The power of a well done cost-benefit analysis

Normally at Scioto Analysis, we talk about issues related to state and local government. This is because we believe that not enough rigorous policy analysis goes into decision making at the state and local level. To understand why this is an issue, we will explore a recent analysis done around a revision to the EPA’s lead and copper pipe rule. 

For context, the EPA is required to perform economic analysis of any potential policy proposal that they expect to be “economically significant.” The threshold they commonly use is any policy that they expect to have either costs or benefits of at least $100 million. There are other rules that require an economic analysis to be performed, but this is the most common. 

In January 2021, the EPA issued new Lead and Copper Rule Revisions with the goal of reducing contamination in water supplies across the country. This revision strengthened the initial Lead and Copper Rule introduced in 1991. 

This change was large enough to trigger an economic analysis, and sure enough the EPA found that these changes would cost about $335 million and produce $645 million in benefits. The costs included things like water sampling, lead pipe replacement, and education about lead pipes among others. The only benefit they monetized was the increased earnings children would experience due to a reduction in lead exposure. 

From a policy perspective, these changes are positive, but not overwhelmingly positive. Benefit-cost ratios of 3:1 and 4:1 are not uncommon with well designed policies. Given that government agencies have to act within budget constraints, it would not be unreasonable to think that there are better ways to use our limited resources than enforcing these particular rule changes. 

However, the EPA only monetized one health benefit from this change. While on one hand we might think that being conservative in our assumptions protects us from investing into unhelpful programs, being overly conservative prevents us from maximizing our limited resources. 

Researchers Ronnie Levin and Joel Schwartz from the Harvard School of Public Health took a second look at these changes and found that the benefits from this rule change could actually be as high as $9 billion, a benefit-cost ratio of 35:1.

The two major changes these authors made were to monetize the infrastructure benefits that are associated with removing lead pipes and to expand the monetized health benefits to include a wider range of expected improvements than just future earnings increases.

With such wildly different estimates of the costs, it is important to take a step back and think about which research has the more believable assumptions. From my perspective, I tend to agree with the $9 billion figure more. 

While it is generally best practice to make conservative assumptions, there is a ton of research on the effects of lead pipes that stretches far beyond future earnings. Not monetizing those other effects doesn’t really make sense from a research perspective.

Taking it a step further, it would even be possible to make the case that Levin and Schwartz were also being a bit too conservative with their estimates. There is a broad literature on the effects of lead exposure on crime, and excluding those benefits might mean the actual benefit of this policy is even higher than just $9 billion.

Bringing it back to the policy decision, there is obviously a major difference in how we would think about a proposal that we expect to have 35:1 returns. It would be foolish for policy makers to ignore something with this much potential. It also makes it much less likely going forward that this rule would get rolled back in the future to fund some other policy proposal.

This is what well done policy analysis can bring to the table. Instead of this change being thought of as a good but not great policy that could be cut if something else were to come along, we should think of it as a critical investment that should be prioritized over other less valuable programs.

While the stakes might be higher at the federal level because of the size and scope of the policies, state and local governments often have to operate under much tighter budget constraints. Effectively maximizing those budgets is a critical part of ensuring that everyone in our society gets to lead their best lives. 

A national look at jobs and income

Earlier this month I wrote about what the middle class looks like in Ohio, using data from the American Community Survey to break out what the most common jobs were in each income bracket. This week, I performed the same exercise but with data from across the country. 

There is a lot of good information in this table, so I will share some of the things that jumped out to me. 

• Because this is a national survey, all of the most popular jobs across all income brackets are pretty universal. The industries represented are parts of basically every local economy. I would argue the exception to this is with the top 1% of earners, but that is  because there are much fewer people with those jobs, and not many industries can support incomes that high for individuals.

• There is a surprising amount of consistency between the lower and upper middle class (21-40 / 61-80). There are changes in the order of the top 10 most common jobs, but we see many of the same jobs appear multiple times across this range. For example, drivers/sales workers and truck drivers are in each of the middle class brackets..

• Most jobs that appear multiple times are in neighboring income groups. This suggests that these are the likely income ranges for those particular jobs that span across deciles. 

• The upper income groups (80th percentile and up) are dominated by managers and specialized professionals. It appears to be extremely difficult to earn in the top 20% without going beyond a bachelor’s degree.

Unlike the middle class jobs, some categories at the low end of the income spectrum do not appear multiple times on this chart. This tells me that people working these jobs do not have a lot of potential to increase their earnings without making a big career change. Programs to help facilitate these changes could be effective in helping increase their wages. 

Looking at differences between the United States as a whole and Ohio, there are a few key differences to note. One important difference is that laborers/freight workers are not as prominent in the higher income brackets in the United States as they are in Ohio.

This is a good demonstration of how local economies can influence wages. I imagine that if we looked at the top jobs in the other rust belt states, we would find more people with high incomes working in this industry. 

One thing that is similar between Ohio and the nation as a whole is the types of jobs  low income people are working. From a classical economic view of labor markets, it makes sense that if the supply for people to work these jobs is high everywhere, then wages would be driven down. 

Similarly, the highest paying jobs are very close between Ohio and the nation as a whole. The only job in Ohio’s top 10% that does not appear nationally is sales representatives for wholesale and manufacturing. This again reflects the importance of manufacturing to Ohio’s economy. 

Hopefully these charts shed some light on what the economy actually looks like for people across Ohio and the country. I know that I only scratched the surface, and I’m sure this data will be useful for all kinds of future projects.

Ohio economists think merit scholarships could combat brain drain

In a survey released this morning by Scioto Analysis, 13 of 17 economists surveyed said that a merit based scholarship program for Ohio high school students who are in the top 5% of their class and attend Ohio colleges and universities could help combat brain drain in the state. The other 4 economists were uncertain, and none said that they disagreed.

Kathryn Wilson from Kent State commented “keeping high-achievement students in Ohio for college is a good way to increase the likelihood that they ultimately become workers in the Ohio workforce.”

Respondents who were uncertain about the impact of merit based scholarships pointed out that high school students at the top of their class might be eligible for other scholarships at more prestigious out of state universities. “I am not sure how much this would actually keep high-performing students in state for college. Out-of-state tuition is much higher so some will stay in state anyways, but these are all high-performing students who may get scholarships anyway. Furthermore, it is not clear that they would stay after college” wrote Curtis Reynolds from Kent State.

From an inequality lens, the economists are uncertain about the effects of merit based scholarships. On one hand, merit based scholarships based on high school grades might increase inequality, since performance in school is correlated with other indicators of inequality such as parents' education level. 

However, as Kathryn Wilson pointed out in her comment “The award will go to those who graduate in the top 5% of their class, which includes the top 5% of students in lower-income school districts. While within any given school it is likely that the top 5% come from families with higher socioeconomic status, giving the scholarship across all the schools may not increase inequality as much as expected.”

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. 

Health inequity alive and well in Ohio

Last month, the Health Policy Institute of Ohio released its annual Health Value Dashboard, its marquee report analyzing health outcomes and cost of healthcare in the state of Ohio. This year, the report had a special focus on health equity. This section covers both the progress the state has made on the health equity front and the challenges Ohio still faces in reducing disparities between different groups in the state.

The dashboard shows how persistent disparities are in Ohio.

For instance, Black Ohioans are much more likely to experience racial discrimination than white Ohioans. A Black state resident is 10 times more likely to be treated worse in healthcare or at work due to race. A Black child in Ohio is nine times more likely than a white child to experience unfair treatment due to race. And Black residents are six times more likely than white residents to experience physical or emotional symptoms due to this discrimination.

Black Ohioans also suffer from social constraints compared to white Ohioans. They are six times more likely to be incarcerated compared to white Ohioans, four times more likely to not own a car, and three times more likely to be unemployed.

Black children are especially vulnerable, four times more likely than white children to be food insecure, three times more likely to be in poverty, and three times more likely to die in infancy.

Hispanic Ohioans suffer their own struggles. They are nine times more likely to suffer unfair treatment due to race as children and four times more likely to have physical or emotional symptoms due to discrimination than non-Hispanic whites.

Hispanic children are three times as likely to be food insecure and twice as likely to be in poverty as non-Hispanic white children. And adults are three times as likely to not have health insurance, twice as likely to not have a college degree, and twice as likely to be unable to see a doctor due to cost as non-Hispanic white Ohioans.

Having a disability also puts Ohioans at a disadvantage. People with disabilities in Ohio are three times as likely to be depressed, to be out of the labor force, or to not graduate from high school than those without a disability. They’re also twice as likely to be food insecure as children or suffer adverse childhood experiences like abuse or neglect.

Being low-income also creates challenges for Ohioans. Low-income Ohioans are 190 times more likely to be severely housing cost burdened than higher-income Ohioans and 55 times more likely to be food insecure.

LGBTQ+ Ohioans, too, experience disparities. LGBTQ+ youth in Ohio are five times more likely to consider suicide and four times more likely to attempt suicide than heterosexual youth. LBGTQ+ adults in the state are three times as likely to experience depression than heterosexual adults.

This snapshot gives us a picture of what health inequities look like in Ohio. A blend of economic and social changes will be needed to level the playing field. People need resources in order to access health care, but they also need a society that provides fair treatment and accepts all people, no matter what their ethnicity, physical or mental challenges, socioeconomic status, or sexual identity happen to be.

In particular, the report highlights workforce interventions like career technical education, childcare subsidies, and paid family leave. It also proposes mental health interventions like mental health and addiction workforce recruitment and retention, integration of physical and mental health, and recovery housing. Last, it suggests healthcare system improvements like primary care workforce training, school-based health services, and cost containment.

These sorts of interventions could improve health quality in Ohio and potentially close gaps between key groups in the state.

This commentary first appeared in the Ohio Capital Journal.

Will Ohio legalize recreational cannabis?

A group called the Coalition to Regulate Marijuana Like Alcohol is currently collecting signatures to legalize the cultivation, manufacturing, testing and sale of cannabis to Ohioans age 21 and up via a ballot initiative later this year.

The argument that cannabis should be treated the same as alcohol is a common one in legalization circles.

Experts tend to say that alcohol is more dangerous than cannabis. A 2010 study of public health and safety experts rated alcohol as the most dangerous of a list of 20 drugs, ahead of heroin, crack cocaine, and methamphetamine, the next three highest. Cannabis came in eighth in the rankings, far behind alcohol but far ahead of hallucinogens like LSD and mushrooms, which were at the bottom of the list.

As I’ve written before, cannabis is not a harmless drug. States that legalize recreational cannabis have higher rates of hospitalization for cannabis use. Legalization can also lead to more instances of impaired driving, which can extend harm to others besides the consumer of the drug. Long-term cannabis use is also related to higher levels of persistent elevated anxiety.

Meanwhile, alcohol continues to be a public health problem in Ohio. The CDC estimates over 5,700 Ohioans die from alcohol use per year, which is more than died in 2022 from Alzheimer’s or diabetes. 

These alcohol-related deaths come from poisonings, liver disease and cirrhosis, hypertension, homicide, suicide, motor vehicle crashes, heart disease, and a range of other effects of alcohol use. Alcohol is readily available and a big part of the culture. These facts combined with the danger of the drug itself leads to a lot of loss of life over a given year.

Mass legalization of cannabis is unlikely to have as big an impact on public health as alcohol currently does. Even in highly deregulated environments, it is hard to imagine cannabis use being as common as alcohol use in Ohio. To the extent that cannabis is a substitute for alcohol use, it could even curb some of the excesses of alcohol in the state. For instance, cannabis has not been found to have the relationship with suicide risk that alcohol does.

Now that nearly half the states in the U.S. have legalized recreational cannabis, we have seen that mass pandemonium has not ensued. As states like Missouri and South Dakota have passed recreational cannabis ballot initiatives, Ohio pursuing its own initiative seems almost…mundane at this point. 

Once again, Ohio is following in the footsteps of other states, making some false starts with a failed oligopolistic ballot initiative in 2015 and now potentially putting forth a more viable option in 2023.

Policy doesn’t need to be innovative to be good for people. There is certainly a prudence to waiting and seeing what happens in the other “laboratories of democracy” before adopting a policy locally. But it does make me wonder if there are better ways for us to do policy like this. 

Medical cannabis is a good intermediate policy, why haven’t we seen a thorough evaluation of that program? The only evaluations that have been done focus on “customer satisfaction,” leaving out goals like public health impacts.

And I have a feeling that’s what we’ll see when recreational cannabis becomes legal here, too. A bunch of tax revenue and a dearth of evaluation of the public health impact associated with it. Yes, programs with the revenue could make up for the public health impacts, but there is no way for us to know without evaluation.

This commentary first appeared in the Ohio Capital Journal.

What does Ohio's middle class look like?

Back in 2014, NPR reported a table of the top jobs by income category. It was a super insightful snapshot that helped contextualize what sorts of occupations people have across the income spectrum.

This has been an important reference for a lot of our work at Scioto, and we thought it would be interesting to update it for Ohio. So we went to the data, and made our own list of the top 10 jobs by income category for Ohio. 

This table was made using data from the American Community Survey which is collected by the Census Bureau annually. We used the five-year estimate from 2021 for this, meaning the data represented here are actually from as far back as 2017.

One interesting takeaway is how jobs that require education beyond a high school diploma are distributed across the chart. For example, the most common jobs in the six lowest income brackets don’t typically require advanced degrees. Additionally, the only category that has only jobs that typically require an additional degree is the top bracket.

Another interesting takeaway from this chart is how incomes for individual jobs are spread across multiple brackets. Because we are looking at the 10 most frequent jobs in each income group, it makes sense that we see the same jobs pop up multiple times. These are all the most common jobs, and with so many people working in these professions, there is bound to be differences in wages. 

People who are laborers and freight workers stand out as having one of the most variable incomes. These jobs are very common among a range of people, from those whose income would likely mean they fall under the federal poverty line all the way to people who would probably be categorized as upper-middle class. One potential cause of this might be the role of labor unions in these industries. 

One final takeaway that stood out to me is that the only income brackets that have jobs that don’t appear somewhere else are the very top and very bottom brackets. For the majority of the most common jobs in Ohio, There is a lot of variability in the potential wages. The exception is if you are at the very top or very bottom of the income distribution. 

I think this is important because for most jobs, there appears to be some potential for upward mobility. While this is not universally true and depends a lot on context, it is possible for people to stay in the same industry and earn more with more experience. This may still involve changing employers, but it seems like major career changes are not the only path to higher earnings.

The exception is if people are at the very bottom or the very top of the distribution. These people have much less ability to move around without making a big career change. For the top of the distribution, this isn’t too bad. Chief executives aren’t often looking for a new vocation.

For the bottom of the income distribution, this presents a pretty significant problem. Namely that it is much harder to develop human capital when you are spending all of your time and energy just trying to get by. From a policy perspective, job training for people in these lines of work could be an avenue for reducing poverty.

Take a look for yourself at the table and see if anything interesting jumps out to you.

Will AI destroy the middle class?

With the arrival of ChatGPT, economic prognosticators across the country are talking about what AI could do for the economy.

A recent poll by the University of Chicago’s IGM forum (the inspiration behind Scioto Analysis’s Ohio Economic Experts Panel) found only 2% of panelists disagreeing with the statement that artificial intelligence will have a big impact on incomes over the next few decades.

One large impact of artificial intelligence on the U.S. economy could be to transform middle-class jobs. Artificial intelligence could make jobs easier or even eliminate the need for certain jobs by fulfilling the key functions of these jobs. Many of these jobs are those currently in the middle class.

For our purposes here, I will define “middle class jobs” as jobs among the most common in the 20th to 80th income percentiles. I picked these from a great NPR analysis from 2014 that presents American Community Survey data on the most common job categories per income percentile.

Primary School Teachers

Primary school teacher is arguably the most common middle- and upper-middle-class job, making up the most common job category in the 50th to 60th and 60th to 70th income percentiles and coming just behind managers in the 70th to 80th income percentiles.

A 2020 study by McKinsey estimated that the average K-12 teacher spends 22 hours a week on preparation, evaluation and feedback, and administration, 10.5 of which could be reallocated through AI and new technologies. Student instruction and engagement, coaching and advisement, and behavioral-, social-, and emotional-skill development, takes an average of 24.5 hours, only 2 of which can be reallocated. This suggests that AI could free up time teachers spend on preparation, evaluation, and administration in favor of more face time with students.

Seeing as this study also found that 70% of teachers identify lack of time as a primary barrier to personalizing learning, this could also help teachers tailor learning more toward students.

Managers

Managers are the top employment category for the 70th to 80th percentile of incomes and right behind primary school teachers for the 60th to 70th percentile.

Managers are often the people who will have to make decisions about when artificial intelligence is employed or not. According to a Wharton School blog, AI could be relevant to managers who spend a lot of time on administrative tasks, screening resumes of job candidates, customer service, marketing, and merchandising.

Truck Drivers

Truck drivers might be the jobs that are most threatened by artificial intelligence technology. Trucking is a top 3 profession in every income decile from the 20th to the 70th percentile in the United States.

Autonomous trucking could functionally eliminate the need for millions of trucking jobs across the United States. Goldman Sachs projects that in 20 years, the United States will lose 300,000 trucking jobs a year to automation. Given that the average trucker in the United States is 47 years old, it is probably not a good time for young people to get involved in the trucking industry.

Secretaries

Secretaries are the cornerstone lower-middle-class employment category in the United States. They are the most common employment for jobs at the 30th to 40th and 40th to 50th percentiles of income and are only behind nursing aides for the 20th to 30th percentile of income.

While a lot of the duties of secretaries (scheduling, managing records) are being automated, there are other roles that could be harder to automate. As long as managers and professionals need someone with a human touch to assist them, secretaries will be in demand.

What I take away from this list is that the idea that AI will take away middle class jobs is a bit simplistic. While trucking is almost sure to suffer as an area of employment, I don’t see primary school teachers, managers, or secretaries being quickly automated away. If anything, their jobs will get easier and free them up from tedium in order to carry out more mission-critical work. I also wonder which jobs could arise from AI: will AI open opportunities for people to do things they weren’t able to do before?

It seems that the best way to be realistic, looking at the data available, may be to be optimistic.

What is "human capital?"

Recently, I’ve been working on updating the Genuine Progress Indicator indicators for Ohio, and one of the new categories I’ve been working on has been services from human capital. In the context of GPI, this is basically how much value things like green jobs and education generate in terms of increases in long-term earnings. 

More generally though, human capital refers to the knowledge and skills that helps an individual be productive. In practice this means that if two people are performing the same job, the person with more human capital would be more efficient because she would know how to do it better.

In many ways, human capital is a lot like the economic concept of utility. It’s not something that we can ever actually measure, there doesn’t exist a well defined human capital statistic, but we know as a society what sorts of things contribute to it. 

Breaking it down even further, human capital can be separated into two main categories: general and specific. As the names suggest, general human capital refers to knowledge and skills that are universally applicable. Things like basic levels of education and communication skills. Specific human capital is the specialized training that individuals have. This might include higher education or mastering a trade.

The reason it’s important to understand all of this is because policymakers are always trying to find ways to invest in the development of human capital. One of the easiest ways to grow a local economy is to invest in training those individuals. 

From an equity perspective, this is especially important for marginalized communities who have historically had much less access to pathways to human capital development like higher education. There is a positive generational feedback loop where individuals who have more human capital are likely to earn higher wages, which leads to their children having greater access to human capital development. 

Another important consideration when investing in human capital is retaining the individuals receiving the training. A common issue in areas with relatively low human capital is brain drain, which is when people who grow their human capital leave to go live somewhere where they can presumably earn more. To counter this, policymakers can offer incentives to highly skilled workers who choose to stay in their communities.

Another issue relevant to human capital is how it interacts with the criminal justice system. People who end up in prison have an especially hard time finding jobs after their sentence. This struggle makes it more likely that in order to survive, they may need to turn to more illegal activity.

This problem has deep roots that no one program can fix, but increasing job training programs in prisons is a step in the right direction. If inmates have the opportunity to increase their human capital during their incarceration, their odds of integrating back into the workforce upon release can be increased.

Improving human capital will not fix all the problems in society, but it can give individuals more opportunity. People with greater human capital have greater career flexibility, more opportunities, and can contribute more to their local economies. Policymakers interested in economic growth should always be looking for ways to invest in people and retain their skills.

Five myths about carbon pricing

There is a new working paper out this month titled Five Myths About Carbon Pricing by Gilbert Metcalf, professor of economics at Tufts. The goal of the paper is to explain some of the common misunderstandings non-economists might have about carbon pricing.

Usually it is our job as analysts to translate the work of academics into a more accessible format for policymakers, so it is refreshing to see work like this. The paper is excellent and well worth a read for someone interested in better understanding carbon pricing. 

Still, the paper proves its points by examining theories and formulas, so it is not necessarily an easy read. So if you want to understand carbon prices more but don’t have the background to take in lots of math, let's go over the five myths Metcalf talks about, trying to focus on the real world implications. 

Myth 1: Carbon pricing will hurt economic growth

Policymaking is all about making tradeoffs, and of course there must be some economic tradeoff to carbon pricing. This tradeoff is the justification the Trump administration used in 2017 when they backed out of the Paris agreement. 

Although some tradeoff does exist, we need to ask how big the potential economic loss is. Fortunately, some parts of the world have begun to implement carbon taxes, allowing researchers to compare how these areas perform against their peers. 

Using methods such as differences-in-differences and panel regressions, researchers have found that the economic downsides of carbon pricing are likely very small, if they exist at all. With all new research, there should be healthy skepticism about how these results can be applied going forward. Still, the fact that their carbon pricing has not dramatically harmed the economies of any of the places that have implemented it makes it extremely unlikely that a carbon tax would cause much harm.

Myth 2: Carbon pricing is a job killer

If you accept the notion that carbon pricing does not harm economic growth, it should not be too surprising that it does not have a major impact on total employment either. In fact, some studies have found that there are slight increases in employment after a carbon tax. 

The employment effect that is more important is the fact that there is a significant shifting between sectors. Carbon intensive jobs are being replaced by non-carbon intensive jobs. 

While it is encouraging that carbon prices can be the catalyst for this shift without a net-loss in employment, Metcalf acknowledges that there hasn’t been any research done into the transitional costs of this shift. Even with transitional costs, it is good news for carbon taxes that new green jobs have the potential to replace old carbon intensive jobs.

Myth 3: Carbon taxes and cap and trade programs are equivalent

For those who are new to this topic, a carbon tax reduces pollution by making it more expensive while a cap and trade program defines the maximum amount of pollution and lets the market set a price by allowing producers to trade their allowances of pollution. From an economic theory perspective, these two instruments are two sides of the same coin.

However, the operation of these two systems in practice leads to many important differences. Metcalf talks about how carbon taxes might be preferred because the infrastructure to collect taxes already exists and taxes are easier to plan for than market fluctuations.

The most important advantage of carbon taxes is how they interact with other pollution reduction policies. Under a cap and trade program, new pollution reduction policies are unlikely to reduce the total pollution. This is because it allows the industries where the policy takes effect to simply sell their excess pollution allotment to other sectors. 

Myth 4: Carbon taxes are incompatible with emission reduction targets

One major concern with carbon taxes is that they never actually require polluters to reduce their emissions. It just makes them pay more, and if polluters have the resources then they can just keep paying. 

While this point is true, taxes can be adjusted to meet emission reduction standards. Metcalf proposes a tax schedule that is tied to emission reduction targets. This would make it clear to everyone when taxes would change and by how much, so firms could easily plan in advance. 

Another point Metcalf makes is that emission reduction targets are often flawed. This is because greenhouse gas emissions are a stock pollutant rather than a flow pollutant, meaning they stick around for a long time. 

For example, if the goal is to reduce emissions by 50% by 2050, then this could be accomplished by halving emissions in the first year and staying at that level or by slowly reducing emissions every year until 2050. The former would result in much lower total emissions than the latter. 

Metcalf’s proposed tax schedule would be tied to cumulative emissions to counteract this. If emissions are too high in one year, that would lead to taxes being higher for a much longer period of time until the cumulative emissions were back in line with targets. 

Myth 5: Carbon pricing is regressive

Regressive taxes are taxes that take a larger percentage of someone’s income the lower their income is. Think a flat tax of $100, that would be 1% of someone’s income who makes $10,000 but only 0.01% of a millionaire’s income. 

We might expect carbon taxes to be regressive because they are a tax on energy consumption,  and generally speaking low income households spend a larger portion of their income on energy usage. We call this part of the equation a “user side impact,” since it impacts the user of the taxed good. 

What Metcalf goes into detail about is the “source side impacts” of a carbon tax, or how this policy could affect wages and transfer incomes. 

The main point of this section is that despite the fact that this tax is on its surface regressive, it is still a tax which increases public revenue, which then often gets passed through to lower income individuals. The revenue generated by a carbon tax could be used to fund anti-poverty programs. Through good policymaking, we can offset the downsides of a carbon tax. 

New research: U.S. poverty associated with 180,000 deaths in 2019

If you are born in the Buckeye-Woodhill neighborhood on the east side of Cleveland, your life expectancy will be 65 years. Meanwhile, if you are born in Shaker Heights, less than two miles away, your life expectancy is 89 years.

What’s the difference between these two neighborhoods? Among other things, poverty.

A 2019 report from the Center for Community Solutions details the relationship between poverty and life expectancy in Ohio neighborhoods, finding a strong negative relationship between poverty rates and life expectancy at birth.

While we have information on how poverty interacts with life expectancy, we don’t have a great estimate of how many people die every year because of poverty. A new study out this week by an international team of policy researchers and sociologists tries to estimate this number.

In “Novel Estimates of Mortality Associated With Poverty in the US,” researchers David Brady from the University of California, Riverside’s School of Public Policy, Ulrich Kohler from the University of Potsdam in Germany, and Hui Zheng from Ohio State University estimate the impact of poverty on mortality by looking at a cohort dataset of income and comparing it to a similar dataset on mortality.

By combining these two datasets, the researchers were able to estimate not only how many people were dying because of poverty, but how quickly they were dying. The chart below shows how quickly people die at different ages due to being in poverty. As the line goes down, it shows the percentage of the cohort still alive at different ages as expressed on the horizontal axis. So for instance, at age 60, about 90% of people in poverty are still alive.

A detectable trend starts in the 40s, with people in poverty dying quicker than those not in poverty. The gap is wide between the two groups over the next few decades, with 10% of people in poverty dead by age 60, a figure not matched by those not in poverty until they are nearly 75. Death rates for the two groups don’t converge until both groups are nearly 90, at which point about half the population of both people in poverty and not in poverty have died.

The figure below compares how many deaths poverty is associated with in the United States compared to other major causes of death. Notably, according to this estimate poverty ranks as the fourth-highest cause of death in the U.S., only behind heart disease, cancer, and smoking and similar to causes of death like dementia and obesity that kill hundreds of thousands of Americans a year.

Notably, poverty also kills many more Americans per year than headline-grabbing causes of death like drug overdose, suicide, firearms, and homicide.

The researchers found that someone in poverty is anywhere from 26-60% more likely to die in a given year than someone not living in poverty. Someone living in chronic poverty over the past ten years had anywhere from a 45-102% higher chance of dying.

These findings have big implications for public policy. The United States has consistently had a higher poverty rate and shorter lifespans than a number of other similar countries–the link between the two phenomena may help explain this trend. Similarly, this may help explain why racial minority groups have higher rates of poverty and lower life expectancy than non-Hispanic whites in the U.S.

Lastly, the authors offer that cost-benefit analysis of anti-poverty programs should incorporate mortality impacts into the benefits of programs that alleviate poverty. This seems like a natural use of this research. If pulling people out of poverty has health impacts, especially on the scale of mortality reduction, those benefits should be monetized along with other important benefits of the policy. 

This is another example of how anti-poverty programs can rise beyond the equality-efficiency tradeoff. If a program that reduces poverty also has health impacts, that is a win-win for society on the dimension of these two social goals. And that is an insight that needs to be a part of our analysis.