Two transportation policies that would change how we travel

Households in the United States spend a lot of money on transportation. According to the bureau of transportation statistics, transportation is the second largest category of household spending behind housing–higher than out-of-pocket medical spending, apparel and services, and food. Additionally, transportation is the largest source of greenhouse gas emissions according to the EPA.

For those of us that are always looking for inefficiencies in society to try and improve, these are two pretty significant red flags. Policymakers tend to agree, and often we look to public transportation as a way of improving economic and environmental conditions.

The thought process is fairly straightforward. If fewer people drive their own cars and instead substitute shared transportation into their lives, we can cut back on costs and reduce emissions. 

Broadly speaking, this is a question of a market with an externality and trying to figure out what the best way to correct it is. In situations like this, the two most straightforward policy levers we have to pull are to subsidize public transportation and tax private transportation. Both in theory should make public transportation more appealing to consumers. 

In practice, we most frequently see a tax on private transportation via a tax on gasoline while subsidies on transportation come in all shapes and sizes. These policies present a very interesting case study to talk about efficiency and equity in the context of externalities, so let’s examine two of the most bold proposals that some governments have adopted. 

Vehicle miles traveled (VMT) tax

A vehicle miles traveled (VMT) tax levies taxes based on the number of miles driven in a year. The VMT tax is an alternative to the gas tax that ignores differences in gas consumption between cars. Because wealthier individuals often have better access to high gas-mileage or electric cars, this prevents the tax from being as regressive. It also helps efficiently price the cost of wear and tear on roads, one of the main reasons car use is taxes in the first place.

In theory, a VMT tax would reduce the number of miles traveled in cars by making those miles marginally more expensive. All else equal, we would expect this lost car travel to be replaced by public transportation, carpooling, walking, biking, or reducing numbers of trips. 

One important equity consideration around the institution of a vehicle miles traveled tax is that many people are unable to substitute public transportation because the current infrastructure doesn’t meet their needs. You might think of someone who has to work a night shift after buses stop running.

Another equity consideration is that the number of miles traveled in a year by an individual does not typically increase proportionally with income. So low-income people would still spend a larger proportion of their income on vehicle miles traveled fees than upper-income people. For this reason, a vehicle miles traveled fee would still be regressive, though not as regressive as a gasoline tax.

From a pollution-reduction perspective, this probably would not be as effective at reducing pollution as a gas tax. In fact, if enough people substituted away from electric cars to public buses with gas engines, it could actually worsen pollution. Carbon or other pollution taxes could supplement a vehicle miles traveled fee in order to efficiently price these externalities.

The most interesting question about a VMT is how extra tax revenue would be spent. This is probably the most important question that would determine whether or not this policy would be efficient. After paying for roads, would money be used to upgrade public transportation, to fund other environmental projects, or maybe as a rebate to low income individuals? These options would have different efficiency and equity implications.

Free public transportation 

In five months, Washington D.C. will become the largest city in the country to completely eliminate its bus fees. Other cities like Olympia, Washington and Kansas City, Missouri have already done so. 

This type of policy is becoming increasingly popular due to the argument that it helps low-income riders. The goal of free public transportation is to increase mobility for people who don’t have access to other transportation and to encourage people who do have access to other transportation to instead use public transportation when possible.

In theory, by reducing the price of public transportation people on the margins would begin to choose it over driving their car. In practice, for there to be much of an impact there would likely have to be an expansion of public transportation infrastructure to match its increasing demand and to make sure that it is far-reaching enough to allow everyone to ride. 

From an equity perspective, the program is targeted at lower income individuals. Currently, public transportation is a less expensive way to get around so by making it even less expensive, lower income people will have more discretion with their income. The extent to which it fulfills this goal, however, is up for debate. Many low-income people in a number of categories have subsidized bus passes provided through other public programs. Eliminating bus fares may fill some gaps, but it might not have the equity impact its boosters hope for.

Eliminating fares also targets benefits narrowly on bus riders. Low income people who walk, bike, or use other forms of transportation receive no benefits under this scheme. A vehicle miles traveled fee used to finance a low-income tax credit can theoretically help more low-income people and reduce single-occupancy driving more efficiently and equitably than eliminating bus fees.

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How state and local governments choose to handle their transportation policy will depend on local factors. If a city already has a robust public transportation infrastructure, then making it free through some sort of progressive tax could reduce equity and pollution. 

If there would have to be a big capital investment to make free public transportation equitable and efficient, then maybe those resources could be better spent on some other poverty reduction or environmental project. Either way, by understanding the potential outcomes of certain policies, policymakers can make the best decision for their constituents with the resources they have.

Which discount rate should I use?

One textbook on cost-benefit analysis says that discounting is not controversial in cost-benefit analysis, but that the rate at which we should discount is. While there are still some fringe voices among cost-benefit researchers who argue for a 0% discount rate, the point is well-taken that most agree discounting is necessary. The point is also well-taken that researchers have not coalesced around a “right” rate to discount at.

Part of this is the nature of the discount rate. The purpose of discounting in cost-benefit analysis is to capture differences in time preference for income for society. If you were offered $100 today or $100 in a year, you’d probably take $100 today. You would need to be offered $103, $105, or maybe even $111 in a year for it to be worth it for you to hold out. Because of uncertainty about the future, a certain amount of income today is widely agreed to be preferable to income later, all else being equal.

A discount rate is ultimately supposed to be about adjusting for how much society prefers current income to future income. This is a hard thing to account for and depends on which society we’re talking about, how they think about income, and the quality of the income (the latter of which can vary significantly in cost-benefit analysis due to the range of outcomes that are monetized).

So if we are going to discount future costs and benefits in a cost-benefit analysis, how do we go about choosing the correct discount rate? Despite the controversy over which discount rate to choose, analysts have coalesced around a few specific recommendations.

3% Discount Rate - The “Consumption Rate”

If you put a gun to my head and asked me what the best discount rate was for any given cost-benefit analysis, I’d have to turn to the 3% discount rate. In a 2021 Resources for the Future issue brief, researchers Qingran Li and William A. Pizer refer to this discount rate as the “consumption rate.”

Li and Pizer say that the 3% discount rate comes from the after-tax earnings on investments. The logic here is that households (who they say are considered the “ultimate authority on ‘welfare value’”) will not favor government policies that yield lower returns than they could receive in the private market.

The 3% discount rate is the rate I have generally seen most and is favored by many because of its focus specifically on time preference and its adjustment downward from the 7% figure due to taxes being included. But don’t count 7% out yet.

7% Discount Rate - The “Investment Rate”

While 3% is the rate I tend to see, it only holds a slight edge over the 7% discount rate, which is based on the average long-term rate of return on a mix of corporate and noncorporate assets. This is the rate of return before taxes, but is still generally considered as a leading discount rate for conducting cost-benefit analysis.

The federal Circular A-94 guidance for discount rates for federal agencies endorses a 7 percent rate for the above reasons, though suggests that higher rates should be used in circumstances where purely business income will be at stake for example since costs will likely be higher due to business’s steeper time sensitivity.

11% - The Developing CountRy Rate

A 2015 technical note from the Inter-American Development Bank says that “In general, developed countries tend to apply lower rates (3-7%) than developing countries (8-15%), although in most cases these rates have been reduced in recent years.” Discount rates are just as controversial in developing countries as they are in developed countries, but traditionally have tended to land around 11%.

More recent reports have recommended lower discount rates in developing country contexts. These are often in light of evaluating the costs and benefits of interventions to mitigate climate change, which tend to front load costs in favor of generating long-term benefits. But there is another way to tackle this problem.

Variable Rate - Accounting for Future Generations

A common approach to dealing with the problem of costs and benefits incurred far in the future is to adopt a variable discount rate, or a discount rate that changes over time. An argument in favor of this approach is that applying a steady discount rate generations into the future privileges the time preference of people in the present over those in the future.

This approach is endorsed by the UK Green Book, the official guidance document from the United Kingdom’s Treasury on how to conduct cost-benefit analysis and other economic analysis. The Green Book recommends a 3.5% discount rate that then declines over the long term.

Sensitivity Analysis

Because of the range of different possible discount rates, my recommendation is to incorporate discounting into your sensitivity analysis. Conduct a partial sensitivity analysis of 3% and 7% discount rates to see how using alternate discount rates impact your results. If costs fall on business earnings, try higher rates to see what happens. Then vary sensitivity analysis in a Monte Carlo simulation to see what range of impacts are possible under different discount rates.

We may not have one discount rate in cost-benefit analysis, but we certainly know enough to try some out and see what they tell us about the policy we are analyzing.

Majority of Ohio economists think "right-to-work" law would deepen inequality

In a survey released this morning by Scioto Analysis, 13 of 22 economists agreed that a “right-to-work” law would increase inequality in the state. Economists who agreed pointed out that right-to-work laws would likely decrease union membership and therefore lower union bargaining power. In theory, this would lead to lower wages for union members, and higher profits for their employers.

On questions of economic growth and employment, economists were evenly split about the impacts of right-to-work laws. In comments, some economists said making union membership non-mandatory could increase employment in some sectors. Others stated this effect might be counteracted by lower wages and slower economic growth. 

One economist points out that states with right-to-work laws don’t experience different economic growth compared to other states, meaning employment effects could be offset by other economic effects. Another mentions that the academic literature on the subject fails to reach a consensus about impacts. 

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.

What goes into a poverty measure?

As with any problem in public policy, we need to be precise in how we define poverty. Using cutoffs like the poverty line is crucial to understanding how our society is faring and how well-being conditions are trending. If the goal of defining poverty is to identify when people don’t have access to enough resources, then we need to figure out both how people access resources and what counts as “enough.” In other words, we need to know what we are counting and where the cutoff is for being considered “in poverty.” 

To better understand how we define poverty in the U.S., let’s take a look at the official poverty measure. The official poverty measure is one of two poverty measures reported by the Census bureau annually. Its key characteristics are (a) that it measures only wage income, and (b) that its thresholds vary based on family size. 

Using wage income as the measure of resources has many advantages, most importantly that it is easy to collect for the population each year. This makes it easy to keep an accurate time series and to see how trends change from year to year.There is a balancing act in policy analysis between accuracy and accessibility of data, and sometimes it is worth trading off some useful information to have more measurements in time. 

A drawback of using wage income as the only measure of resources is that it fails to account for other factors such as access to public benefit programs. For example, some states may deny individuals convicted of felonies access to programs like the Supplemental Nutrition Assistance Program (SNAP, formerly known as “food stamps”) or Temporary Assistance for Needy Families (TANF), two common subsidies for low income individuals. Those people will have to spend more of their income on necessities and will be worse off than someone with the same income who has access to those programs. 

The other annual poverty measure reported by the Census bureau, the supplemental poverty measure, includes these benefits in its measurement of family resources. Additionally, the supplemental poverty measure only counts income after taxes and subtracts certain expenses such as medical or work expenses. We assume these other costs are unavoidable, and count them as a subtraction from a person’s total income. 

These adjustments are significant in that they are still easy to measure annually and they paint a much more accurate picture of what resources an individual has to spend on their needs. Going a step further, it is possible to imagine how having even more granular information about an individual’s resources might tell us more about whether or not they are experiencing poverty. 

One example could be if we had survey data about how often an individual has to pay for their own food. Perhaps a nearby friend or relative can offer some assistance. Someone who has this network of support would have more discretion with how they allocate their resources than someone who does not. 

Of course, this exact information would be difficult to find for every person. It might be useful to collect just one time or once every few years to fill in the gaps in our understanding of poverty, but it just isn’t realistic to get this data every year. Balancing accuracy and accessibility is difficult, but as we see between the official and supplemental poverty measures there is often easily accessible information that improves our understanding. 

The second defining characteristic of the official poverty measure is that the cutoff for determining who is in poverty depends only on the number of people in the household. If a household’s income is below the threshold for the number of people living there, then everyone in that household is considered to be in poverty. 

This is an example of an absolute poverty threshold, which is constructed based on some conception of “basic needs.” In other words, this poverty measure defines a strict cutoff where everyone below is considered in poverty and everyone above is not. Absolute poverty thresholds are based on the idea that there is some universal amount of resources that people need to live. The idea is that if someone falls below the threshold, then they must be sacrificing something critical just to get by.

One benefit of an absolute threshold is that it makes comparisons between places easier. If one state has lower poverty than another, then we might look to the first state as an example for ways to alleviate poverty. It also makes it easier to compare trends across places. If poverty rises in some regions then we know something needs to change. 

The major downside of absolute thresholds is that they often fail to acknowledge differences between regions. For example, the cost of living in a big metropolitan area like New York or Los Angeles is much higher than in a small Midwestern town. 

These alternatives are called relative poverty measurements, and although they are more difficult to compare and understand, they add a lot of useful information to the poverty discourse. While absolute poverty measures are about a universal definition about what is “enough,” absolute poverty measures are about how individuals compare to those around them.

The supplemental poverty measure makes this adjustment by including adjustments for geographic differences in housing costs. By using a simple adjustment, the supplemental poverty measure still makes comparisons between geographic groups fairly simple. 

Exactly how to trade off simplicity for local accuracy is a decision that relies a lot on the story you are trying to tell. If an analyst is trying to tell the story of poverty in a single city over time, tailoring the poverty measure to that place very specifically makes a lot of sense. If instead the goal is to compare poverty across states or nations, then a simpler and more general measure might perform better.

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A lot goes into defining a good measure of poverty. At Scioto, we have our own Ohio Poverty Measure that is a more accurate representation of poverty in Ohio than either of the Census measures. It would be beneficial for more state and local governments to seek out more locally accurate pictures of poverty in order to help guide their policy decisions. 

As useful as it is to compare poverty across states, the barriers that prevent extremely detailed national poverty analysis are much easier to overcome for smaller areas. Taking the time and doing this additional research could bring new information to light and hopefully would give policymakers the information they need to improve the lives of those who need help the most.

“This data” or “these data”: which is correct?

When I enrolled as a public policy analysis graduate student at the University of California, Berkeley, my goal was to learn how to better distinguish between good policy and bad. In school, I learned a lot about statistical methods, microeconomics, cost-benefit analysis, and policy analysis. But another thing I learned about was that some people have strong opinions about the word “data.”

The particular linguistic controversy around the word “data” revolves around its use in a certain phrase: “this data.” Many academics strongly prefer the phrase “these data” to “this data,” claiming the former use of the phrase is “correct” grammatically.

Why do academics often insist that “these data” is “correct” grammar while “this data” is incorrect? The logic behind this argument is that “data” is actually a plural of the word “datum.” Historically, this is true. The words “data” and “datum” first appeared in the early 17th century, in particular for use in math. The original meaning of the word “data” was “facts given as the basis for calculation in mathematical problems.”

Why does “these data” sound so wrong to the average person, then? It may be because the word “datum” has nearly completely dropped out of use in the English language. Over the past five years, there is only one week (late February/early March 2022) where the word “datum” registered 1% of the peak popularity of the term “data” on Google Trends.

Despite the near extinction of the term “datum,” “these data” still has some life. According to Google Trends, the phrase “these data” gets one mention for every two mentions of “this data.” This means the phrase “these data” is still competitive, but is still the minority linguistic composition to “this data,” much to the chagrin of many academics.

How can “this data” be justified linguistically? The use of the phrase “this data” makes linguistic sense if the word “data” is treated as a mass noun like the words “money” or “food.” We all accept that the words “money” and “food” are plural, but we would look askance at someone saying “look at all these money I made” or “I can’t believe I ate all these food.”

While most grammar references will tell you both uses of the word are correct in modern English, the two alternate grammars put policy analysts in a hard place. People who have spent a lot of time in the academic world tilt strongly toward the use of the phrase “these data” based on storytelling within academia about the noble, dying “datum.” Policymakers, on the other hand, tend to exist in contexts where more standard English prevails. This can lead policymakers to prefer the phrase “this data” and see “these data” as an incorrect use of the phrase.

The rule of thumb I lean toward is this: if you can code switch masterfully, use “this data” in policymaking contexts and “these data” in academic contexts. If you, on the other hand, are like most people and can’t reflexively change your grammar from context to context, pick a use of the noun that fits your context better. If you work mostly with policymakers, use the standard mass noun phrasing of “this data.” If you work mainly in academic circles, use the count noun formulation “these data.”

And if you work with both? Well, suffer with the rest of us until academia gets on board with the public or finally convinces the public to buy into the good ol’ count noun formulation.

What does it mean to call natural gas “green?”

Earlier this month, Ohio Gov. Mike DeWine signed legislation to redefine natural gas as “green energy” and to require state agencies to lease out state lands for oil and gas exploration and production.

With the redefinition of natural gas as “green,” Ohio follows a summer decision by the European Union aiming to provide guidance for investors, policymakers, and companies interested in climate change. While natural gas still emits carbon dioxide, its emissions are 40% lower than coal according to the U.S. Energy Information Administration.

Natural gas can’t get Ohio to a zero emissions future, but it has been instrumental in reducing Ohio’s carbon emissions, helping Ohio and its neighboring states reduce carbon emissions by a quarter over the past decade.

Some environmental experts are not happy with the redesignation, however. Ohio State University Environmental Law Professor Cinnamon Piñon Carlarne called the redefinition of natural gas “regressive and a fallacy” and “a little bit Orwellian.”

The new law also changes a permissive 2011 law that allowed state agencies to permit oil and gas exploration on state lands to a mandate for state agencies to open lands to oil and gas exploration. This takes oil and gas exploration from a case-by-case basis in places like state parks to a blanket allowance by oil and gas companies.

What stands out to me about these decisions is the difficulty in assessing the real impacts of policies. The new law defines “green energy” as energy that either (a) Releases reduced air pollutants, thereby reducing cumulative air emissions, or (b) is more sustainable and reliable relative to some fossil fuels. However, it still excludes energy generated through use of natural gas from eligibility for renewable energy credits.

It’s easy to get caught up in the rhetoric about public policy decisions. But what will this law actually do? It’s pretty hard to tell on its own.

Part of this is because while legislators and administrators in Ohio have access to robust legislative and budget analysis, they don’t have access to much policy analysis. This means that policymakers have good resources to figure out what their policy says and how much it costs, but little support in figuring out what it does.

The Legislative Service Commission is a well-funded nonpartisan agency that has hundreds of staff members analyzing bills to explain what the text says and what they will cost the state government. The governor has dozens of budget analysts working for him and agencies have legislative analysts who can help interpret the meaning of text.

On the policy analysis side? There isn’t as much capacity. In 2019, I conducted a study on the use of cost-benefit analysis in Ohio and found there were only 27 studies published from 2012 to 2018 that assessed costs and measured outcomes in some way for programs in the state. I was able to find no studies over the past decade that conformed to all the best practices of a cost-benefit analysis.

Wouldn’t it be useful to know how many metric tons of carbon we can expect to see reduced with reforms like this? Wouldn’t it be useful to have projections for how many lives could be saved through adoption of cleaner energy technology? More investment in cost-benefit analysis and policy analysis in our legislative and administrative processes could bring these statistics to the table for policymakers and the public.

Under a democratic system, politics will always be in tension with analysis. But analysis needs a fighting chance if we are going to have any idea what crucial changes like this will end up doing.

This commentary first appeared in the Ohio Capital Journal.

Effectiveness, Efficiency, and Equity: the Three "E"s of Policy Analysis

Policy analysis has a broad range of tools that help us better understand the impact of policy proposals. The purpose of policy analysis is to help policymakers better understand the potential outcomes of a proposal based on socially-relevant criteria. 

There are many different lenses through which an analyst can measure the impacts of a policy. Among these different lenses, three loom above all the others: effectiveness analysis, efficiency analysis, and equity analysis. 

These three criteria help us understand something different about what we expect a policy to do. Understanding the pros and cons of each is vital to performing meaningful analysis of public policy. 

Effectiveness

Measuring the effectiveness of a policy is entirely dependent on the criteria an analyst selects. The big question we are trying to answer when we ask how effective a policy is is “what are the outcomes of this policy? Does it accomplish its goals?” 

Imagine we are analyzing a policy that is designed to reduce pollution. We could measure its effectiveness by estimating how much carbon emissions are reduced, or perhaps by how we expect climate trends to change. We also could measure how much NOx or PM2.5 pollutants decrease, how many instances of low birthweight could be prevented by reduction of the pollutants, or even how many instances of respiratory illness or death are prevented by these reductions.

This means measuring how effective a policy is at improving both primary and secondary criteria. Along with estimating how much a policy reduces pollution, we can also estimate health benefits or recreation benefits associated with reduction of pollution. 

The gold standard of effectiveness is the “randomized controlled trial,” an experiment where participants are exposed to the policy randomly and their outcomes are compared to those of a control group. Not all policies are good candidates for randomized controlled trial, though: often quasi-experimental studies like difference-in-difference or regression discontinuity analysis can provide some evidence of effectiveness. Even lighter evidence can come from pre/post data or point-in-time data.

Efficiency

In contrast to effectiveness–which is only concerned with the outcomes of a policy–efficiency analysis depends on both a policy's inputs as well as its outputs. 

The most comprehensive tool policy analysts have to estimate economic efficiency of a public policy is cost-benefit analysis. Cost-benefit analysis is easy to understand, theoretically straightforward to compute, and if done well paints a clear picture of a policy’s efficiency. 

Although efficiency and effectiveness are similar to each other, they can sometimes be in tension with one another.  One example of this is the example of scaling policy–taking policy from the pilot level to broader application. When we increase the scale of policies, we often find increases in effectiveness and decreases in efficiency. 

Whether the most efficient or the most effective policy is preferred by a policymaker depends on the context of that policy. Maybe in the case of a pollution-reducing policy there is some emissions target that needs to be reached, even if it is not the most efficient. On the other hand, a policy may be efficient at a local level and not as desirable at a national level because of decreasing returns to scale. These are ultimately judgment calls that policymakers need to make, but that good analysis can make more clear.

Equity

The equity component of policy analysis is often the most difficult to understand, largely because it is the least well defined. Generally speaking, the goal of equity analysis is to understand how costs and benefits are distributed throughout society. 

Currently, methods for equity analysis tend to be less robust than methods for effectiveness or efficiency analysis. Partially this is due to the fact that accurate social/demographic data is often harder to access, and partially due to the fact that equity analysis is often an overlooked step. Equity also exists on multiple dimensions: income, race, gender, age, disability status, national origin, urban/rural etc. While some fields of policy such as tax policy have good standard practice for analyzing equity, examples such of this are few and far between due to the plethora of dimensions equity can be analyzed on.

Still, it is important to understand how equitable the outcomes of any policy are. Good questions for analysts to ask are “what are the demographics of the people who bear the costs of this policy?” and “Does this policy alleviate inequality?”

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As analysts, it is our responsibility to keep  diverse criteria in mind as we explore the possible outcomes of a policy proposal. There are always trade-offs between effectiveness, efficiency, and equity that will determine how a policy impacts society. 

However, in many cases analyzing a policy on all three dimensions may not be feasible due to time or resource constraints. Policy analysis is an inherently time-sensitive process – sometimes decisions have to be made quickly and policymaking moves faster than academia. 

Under these constraints, it is important to figure out which category of analysis is most useful for policymakers. Sometimes, a policymaker will ask for a specific type of analysis, other times it is up to the analyst to decide which analysis will provide the most useful information. 

In any case, it is important to remember that these three criteria tell us different things. Understanding how they are different and communicating it effectively to policymakers looking for information is one of the most important things a policy analyst can do.

Five policy issues to keep an eye on in 2023

A new year presents an opportunity to reset and take stock of where we are in the policy world. There are lots of issues policymakers have to consider that can dramatically change the way our society operates. Here are five policy issues that we expect to be talked about a lot this year. 

Abortion 

Last summer, the U.S. Supreme Court overturned Roe v. Wade, opening the door for state governments to independently decide how they want to handle abortions. This decision set off a flurry of legal action that has dramatically changed the landscape of abortion access in the country. 

However, once the dust settles from all the legal drama, policymakers will have the opportunity to step in and redefine abortion rights in law. Whether it’s strengthening protections that already exist or writing new laws, this will undoubtedly be a major issue for years to come. 

Electric Vehicles

One important provision of the Infrastructure Investment and Jobs act passed last year was that it provided specific funding for new electric vehicle infrastructure. Since then, all 50 states have submitted plans to improve their electric vehicle infrastructure. 

Looking forward, there are still many decisions policymakers need to sort out. Policies like electric vehicle tax credits, mandates for state vehicles to be electric, and new taxes on electric vehicle charging are all options state governments will consider in 2023. The infrastructure bill was only the beginning of the new debate around electric vehicle policy.

Healthcare

It is now almost three full years since the beginning of the Covid-19 pandemic, and we are still feeling the lingering effects. One important change was that Medicaid enrollment increased substantially as a result of the Families First Coronavirus Response act. This caused  the number of people without health insurance to fall. 

Eventually, Medicaid eligibility will be reduced again. Before this happens, policymakers need to decide how they are going to respond to the potential for millions to lose publicly-funded health insurance. Will they let these protections lapse and return to the pre-covid status quo, or will this be the catalyst for new healthcare reform? Either way, healthcare will continue to be a major focus for policymakers in 2023. 

Cryptocurrencies 

Last year, 37 states introduced legislation that attempted to regulate cryptocurrency in some way. The most important legislation was when the federal government passed the Digital Commodities Consumer Protection act, opening the door for the Commodity Futures Trading Commission to oversee the digital commodity market. 

It seems unlikely that cryptocurrency will go away anytime soon, given that more and more businesses are beginning to accept it as payment. Especially after the Sam Bankman-Fried arrest, policymakers will be forced to address cryptocurrency in the legislature and decide how to manage it. 

Inflation

One of the most pressing issues during the midterm election cycle, inflation continues to impact people across the country. Policymakers will have to find ways to address rising prices and help their constituents get by. 

In their 2023 budget, California approved stimulus checks of $1,050 for eligible households. Other states like Georgia and Virginia offered rebates to people who filed their tax returns. There is a lot of room to determine exactly when and how to support the people who are most impacted by inflation, and we should expect this to be an important discussion among policymakers this year.

What is the “Social Cost of Carbon”?

What do 51.5, 1, and 190 all have in common? There’s no obscure mathematical connection: these are the three values of the social cost of carbon (SCC) as calculated by the Obama, Trump, and Biden administrations respectively. 

What is the SCC, and why is it that three different sets of economists can come up with such a wide range of estimates for exactly the same value? 

Why does it matter?

The SCC is one way we can try to correct an externality in the energy market. All else equal, producers and consumers of energy would often prefer to purchase the cheapest form of energy possible, which tends to also cause a lot of pollution. This means that people who do not pay the cost of buying electricity or producing electricity end up paying the cost of climate change even though they are not a part of the energy transaction. In a well-functioning market, producers and consumers are the only people impacted by a transaction. In this case, future generations and people in more climate-vulnerable environments take on the externality cost of carbon emissions–higher temperatures, heavier precipitation, and rising sea levels due to climate change. 

Considering this bigger picture, we can surmise it would be socially optimal for a larger portion of the energy bought and sold to come from non-polluting sources. 

Normally when there is some negative externality in a market, a government can impose some tax on it to bring the private cost of a good up to the social cost of the good and reduce consumption of that good. In the case of energy, this process would lead to other non-polluting sources of energy becoming relatively more affordable.

The SCC tells us how much damage an additional ton of carbon dioxide emissions causes. This is a guideline that we can follow to better understand when different policy options to reduce emissions are cost-effective.

Why such a big range?

There are many differences between the various calculations for the social cost of carbon, but the most important difference is whose damages count. Deciding whose costs to count in policy analysis is one of the most important decisions to make.

This part of policy analysis is called defining standing. When we define who has standing, we are asking whose costs and whose benefits we are looking at. The SCC example illustrates two of the most common ways of defining who has standing: when everyone has standing and when the people within some set jurisdiction have standing. 

In practice, this leads to differences in the estimates for the SCC because the Trump administration only counted damages to Americans. The Biden and Obama administrations calculated the worldwide costs of additional carbon emissions. The newest estimate for the SCC relies on methodological changes to the way that socioeconomic factors, climate factors, and discounting is incorporated. These updates reflect a better understanding of the impacts of climate change and provide a better picture of the total costs.

International bodies enforcing sanctions on nations is a much more significant task than a local government enforcing a sanction on a single producer. Self-imposed policies driven by estimates of the SCC might be the only way to counteract global climate externalities caused by carbon emission.

Scioto Analysis's 5 biggest studies of 2022

In 2022, Scioto Analysis was busier than ever. We have been working hard to improve the quality of public policy analysis at the state and local level. As the calendar year comes to its end, we thought it would be fun to look back at the research Scioto Analysis put out this year. We got to work on projects that covered many parts of the public policy discussion, from income inequality to water systems to the impact of climate on local government. 

Energy Storage Roadmap for Northern Appalachia

This project was a collaboration with researchers from the Energy Policy Center at Cleveland State University. The goal of this project was to create an energy storage roadmap to help stakeholders transition into a growing market by understanding the assets available in Appalachia for the energy storage sector.

Northern Appalachia has long been associated with energy and natural resources. Now, as energy storage and transportation technologies keep evolving, this region has the opportunity to pivot its infrastructure into this new field. This report summarizes the current state of the energy industry and provides guidance on how to pivot those resources to this emerging industry. 

Municipal Tree Planting Programs: A Cost-Benefit Analysis

This project was a cost-benefit analysis examining the impact of tree planting programs in Ohio cities. Planting trees in a city can help the environment, improve physical and mental health, increase housing value, and even help reduce crime. 

Our analysis found that on average, planting and maintaining a new tree would cost an Ohio city just over $10 and provide benefits between $10 - $21 depending on the city. A single city could receive net benefits as high as $110 million depending on the canopy coverage goal. 

The Bill is Coming Due: Calculating the Financial Cost of Climate Change to Ohio’s Local Governments

Scioto’s biggest project of the year was estimating the local financial costs that will arise because of climate change. This project was a collaboration with Power a Clean Future Ohio and the Ohio Environmental Council.

In this study, we looked at how much new spending local governments will incur as a result of new climate factors like increased temperature and precipitation. For 10 important spending categories, we project that local governments in Ohio will have to spend between $1.8 and $5.9 billion additional dollars per year by 2050 to counteract the effects of climate change.

Income Inequality in Ohio 

This study looked at income inequality in Ohio and analyzed a few policy options that might alleviate it. Ohio currently has slightly more income inequality than the average state, with the top 1% of Ohioans making 10% of the state’s income and the bottom 50% making only 13%. 

The three potential policy options we examined were a refundable earned income tax credit, an increase to the minimum wage in Ohio, and a negative state income tax. Among these options, implementing a negative state income tax was the most effective at reducing income inequality in our simulations. 

Water Quality in Ohio: a Cost-Benefit Analysis

Our most recent project was a cost-benefit analysis about voluntary nutrient management programs as part of the H2Ohio initiative. This was the first cost-benefit analysis conducted by new Scioto policy analyst Michael Hartnett.

Our research showed that under current conditions, the program provides close to $3 million in net benefits from improved water quality to Ohioans. Assuming the program continues to remain effective, allowing farmers from the rest of the state to enroll in the program could result in about $12 million in net benefits.