Analyst perspective: who really pays for taxes?

In our work as policy analysts, we rely heavily on some of the fundamental theories of economics in order to determine the value of impacts. This has recently come up in a research project we are currently working on, where we need to estimate the elasticity of demand for a good in order to understand what the effects of an excise tax will be. In particular, we are looking at the market for recreational marijuana.

Briefly, the elasticity of demand (or supply) is a numerical measure of how much effect a change in price has on the quantity demanded. For example, if a 1% increase in the price leads to a 2% decrease in quantity demanded, then we say a good has a demand elasticity of -2. 

There are many important results that we can derive from elasticities, but I want to focus on how they interact with taxes. Consider a competitive market for a good that we want to tax:

We can see in our simple setup that the demand curve is much steeper (more inelastic) than the supply curve. Intuitively, this means that changes in price have little effect on the quantity demanded. Conversely, the supply curve is quite flat, suggesting that a small change in price would have a large effect on the quantity supplied. In the above picture, the new tax we are adding to this market is represented by the vertical dotted line, often called the tax wedge. 

Because demand is more inelastic than supply in this market setup, the consumers of this product will have a higher tax incidence. In other words, a larger percentage of this tax will get passed onto them. If we assume that this is a flat $10 tax per item sold, then this tax might  cause the price consumers pay to rise by $8 (the suppliers eat the other $2 via lost revenue).

Who ends up paying for taxes is an interesting question in its own right. In our forthcoming cost-benefit analysis of recreational cannabis legalization in Ohio, this matters to us because we need to fully understand how the proposed excise tax will impact consumer surplus.

As a reminder, consumer surplus represents the difference between the cost of a good in a market and people’s willingness to pay for that good. If people are willing to pay large amounts, but the market price for a good is very cheap, then there is large consumer surplus. People are getting a lot of value for the price they pay. 

In our model for the benefits of legalizing recreational cannabis, one of the major components is going to be how much consumer surplus is generated in a regulated and taxed market. In order to accurately estimate this, we will need to know about the elasticities in our market so that we can accurately assign the tax incidence. 

In practice, this is going to require empirical data. There are econometric methods for estimating supply and demand curves, and because many other states have legal cannabis markets we should have some data to work with. 

Hopefully, we can accurately predict the shape of the recreational cannabis market in Ohio. Then, it will be up to the voters to decide whether they want this market to exist in November.

Three steps for analyzing equity in public policy

Earlier this month, Scioto Analysis released an analysis we did in partnership with the Center for Climate Integrity on the cost of climate change in Pennsylvania.

In this study, we built on the work we did on our Ohio cost of climate change report to estimate how climate change will impact different types of communities.

A lot of lip service is paid to “equity” in public policy analysis these days. Equity is one of the “big three” criteria for analyzing public policy along with effectiveness and efficiency. But unlike effectiveness analysis, which has tools like randomized controlled trials and quasi experimental methods, and efficiency analysis, which has cost-benefit analysis, equity analysis has no go-to methodology for its conduct.

So how do we conduct equity analysis? The steps we took ended up looking a lot like a broader policy analysis. While you could go through the steps of the Eightfold Path to conduct an equity analysis, our approach in this study boiled down to three major steps.

Step 1: Define Criteria

One of the major reasons equity analysis is not as standardized as effectiveness or efficiency analysis is because of the many dimensions equity can be analyzed on. Race, income, sex, rurality, age, education, sexual orientation, geography, employment, immigrant status, language spoken at home, and housing are just a handful of different examples of dimensions of equity.

Criteria should be defined based on (a) what is informative to your client, (b) what you have reliable data on, and (c) what is relevant to the content of the study. 

The former consideration is always paramount in public policy analysis: what will people listen to? When speaking truth to power, an analyst needs to be aware of what her client is interested in and what she will listen to if policy analysis is to be useful.

Also important is whether data is available. A client might be eminently interested in how their policy impacts high-IQ students, but if the data is not available on how those students are impacted by the policy, an analyst is not very useful.

Step 2: Calculate Impacts

This is the “technical” part of the analysis. This phase of equity analysis consists of gathering data and estimating what the range of impacts are for different groups. 

For our Pennsylvania analysis, we had defined municipalities as municipalities of interest based on them having larger racial minority, impoverished, rural, non-English-speaking, or foreign-born populations. We then calculated what these communities’ per-capita costs were compared to the per-capita costs of the average municipality statewide.

Step 3: Communicate Results

Policy analysis requires good communication, and equity is no exception to this. Communication of equity results can demand extra care because of the sensitive political dynamics associated with communities of interest defined by equity categories. Knowing the right language to use around race, income, and gender is tantamount, especially when making sure a client will take this analysis seriously.

Also important is making sure that results are communicated in a way that transparently and clearly shows the differences between equity categories. In our results, we showed the dollar figure difference between communities. This showed how much different communities would have to pay based on the type of community they were.

When all was said and done with this study, we found that high-poverty and rural municipalities would pay more per-capita than the average Pennsylvania municipality. This was a useful insight: it told us something about who will shoulder the burdens of the cost of climate change. And this is just the sort of insight that helps us understand who will benefit from policy interventions.

Ohio economists agree abortion protections will improve outcomes

In a survey released this morning by Scioto Analysis, 13 of 18 economists agreed that women who receive abortion services will have improved economic outcomes, such as higher educational attainment, higher labor force participation, and higher wages. However, there is some disagreement about how large the effect might be. 

Additionally, economists indicated they believed abortion protections in Ohio will spill over into neighboring states like West Virginia, Indiana, and Kentucky, all of which currently have bans on abortion.

The majority of survey respondents agreed that abortion protections in Ohio would likely lead to people crossing the border in order to access those services. Assuming people who receive abortions will have higher economic attainment, this could mean a slight boost in the economies of those neighboring states. 

As Ohio prepares to vote in November on a ballot initiative that would in practice make the majority of abortions legal, these results are an important consideration. Although this is often painted strictly as a social issue, there are important economic considerations that policymakers should be aware of. 
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.

What would recreational marijuana in Ohio look like?

While the conversation about the November election has revolved around abortion protections, that won’t be the only issue Ohio voters decide on. The Coalition to Regulate Marijuana Like Alcohol recently submitted enough signatures to get a vote on recreational cannabis on the ballot.

The proposal, known as the Ohio Marijuana Legalization Initiative, would legalize the cultivation, processing, sale, purchase, possession, and home growth of marijuana for individuals age 21 and up. It would create a state Division of Cannabis Control, which would regulate and license marijuana operators and facilities and would oversee the compliance and standardization of marijuana in Ohio.

The proposal would also tax marijuana sales at 10%, putting funds toward a “cannabis social equity and jobs program” that would support individuals affected by marijuana law enforcement.

Currently, Michigan is the only neighboring state that has a recreational marijuana program. Like Ohio, Kentucky, Pennsylvania, and West Virginia each have medical marijuana programs in place. Indiana is the only state bordering Ohio without a medical marijuana program, though it allows CBD and low-THC products.

If this initiative gets on the ballot and Ohio voters approve it, then Ohio would join Michigan in having a recreational marijuana program with a 10% excise tax. Unlike Michigan’s excise tax, though, which goes toward public schools, transportation, and other local initiatives, Ohio’s would be specifically earmarked for the cannabis social equity and jobs program.

In 2022, Michigan brought in $325 million in excise and sales tax from marijuana sales. Since Ohio has a similarly-sized population to Michigan, it is likely to bring in hundreds of thousands of dollars in revenue as well once its program is up and running.

Besides raising revenue, legalization of marijuana in Ohio would have some other impacts as well.

First, it will almost certainly increase consumption. While many people inside Ohio use marijuana for medical reasons and others purchase marijuana in informal markets currently, full recreational legalization would increase access and would likely lead to more consumption of marijuana.

It also would provide more opportunity for residents of Indiana, Kentucky, Pennsylvania, and West Virginia to purchase marijuana legally, which may lead to more cross-state travel and sales. It could even lead to arrests in those states.

While it is not guaranteed, legalization of sales of recreational marijuana could lead to a reduction in the informal (often called “black”) market in marijuana sales. This could lead to less criminal activity associated with sales and less public costs around arrests and imprisonment.

There are also likely to be public health costs associated with legalization of recreational marijuana. One impact is car crashes. We could see an increase in car crashes due to an increase in driving while under the influence of marijuana. We also may see a decline in productivity as marijuana use may have an impact on the workforce.

Some evidence shows we could also see a reduction in some public health ills. In particular, some states that have legalized medical marijuana use have seen declines in male suicide rates driven by substitution away from alcohol use, a risk factor for suicide.

We don’t yet know which of these effects will predominate and how drastic they will be. My practice Scioto Analysis is currently conducting a cost-benefit analysis on recreational marijuana legalization and should have results in the next couple of months. We look forward to studying this question and seeing what legalization of marijuana will do to the public.

This commentary first appeared in the Ohio Capital Journal.

Spillover effects of early childhood education

Among policy analysts, the Perry Preschool Project is one of the most famous randomized control trials ever undertaken. In the 1960s, over 100 disadvantaged black children in Michigan were randomly selected to receive additional education and support starting at age three.

What makes this study unique is that researchers have been able to follow up with the participants in both the treatment and control groups for decades after the formal program ran its course. This means that we can directly see how this program influenced outcomes like adult wages directly, instead of relying on intermediary effects like school test scores.

What’s even better is that there is robust data not only on the control and treatment groups, but on those peoples’ siblings and children. This makes the Perry Preschool Project not only one of the best studies ever conducted for understanding the effects of early childhood education, but it also makes it one of the best examples of how programs focused on benefiting disadvantaged children can have effects that spillover to nearby children and even the next generation. 

Results from the most recent follow-up survey have been analyzed and published in a new working paper. Today, I want to explain what these effects are, and what the implications might be for related policies.

Intergenerational Effects

The larger of the two spillover effects was how the Perry Preschool Program influenced outcomes for the children of the participants. The way this was measured was by comparing adult outcomes such as employment for the children of participants in the control and treatment groups. 

The researchers found that children of people in the treatment group were more likely to be employed as adults, were more likely to be in good health, they were less likely to be divorced, and perhaps most importantly they were less likely to be arrested. 

Additionally, male children of people in the treatment group were more likely to have a college degree, while female children were more likely to have a highschool degree. 

What's so exciting about these intergenerational effects is that despite not taking place until over 50 years after the intervention, and therefore being subjected to over 50 years of discounting, they still have massive and positive net present benefits. 

Sibling Effects

When exploring the effect that the Perry Preschool Project had on participants' siblings, the researchers chose to limit themselves to a subset of all siblings. Specifically, they only considered siblings that were older than the participants, but not more than 5 years older. 

The justification for this small range is that (1) Younger siblings might experience different outcomes because their parents might act differently after participation in this program (a worthwhile effect to understand but not the effect they were interested in) and (2) Children more than five years older might already be experiencing some of these outcomes. 

The most notable sibling effects are that female siblings of participants were more likely to finish high school and male siblings were less likely to have been arrested. The effect sizes for siblings are smaller than those for children of program participants, but they still make a big difference. 

From a policy perspective, there are a few key takeaways from this research. First, we may be underestimating the value of anti-poverty programs. The intergenerational effects of these projects is enormous, and unless we account for that we are likely not accurately portraying the benefits of many projects. 

Second, we should be aware of who we are enrolling in pilot programs. The total benefits of programs will often spillover into nearby people and communities. On one hand, targeting communities to maximize these spillover effects will likely increase the value of a project, but we must also be careful and understand that if these effects exist, then people or communities with less opportunity for spillover might have smaller effects. 

November vote could give Ohio among the strongest abortion protections in the region

Last week, Ohio voters rejected Issue 1. This was a constitutional amendment put forth by abortion opponents in the state legislature to make it harder for Ohio voters to enshrine abortion protections in the Ohio Constitution.

After this vote, the next big policy decision for Ohio around abortion protections will be the “Ohio Right to Make Reproductive Decisions Including Abortion Initiative,” which Ohio voters will decide on in November.

This initiative will establish a right for Ohio residents to make and carry out their own reproductive decisions. This will include the right to abortion, contraception, fertility treatment, miscarriage care, and continuing pregnancy.

The bill puts specific limits on how the state can restrict this right, in particular only allowing the state to restrict abortion after the point of fetal viability, which is at about 24 weeks. It also specifically prohibits restriction of abortion in any case where abortion is necessary to protect the pregnant patient’s life or health.

Currently, Ohio has a six-week abortion ban on the books which has been frozen by the courts. The current practical limit on abortion is up to 22 weeks of pregnancy, the threshold that existed when Roe v Wade was the law of the land. This means that Ohio would likely have more abortion protections under this new change than it did before the Supreme Court overturned Roe v Wade.

Currently, states bordering Ohio have a range of different laws regulating abortion. According to the Guttmacher Institute, Kentucky and West Virginia have essentially banned abortion in its entirety in their states. Indiana took a similar step this month when legislation was enacted banning essentially all abortions in the state. 

Michigan and Pennsylvania have more protections for abortion rights. Pennsylvania guarantees abortion rights up to 24 weeks of pregnancy and Michigan guarantees up to fetal viability, around the same timeframe. 

So as far as weeks of pregnancy go, adopting this constitutional amendment would put Ohio on the forefront of its neighboring states, using the viability threshold Michigan also has.

I’ll be interested to see what happens to many of the other abortion restrictions Ohio has in place if this amendment is put in place. For instance, Ohio requires two separate visits to a clinic at least a day between. It also requires parental consent, which can pose problems for victims of incest and rape. There also are a number of laws put in place designed to make it harder for clinics to operate.

The amendment requires that the state uses the least restrictive means to advance the individual’s health in accordance with widely accepted and evidence-based standards of care. This would likely lead to many of Ohio’s abortion restrictions being struck down.

Overall, this constitutional amendment would give Ohio some of the strongest protections for abortion rights in the region. This could be a lifeline for not only Ohioans, but also people living in Indiana, Kentucky, and West Virginia would need access to abortion care. In November, we will find out if Ohio is ready for a right to abortion.

This commentary originally appeared in the Ohio Capital Journal.

New research: the cost of climate change in Pennsylvania

For the past year, Scioto Analysis has been working with the Center for Climate Integrity on a project estimating the financial costs that local governments will incur as a result of climate change. The study was released last month and has already been getting some coverage in the Pennsylvania media.

The study looked at the costs associated with eight different categories of infrastructure that would be impacted by rising temperatures, increased precipitation, and rising sea levels. An online visual representation of these costs estimated by engineering firm Resilient Analytics can be found here.

We contributed the equity and budget analysis to this project, helping explain who is going to be bearing these costs and providing some context for what these costs might actually feel like in a municipal budget. Here are some of our key findings and what they mean for Pennsylvania.

Rural municipalities have the highest per capita costs

Despite incurring lower total costs, rural communities across Pennsylvania are going to face higher per-capita costs compared to the statewide average. 

This is mostly due to the fact that rural municipalities have smaller populations spread out across a large area. This often means that a smaller number of people are responsible for maintaining a large area of roads. 

Road related costs ended up being some of the most significant drivers of spending across the state. Increased heat and precipitation will necessitate greater spending on road maintenance and the increased risk of landslides (a problem that is particularly prevalent in rural, Western PA) will lead to significant spending on prevention and road repair. 

All of this adds up to people living in rural municipalities needing to spend a greater amount on climate change adaptations. 

Communities subject to sea level rise have large racial minority populations 

Because the Delaware river is a tidal river, people who live along its banks are going to experience the effects of sea level rise despite not living on the ocean. As a result, those people are going to need to build preventative infrastructure to hold back the rising water in order to keep their homes on dry ground. 

In Pennsylvania, the municipalities projected to be affected by sea level rise have much higher poverty rates, are less white, and have larger immigrant populations than the rest of the state. 

Compared to Pennsylvania as a whole, there are only a small number of municipalities that are going to feel the effects of sea level rise. However, these costs represent one of the greatest examples of climate inequity in the state. The populations that are going to be exposed to sea level rise have historically been marginalized, and will not have the capacity to adapt that other municipalities might. 

Four municipalities will experience severe fiscal stress

Climate change will be costly for everyone in Pennsylvania, but for a select few municipalities the costs will be nearly impossible to bear. 

Our measure of severe fiscal stress is based on our research of the Census Bureau’s Annual Survey of State and Local Government Finance. What we found was that over the past 20 years, local government budgets have increased by roughly $1,000 per person per year. 

This means that if a municipality is projected to have per capita annual costs of $1,000 by 2040, then they would essentially need to commit the entirety of their budget growth to climate adaptations.

These municipalities will have no money to offer raises for city employees over the next 17 years, no ability to renovate old buildings. If some other emergency occurs, the city would have no capacity to respond. 

Thankfully, only four municipalities across the state have per capita costs this high. Still, policymakers need to be aware of these costs, and understand just how debilitating they could be. 

How to determine thresholds in policy analysis

For the past year, Scioto Analysis has been working with the Center for Climate integrity on a series of projects that attempt to measure the financial impact climate change will have on local governments. The first report on Ohio was released in July of last year, and just last month the newest report on Pennsylvania came out. 

In total, the report projects that Pennsylvania’s local governments will have to spend over $15 billion by 2040 in order to adapt to climate change. That’s $15 billion to roughly maintain the same living conditions we have today in the face of climate change.

The report goes into a ton of detail about these costs, and there is even an excellent interactive tool our partners on this project, Resilient Analytics, put together in case you want to learn more about this project.

However, I wanted to dive deeper into one aspect of this report. How we defined the categories for our equity analysis. 

Briefly, the equity analysis for this project involved identifying certain criteria that municipalities had to meet in order to get labeled as a certain equity category. For example, a municipality that has a poverty rate over 20% was labeled as high poverty. 

Conceptually, we are trying to identify municipalities that are more vulnerable to climate change, that have less adaptive capacity, and that have been historically marginalized. The difficulty comes from the fact that we need to determine a specific cutoff point.

This problem comes up across all sorts of contexts. Think about how small the difference in wellbeing is for one person who earns $1 above the poverty threshold and a person who earns $1 below the poverty threshold. Small differences in experience but entirely different categories. 

In most cases, we can turn to the standards set by others who have done research on this before. If there is a consensus among the community that studies these topics, then it is usually not our place to come to a different conclusion. Having consistency with past research on the same topic not only helps tie our work to the established literature, it also makes it easier to communicate the final results.

One way we defined our thresholds when there was not outside guidance was to try and make the new groups comparable to those we had already defined. In the case of our Pennsylvania analysis, we did this by making sure that roughly the same number of municipalities fell into each equity category. 

This approach also ensured that we were getting enough of a sample size in each category to make reasonable inferences. Making sure that one group isn’t too small is very important to this type of analysis. 

Whatever thresholds we choose to use, we need to make sure that they are well defined before we begin our analysis. It might be tempting to wait until the analysis is done and see what thresholds provide interesting results, but that is answering a different question. It would be dishonest to determine the thresholds after the fact and report comparisons between the two groups.

What to do with conflicting information

As policy analysts, we are often at the mercy of the research others have done in order to estimate some outcome. Rarely in this job do we have the time to sit down and research a problem the same way an academic does. Instead, we focus on finding creative ways to take the research that others do and apply it to the context that we are interested in understanding.

Unfortunately, academic research doesn’t always agree with itself. 

Sometimes it’s a small difference, and the final results don’t really change all that much. But sometimes other research is directly contradictory, and our outcome will depend heavily on what estimate we choose to use.

In the case of a small difference, we could do something like use the average effect and wait until sensitivity analysis to explore the full range of outcomes. In the case where there are contradictory results, we need to make a decision. 

Anytime we make a decision like this, we need to be careful to fully understand what the implications are and communicate them effectively. Here are a few things I try to think about when I am presented with conflicting information. 

What context is most similar?

The first place I always begin when thinking about what research I want to use in my own projects is how similar is the context of my situation. It is more reasonable to think that the estimates others have come up with will more closely hold if we change fewer things about the situation in which we are applying them. 

For example, when working on a project estimating the cost of climate change in Pennsylvania, it would be much easier to use climate research from places with similar climates today. Ideally, we’d want studies that look at climate change in Pennsylvania specifically, but if someone measured the cost of climate change in Ohio that would still be a useful piece of research. 

Conversely, it would probably be incorrect to use estimates for the cost of climate change measured in Brazil. Even though those researchers might have come up with a very detailed causal equation that neatly ties increased temperatures to monetary losses, we know for a fact that the underlying assumptions about the climate are different there than in Pennsylvania.

Which estimate is the easiest to translate?

Another important consideration is how much work is it going to take to manipulate someone else's results and make them usable. I don’t necessarily mean how much effort it takes (though time is a limited resource and should always be considered) but rather how many steps and assumptions are required to use someone else’s result.

Each time we inflate a value, adjust for regional differences in prices, or change units, we introduce opportunities for estimates to become less meaningful. Some of these are more important than others, changing from pounds to kilograms for instance should not interfere with anything. 

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These are just two things to consider when deciding what estimate to include in a study. Ideally, during the sensitivity analysis phase we can explore and report how these different estimates would impact the results. 

How can we use policy to reduce disparities in Ohio?

Earlier this month, the Health Policy Institute of Ohio released a brief on the prevalence of racial disparities in Ohio. The study quantified the disparity between racial groups of Ohio at $79 billion from gaps in income, consumer spending, tax revenues, health care spending, productivity, and corrections spending.

Racial inequality has economic implications, but it’s also something worth eliminating for its own sake. But how do we do this?

The brief from the Health Policy Institute of Ohio has a few suggestions. It suggests implementing policies that promote justice and fairness, tailoring policies to support people of color, allocating resources to community-building policies, evaluating disparities, reforming criminal justice, and ensuring equitable access to financing.

I’ll add some other policies to consider.

Income

One of the biggest disparities between different racial groups in Ohio is income. According to 2021 5-year American Community Survey data, Black Ohioans are 2.6 times more likely to be in poverty than white Ohioans.

Income disparities could be closed through tax policy. One option is the state earned income tax credit, a tax credit that goes to low-income workers. Ohio’s earned income tax credit rate is rather high, but because taxpayers can’t receive any more from the credit than they pay in taxes, its ability to alleviate poverty is hampered compared to the federal credit. Changing this policy would put more dollars in the pockets of low-income workers, which would help reduce the racial income gap.

Another option is the child tax credit. This credit gives cash assistance to families with children. Recent analysis by my practice revealed that creating a state child tax credit in Ohio could generate anywhere from $60 to $300 million in net economic benefits, mostly realized through higher future incomes for children in families that receive the credit. Adopting a state child tax credit could help narrow the racial income gap by helping low-income households with chilren.

Housing

Housing is a living cost no one can avoid. A National Association of Retailers study from earlier this year found 30% of Black homeowners in the United States are housing cost burdened, meaning they spend over 30% of their income on housing. This compares to only 21% of white homeowners. If housing costs can be controlled, it could have an impact on closing the gap between Black and white households.

One way housing costs can be controlled is by encouraging construction of housing. Reducing restrictions on construction of multifamily housing such as those promoted through single-family zoning could ease the cost of housing, especially in more tight housing markets.

Education

A 2020 brief from the National Institute for Early Education Research found black children are nine months behind white children in math achievement and seven months behind white children in reading achievement as early as kindergarten entry. Promoting access to early childhood education can be a tool for leveling the playing field between people from different racial backgrounds.

We have tools for reducing disparities between different groups. Reducing these disparities will help Ohio’s economy, and it is also just the right thing to do. Ohio should not be a place where people’s outcomes are predetermined at birth based on skin color or ethnic background. It should be a place where the field is level for people of all different backgrounds to contribute. Policy can be a tool for making that a reality.

This commentary first appeared in the Ohio Capital Journal.