How do we incorporate animals into cost-benefit analysis?

A book I’m particularly excited to read that came out earlier this year is Dog Economics: Perspectives on our Canine Relationships. This book, written by two of the leading economists in the public policy field, applies a range of economic concepts to dogs and their relationships with human beings.

I first was exposed to David Weimer’s work on dog economics when I was serving as editor for On Balance, the blog for the Society for Benefit-Cost Analysis. Weimer had recently won the Society’s Journal Article of the year award for an innovative analysis he had done to estimate the value of a statistical life for dogs.

Why calculate the value of a statistical life for dogs? Well the reason is because federal agencies sometimes promulgate regulations that will directly impact canine well-being. Weimer was drawn to this idea because the Food and Drug Administration had conducted a benefit-cost analysis on approval of a new type of dog food. A glaring omission from the analysis? The risk to health for dogs who consumed the product.

But how do we calculate the value of safer dog food? When human beings participate in the labor market, they trade off risk of death for more pay. Using these tradeoffs, we estimate how much people value marginal reductions in risk of death, which allows us to come up with the value of a statistical life.

Dogs don’t participate in the labor market. In a literal sense, dogs don’t participate in markets. They don’t have income, they don’t have assets, they don’t purchase goods and sell their services. So how can we estimate a dollar value they put on reductions of their risk of death?

Weimer’s answer is to ask their owners. Weimer conducted a “contingent valuation” study to see how much value people put on their dogs’ lives. He did this by posing questions about hypothetical vaccinations for dogs that would reduce dogs’ likelihood to contract potentially deadly diseases. By eliciting the willingness to pay dog owners have for these vaccinations, Weimer was able to derive a value of statistical life for dogs.

This is certainly a step in the right direction for incorporating the value of public policy to animals into cost-benefit analysis. But this approach still falls short of fully incorporating animals into cost-benefit analysis. Why? Because it defines the benefits that accrue to animals through their owners’ altruism rather than on their own terms.

This would be sort of like if you surveyed people on how much less income they’d be willing to take on as a family in order for their spouses to have the opportunity to take on less dangerous jobs. It should be a good proxy for the value of a statistical life, but a more accurate measure would come from seeing how much people actually take on themselves, not relying on a report from someone who cares about them.

This approach is taken often when assessing benefits to children. But even here we see problems. In his book The Child Care Problem: An Economic Analysis, economist David Blau talks about the market failure caused by parents underconsuming high-quality child care. This happens because parental demand for child care that will nurture their children and lead to better outcomes for them and society falls short of the optimal social benefit. If children could rationally decide for themselves what quality child care to consume, they would likely be willing to pay more for quality child care than parents do.

For now, though, it is difficult for us to divine how much of a value of risk of death dogs take on themselves. Some cutting-edge researchers are conducting studies with chickens, seeing how much feed they are willing to give up in order to live in more free-range environments. The results are promising: initial studies have found chickens exhibit a rational demand curve for more open space.

Non-human animals are not as different from humans as we act like they are. They have to operate under conditions of scarcity just as much as humans do. And they are often subjected to the impacts of public policy just like human beings are. Hopefully as we innovate new ways to study public policy, we will also find ways to incorporate their interest into our benefit-cost models.

How do immigrants impact the labor market?

It’s presidential election season again, which means that debates around the most politically divisive issues are front and center in the national news. Immigration is one of the most commonly debated topics, and according to data from Pew Research, 57% of Americans think that it is a top priority for the President and Congress. 

Political narratives aside, what actually happens when immigrants come to the United States? You will get extremely different answers to this question depending on who you ask, so let’s instead use this opportunity to explore some of the data surrounding immigration. Specifically, what happens to the labor market when immigrants move in. 

Before we talk about actual data, it will be useful to visit some of the basic economic theory behind this question. If a wave of immigrants move into a community and begin participating in the labor market, then theory says we should see a growth in the labor supply. Assuming that nothing else changes, this would have two main effects.

First, unemployment should rise. Assuming that the demand for labor is unchanged, then there would just be more people competing for the same number of jobs. 

Second, wages would go down. Competition among potential workers should cause a race to the bottom for wages. 

These two outcomes only happen in a perfectly competitive labor market, where all workers are competing for the same jobs and wages can fluctuate with supply and demand. In the real world, the labor market functions very differently. 

To see just how differently this plays out in real life, we can look at the example of the Mariel boatlift, a mass migration event where thousands of Cubans moved to Florida in 1980. This event was the subject of a landmark 1990 paper by the economist David Card, who explored the labor market impacts of such a sudden shock.

Card’s study was a pioneering deployment of “difference-in-differences” economic evaluation. Card looked at unemployment and wages in Miami before and after the Mariel Boatlift then compared these to changes in unemployment and wages in comparable metropolitan areas. These comparable metropolitan areas served as “synthetic controls,” mimicking an experimental design and allowing Card to see what the actual impact of the sudden influx in workers had on employment and wages

Card found that, when compared to comparable metropolitan areas, the sudden wave of immigration actually had no effect on unemployment or wages in low-skill industries. Instead, the Miami labor market was able to immediately grow and absorb these new workers. 

Why was this able to happen? The dynamics of labor markets are extremely difficult to understand, but I think there are a few main reasons.

One reason is that in a healthy economy, there should be room for growth. Many economists consider there to be “full employment” not when the unemployment rate is 0%, but rather when everyone who wants to work is able to with relative ease (the rule of thumb I learned is that full employment is equivalent to an unemployment rate of about 3% - 4%). There are far more technical definitions of full employment, but the idea is we should always expect there to be some job openings. 

The second reason is that immigrants don’t just come here to work, they come here to live. They spend money in their new communities, they start their own businesses, they grow the local economy. By moving to Miami, the immigrants from the Mairel boatlift created the opportunity for employers to expand their operations. 

The political discourse around immigration isn’t very data-driven these days, and that can be an issue. Hopefully this example helps shed some light on what actually happens when immigrants move to the United States. 

Ohio economists say immigration counteracts Ohio’s brain drain

In a survey released this morning by Scioto Analysis, 17 of 19 economists agreed that an increase in the proportion of skilled immigrant workers has helped counteract human capital loss known as "brain drain" in Ohio over the past ten years. “For a long time, Midwestern states have lost college-educated labor to other regions.  In Ohio, at least, immigrants are somewhat more likely to have a bachelors degree (about 3 percentage points) and substantially more likely to have graduate degree (about 12 percentage points) according to American Community Survey data,” wrote Curtis Reynolds from Kent State.Wesleyan. 

Similarly, 14 of 19 economists agreed that international immigration has driven a significant portion of Ohio’s Gross State Product over the past 10 years. “Although only six percent of the workforce is foreign born they tend to be in high productivity sectors such as technology and health care,” wrote Robert Gitter from Ohio Wesleyan. 

When asked about the impacts that immigration had on low-skill wages, 11 of 19 economists disagreed that international immigration led to decreased wages. Seven more were uncertain about the impact, and only one respondent agreed that immigration resulted in lower wages. 

Will Georgic from Ohio Wesleyan wrote in his comment “Since the immigration is disproportionately high-skill, it seems plausible that recent immigration has applied upward pressure on low-skill wages in the state by increasing demand for goods/services to a greater extent than the competition for low-skill jobs increased.” 

The Ohio Economic Experts Panel is a panel of over 40 Ohio Economists from over 30 Ohio higher educational institutions conducted by Scioto Analysis. The goal of the Ohio Economic Experts Panel is to promote better policy outcomes by providing policymakers, policy influencers, and the public with the informed opinions of Ohio’s leading economists. Individual responses to all surveys can be found here. 

Ohio officials turn down billions of dollars in federal aid

On April 7, Jake Zuckerman from Cleveland.com reported nearly 400,000 Ohioans have lost Medicaid coverage since pandemic-era enrollment rules were rescinded.

We all know the importance of health insurance coverage to a family. Illness or injury can plunge even a financially sound household into dire straits. But for those teetering on the edge of poverty or living in it, insurance coverage is even more important.

But there is another story to policymakers’ choices to restrict health insurance coverage for low-income residents: the lost economic development potential from federal Medicaid money left on the table.

According to the Kaiser Family Foundation, a leading source for health policy research, over two-thirds of Medicaid spending is picked up by the federal government. Kaiser also reports annual state Medicaid spending in Ohio averages over $6,900 per enrollee.

Assuming those who lost their Medicaid coverage are similar to the average enrollee, Ohio lost nearly $1.9 billion of federal dollars from denying Medicaid coverage for these Ohioans.

Let’s put that number in perspective. $1.9 billion is larger than the FY2024 budget for 57 of Ohio’s 61 state agencies, coming only behind the Departments of Medicaid, Education, Higher Education, and Rehabilitation and Correction. This is more money than Ohio spends on 44 different state agencies combined.

What does leaving $1.9 billion of federal funding on the table mean for the economy? It means less dollars going to hospitals and the greater health care system to treat Ohioans struggling with disease and disability. This means not only less resources for our health care system, but also less money in the pockets of nurses, doctors, and administrative staff to spend in the state economy.

This matters for a state like Ohio. Five of Ohio’s top ten employers are in the health industry including its top statewide employer, the Cleveland Clinic, which employs nearly 60,000 Ohioans. Health care is a $59 billion industry in Ohio, making health care 17% more concentrated in Ohio compared to the United States as a whole.

Much of this is driven by Ohio’s aging population. 18.3% of Ohioans are retirement age, higher than the national average and making Ohio older than 31 other states and the District of Columbia. This means Ohio has a greater need for a strong health care industry than the average state.

Beyond the direct economic effects of turning down federal funds for Ohio’s health care industry, there are also dynamic problems that come from reducing investment in health care. Losing out on incentives for preventative screenings can take small problems and make them worse, leading to chronic issues for workers that impact productivity.

Burdens from the cost of health care can cause people to skip out on medication or procedures which can help treat illnesses or conditions that end up hurting them in the workplace.

Legislators and other policymakers have made it clear that they care about Ohio’s workforce and economic vitality. Leaving billions of federal dollars on the table is not the way to do that.

This commentary first appeared in the Ohio Capital Journal.

Defining standing: whose benefits count?

One of the most important parts of cost-benefit analysis is clearly defining standing, or whose costs and benefits will be estimated in the analysis. In many ways, the problem of defining standing is similar to the problem of choosing which outcomes to quantify and monetize. In theory, we could keep thinking of progressively distant outcomes that may be influenced by our policy change. We could similarly ask which people are affected by our policy change further and further downstream. 

As an example, when looking at a minimum wage change in Ohio, we often focus on the impact of the policy on Ohioans. With infinite time and resources, we might be interested in modeling how an Ohio minimum wage could influence interstate commerce, but with limited resources, we might limit our standing to only those impacts on residents of Ohio. 

However, defining standing is not just an issue of deciding whose impacts we have the capacity to analyze, it often requires us to make judgements about what should or should not count in a cost-benefit analysis. One question that regularly comes up is whether or not we should give standing to impacts that are perceived as immoral or wrong. 

At last month’s Annual Conference of the Society for Benefit-Cost Analysis, I heard Richard Zerbe, one of SBCA’s founding members, talk about this issue. 

He suggested that it was sound guidance to only give standing to activities that are legal. If we accept that our laws are a reflection of the things our society values, it makes sense to let our laws dictate what has standing. Leave the questions of right and wrong to the lawyers and lawmakers. 

Of course, laws change over time, and there are countless examples of historical laws that certainly do not reflect the values of our modern society. This question became relevant to our work late last year, when we were working on our cost-benefit analysis of recreational marijuana in Ohio. 

One of the benefits we calculated was the consumer surplus that people would receive from participating in the recreational marijuana market. From an ECON 101 perspective, this is a critical part of the welfare equation. 

The question we were faced with was whether all of this consumer surplus was a new benefit, or if there was already  some consumer surplus that people who use recreational marijuana illegally were receiving. While it seems odd to only count that benefit assuming legalization occurs, it would go against the norm for cost-benefit analysis to include the illegal consumer surplus.

In the end, we ended up not counting the consumer surplus from the illegal market in our analysis. This was more a factor of the difficulty that came with estimating exactly what that would be, given the fact that we could never fully understand the size and prices that existed in an illegal market. 

I think if we were able to better estimate the illegal market for marijuana, this would have been a situation where giving standing to an illegal good would have been appropriate. Clearly, the legality of marijuana did not reflect its current social acceptance. This is evidenced by the fact that it is legal in almost half the country, and the vote passed quite convincingly in Ohio. As with every part of a cost-benefit analysis, the most important thing is to be extremely clear about what assumptions are being made. 

What is equity’s place in cost-benefit analysis?

Last month, at the Annual Conference of the Society for Benefit-Cost Analysis, I had a fascinating conversation with an economist from North Dakota. He was struggling with a concern he had in his work about the implications of his cost-benefit analysis methods. Essentially, he was trying to calculate what the impact would be if a city stormwater system got overwhelmed. 

From an engineering perspective, the answer was quite clear. The excess water would move downhill, and flood two adjacent neighborhoods: one with moderate-income single family houses, one with low-income trailer homes. Barring a complete overhaul of the entire stormwater system, it appeared as if the city had to choose which way to direct the water in the event of a severe storm.

Using a basic cost-benefit analysis framework, it seemed that allowing the trailer homes to flood would be a much less costly outcome. This is due to the fact that property values are much lower in this neighborhood and the monetary value of damages and the needed repairs would be much smaller. 

However, this doesn’t take into account the structural disadvantages that residents of this neighborhood face. Although it might cause a higher amount of property damage should the single-family homes flood, people living in them would be more resilient to that event. 

This type of equity concern is extremely difficult to account for in cost-benefit analysis, and it has become one of the most pressing current issues in the field. 

Beyond this lunchtime conversation, multiple paper sessions and panel discussions focused on this same question. How can we take this analysis tool that is so narrowly focused on measuring efficiency, and use it to ensure we achieve equitable outcomes as well? 

This is an interesting element to try and add into a cost-benefit analysis framework, because often we assume that there is some tradeoff between equity and efficiency. In his 1975 book on the topic, Arthur Okun describes this tradeoff using the metaphor of a leaky bucket. 

He posits that pursuing an equitable outcome is like moving water between wells with a leaky bucket. The resources we use to improve outcomes for less welloff individuals comes from people who are more well off, and to get them where they are more needed there will be some economic loss along the way.

While it is not impossible for some policy decisions to improve both equity and efficiency, it is often acknowledged that we need to balance these two policy goals. 

The most commonly proposed method to incorporate these equity considerations into cost-benefit analyses is distributional weights. In practice, distributional weights suggest that costs and benefits matter more or less depending on who accrues them. 

From a theoretical perspective, we can justify this by looking at the marginal utility gained or lost by different groups of individuals. For example, we might expect very wealthy individuals to lose less utility from some income-reducing policy, because they care less about that marginal amount of income. 

In practice, this really isn’t an efficiency concern. If we weight outcomes for different groups, then we are no longer looking for the most economically efficient outcome. Instead, we are trying to find the “socially optimal” outcome. 

This blend of efficiency and equity analyses doesn’t neatly fit into the rigid lines of economic analysis. When we combine these two into a single algorithm, we are trying to pursue two goals at once, and distributional weights are an attempt to make them comparable. 

If it sounds like I don't think distributional analysis is a good addition to cost-benefit analysis, that is certainly not true. Theoretical questions aside, the current state of economic analysis does tend to favor currently advantaged groups, like the owners of those single family homes. Economists should be considering equity in their analyses, they just maybe should be willing to say that equity is important out loud. 

What is distributional analysis’s place in benefit-cost analysis?

At last week’s Annual Conference of the Society for Benefit-Cost Analysis, one topic was all the rage: distributional issues. Much of this discussion stems from the new Circular A-4 produced by the Office of Information and Regulatory Affairs, which emphasized the importance of distributional analysis in federal economic analyses.

At its heart, benefit-cost analysis is not a tool designed for distributional analysis. It is focused on economic efficiency, helping policymakers understand whether a policy change will “grow” the proverbial economic pie rather than just slicing it differently.

A rationale economic theoreticians often appeal to when justifying the use of benefit-cost analysis in policymaking is the Kaldor-Hicks efficiency criterion. This “test” says that if a policy changes the amount of resources in the economy such that the “winners” can compensate the “losers,” that it is economically efficient.

Kaldor-Hicks has come under fire over the years. One of the criticisms lobbed at Kaldor-Hicks at the conference is that losers of a policy are rarely compensated through the policy change itself, so the test seems more like a theoretical exercise than a practical concern.

I personally have never understood the appeal of Kaldor-Hicks. I understand the intuitive appeal of a policy that makes everyone off without making anyone worse off, but I also think benefit-cost analysis offers an even more intuitive insight. It simply tells us how much aggregate welfare is increased by a policy.

Okay, “aggregate welfare” is kind of a vague term. What I really mean by this is the total amount and value of stuff that people want in the economy. And by “stuff,” we’re talking broadly: not only goods, but also services, free time, and environmental goods in the economy.

This is utilitarianism in practice, but utilitarianism as defined by how people value things in the economy. When we utilize the value of a statistical life in an analysis, we’re using estimates of how much people themselves are willing to reduce risk of death as a way to measure the intervention against taking away their own resources to finance these small reductions in risk of death.

Now what does this all mean for distributional analysis, or our analysis of who gets what after a policy change? Benefit-cost analysis isn’t at its heart designed to answer this question, but it is a valuable tool for doing that. Knowing how a policy will impact the economy then lets an economist understand who will be impacted by a policy.
In light of this discussion, here are a couple tips for incorporating distributional analysis into benefit-cost analysis.

Show distributional impacts of policies

Wherever possible, show who will be impacted by a policy. Sure, a tax that takes money from workers in order to pay administrators will be a transfer, but showing who will be impacted is valuable for a policymaker, especially someone interested in the impact on workers.

Apply distributional weights as sensitivity analysis

Distributional weights, or multiplying different people’s impacts by a certain amount, have been controversial in benefit-cost analysis. The reason for this is that they seem to apply a judgment by the analyst to the inherent “value” of impact to different groups of people. Analyst after analyst at the conference, though, put forth the same point: assuming multipliers of one to every group is making a judgment in itself. By applying distributional weights as a type of sensitivity analysis, like through use of break-even analysis, it can help a policymaker understand how much a policy impacts one group compared to another.

Allow distributional analysis to stand on its own

Don’t think that benefit-cost analysis can fully incorporate distributional analysis. At its heart, benefit-cost analysis is designed to measure aggregate welfare. Thinking equity analysis can be “smuggled” into benefit-cost analysis is covering up the insights of equity analysis. Presenting the insights of efficiency and equity analysis separately allows the policymaker to weigh the two concerns against one another and make the decision herself.

Distributional analysis is still a frontier of benefit-cost analysis. That doesn’t mean we can’t apply it and use it today. Good policy analysis requires good equity analysis, and distributional analysis drawn from insights in benefit-cost analysis is a powerful tool for helping policymakers understand the impact of public policy.

Policy Analysis: Whose Perspective Matters?

Last week at the Society for Benefit-Cost Analysis’s annual conference, I had the opportunity to listen to Catherine Wolfram speak on carbon pricing in the United States. Dr. Wolfram is an economist from MIT who previously served as the Deputy Assistant Secretary for Climate and Energy Economics at the U.S. Treasury. 

As an economist who worked in government, Dr. Wolfram spoke not just about the economics surrounding carbon pricing, but also went into a lot of detail talking about the politics surrounding this question. This was a unique moment during this conference, where speakers often appear agnostic about politics, despite addressing very politically charged questions. 

The interesting thing about this talk in particular was not the claim that carbon pricing has become a polarized topic, we need only look at the past administrations’ social cost of carbon to see that clearly. The talk was interesting because most economists try to avoid the fact that they are talking about political issues. 

On one hand, avoiding politics allows economic analysis to remain more objective. While every analysis is biased by the analyst's context, at the end of the day math is math and presumably a well-done study should yield the same results regardless of who is performing it. 

However, the goal of policy analysis is to inform public policy decisions, and in a democracy, public policy decisions are largely made by politicians. If there isn’t any political will to explore a policy option, then a policy analysis might not be worth the effort. It could be the case that policy analysis will be used to help create political will for a project, but we don’t want to waste time on projects that have no chance of being informative. 

Another reason policy analysis is sometimes overlooked in the decision making process is that not everyone agrees that economics is the most important tool for evaluating impact. 

One question posed by Dr. Wolfram during her talk was whether the importance of economists and policy analysis has actually been a hindrance to the advancement of environmental policy. Her argument for this was that while economists have debated over the fine points of carbon pricing, trying to figure out the best solution to an extremely important issue, climate change has kept progressing. Perhaps, this is a situation where great has been the enemy of good. 

Another point she made was that people who specialize in other disciplines dislike the importance that economics has in policy decisions. It is a little strange to them that some of the most important environmental policy decisions are being influenced by economists and not ecologists. 

All of these points led me to the same conclusion: economics is an excellent tool for understanding public policy, but it is not the only perspective that matters. While a well done cost-benefit analysis might rely on the most recent biology or psychology research, to create the best public policy, we need to involve biologists or psychologists. 

It is not enough to do an economic policy analysis, and pass it along to the policymakers. When possible, the opinions of other experts and decision makers should be included. By collaborating more frequently, we can get closer to the ultimate goal of improving public policy.

Is the Value of a Statistical Life lower for older adults?

Last week, I was at the Society for Benefit-Cost Analysis’s annual conference in Washington, D.C., where I had the opportunity to learn from some of the leading experts in economic analysis about the newest developments in the field.

One topic that has been a major talking point for decades is how analysts monetize changes in the risk of death, which we commonly refer to as the value of statistical life (VSL). We’ve written in detail about VSL in the past, but as a reminder, VSL is not based on how valuable human life is, it is based on how much people are willing to pay for small reductions in the probability of death. 

One of the most debated questions in the VSL literature is whether or not its value should be the same for everyone. My colleague Rob Moore has written about this problem and some of the main questions that surround this topic. However, at this year’s conference, one presenter was focused specifically about how we should think about VSL for elderly adults. 

This presentation was somewhat responding to a talk that was given at the 2023 annual conference, talking about the VSL for young children, which concluded that the child VSL should be higher than the adult VSL. In that presentation, the key insight the researchers had was that children do not have resources to trade for risk of death reduction, because their resources are managed by their parents. As a result, we should look at how much parents are willing to pay for risk of death reduction on behalf of their children, which is indeed higher than what they would pay for reducing their own risk. 

The presentation I attended at the conference last week took some issue with this approach. The main problem the presenters had was the fact that assuming child VSL is higher than adult VSL is a dramatic departure from the labor market approach that the original estimate of VSL is derived from. 

Although this is not the main argument last year’s researchers made, it is a logical extension that if we depart from the labor market method to arrive at a higher VSL for children, we could follow the same rationale to arrive at a lower VSL for elderly adults. 

Researchers have shown that ageism is an extremely costly type of prejudice that exists in our society. While the technical minutia that informs cost-benefit analysis might not appear to have significant equity concerns, if we understand their full implications we can see how these decisions could inform policy that disadvantage certain groups. If we assume that VSL is lower for elderly adults, then things like high quality elder care become much less valuable from an economic perspective. 

The point I took away from this discussion is not that there is a particular right way to calculate VSL, but rather that we need to be fully aware of the implications our calculations come with. If we decide a variable VSL is appropriate because it allows for a more efficient allocation of risk reduction, then we need to make sure we are not reinforcing negative biases with our decisions.

What are the frontiers of cost-benefit analysis?

Last week, Michael and I attended the annual conference for the Society for Benefit-Cost Analysis in Washington, D.C.

This is the once-a-year conference where academics, agency economists, and independent consultants get together to talk about the state of and cutting-edge research around benefit-cost analysis.

One of the sessions I went to that stuck out to me was on the topic of frontiers of benefit-cost analysis being investigated by federal agencies. In March 2023, the National Science and Technology Council established a subcommittee on the topic. The subcommittee was focused on helping agencies conduct three specific tasks:

• Sharing knowledge and expertise on advancing benefit-cost analysis,

• Aiding each other in accessing new data, methods, and expertise, and

• Identifying areas where additional research, including by non-governmental actors, could meaningfully advance agency capacity to quantify or monetize costs and benefits.

At the Annual Conference, representatives from the Subcommittee presented their 2023 Annual Report, which covered some of the most pressing needs by agencies in the area of benefit-cost analysis. In this report, the authors identified five areas where federal agencies needed help understanding how to quantify and monetize costs and benefits for use in regulatory analysis.

Non-Fatal Health Effects

The value of a statistical life is a well-traveled path in benefit-cost analysis. Much more sticky for analysts is how to value the cost of effects that reduce quality of life without ending lives. Quality Adjusted Life Years (QALYs) have been used in medical cost-effectiveness analysis for decades, but this methodology has some limitations that make them hard to use in benefit-cost analysis. Guidance on how to quantify and monetize the impact of non-fatal health effects could be valuable for evaluators doing regulatory impact analysis.

Ecosystem Services Effects

When an ecosystem deteriorates, it can cause harm to humans. For instance, removal of wetlands can reduce the ability of the water system to remove toxins. Evaluation and monetization of these impacts could help agencies such as EPA better evaluate their interventions.

Wildfire and Extreme Weather Effects

Estimating what the future impact of extreme weather will be comes with a lot of uncertainty. Creating estimates that are credible and sound will help policymakers create regulations related to climate change in assessing the impact of their interventions, as well as giving tools to emergency management agencies such as FEMA to use in planning.

Information and Transparency Effects

Much of what federal agencies like the Food and Drug Administration (FDA) do is require information-sharing so consumers have a clear idea of what they are purchasing. Capturing the economic value of these information requirements can be difficult, however. Clearer estimates of these can help agencies balance the costs of information distribution with their potential benefits.

Effects of Public Benefit Programs

Many policymakers are interested in the multigenerational impacts of public benefit programs like SNAP (formerly known as “food stamps”). Giving a full picture of what the impacts of these programs are will help policymakers assess the costs and benefits associated with extension or regulation of benefits.

In addition to these five, the subcommittee also identified two cross-cutting issues: analyzing distributional effects and analyzing risk.

If you are an early-career researcher who wants to make a difference, focusing on one of these topics would put your work in line with what policymakers need today. Now is a good time for researchers who want to be able to make a difference in the policy space.