognize these future improvements or reductions in allocative efficiency, as it focuses exclusively on static outcomes in the present, not dynamic ones across time. The task for economists, then, is to think about policy impacts in a dynamic way. President Biden’s recent memo on scientific integrity foreshadows increased use of behavioral science to justify regulations. The irony, though, is that the same regulators and academics who presume to be able to correct the irrational decisions of others suffer from their own form of present bias, at least when it comes to cost-benefit analysis and the market failure theory that supports it. This irony prompts an obvious question: Who is it that is irrational? Is it consumers—or is it economists