Ohio economists split on affirmative action decision

This morning, Scioto Analysis released a survey of Ohio economists exploring the impact of the Supreme Court’s recent decision to end affirmative action on Ohio’s colleges and universities. The plurality of respondents believe that this decision will reduce diversity among college students in Ohio.

Many respondents point out that the effect of this decision might be small and isolated to only a few select schools. “There is research based on states that removed affirmative action previously. That research generally shows that on average there was not much change in college attendance except at the most selective schools, where minority enrollment decreased and White enrollment increased,” wrote Curtis Reynolds from Kent State. “So it could decrease diversity at the most selective schools, but will likely not have much effect at most institutions.”

There was less consensus on the question of how this decision would affect the ability of colleges and universities to promote economic mobility. As many respondents pointed out, these effects have historically been isolated to historically selective schools. If enrollment doesn’t change much in places like large state schools, then the economic mobility provided by a college degree may not be affected very much. 

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.

Who benefits from a child tax credit?

Earlier this week, Scioto released a new cost-benefit analysis looking at what the impacts of a state child tax credit might be for Ohio. We chose to estimate the impacts of a state child tax credit because the federal child tax credit that was introduced as part of the American Rescue Plan Act went away last year. 

In its short time, that child tax credit was one of the most effective anti-poverty programs in decades, lowering child poverty to its lowest percentage ever recorded. Because it no longer exists at the federal level, we were interested in seeing how it could be implemented at the state level. 

Our analysis looked at three different plans for administering a child tax credit that varied based on the age of children eligible for the credit and the size of the credit. In all three of the plans, we found that economic benefits outweigh costs of the program. 

In addition to measuring the society-wide costs and benefits, we were also able to perform a distributional analysis to see exactly where these costs and benefits landed. 

Unsurprisingly, the majority of the benefits were received by the recipients of the tax credit and the majority of the costs were borne by those who don’t qualify for the tax credit. An important result of this distributional analysis is that if we only look narrowly at the impacts on households that don’t receive the tax credit, we find that there are actually slightly negative net benefits.

This is not to say that the people who don’t receive the tax credit don’t see any benefits from this program. Things like the expected reduction in future crime benefit everyone in society as taxpayers and possible victims of crime. However, for the people who would not qualify for this credit, this probably isn’t the most efficient way to achieve those same benefits.

For the people who qualify for this tax credit, the benefits of the program are enormous. The most significant benefit is the expected increase in future earnings for children who grow up with this extra income. For a poverty program, the ability to keep people out of poverty in the future is extremely important, and this intervention achieves that goal effectively and efficiently.

Generally speaking, this program is a small loss for people who don’t qualify, and a much larger gain for those who do. Because the qualification criteria is based on income, we know that this program benefits those who are less well off and does not benefit people at higher incomes. 

These insights should hopefully help policymakers understand exactly what tradeoffs come with this sort of policy. Yes, some upper-income households will have less resources and less ability to get the things they want. However, this is overwhelmingly offset by massive gains for those in our society who are struggling the most. 

Our analysis also highlights the fact that the majority of the benefits for this policy are realized in the long-term. In the short term, this helps people who are currently in poverty get a little extra income. As an investment, we are helping today’s children stay out of poverty as adults, and reducing the future burden on our social safety net. Even though there are tradeoffs, once we add everything up we expect this policy to make our society better on net.

Ohio child tax credit would boost state economy

This morning, Scioto Analysis released a new cost-benefit analysis estimating the impact of a state child tax credit for Ohio. We find that depending on the size of the credit, the state could generate between $60 million and $300 million in net benefits.

A child tax credit is a program that provides cash support for families with children. In addition to the federal child tax credit, 12 states have child tax credits of varying levels and availability, with credit amounts ranging from $100 to $1,000. A November 2022 Census Bureau analysis found that the federal child tax credit expansion in 2021 lifted 2.1 million children out of poverty.

“The expansion of the federal child tax credit in 2021 was the most significant antipoverty policy change in the United States since Lyndon B. Johnson’s Great Society,” said Scioto Analysis Principal Rob Moore.

According to the analysis, the majority of the benefits of a state child tax credit would be realized as increases in future earnings for children who qualify for the tax credit today. This means that in addition to lifting children out of poverty today, a child tax credit could help disrupt the cycle of intergenerational poverty.

Additionally, there are major social benefits in the form of reductions in future crime and healthcare expenses. 

“Programs like this that reduce child poverty are not just band-aid fixes that help people get by today, they are investments in the future that have major positive impacts for society” said Michael Hartnett, policy analyst. “Everyone in our society, even those who never see a dollar of this tax credit stand to receive major benefits.”

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.

Is paid family leave an anti-poverty program?

The first months of a child’s life are crucial for her development. Children who have more individualized attention from a caregiver have more of a chance of succeeding down the road. Encouraging parents to spend time taking care of infants could pay dividends for society down the road.

Throughout much of the United States, though, it is hard for parents with careers to do this. According to the New York Times, the United States is one of eight countries in the world with no guarantee of parental leave. The average country guarantees 29 weeks of parental leave and nearly all of Europe plus countries like Iran and Russia guarantee at least 24.

Among U.S. states, paid leave is also rare. Only seven states, five in the northeast and two on the west coast, had implemented paid family and medical leave laws as of June of last year according to the National Conference of State Legislatures. Four additional states had enacted legislation but not yet implemented it.

Despite only a smattering of states enacting paid leave guarantees, local governments across the country are considering paid leave as a way to improve working conditions and bolster prospects for children and youth. Because of this dynamic, Scioto analysis is working with the Rise Together Innovation Institute to explore opportunities for expanded parental leave in Franklin County, Ohio.

Lack of parental leave policies put families on the edge of poverty in a difficult position. They have to decide to either have children and not spend time with them in some of their most crucial developmental moments, have children and give up income to spend time with them, or postpone or forgo having children at all.

Putting a robust parental leave program in place can be an effective tool for empowering these families on the edge of poverty. First, they ensure families have resources when they have children and can spend time with those children during key developmental months. Internationally, more extensive parental leave policies correlate with a decreased risk of poverty for both two-parent households and single mothers.

As hinted at the start of this blog post, paid family leave is also an intergenerational policy. Early interactions between parents and infants have a large impact on long-term outcomes for children when it comes to cognitive and social development. Children who enter school and then the workforce with better cognitive and social skills have a leg up in escaping poverty in adulthood. By promoting time between parents and children at the earliest ages, parental leave is a multigenerational anti-poverty program.

Lastly, paid leave laws could be an effective tool for closing the gender wage gap. Since women are more likely than men to take leave, employers could be reducing wages for women to make up for the paid leave they currently provide. Requiring paid leave for all parents could theoretically combat this race to the bottom.

In May, Mayor Justin Bibb of Cleveland proposed a parental leave program that would apply to all city employees with a month of service, giving them 500 hours of parental leave at 100% of salary (20 hours are reserved for prenatal appointments/preadoption appointments). This is more robust than either the City of Columbus or Franklin County’s current policies.

Enacting new paid leave laws could be an effective tool for supporting parents now and children over the trajectory of their lives. And the policy is not only a workforce policy, but also an anti-poverty policy.

What is an Accessory Dwelling Unit (ADU)?

Recently Scioto Analysis has been working with the RISE Together Innovation Institute to research the current state of poverty in Franklin County and policy to improve it. One policy we’ve been looking at has been loosening regulations surrounding Accessory Dwelling Units (ADUs) as a way to improve housing affordability. 

One major challenge faced by people in poverty is finding affordable housing. We found during our research that families with income under $20,000 annually were 28 times more likely to be housing burdened (spending over 30% of their income on housing) as families making over $70,000 annually. 

In Columbus, housing prices have risen by over 60% in the last five years. To make matters worse, the Mid Ohio Regional Planning Commission projects that in 25 years, the Central Ohio region will grow by as many as 756,000 people. 

If nothing changes, Franklin County could be on track for a housing crisis.

The good news is that rising prices and a lack of housing have the same solution: increase the supply of housing. 

This can be achieved by building new affordable housing, but that takes a lot of time and resources. Even worse, developers are often incentivized to build new luxury housing in order to maximize the value of their property. 

This is where ADUs come in. An ADU is a separate living space that exists on a lot in addition to a single family home. It could be part of a single family home like a basement or an attic, or it could be a separate structure like a detached garage. 

In addition to increasing the supply of housing, ADUs also make neighborhoods with single family homes more affordable to live in. Without dramatically changing the existing infrastructure, we can put more families in neighborhoods that historically have been limited in the number of people that can physically live there. 

Another side effect of ADUs is that it gives low-to-middle income earners more access to neighborhoods with higher income. Those neighborhoods have higher upward mobility thanks to things like high quality schools and low crime rates.

Arguably the best benefit of ADUs is that from a policy perspective, they are extremely cost effective. While ADUs are not going to solve the problem of a massively growing population (that will require large investments into affordable housing), ADUs are a low-cost strategy to get more people into stable housing situations. All it requires from policymakers is a change in zoning rules. 

The benefits of having stable housing are significant as well. People with reliable housing have better health, employment, and education outcomes. All of these things reduce the burden on the social safety net, and free up resources to be used elsewhere in our society. 

ADUs are likely to face pushback from people who don’t like the thought of single-family neighborhoods allowing multiple families to live separately on the same plot. Educating these people about the benefits of affordable housing could help garner support for ADUs. 

As Central Ohio grows over the next 25 years, policymakers are going to have to come up with creative ways to make housing more affordable. New construction of affordable housing is likely going to be required at some point, but we don’t have to wait that long to improve conditions. 

ADUs are not going to solve the housing crisis by themselves. However, if policymakers change the zoning rules and allow ADUs to be built, we could see short-term improvements. Policymakers should be looking for low-cost ways to bridge the gap until a more permanent solution can be implemented.

Secondary Market Effects in Cost-Benefit Analysis

According to a recent paper published in the Journal of Benefit Cost Analysis, most policy analyses underestimate the costs of interventions by ignoring the effects they might have on secondary markets.

For context, policy often impacts secondary markets that are for goods that are substitutes or complements to the market we are making a direct policy change in. An example given by the researchers is how a tax on coffee consumption might impact the market for tea.

In most cost-benefit analyses, these effects are largely ignored. From a practical perspective, they are quite difficult to measure, and from a theoretical perspective this exclusion has been justified by the assumption that because these markets are linked, our initial primary market analysis will inherently account for secondary market changes. 

Mathematically, we can think of the total effects of a policy as an equation:

Net Effects = Primary Market Effects + Non-Market Effects + Secondary Market Effects

In most cases, the primary market effects and the non-market effects have different signs (e.g. a tax on cigarettes that improves public health). If the assumption that the secondary market effects are accounted for in the primary market effects holds, then we can ignore that part of the equation.

Unfortunately, most cost-benefit analyses do not take the required steps to satisfy this assumption. Instead, they often underestimate the second market effects and miss a key cost.

Fortunately, the researchers have developed a solution to this problem. By using fairly simple to calculate elasticities, they found a way to estimate the impact of a primary market change in a secondary market. 

If analysts incorporate these simple elasticity-driven analyses into their cost-benefit analyses, they will be able to determine an estimate for the impact of a policy change on a secondary market. This means that these future cost-benefit analyses will be more accurate and can better inform policy decisions. 

One interesting note the authors highlight at the end of their paper is that these effects are often quite small in practice. In the examples they provide, secondary market effects are small enough that ignoring them entirely likely would not affect a policy maker's decision. 

In fact, in both examples provided by the authors the inclusion of the secondary market effects failed to move the point estimate outside the confidence interval for the initial primary market effects. In other words, there was no statistically significant difference between the model that included secondary market effects and the model that did not.

Still, these small improvements on the margins add up to better policy analysis. Although the examples presented by the authors could have potentially ignored these secondary effects, their inclusion increases the accuracy of the model. Additionally, some policymakers may be interested in the impacts on secondary markets. For instance, a Congressmember who represents a district that produces tea would be interested in the impacts of a tax on coffee, even if they look small on a national scale.

I imagine that given the size of these effects, many analysts will continue to ignore them. In a world where limited resources can go into performing these analyses, spending those resources on such a small effect will often not be deemed valuable.

This advancement illustrates the value that academics researching policy analysis can add. As cost-benefit analysis becomes more widely used, these small marginal adjustments will become more and more important. Hopefully, policymakers will be able to make use of the information that comes from these changes to the process too. 

Is the value of a statistical life the same for everyone?

In an essay published earlier this year in the Journal of Benefit-Cost Analysis, former OIRA director Cass Sunstein grapples with a tough question: should the value of a statistical life vary for different populations?

The value of a statistical life is a key tool analysts use to understand the economic implications of regulatory actions at the federal level. Sunstein calls the value of a statistical life (VSL) “the workhorse of cost-benefit analysis,” saying “it is the principal driver of benefits in multiple domains, whether we are speaking of highway safety, road safety, food safety, cigarettes, or pandemics.”

The value of a statistical life has a long history. The statistic was first an estimate of how much it would cost to replace a worker. It later evolved into the sum of future earnings of an individual. Its current approach is drawn from risk of death reductions based on the relative safety of workplaces. 

The modern method is focused on labor market data. Economists use the relative danger of different workplaces combined with wage data to see what wage premium is paid to people to take on additional risk of death. For instance, a welder who works on skyscrapers is usually paid more than one who works in a shop, all other things being equal. This extra payment represents a market for risk of death reduction, where workers will take lower pay for lower risk of death and will require higher pay for higher risk of death.

One complication of this approach is that lower-income people have less money, so they are willing to pay less to reduce their risk of death than upper-income people. This is because minute reductions in risk of death could cost the same as a meal or even a bill payment, something low-income people would not want to trade off.

This unearths an insight about regulatory policy: it can in theory be quite paternalistic, forcing poor people to spend their limited resources on minute reductions in risk of death that they would never make on their own.

Despite all this, federal agencies and most analysts in general use a uniform value of a statistical life. This means that low-income, middle-income, and upper-income people’s willingness to pay for risk of death reduction is simply assumed to be the same.

Sunstein’s article talks about the implications of this assumption. He first tackles the question of subsidies, saying that low-income people, all other things being equal, will benefit from subsidies allocated according to this principle, though less than they would from a cash transfer or some alternate uses of these funds. This is because they are likely to receive some benefit from the subsidy, even if it is not as much as middle- or upper-income people.

Note that this is only true if they are not the ones paying the bill for the subsidy. If low-income people are footing the bill, they could end up less well-off than they would if the program were not in place.

As for regulations, Sunstein argues that it all depends on incidence. If low-income people accrue much of the benefits of a regulation and little of the costs, they will of course benefit. But if they pay a large proportion of the costs and accrue little of the benefits, they will do poorly under a situation where their value of a statistical life is assumed to be higher than it is.

Overall, what I take from this essay is that decomposition of benefits and costs matters. We should know who benefits and who will pay for a regulation not only for equity reasons, but for efficiency reasons, too. This will give us a better idea of what really happens when we put a regulation in place.

How can we do tax cuts better?

Currently, the Ohio House and Senate are in negotiations over the Ohio state budget. The budget for the Ohio House included broad-based income tax cuts while the budget for the Ohio Senate focuses on business and income tax cuts that largely benefit upper-income residents.

A recent analysis by Richard C. Auxier and David Weiner of the Urban-Brookings Tax Policy Center looks at an alternate strategy for tax cuts and its impact on the income distribution in Ohio. Rather than approaching tax cuts through reductions in income tax rates, this analysis looks at how a child tax credit would impact household incomes. The analysis looks at a proposal for a child tax credit that is cheaper at $550 million per year than the current Senate tax cut proposal of $780 million per year.

Using the TPC state tax model, they find the Ohio Senate’s tax reduction would lead to no additional income for households making under $50,000, less than $100 annually for households in the $50,000 to $75,000 range, a few hundred dollars annually for families in the $75,000 to $200,000 range, and over $800 annually to the average family making more than $200,000.

As we have written before, this sort of distribution of public funds can be undesirable from a welfare standpoint because households with more income have less use for additional dollars than low-income households.

A $250 tax credit for households with children, on the other hand, would have benefits distributed throughout income categories. Their analysis projects that the average household across nearly every income bracket would save $100 under the proposal. The results are even more dramatic for families who would receive the credit, with them receiving an average of about $400 per household across all income brackets. This impact comes 30% cheaper than the Senate proposal, suggesting the intervention could be more equitable and cost less than the current proposal.

Why does the child tax credit offer benefits to low-income individuals that overall tax cuts do not? This is because upper-income households pay more in income taxes than lower-income households currently, so they have more to gain from an income tax cut. A flat child tax credit, on the other hand, applies to all households with children.

Their analysis also puts forth a number of policy alternatives that can be adopted by policymakers in Ohio. This includes a couple of cheaper policy options than the $550 million child tax credit proposal including a $440 million proposal that limits benefits to young children and a $315 million proposal to make the CTC nonrefundable, limiting its benefits to middle- and upper-income households. They also offer a $640 million proposal to make the state earned income tax credit refundable, which would help lower- and middle-income households. All three of these alternatives are more affordable than the current $780 million income tax cut proposed by the senate.

Analysis like this is important because it challenges policymakers to get creative. If policymakers want to reduce taxes, they can do that in a way that also promotes equity goals. It just means taking potential policy options seriously.

How does inequality impact well-being?

Our most recent published study looking at GPI in Ohio has received some coverage recently. As a result, I wanted to revisit it one more time to cover one of the most significant differences between GPI and GDP: the income inequality adjustment. 

In Ohio, we estimate that income inequality costs the state over $100 billion annually, by far the largest cost measured by GPI.

Taking a step back, we should think about why we want to measure the cost of income inequality. People have very different opinions about how much inequality is acceptable. Most people tolerate some of the inequality that comes with more efficient growth (e.g. employees might get small raises when their company’s profits double), but find extreme inequality undesirable.

From a theoretical perspective, this ties into the idea of the marginal utility of money, in other words how much our money is worth to our well-being. If your first thought is that every dollar is the same, consider this example. 

Imagine two people. Person A spends $300 a month on food, never goes hungry, and budgets carefully. Person B spends $3,000 a month on food and goes to expensive restaurants regularly. Is person B really getting 10 times the value from their money as person A?

We should expect person B to have some added value from their extra $2,700 a month in food spending: you don’t have to prepare your own food at a fancy restaurant and it is a form of recreation that is valuable. Still, it is unlikely that person B is getting 10 times as much value from that spending as person A.

Another way to think about it, imagine instead of person B living lavishly, they adopted the same budget as person A, and used the leftover money to feed 9 additional people on the same budget who would otherwise have to skip meals or stretch their budget to eat. Surely those people would receive more value for that money than person B otherwise would.

So how do we factor this intuitive insight into an economic model?

When calculating GPI, we follow the approach used by John Talberth and Michael Weisdorf. They propose a logarithmic adjustment for the diminishing marginal utility of income. Essentially, $100 is worth less to a rich person than a poor person, and is worth almost nothing to the people with the highest incomes.

There are a few key factors in this adjustment. First, we only adjust incomes above the median. This assumes that people earning less than or equal to median income in a given area are receiving the full economic benefit of their income.

Additionally, because of the logarithmic reduction in utility, very high earners contribute very little additional benefit to the economy with their consumption. The natural extension of this idea is that these individuals would contribute more to the society if they didn’t consume all of their resources, but rather distributed them throughout the income distribution.

A final consideration with the income inequality adjustment is that more than any other indicator, it measures the efficiency of our economy rather than the effectiveness of it. Other costs like pollution and underemployment are designed to shine a light on tangible economic activity that GDP ignores, but this adjustment is an inherently distinct concept.

Its inclusion is certainly relevant, there is research showing that including measures for inequality is important to public policy. Despite the fact that it is the most unique of all the measures in GPI, accounting for income inequality helps us better understand how our economy truly is faring.

Economists agree subsidies will improve housing affordability

In a survey released this morning by Scioto Analysis, the majority of economists agreed that a tax credit for low-income housing developers would significantly lower housing prices for low-income renters.

This policy has been a point of contention between the Ohio House of Representatives and the Ohio Senate. House lawmakers supported spending $500 million on tax credits for low-income housing developers in their most recent budget proposal, but the Senate removed this item.

Among economists who agreed, questions remained about whether this policy would be an efficient use of government resources. As Jonathan Andreas of Bluffton University wrote, “housing costs (particularly for starter homes) are higher now than they have been historically because of a supply problem… I doubt this is the most cost-effective way to increase the supply of housing, but the best way is often politically infeasible and this should increase the supply of housing.”

Charles Kroncke from Mount St. Joseph university strongly disagreed that this policy would lower prices for low-income renters, writing “only if government mandates specific rental prices. Even then, I would expect developers to have a preference for market priced developments.”

Economist opinions were mixed on the prospects of these subsidies for economic growth or reducing income inequality.

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.