Tax revenue from recreational marijuana will generate hundreds of millions of dollars in social benefits

Earlier this morning, Scioto Analysis released its most recent cost-benefit analysis looking at the impact of Ohio’s Ballot Issue Two, recreational marijuana legalization. We found that this policy will likely generate about $260 million in net benefits for society, though likely results ranged between $200 million in net costs and $1.9 billion of net benefits. Although there is a chance the costs outweigh the benefits, our simulation model suggests that in 90% of likely scenarios, recreational marijuana legalization will have a positive net economic benefit on society. 

The largest reason for these benefits is the estimated $190 million of tax revenue that we expect to be collected from the sale of recreational marijuana. In the language of ballot initiative, this revenue would be earmarked in part for two highly beneficial programs: the Cannabis Social Equity and Jobs Fund and the Substance Abuse Addiction Fund. We expect both of these programs to generate over $800 million of social value by themselves. 

The most significant cost we monetized was the lost productivity of workers in certain industries. Past research has shown that states that legalized recreational marijuana experience reductions in productivity amounting to roughly a 1% decrease per worker. In Ohio, we calculate that this will lead to roughly $760 million worth of lost productivity in the short run. 

“The key reason benefits are likely to outweigh costs when it comes to marijuana legalization is how the tax dollars raised are going to be used,” said Michael Hartnett, policy analyst. “The programs outlined in the ballot initiative have historically been very efficient ways to use public dollars, and will likely generate a lot of value for Ohioans.”

This study is the most recent cost-benefit analysis conducted by Scioto Analysis. Previous cost-benefit analyses include research on water quality programs, municipal tree planting, volunteer programs, and school closures for COVID-19.

New ways to think about the economy

Part of Scioto Analysis’ mission is to change the way people think about the economy. We believe in looking beyond GDP and output, and actually trying to measure how well-off people are. We think that this base understanding will be a better basis for good public policy than the current tools we have.

To do this, our goal is to conduct a series of five analyses looking at different ways to measure the economy. We’ve done three already: the Ohio Poverty Measure, an Inequality Report, and most recently our GPI 2.0 report.

We are currently working on the fourth leg of this plan, a human development report that we are working on with the help of a group of undergraduate students at Ohio State University’s Department of Agriculture, Environment, and Development Economics. The final step that we will tackle sometime in 2024 is going to be a report on subjective wellbeing. 

Our subjective wellbeing report will come just over a decade after the Organization for Economic Cooperation and Development (OECD) released their initial guidelines on how to do subjective wellbeing measurement. Just last month, OECD released a new working paper exploring the current practice of measuring subjective wellbeing. Below are some of the highlights from that report. 

What we want to measure

The OECD report breaks up subjective wellbeing measurement into four categories:

1. Evaluative - Overall life satisfaction

2. Positive Affect -  How often people feel positive emotions

3. Negative Affect - How often people feel negative emotions

4. Eudaimonia - Feelings of purpose, motivation, and optimism/hope

Exploring all four of these categories is necessary to capture a more complete picture of how people feel about their wellbeing. These four categories are a great example of how to design an effective survey.

These four categories may be correlated with one another. For example, we might expect someone with higher overall life satisfaction to have more positive emotions and fewer negative emotions. But without all four categories, we could be missing a key piece needed to understand how people feel. 

A good example of these questions in practice is the recent US Happiness Survey released by Gross National Happiness USA. This is a grassroots organization that Scioto’s principal Rob Moore is president of. Their work is focused on measuring subjective wellbeing at the national level.

How wellbeing can influence policy

One of the main findings from OECD’s follow-up was that despite a growing desire to measure subjective wellbeing, there are still very few examples of policy decisions being made with these figures in mind. Some of this may be because policymakers still don’t understand how to use this new data to make decisions. 

One area subjective wellbeing could help improve policy decisions is when analysts examine policies that are going to have large non-market effects. Our economic toolbox often relies on market principles that sometimes aren’t relevant. In these cases, it may be better to take a subjective approach that more accurately represents how people act than to try and fit our current models to the situation. 

Future considerations

Finally, the report talks about how future subjective wellbeing research can help advance the field. One of the main points of interest is what other categories we can use to measure wellbeing.

Some potential affective measures could be how often people feel tired or relaxed. Generally, how often people feel things that don’t exactly fit into a positive or negative box. However, before we start asking these questions, we need to know how they relate to our current subjective wellbeing measures. The promise of subjective wellbeing measurement is big: we could help craft policy that makes people happier.

How are local governments forgiving medical debt?

There’s a dirty secret about medical debt held by debt collectors: they don’t want it.

In a health care market that requires much from payers receiving medical services, medical debt is built into the structure of the market. The market for medical debt in the United States is nearly $200 billion in a 2022 estimate.

Much of that debt is not going to be repaid. And the holders of that debt know that. They will do what they can to get those debts paid off, but many people holding these debts do not have the means to pay them back.

Debt collection executives Craig Antico and Jerry Ashton saw this and had an idea. What if holders of debt were given an option to cut their losses and get paid to let go of that bad debt? How much would these collectors be willing to accept to let go of that debt?

The answer is very little. Antico and Ashton started a company called RIP Medical Debt that used advanced analytics to identify where some of this debt existed. They raised money from donors, partnering with nonprofits and community organizations to buy debt at a penny on the dollar.

Then they forgave that debt in full.

More recently, governments have been getting into the debt forgiveness game. Last year, Chicago’s Cook County, Illinois allocated $12 million of American Rescue Plan dollars to erase $1 billion in medical debt across the county.

Toledo, Ohio has become the first city in Ohio to start forgiving medical debt. The city allocated $800,000 in ARPA funds and got a match from Lucas County. This could lead to nearly a quarter billion dollars in medical debt forgiveness in the county.

Then-Toledo City Councilmember and Now-State Representative Michele Grim sponsored the program and is now looking to expand it statewide.

Other cities across the state have had legislation introduced to allocate funds to buying and forgiving medical debt. Last night, city leaders in Columbus announced $335 million in medical debt forgiveness achieved through a partnership with the Central Ohio Hospital Council. Councilmembers on the Cleveland City Council have introduced legislation to allocate $1.9 million to forgive $190 million in medical debt.

These publicly-funded allocations are cropping up outside of Ohio, too. Less than a year ago, Pittsburgh, Pennsylvania earmarked $1 million in city funds to forgive $115 million in medical debts across the city.

Cancellation of medical debt can be a lifesaver for a family struggling with paying off debt. Mounting medical debt can keep someone away from preventative care and can help families that are putting off refill of prescriptions to get access to care they need. It also has the potential to remove a barrier to access to credit for families, which can be important for families working to buy a house or get a car payment.

Medical debt forgiveness will not end poverty or solve all problems, but it is one tool in the arsenal of local governments looking to provide relief for residents. And for some people, this sort of intervention could mean the world.

Can clean jobs drive Ohio’s employment future?

My first job as a policy analyst was with a group called the American Jobs Project. We were a group of researchers and graduate students at UC Berkeley working under the guidance of former Michigan Governor and now Secretary of Energy Jennifer Granholm to understand the possibility of clean jobs in the United States.

In this position, I drove across Ohio interviewing people in the clean energy and manufacturing sectors about the wind and advanced manufacturing industries. 

A decade ago, a lot of these ideas seemed abstract. Many parts of these industries were starting to get footing, but there had been starts and stops in Ohio’s clean energy industries.

I was excited recently to come across last month’s Clean Jobs America 2023 report. In this report, researchers found Ohio is home to over 110,000 clean energy jobs. These jobs make up over a third of the energy jobs in Ohio despite renewable energy only generating 4% of electricity in the state.

About 76,000 of these jobs were in energy efficiency with the rest made up of clean vehicle, renewable energy, storage and grid, and biofuels jobs.

Maybe more importantly, the number of clean jobs available is outstripping the general growth in employment in Ohio. While statewide job growth in 2022 was 2.1%, clean job growth was over double the rate at 4.6%.

The fastest-growing clean job subsectors were clean vehicles (13.0%), storage and grid (7.7%), and renewable energy (6.7%). Each of these grew three times faster or more than statewide employment.

An especially promising element of clean job growth is their concentration in industries that support working-class professions. About half of Ohio’s clean jobs are in the construction industry and another quarter are in the manufacturing industry. These jobs also support white-collar employment: about 15% of these jobs are in the professional services industry.

Breaking down Ohio’s energy efficiency jobs, they are split pretty evenly between efficient lighting, traditional HVAC, renewable HVAC, and advanced materials jobs. This indicates Ohio’s energy efficiency employment is diversified and split between upgrading existing infrastructure and building new infrastructure.

Over the past two years, Ohio’s clean energy employment has grown from 103,000 to 114,000 and its clean vehicle employment has grown from 16,000 to 22,000. And while its fossil fuel employment has hovered around 30,000, its gas and diesel vehicle employment has grown from 119,000 to 130,000.

If lawmakers in Ohio want the state to be a leader in the growing clean job market, it has options at its disposal.

Strengthening Ohio’s renewable portfolio standard will spur job growth in the renewable energy sector and help ease Ohio’s transition from a fossil fuel dependent electricity market to a more diversified market.

Joining the regional greenhouse gas initiative would introduce Ohio’s electricity companies to a new market of carbon allowance trading with twelve other states, including neighboring Pennsylvania. This would allow power companies to efficiently reduce carbon emissions through trading of allowances and incentivize creation of more clean jobs.

Enacting a carbon tax would have a similar impact, incentivizing the creation of clean jobs by discouraging the use of fossil fuels in the electricity and transportation sectors.

In 2021 Scioto Analysis found each of these strategies would abate carbon and have large economic benefits. Less climate change, a stronger economy, and more jobs: what’s not to like?

This commentary first appeared in the Ohio Capital Journal.

How much inequality is too much?

One thing that most people would agree on is that inequality is a problem. Especially in the United States where there is a great deal of inequality, I imagine it would be near consensus that our society would be better off with less inequality, all other things being equal. 

Inequality exists on a wide variety of dimensions such as race, gender, sexuality, and religion. These social examples of inequality are generally agreed to be bad for society. Unfortunately, because they are social issues, it is difficult to design policy focused on correcting them. 

In a liberal society, the government has little power to enforce social equality. This is not to say it is impossible, nor that policymakers shouldn’t work to correct these inequalities, but the levers policymakers have at their disposal to spur social change are few and far between.

However, policymakers do have very straightforward ways of reducing one type of inequality: income inequality.

A governing body that has the power to levy taxes could in a single bill completely exterminate income inequality. So, why doesn’t this happen anywhere?

The answer is because even though we might accept that income inequality is bad, we are willing to tolerate some amount of it when the benefits to society outweigh the negative impacts of income inequality.

It is true that our economy is set up to reward individuals who come up with ideas that people like. Jeff Bezos is a billionaire because people would rather have anything they want delivered to their doorstep than go to a store and buy it. 

In a vacuum, the creation of Amazon seemed to have made society better, despite the fact that it increased income inequality. But what if we could do better? Really, what I am asking is this: are we currently allocating these resources optimally?

What if some of the money that goes to the highest earners was taken and given to those who don’t have enough to get by. This already happens to some extent via taxes and social safety net programs, but are we doing enough?

The answer to this question largely depends on an individual's preference. One way to think of reducing inequality like this is by imagining a leaky bucket (an image first presented by Arthur Okun). If we want to take income from one individual and move it to another, we need to do so with our leaky bucket (taxes). The more income we try to move, the more we expect to leak out of our bucket in the form of administrative overhead and more importantly, distortions to our economy.

This presents two interesting challenges for policymakers. How much income should we try to move with our leaky bucket, and what can we do to minimize the leak? Both of these are important questions, and both can directly be influenced by policy.

How much we should reduce income inequality really is a function of how leaky our bucket is. If policymakers are able to design efficient ways of reducing inequality, then bigger reductions of inequality could reduce losses for society. We could even see gains in the long run, as studies of the child tax credit suggest.

Even though it’s impossible to close the leak entirely, our society could still benefit from reduced inequality. At extremely high levels of income inequality like we have in the United States, we might not even notice leaks at a societal level at all.

Quantitative vs qualitative analysis

At Scioto Analysis, our mission is to provide policymakers and policy influencers with evidence-based analysis of pressing public problems. For us, this almost always means analyzing data and finding ways to quantify impacts. 

However, there are still times when data isn’t quantifiable but analysis is still helpful. For example, when we want to draw insights from written comments on a survey. 

In these instances, there are two main options we have as analysts. First, we can try to quantify these abstract data points. The main issue with this is that quantifying inherently means simplifying the data. When done well, key points can be preserved, but some amount of nuance is always lost when quantifying qualitative data. 

The other option we have is to perform qualitative analysis. 

As someone who went to school specifically for statistics, I am much more comfortable with quantitative analysis compared to qualitative analysis. It is possible even to take qualitative data and manipulate it into quantitative data that you can analyze.

That being said, the methods used to encode qualitative data are quite robust and can provide extremely useful information to analysts. 

But, the more policy analysis work I do the more I have come to value the insights that qualitative analysis can provide when conducted well. Additionally, I think the additional cost that often comes with quantitative analysis is not always worth the additional insights it provides. 

For example, we’ve recently been working on a project trying to better understand the impacts that climate change will have on health equity by midcentury. One of the main areas of focus for this project is on climate change related mental health issues. 

Mental health is a notoriously difficult subject to quantify. Because there is such a wide spectrum of outcomes and there is not any tangible way to measure people’s experiences, quantitative analysis of mental health is almost always limited in its ability to give analysts useful information. 

For this project in particular, a quantitative estimate of how climate change would affect mental health was unnecessary. There is a great deal of qualitative evidence from mental health professionals about how climate change affects mental health. 

To gather some expert opinions on the topic, I spoke with a few mental health professionals about the subject. The context they were able to provide helped us convey the main message of the report without needing to get bogged down with math that would ultimately be unhelpful to the project as a whole. 

One final consideration when comparing quantitative and qualitative analysis is who the intended audience of a study is. Quantitative methods can produce extremely robust results for use in tasks like budgeting by professionals who use math from day to day, but it might not be worth doing if you need to explain how you encoded peoples' written comments and performed a proportional odds logistic regression to a group that doesn’t have any background in statistics. 

In this case, it might be more helpful to those people if instead of throwing hard-to-interpret regression results at them, you instead drew some qualitative insights that get the same point across. 

Overall, I generally believe that quantitative analysis provides better insight into many questions than qualitative analysis. However, the more work I do in policy analysis, the more I have come to appreciate what qualitative analysis can provide. Understanding the pros and cons of both methods is important for anyone who wants to improve decision making in the public sector.

How Ohio can reduce poverty

Last month, the Census Bureau released its 2022 report on poverty in America. This report confirmed that poverty numbers increased dramatically in 2022 as the pandemic expansion of the child tax credit lapsed.

With after-tax poverty increasing more in a single year than ever before and child poverty more than doubling in 2022, the outlook on poverty in the United States is bleak. But state lawmakers still have tools at their disposal to fight poverty.

The National Academies of Science, Engineering, and Medicine released a report last month on policies to break the cycle of intergenerational poverty. While this report runs the gamut of different interventions, here I will list five policies specifically tailored toward increasing incomes for people in poverty.

Minimum Wage

Ohio last increased its real minimum wage in 2006 and it has increased every year since in nominal terms due to inflation adjustments built into that change. Minimum wage increases can help people in parts of the state with few options for work who can have their wages artificially depressed by employer market power.

Temporary Assistance for Needy Families (TANF)

TANF is designed to be the low-income cash assistance program for the United States and states have wide latitude for how to spend these funds. Ohio only spends 19% of its TANF budget on basic assistance, which is generally paid out as cash assistance to families in deep poverty. Others are earmarked specifically toward child care, eaten up by administration, or pay for work activities.

Nationally, states spend 23% of their TANF budgets on basic assistance. West Virginia spends 34% of its TANF budget on basic assistance and Kentucky spends 75% of its budget on basic assistance. Spending more of this budget on cash could instantly pull families out of poverty who are experiencing it now.

Earned Income Tax Credit

The Earned Income Tax Credit is a cash program that pulls more people out of poverty than any program nationwide besides social security. Many states, including Ohio, have a state-level earned income tax credit. A 2019 study by my firm Scioto Analysis found refundability reforms could put an extra $150 to $900 per household in the pocket of low-income Ohioans.  By expanding the state credit by changing a refundability loophole that makes most families in poverty ineligible for the program, the state could improve incomes for hundreds of thousands of low-income Ohioans.

Child Tax Credit

The culprit for the increase in poverty rates in 2022 was expiration of pandemic-era expansion of the Child Tax Credit. The Child Tax Credit gives cash to families raising children and has been shown to improve future health and labor market outcomes for those children. Scioto Analysis has estimated that a state child tax credit in Ohio could generate between $60 million and $300 million in net benefits.

Negative Income Tax

If Ohio wanted to swing for the fences on poverty, it could do that with a negative income tax. This is a cash transfer program that could theoretically abolish poverty with the stroke of a pen. Cash transfers funded by income taxes on people earning more are the most straightforward way to tackle poverty. A negative income tax was proposed during the Nixon administration, but never implemented at the federal or state level.

Ohio has options to raise incomes for people in poverty. Poverty is a policy choice and one that is made by policymakers every single day. It has options to make better ones.

This commentary first appeared in the Ohio Capital Journal.

Five policies to break the cycle of intergenerational poverty

Last week, the National Academies of Sciences, Engineering, and Medicine released a groundbreaking report on reducing intergenerational poverty.

This study looked at the subject areas of health, education, safety, income, and housing as determinants of intergenerational poverty. Crucially, the study offers policy interventions that can be used to break the cycle of intergenerational poverty. Here we highlight some of the most promising policies in the study.

Expanding access to Medicaid

Children in poverty start with worse health outcomes than those not in poverty before birth and those disparities only grow as they age. Access to health care is a key strategy to close that gap. Medicaid is the most common form of health insurance among Americans experiencing poverty. 

The report says that Medicaid expansions in pregnancy and childhood leads to not only better health at birth and throughout childhood but even improved labor market outcomes. This means investment in health insurance coverage now can reduce risk of poverty decades into the future.

Increasing K-12 spending in low-income districts

Currently, Scioto Analysis is conducting a cost-benefit analysis of increases in school spending. While our final results are still pending, we are currently certain of one thing: investments in low-income districts will have more benefits than investment in upper-income districts.

Children from low-income households tend to start school behind their peers in achievement and these gaps do not tend to close over time. Investment in low-income districts can help provide resources which could help reduce those gaps and break the cycle of intergenerational poverty.

Increasing mortgage lending

Crime disproportionately affects people with low-incomes. One result I was surprised to find in this study was that communities with more mortgage lending tend to have lower crime rates. This was found in a study of Seattle lending patterns which found that lending impacted violent crime but not vice versa. More investment in a community can lead to reductions in crime prevalence.

This could have long-term impact on poverty as well. Cleveland Fed Economist Dionissi Aliprantis finds black men who witnessed a shooting as a child have 31% lower earnings than those who did not and that it can be attributable to toxic stress. Reducing gun violence can be a tool for reducing intergenerational poverty.

Expanding housing vouchers

High lead levels, homelessness, overcrowding, frequent moves, and high housing costs are all both symptoms of and causes of future poverty. Increasing access to housing through programs like housing vouchers and coupling those resources with counseling and case management can reduce future incidence of housing insecurity and intergenerational poverty.

Expanding the earned income tax credit

Scioto Analysis has done a cost-benefit analysis of the earned income tax credit and recently conducted a cost-benefit analysis of a statewide child tax credit. These credits, targeted toward low-income households, put cash in the pockets of households with children. This leads to better educational and labor market attainment and health for children down the road.

All of these interventions can be conducted at the state level. States have a wide berth on how they can expand or contract access to Medicaid. States control how much funding goes to school districts. States can encourage lending in communities bereft of investment. States can create voucher programs. And states can create their own earned income and child tax credit programs. Now the only question is whether they are willing to make the investments to make intergenerational poverty a thing of the past.

Why sensitivity analysis matters

In policy analysis, the primary goal is often to make a best guess as to what the impacts of a potential proposal are. Because this work is often on a pretty short timeline, the end result is often a single estimate for the effects of some policy, with much of the behind the scenes work failing to make it into the headlines.

In my opinion, the biggest downside of focusing on point estimates as the main result of interest is that they don’t communicate anything about how likely the result actually is. This is a question that can be answered by a well done sensitivity analysis. 

Through sensitivity analysis, we get the opportunity to test the boundaries of our results. This allows us to see what happens in the best and worst case scenarios. 

I would argue that this information is far more important than a point estimate when it comes to making informed policy decisions. Policymakers should be thinking about questions like what the probability is that we see an effect size of at least 10%, or how likely is it that an intervention breaks even.

These questions require a greater deal of probabilistic thinking which can be challenging. Still, these are the insights that can tend to lead to better decision making. A point estimate is a good talking point, but understanding uncertainty is the key to evaluating risk correctly. 

Sensitivity analysis is also an important way to check and make sure results are credible. Point estimates can sometimes be misleading when the variance of an estimate is extremely high. Just because a point estimate is the best guess does not necessarily mean that the actual outcome of a policy intervention is likely to be close to it. 

On the other hand, sensitivity analysis can help make the case for programs that have lower variance as well. Consider an example from Washington State’s Institute for Public Policy involving cognitive behavioral therapy (CBT). 

One program is targeted at youth in state institutions, the other at moderate-to-high risk adults in the criminal justice system. The first program has a net present value of over $16,000 compared to just under $800 for the second. However, the first program is only expected to have positive value 68% of the time compared to 98% for the second. 

Maybe the effect size of the first program is worth the added risk, or perhaps policymakers are more willing to take that risk with children compared to with adults. Either way, by understanding the full range of outcomes policymakers can make smarter decisions about how to allocate limited resources. 

These are just a few reasons why policymakers should care more about sensitivity analysis than point estimates when evaluating the work of analysts. Point estimates are still important, and the fact that they are easier to communicate to the public should not be underestimated. 

Still, in order to make the best possible decisions, policymakers need to understand uncertainty. It will lead to more effective decisions, and better outcomes for society as a whole. 

Exploring the credibility revolution in economics

One of the three main criteria policy analysts use to measure policies is by their effectiveness. In order to do this, we use estimates from academic research to determine the likely effects of a policy change.

When we are looking for estimates to use in our analyses, we often give priority to research that uses sound empirical methods. For instance, we prefer to cite research drawn from randomized control trials or differences in differences approaches to observational studies. This allows us to better estimate whether a policy had an actual effect rather than some other variable that may have caused a change in the population.

These methods broadly represent what is known in economics as the “credibility revolution.”

In short, the credibility revolution refers to the explosion of econometric methods that has happened in recent decades. Gone are the days relying on theoretical models to estimate policy impacts: now we have the tools to understand real data and apply it to problems in economics and public policy. 

However, as beneficial as the credibility revolution has been, some argue that we have gone too far and begun to overemphasize the results of unreplicable experiments. Kevin Lang, an economist from Boston University, argues this point in his new working paper.

The key result Lang reports is that by his estimate, 41% of rejected null hypotheses in the economics literature are false rejections. This means that more than two in five economics studies could be reporting incorrect results.

This is an extremely surprising result. We base our policy analyses on results from journals like those Lang studied. If economists are truly coming to that many incorrect conclusions, our final policy analysis estimates could be way off. 

One of the main drivers of this conclusion is the fact that in economics, there is very little replication of studies. This is because of practical reasons like the difficulty of finding natural experiments on which to test. Additionally, there is often very little incentive for economists to test each other’s work. Replications rarely get published.

So, what can we do about this problem?

This is a moment where the differences between academics and policy analysts are quite clear. For academics, accurately measuring and reporting results is the most important job. It certainly makes sense for academics to require more stringent guidelines for reporting their findings. This will certainly slow these processes down, but the purpose of academia is truth finding.

Policy analysts and policymakers operate on a much shorter timeline. Because policymaking is subject to political pressures, there are always external considerations when policies are being decided beyond what their expected impact will be.

As a result, our goal as policy analysts is not always to find the best answer, but rather to improve the decision making process. This is not to say that policy analysts don’t have any obligation to the truth, far from it. Instead, our job involves making some prediction, then being honest about the strength of that prediction. 

If 41% of rejected null hypotheses in the economic literature are false rejections, that should not exclude those results from being incorporated into a perfectly reasonable policy analysis. We instead should understand that we might have to be more skeptical about our results, and be effective in communicating that skepticism.

The credibility revolution has been an amazing change in the field of economics. The overall quality of the research being done today is still extremely high. Lang’s paper does not decry the entire field of economics, but rather offers a reminder. There is always uncertainty in the work we do, and we need to be aware of and transparent about that uncertainty when communicating the results of our analysis.