Scioto Analysis releases cost-benefit analysis of Moving to Opportunity programs

This morning, Scioto Analysis released a cost-benefit analysis on an economic mobility program to help low-income families move to neighborhoods with more economic opportunity. The program is modeled after Moving to Opportunity, a 1994 experiment by the Department of Housing and Urban Development, and Families Flourish, a non-profit organization based in Columbus, Ohio. Based on evaluations of these programs, analysts estimate that a program expanded to 1,000 families would create $320 million in value through reduced crime, increased lifelong earnings, reduced welfare spending, and other impacts. 

Studies have shown that neighborhoods with lower rates of poverty produce better outcomes in health, economic standing, and education for children who live in them. The original Moving to Opportunity program enrolled 4,600 low-income families and moved roughly half of them to lower-poverty neighborhoods through subsidized housing vouchers. Children who moved before the age of 13 experienced the greatest benefit from the program. Through our analysis, analysts estimate that a Moving to Opportunity-styled program for 1,000 families in Ohio would result in:

• $140 million in increased lifelong earnings

• $9.5 million in reduced crime

• $450,000 in reduced welfare spending

Per family, the program is expected to cost $40,000 per child in discounted present dollars. Analysts conducted a Monte Carlo analysis with 10,000 simulations of the program. From this, they estimate the program will generate $5 to $7 in benefits for every $1 in costs. Net social benefits are expected between $250,000 and $310,000 per child. Analysts expect this program to be largely beneficial for low-income Ohioans, providing long-term benefits in income, crime, and health.

Minimum wages can improve public safety

Earlier this week, This Land Research released a study conducted by Scioto Analysis looking at the impact that raising the minimum wage in Oklahoma would have on crime rates. Crime and public policy  is one of my favorite applications of economic theory, and I think it is extremely important to understand the incentives behind crime if we want to address its root causes. 

In the study, we found that higher minimum wages would lead to reduced crime rates in Oklahoma. The main reason for this is that the effect of increased wages outweighed the effect of reduced employment in the majority of scenarios we simulated. 

When we look at what types of crimes would be prevented by a higher minimum wage, we find the largest impact would be for young adults committing larceny. Intuitively, this makes a lot of sense. Larceny is non-violent and very often financially motivated, so if people have better outcomes in the labor market they might prefer to work in the legal market rather than steal. 

However, when we look at the monetized value of the crime reductions, the vast majority of the social benefit came from a reduction in homicides.

A 2020 study found that there was a connection between higher state minimum wages and reductions in the number of firearm homicides. We used this insight to determine what a similar effect might mean for the number of homicides in Oklahoma, and we calculated it would lead to 55 fewer homicides on average.

Despite making up less than 1% of the total number of the avoided crimes, these 55 fewer homicides were responsible for almost 90% of the total social value of crime reductions due to the higher minimum wage. Conversely, the prevented larcenies accounted for nearly 70% of the total avoided crimes, but only 2% of the total social benefit. 

I think there are two major takeaways from this particular finding, one for policy analysts and one for policymakers.

For analysts, this demonstrates the importance of looking for costly connections between a policy and an outcome, even if the connection between the two is small in magnitude. This doesn’t mean it is appropriate to shoehorn in a mortality impact where it doesn’t belong, but if there is empirical evidence to suggest a connection between the policy you are studying and some very important outcome, it is often worth exploring. 

For policymakers, this shows how certain policies can have effects on things they are not designed to change. The purpose of this study was to highlight the connection between minimum wages and public safety, but most people who participate in the discussion about minimum wages are solely focused on the labor market impacts. Minimum wage policies are not implemented as crime reduction policies, it just so happens that they have a spillover effect.

We can learn a lot about policies when we focus on the social impacts. The total volume of homicides prevented is not close to the total number of other crimes, but because we know how much more severe a homicide is we see that preventing even a handful can lead to major benefits for everyone.

New Report Finds Raising Minimum Wage to $15 Would Deliver Major Public Safety Benefits in Oklahoma

A new report “Public Safety and the Minimum Wage” released today by This Land Research and Communications Collaborative highlights the connection between wages and public safety in Oklahoma. The analysis, conducted by Scioto Analysis, shows that raising the state’s minimum wage to $15 an hour by 2029 could reduce crime, incarceration, and corrections spending—while delivering hundreds of millions of dollars in social benefits to Oklahoma families and communities.

In this analysis, we estimate a $15 minimum wage in Oklahoma will lead to the following:

• Nearly 7,000 fewer crimes each year — including an estimated 55 fewer homicides annually and over 4,900 fewer incidents of larceny. 

• $840 million in avoided social costs each year, with the majority of savings driven by reductions in violent crime. 

• The public safety impact of a $15 minimum wage would be equivalent to hiring nearly 1,000 additional police officers—without the additional $58 million in costs to taxpayers. 

• Oklahoma’s incarcerated population could decline by 370 individuals annually, reducing corrections spending by an estimated $5.7 million each year. 

• Recidivism rates are projected to fall by six percentage points, helping more Oklahomans successfully reenter society and stay out of prison. 

“These findings make clear that wages are not only an economic issue, but a public safety issue,” Rob Moore, Principal for Scioto Analysis, said. “When wages rise, workers are less likely to be pushed toward crime and more likely to build human capital in the legal workforce. This new analysis shows raising the minimum wage isn’t just about higher wages, it’s about building better, safer communities, while saving taxpayers millions of dollars.” 

While Oklahoma has made great strides in reducing the number of people in prison, it still has one of the highest incarceration rates in the nation, with 1 in 178 residents behind bars. The report underscores that higher wages could help reduce that number even further and reduce the burden on law enforcement and Oklahoma’s corrections system.

What are “sticky prices?”

In a recent blog post, I talked about different types of market failures and how they can change the way we see markets operate in practice. In this post, I covered externalities, information asymmetries, public goods, and monopolies. 

Of course, this is not an exhaustive list of every possible type of market failure. These are just the ones that we most frequently run into with our work. 

I wanted to talk today about another aspect of markets that don’t quite qualify as market failures but do play a big role in real world markets deviating from our simple economic models: “stickiness.”

When we talk about stickiness in markets, we refer to the fact that in many cases, prices don’t respond right away to changing market conditions. The best example of this is in the labor market. Many employees work under contracts that specify what their wages will be for some fixed duration. 

It sounds crazy on its surface, but if every person was a totally rational economic actor and had perfect information, it might increase efficiency to allow people to negotiate their wages every single day. The reason this is crazy is because nobody is a perfectly reasonable economic actor, perfect information rarely exists, and there are significant transaction costs to renegotiation of contracts. Imagine the first hour of every work day being taken up by wage negotiations. Not only would it waste time, it would be mentally exhausting.
Instead, both employers and employees agree that signing contracts and having some certainty about the future is a better system. The reasons I wouldn’t classify this as a market failure are 1) I suspect that nobody would be better off without these types of contracts and 2) it does not lead to an inefficient allocation of resources. 

At any single point in time, participants in the labor market can negotiate the price of labor and choose an efficient rate that takes into consideration some basic ideas about what both parties expect the economy to look like in the future. If the economy is generally stable, then a fair price today should largely be a fair price into the future. 

Another key point about sticky markets is that they do eventually respond to changes in broader economic conditions, just not right away. This can create issues in the short run that public policy might need to address. Take the recent high inflation as an example. 

There is mixed evidence about how well wages have kept up with inflation in recent years, but in general they have been able to keep pace overall. Still, we all remember how challenging it was for so many people in the early days of that high inflation. 

Many people struggled with paying for basic needs because their wages were still stuck behind. Eventually they may have caught up, but by that point, the damage had been done for a lot of people. 

While sticky wages can be a rational choice for businesses and employees, they do create problems that public policy can help address. A classic example is rising unemployment during an economic downturn. Because wages are sticky, companies are unable to reduce wages when they lose revenue. Instead, they have to lay off a portion of their employees. 

Unemployment insurance is a direct response to this problem. By requiring companies to contribute to a shared fund, the government can provide a safety net for workers who lose their jobs due to these rigidities. This helps to stabilize the economy and reduce the harm caused by sticky prices.

Another market that is affected by sticky prices is housing. For many people, rents and mortgages make up the majority of their housing costs and those almost always come with long-term fixed payment schedules. 

There are some variable housing costs like utilities that can fluctuate more regularly, but for most people participating in the housing market the price they pay is largely fixed. Again, this isn’t the same as a market failure because people participating in the market can account for future uncertainty and make efficient decisions in the short term. It does, however, constitute a departure from our classical economic models.

While we often focus on traditional market failures like externalities and monopolies, it's also important to understand other deviations from our simplified economic models. While sticky prices don’t cause the same type of inefficiency as a true market failure, it can still lead to short-term challenges that policymakers may need to address.

Will state national guard deployment reduce crime in D.C.?

Just over two weeks ago, the Trump Administration declared a “crime emergency” in Washington, D.C. in response to a former DOGE staffer getting injured in an attempted carjacking. As rationale for the emergency, the Trump Administration has described crime in D.C. as “out of control” and has named the District itself as, “by some measures, among the top 20 percent most dangerous cities in the world.” In January of this year, the Metropolitan Police Department announced that crime in D.C. reached the lowest it had been in 30 years. 

The Trump Administration has since taken control of the Metropolitan Police Department and deployed the District of Columbia’s National Guard in an effort to reduce crime in the city. Earlier this week, President Trump signed an additional executive order with guidance to staff the National Park Service with more police officers, hire additional attorneys to prosecute violent crime, and increase law enforcement training and resources, all to help crack down on crime in the District of Columbia.

The 1973 Home Rule Act gives the president the ability to invoke this kind of power by granting presidential control over the national guard in the District of Columbia and allowing presidential use of the local police during emergencies. These emergencies are permitted to last for up to 30 days without legislative authorization. 

The Home Rule Act has been invoked by presidents in the past, but never for reasons as broad and sweeping as crime. Prior to the current deployment in the District of Columbia, the most recent instance a president bypassed a governor to deploy state troops was to protect civil rights advocates in the 1960s. The use of the Home Rule Act and the deployment of the National Guard to fight everyday is unprecedented and is sure to face legal challenges and political pressure. 

Research from Brown University finds that in the past, military policing has done very little to actually reduce crime. The study focuses on Bogotá, Colombia, one of the cities President Trump has compared crime rates in the District of Columbia to. The study finds that military forces seldom decrease crime more than traditional police forces, and in many cases, crime rates end up higher than before once military forces leave. 

This data corroborates the concerns lawmakers in the District of Columbia have voiced. The National Guard does not receive the same type of training that local police officers receive on when and when not to use lethal force. Typically, the National Guard is used for more specific use cases, such as engaging in crowd control for protests, helping in the aftermath of a natural disaster, or controlling civil unrest. And, when it comes to handling crime, the National Guard acts more in-line with the military strategy of neutralizing dangerous threats than the police strategy of solving crime. In conjunction with recent reports of the National Guard carrying firearms, safety concerns in the District of Columbia are not without reason.

This poses the question: is deploying the National Guard an efficient use of resources to stop crime?

As of August 20th, six states have promised to send their National Guard troops to the District of Columbia: Louisiana, Mississippi, Ohio, South Carolina, Tennessee, and West Virginia. These troops will add more than 1,100 new soldiers to patrol the streets in the District of Columbia. 

Using FBI crime data, violent crime rates from these states compared to Washington, D.C. are shown in the chart below. 

Out of the six states that have promised to deploy the National Guard to the District of Columbia, the rate of violent crime per capita in Washington, D.C. is only higher than two, West Virginia and Mississippi, both of which have fewer urban areas than the other states. Even if deploying the National Guard is an effective way to reduce crime, why aren’t states with crime rates higher than the District of Columbia deploying the National Guard in their own cities? In this case, it seems subnational policymakers have deferred local interests to federal interests.

Once troops are sent from the states, Washington, D.C. will have upwards of 2,100 troops deployed. More than half of these troops will be from states, and the total cost of these troops could exceed $1,000,000 per day, meaning that states could be collectively paying upwards of $500,000 per day to keep troops in the District of Columbia. At Scioto Analysis, we focus on state and local policymaking, and the question persists– is deploying troops to the District of Columbia a good use of state resources?

So far, the Trump administration has cited robberies down 46%, carjacking down 83%, and violent crime down 22% in the District of Columbia due to the executive orders. While the source of this data remains unknown, if these figures are accurate, it remains unclear if crime will continue to fall once the National Guard leaves, or if it will rise to levels at or higher than before the National Guard was involved, as historical precedence suggests it may.

Historically, effective strategies to reduce crime in the long-run focus on institutional change and local based efforts such as vocational training, extra police patrol, rehabilitation programs, incarceration for repeat offenders, and quality education for high-risk youth, while ineffective strategies include community mobilization against crime, high supervision based programs, and arrests for minor offenses. Policymakers may have more work to do if they wish to make further progress on crime in major U.S. cities.

Do markets stop at man?

Often when we talk about markets, we treat them like technology.

A history of the economy is often told like this: first, hunter-gatherers took food from nature and shared it among their tribes. Then, tribes began to trade with each other through barter systems. Agriculture brought stockpiling of resources and trade of those stocks. Then eventually cultures developed currencies, easing trade and creating markets like what we see today.

But what if there is something older than this? What if there are “deep markets” that go beyond not just history and prehistory, but beyond mankind? What if economics is not just a human phenomenon, but a biological phenomenon?

In this week’s Planet Money newsletter, economics journalist Alex Mayyasi covered research by Evolutionary Biologist Toby Kiers, who studies “markets” in sugar and nitrogen between soybeans and microbes.

Her theory: that the two were not leeching off each other, but rather were engaging in a free exchange. That meant they could withhold their side of the bargain if the other side was not held up.

So she put the theory to the test. She surrounded the microbes with air that did not include the nitrogen they provided to the soybeans. Kiers observed that when the soybeans were not provided the nitrogen, they did not provide sugars to the microbes. No payment, no product.

She found this played out in trades between plants and fungi, too. Plants in full sun were able to produce more sugars to trade than plants in full shade. The fungi gave more of their phosphorus and nitrogen to plants that provided them with more fats and sugars. The relationship was not parasitic–it was voluntary and reacted to shocks in supply.

With tools developed to track “nanoparticles,” Kiers teamed up with chemist Matthew Whiteside and biophysicist Tom Shimizu to follow these trading patterns. With these tools, they found that fungi save their phosphorus and nitrogen for times when sugars and fats from plants are more plentiful. This implies a “price” for trading that changes based on supply of resources.

According to Mayyasi, primatologist Ronald Noë has been documenting a phenomenon he calls “biological market theory,” that he first developed by observing monkeys trade food and grooming sessions like commodities. He notes changes in the “price” of mating as dictated by food offered during times with more or less balanced sex availability, cleaner fish who take advantage of larger fish when competition decreases, and comparative advantage developing between plant species in evolution as examples of markets developing in biological settings.

But how could this be? Don’t we know that human beings make decisions based on rational thought, weighing their strength of desire for a product against their personal resources?

Wait, do they do that?

Or do we make purchases on autopilot? Do we think through every single purchase we make or do we sometimes buy things bounded by general large bounds for reasonability. If I go to a food stand at a soccer game, I want to buy a hot dog. I will pretty much pay what they ask me for it unless it is extremely expensive. But really I’m just going to go up and give them my money and take what they give me. Their price is constrained not by my choice, but by the hundreds of people all making hunch decisions about what would be too much and walking away if it is too much to pay.

In Mayyasi’s words, “brains are overrated.” We’re not that far off from fungi: we want things and we have resources to trade for them. The mechanism by which fungi and plants make these decisions to trade is mysterious, but so is the neurological process that governs our economic decision making. From an objective, behavioralist standpoint, the two do not seem that different from one another.

This matters to me as a public policy analyst. When conducting cost-benefit analysis, our goal is to estimate how a public policy will change the economy, including how much resources will grow overall and how they will be distributed throughout the economy. When the welfare of non-human animals is raised, the first thought that comes to mind is this: non-human animals don’t take part in markets! How could you value the impacts of a public policy on their markets if they don’t exist?

But they do, and this market behavior stretches beyond non-human animals into plant and even fungal populations. We observe it happening, with populations exhibiting economic behavior that conforms to models eerily similar to that we see among human populations in markets much more familiar to us.

What does this mean for the public policy analyst?

First, it means that we are likely missing key stakeholders when we are conducting cost-benefit analyses. If changes to air quality can disrupt markets in trade between plants and fungi, what other changes to ecosystems come about from changes in public policy?

Second, this means analysts have a tall task ahead of them when it comes to monetizing these impacts. If fungi reproduce at lower rates when nitrogen content in the air reduces in quantity, what does this translate into in dollar terms? Our traditional approach to valuing an impact like this would be to find out how much this impacts human welfare, which is one way to value this. But is it worth valuing fungi in and of themselves? Surely the fungi are valuing their own reproduction since they are optimizing toward reproduction. How do we measure this against other goods in society like health and consumer goods that human beings establish a willingness to pay for through market activities?

I do not have all the answers to these questions. I do know, though, that there is work to be done. The more research uncovers truths about how life interacts with each other, the more certain I become that the line we draw between man and other life on earth is arbitrary and convenient more than scientific and indisputable. Science will keep pushing us to consider the larger impacts of our public policies. It is up to us to listen and to do the hard work of incorporating these broader impacts into our models.

Ohio economists believe BLS turmoil will hurt Ohio economy

In a survey released this morning by Scioto Analysis, 10 of 14 economists agreed that reduced trust in Bureau of Labor Statistics estimates will hurt economic development in Ohio. This comes after the firing of Commissioner Erika McEntarfer and subsequent appointment of E.J. Antoni from the Bureau of Labor Statistics has raised concern about the independence and reliability of Federal employment statistics.

As Curtis Reynolds from Kent State wrote “One of the hidden backbones of the US economy is the quality of economic data.  That provides investors - both in the US and from other countries - with reliable data for making decisions.  A belief that the quality of the data has decreased or is now being manipulated/manufactured for political gain will undermine confidence in the entire US economy.”

One economist who disagreed was David Brasington from the University of Cincinnati, citing challenges the Bureau of Labor Statistics has had recently in their survey response rates “I disagree with the premise.  If the new director makes the BLS use more updated models, trust in the numbers could increase.  Current BLS surveys have 45% completion rate compared to 95% in the past.”

Additionally, 10 of 14 economists agreed that reduced trust in Bureau of Labor Statistics estimates will increase the importance of state-level economic data. This does come with additional challenges of state’s having potentially differing methods, as Kathryn Wilson notes “Yes, but relying more on state-level data is a costly and an uneven substitute. If states vary in their ability or willingness to invest in high-quality data collection, the result will be inconsistent and less useful than national statistics. The costs of duplicating efforts at the state level would be significant, and there’s the added risk that state-level data could become further politicized along partisan lines.”

The Ohio Economic Experts Panel is a panel of over 30 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.

How does working from home impact the labor force?

Since the COVID-19 pandemic, significantly more people have been performing their jobs remotely instead of going into an office. Work-from-home policies have largely stuck around since the COVID-19 related restrictions have been lifted, changing the way people across the country work. 

Some of the effects of this are easy to see. Downtowns that used to be full of office workers are more sparse. Commuting patterns have changed However, we are only just beginning to understand what these policies have done to impact the labor market. 

A recent working paper from the National Bureau of Economic Research by Emma Harrington and Matthew Kahn looks at one outcome that comes from increased work from home opportunities: a reduction in the motherhood penalty, the career setbacks and earning disadvantages often experienced by women after having children. 

The motherhood penalty can result in a variety of negative outcomes for mothers in the labor force, including slower career progression, fewer opportunities for promotions, and lower overall earnings compared to their male counterparts and women without children.

One crucial aspect the researchers considered is who chooses to work from home when the option is available. It's not simply a matter of everyone transitioning to remote work equally. Factors such as the nature of the job, individual preferences, and household circumstances likely play significant roles in determining who takes advantage of location flexibility. Understanding these choices is essential for accurately assessing the broader impacts of work from home policies.

The authors’ analysis focused on technological advancements that made remote work feasible in various college degree fields even before the pandemic. By examining how the potential for working from home in different occupations influenced employment gaps for mothers, they uncovered compelling evidence of the impacts of work from home on the motherhood penalty. 

Their findings suggest that increased opportunities for remote work actually narrowed the employment gaps between mothers and others in the workforce. This effect was particularly pronounced in careers that have traditionally been considered less family-friendly such as finance and business. These are jobs that traditionally have longer and less flexible hours. 

An interesting finding from this paper is that mothers who work from home use their added work flexibility to take care of their children. For example, it’s easier to pick a kid up from school if you don’t have to leave and then return to the office. 

One question this raises for me is whether or not work-from-home policies might lead to fathers taking on a more even share of the child care responsibilities? Studies have found that women are more likely than men to be called by schools, which can lead to labor market challenges for women. If more men work from home, perhaps some of these external norms might begin to shift in such a way that helps further balance the duties of childcare.

While the long-term consequences of the widespread shift to working from home are still unfolding, research like this suggests some potentially positive outcomes. The increased feasibility and acceptance of remote work may offer a pathway to a more equitable labor market, where mothers are better able to maintain successful careers without facing the traditional penalties associated with balancing work and family life.

What would universal prekindergarten do for Ohio’s workforce?

What do Georgia, Oklahoma, Vermont, and West Virginia have in common? 

Despite the wide range of geography, politics, and demographics between these four states, they share a specific statewide policy in common: each has a universal prekindergarten system currently in place.

Over the past few decades, interest in prekindergarten enrollment among economists and education researchers has grown. Randomized controlled trials of high-quality prekindergarten programs conducted in the 1960s and 1970s have shown positive long-term outcomes for labor market earnings, health, and criminal justice involvement.

Researchers like Nobel Prize Winner James Heckman and Leading Economic Development Economist Timothy Bartik have used these findings to estimate the long-term economic benefits of investing in early childhood education, coming to the conclusion that early childhood is one of the best times to invest in education.

According to prekindergarten enrollment data compiled by U.S. News and World Report, Ohio is currently #37 in the country for prekindergarten enrollment, behind states like Alabama, Arkansas, Louisiana, Missouri, and New Mexico.

In April, my firm Scioto Analysis released a cost-benefit analysis we conducted on universal pre-kindergarten. In this analysis, we used results from a study conducted by the Washington State Institute for Public Policy to estimate what the benefits of a universal prekindergarten program would be for Ohio.

We estimated that increased lifetime earnings, reductions in criminal justice costs, and savings in special education would produce about $3.80 in benefits for every $1 of costs of the program.

Universal prekindergarten would not be a small investment for the state. According to 2023 American Community Survey data, Ohio has about 650,000 children under age 5. This means expanding prekindergarten to children age 4 would mean offering it to about 130,000 children and expanding it further would mean providing it to even more. Covering everyone would cost more than a billion dollars.

It is easy to get scared off by sticker shock by programs like this, but policymakers should take into account the impact this spending has on local economies. These funds will go toward paying prekindergarten teachers, who then have resources they can spend in the economy.

They also compare similarly to spending on vouchers for private schools, which often function as a windfall for parents already sending their children to private school rather than an incentive for children to enroll in high-quality programs.

Since the COVID-19 pandemic led to a nationwide reduction in workforce due to early retirements, layoffs, and untimely deaths, workforce has dominated so many of our state and local economic development conversations.

Investment in early childhood education is a strategy to build today’s education workforce and tomorrow’s broader workforce. As Timothy Bartik shares in his book “Investing in Kids,” high-quality universal early childhood education programs have returns to local wages that are on the scale of high-quality tax incentive programs for businesses.

A state that wants to build its long-term workforce needs to look to training people across all years of life. That means investment in postsecondary and technical education, K-12, but crucially early childhood.

Tomorrow’s workforce depends on decisions made, or not made, by policymakers today.

This commentary first appeared in the Ohio Capital Journal.

Why doesn’t the Official Poverty Measure adjust for geographic cost of living?

This week, the Ohio Association of Community Action Agencies released their 2025 State of Poverty Report, their annual report on poverty in the state of Ohio. One of the points that caught my attention in the report was Ohio’s listing as the 15th-highest poverty rate in the country with 13.2% of Ohio residents living under the federal poverty line.

These numbers come from the American Community Survey, which says that Ohio’s poverty rate was 13.3% in 2023 using 2023 survey data, so within the margin of error of what the Ohio Association of Community Action Agencies reports. It seems like their numbers are correct, but using the official poverty measure does mask some important distinctions between Ohio and other states that I would like to talk about.

Each year, when the United States Census Bureau releases their American Community Survey results, they also release a report called “Poverty in the United States” that details the poverty statistics of the previous year. One of the tables they release with the report is a table that shows state-by-state statistics for poverty based on two separate measures: the official poverty measure and the supplemental poverty measure.

We have written about different poverty measures plenty in the past but I will give you a rundown of the difference between these two measures. The Official Poverty Measure is based on Mollie Orshansky’s Great Society threshold of the cost of a thrifty food plan times three. In the 1960s, the average American household spent about a third of their income on food, so Orshansky reasoned that a good measure of “poverty” would be whether the household income does not exceed the resources to pay for three times a bare-bones food plan. The federal government agreed and that measure has stood, adjusted for inflation, for the past sixty years.

In the 1990s, however, a group of researchers were convened by the National Academies of Arts and Sciences to study and improve the federal poverty measure. In their report, this group of researchers addressed problems with the Official Poverty Measure.

The three main problems were as follows. First, the cost of food had plummeted in the United States over the past thirty years due to agricultural science advances. At the same time, health care and housing costs were on the rise. This challenged the idea of “the cost of a thrifty food plan times three” as a reasonable basis for setting a poverty threshold.

Second, the Official Poverty Measure only used pre-tax income to measure household resources. With the expansion of the social safety net and the role of taxes in household budgets, resources given and taken by the government have a substantial impact on poverty rates. The Official Poverty Measure ignores this.

Third, the Official Poverty Measure sets the same poverty threshold for every household in the United States, with the exception of separate thresholds for Alaska and Hawaii. This means someone living in San Francisco is expected to need the same amount of resources as someone living in rural Oklahoma.

The researchers proposed a new measure that ties thresholds to average consumer spending rates, factors in taxes, transfers, and living expenses, and adjusts for geography. The report then sat on a shelf until 2009, when New York City started calculating the New York Poverty Measure. The Census Bureau soon followed, developing the Supplemental Poverty Measure to estimate poverty nationwide. Scioto Analysis’s Ohio Poverty Measure is a version of this measure.

When you are seeing poverty rates cited, you are almost certainly seeing Official Poverty Measure rates. Even though these numbers are not as favored by poverty researchers as the Supplemental Poverty Measure numbers, they are still the numbers that are used for federal resource allocation, so they get top billing.

And so we see this in the Ohio Association of Community Action Agencies report. Ohio is good for 15th highest poverty rate in the country…under the Official Poverty Measure. This is one-year data from the American Community Survey, but it is a little more alarming than from three-year data reported by the United States Census Bureau in their “Poverty in the United States: 2023” report–in that report, Ohio was ranked 21st in the country for poverty.

Under the Supplemental Poverty Measure, though, the story is different. After adjusting for local cost of living, tying costs to average spending, and factoring in taxes and transfers, which the Supplemental Poverty Measure does, Ohio plummets from 21st in the country in poverty to 36th, dropping it from the middle of the pack for poverty to a state with below-average poverty rates. This 15-place drop is shared with Iowa (which drops from 33rd under the Official Poverty Measure to 48th under the Supplemental Poverty Measure) for the largest drop in ranking in the country when looking at the Supplemental Poverty Measure rather than the Official Poverty Measure.

When using this measure that adjusts for local conditions, states like Iowa, Ohio, New Mexico (2nd to 16th-highest poverty) and Maine (39th to 51st-highest poverty, ending up better than all states and D.C.) drop in the rankings due to lower costs of living. Meanwhile, high-costs states like New Jersey, Maryland, and Colorado, which are 44th, 41st, and 47th respectively under the Official Poverty Measure, catapult into the middle of the pack at 20th, 18th, and 26th under the Supplemental Poverty Measure. Large, relatively high-cost states of California, Florida, and New York increase from merely above-average rankings of 19th, 15th, and 17th highest poverty in the country under the Official Poverty Measure to 1st, 4th, and 6th respectively under the Supplemental Poverty Measure.

Cost of living matters. A dollar in Southeast Ohio is not the same as a dollar in the San Francisco Bay Area. If we look at a measure like the Supplemental Poverty Measure, we will get a lot closer to understanding what true relative differences in poverty are from state to state. And that matters. Designing antipoverty policy around good data is important because it will help us bring the end of poverty more quickly and with less pain. And isn’t that what antipoverty policy is for?