Scioto Analysis Releases Most Accurate Measure of Poverty in Ohio to Date

This morning, Scioto Analysis released the Ohio Poverty Measure, the most accurate measure of poverty in the state of Ohio to date.

“The Ohio Poverty Measure draws on poverty measures in California, New York City, Oregon, Virginia, and Wisconsin that use American Community Survey data to assess the state of poverty and how safety net measures, taxes, and local cost of living effect them,” said Rob Moore, principal for Scioto Analysis and co-author of the study.

The study found that in 2018, about 9.7% of Ohioans were in poverty and 3.7% of Ohioans were in “deep poverty,” defined as having less than half the income to meet the poverty threshold. The 9.7% poverty rate is below the 12.9% poverty rate reported in the Official Poverty Measure and the 10.4% rate reported in the Supplemental Poverty Measure.

“Improved measures of poverty such as the Ohio Poverty Measure tend to show lower rates of poverty in low-cost states like Ohio due to cost of living adjustments that don’t show up in the Official Poverty Measure,” said Moore.

The measure also found estimates for local poverty rates. According to the measure, the lowest-poverty regions of the state were suburban northwest Franklin County and Delaware County along with the suburban communities of the Twinsburg area in greater Akron and the Strongsville area in greater Cleveland, all with poverty rates under 4%. Meanwhile, the highest-poverty regions in the state were in urban Cleveland, Toledo, and Cincinnati, with poverty rates exceeding 20%.

Poverty rates also varied by demographic groups. While only 8% of seniors in Ohio were in poverty under the measure, about 13% of children were in poverty. White Ohioans only had a poverty rate of 8%, which was lower than all other demographic groups, especially Black Ohioans, who had a poverty rate approaching 23%.

This measure also provides an estimate of the impact of safety net measures. According to the study, an estimated 150,000 Ohioans were pulled out of poverty by the Earned Income Tax Credit, 110,000 Ohioans were pulled out of poverty by SNAP (formerly the “food stamp” program), and nearly 100,000 Ohioans were pulled out of poverty by child and child care tax credits.

The Ohio Poverty Measure improves on past poverty measures in the state by combining the more nuanced approach of the Supplemental Poverty Measure with more fine-grained data collected by the American Community Survey.

“The Official Poverty Measure is based on an outdated definition of poverty: the cost of food times three,” said Moore, “The Ohio Poverty Measure uses consumer spending as a baseline then makes adjustments based on local cost of living, taxes and transfer payments, and unavoidable expenses, providing the most accurate snapshot of poverty in Ohio to date.”

What would the public health and safety impacts of cannabis legalization be in Ohio?

Last Monday, a panel of state officials approved ballot language for recreational cannabis legalization in the state of Ohio, clearing the way for backers to begin collecting signatures for the initiative. According to reports, the ballot language for this initiative is similar to a bill introduced by state Reps. Casey Weinstein and Terrence Upchurch to legalize recreational cannabis in the state.

I have written previously about the most obvious impact of cannabis legalization in Ohio: hundreds of millions of dollars in new revenue. But tax revenue isn’t the only likely impact of recreational cannabis legalization.

First, many hope that legalization of recreational cannabis will reduce the size of Ohio’s black market in cannabis sales. While other states have not seen a precipitous decline in black market activity and may have even seen increases in black market cannabis sales, Colorado did see a large decrease in cannabis-related crime after legalization of recreational use and sales. 

If Ohio’s marijuana-related arrest rate falls as much as Colorado’s does in the time period after recreational legalization, Ohio could be making 18,000 less marijuana-related crimes per year after legalization.

An exception to this rule was arrests for driving under the influence, which actually increased after legalization. The positive news, though, is that cannabis-related traffic fatalities were flat over this time period, suggesting that it may have been an increase in training to detect cannabis influence that drove this change, not an increase in actual frequency of driving under the influence of cannabis.

A more indisputably negative impact of cannabis legalization in Colorado is cannabis-related hospitalizations. Colorado saw a 100% increase in cannabis-related hospitalizations after cannabis was legalized in the state. These numbers also only capture the short-term effects of legalization. Longer-term impacts of more widespread cannabis use will not be detected for years to come.

On the bright side, despite higher consumption of cannabis in Colorado after legalization of recreational cannabis, there is little evidence this trend has occurred among children as well. Rates of youth cannabis consumption stayed stable after the legalization of recreational cannabis in the state.

Something else policymakers are interested in is the impact of cross-state cannabis consumption. While we do not have much information on what legalization of recreational cannabis in Michigan has meant for consumers in Ohio, researchers at Washington State University have found that legalization Colorado and Washington led to increases in possession arrests in counties bordering these states. This suggests that cross-border consumption is likely taking place.

In addition, researchers at the University of Oregon have estimated that the state of Washington had earned tens of millions of dollars of tax revenue from cross-border shoppers after their own legalization of recreational cannabis. If cross-border shopping from Ohio to Michigan is as prevalent as it was between Washington and Oregon, Michigan may have earned $3 million in taxes from Ohio cannabis shoppers in 2020.

Will Ohio look like Colorado and Washington if it legalizes recreation cannabis? It is hard to say. So far, legalization has meant a larger market, more tax revenue, less arrests, flat youth consumption and traffic fatalities, and more hospitalizations in states that have done so. Policymakers and voters should weigh these considerations when deciding on the fate of recreational cannabis in this state. 

This commentary first appeared in the Ohio Capital Journal.

Do people have the “right” to contract and spread a deadly disease?

From the beginning of the spread of COVID-19, we knew a vaccine would be the most effective tool we would have to stymie its spread, reduce infections, and save lives. While so many things were new about COVID-19, we knew, like with any infectious disease, that vaccine technology would be our best bet for prevention of infection and curbing morbidity and mortality associated with the virus.

But vaccine hesitancy has been brewing for years now. Whether it’s from crunchy yuppie communities trying to shield their children from modern medicine or homeschool fundamentalist households trying to do the same, fringe movements have tried to discredit this technology, largely because people misunderstand the risks involved with it.

These public information problems have bled into the largest public health crisis of the last hundred years. And it is not just fringe groups that are subscribing to vaccine misinformation these days: It is the people crafting policy in our state as well.

Ohio’s House Bill 248, currently in the House Health Committee, proposes law to prohibit basically anyone from requiring any vaccination, not just against COVID-19, and ban anyone from requiring people to tell if they have been vaccinated.

House Bill 253, also in the House Health Committee, proposes a ban on proof of vaccination to enter the state or state buildings.

House Bill 350, currently in the House Civil Justice Committee, proposes a prohibition on people and companies requiring vaccinations or asking for proof of vaccinations.

What is driving these bills? As we have all heard, the FDA has fully approved the Pfizer-BioNTech COVID-19 vaccine, meaning the argument of the “dangers” of vaccination are becoming weaker and weaker. With half the population vaccinated at this point and no serious population-level side effects being reported, it is hard to keep up the argument that these vaccines are unsafe because they are untested.

We know they are saving lives. Death rates from COVID-19 have plummeted since vaccinations began and have been consistently low in Ohio for months, even as hospitalization rates have crept up. So whatever reason the policymakers are making for wanting to discourage vaccinations, it seems to be more pressing to them than keeping death rates low.

In addition to being good for public health, vaccination seems to be good for the economy. The Ohio Chamber of Commerce came forth last week to testify in opposition to House Bill 248, saying that employers should have the power to run their workplaces the way that they wish and hinting that less vaccination would be bad for the economy.

Ultimately, there are some reasons that vaccine mandates should not go too far. There are certain people with compromised immune systems who may not be eligible for vaccination. In that case, regular testing may be a better path. But the idea that the public sector should be intervening to make sure that people have “the right” to contract and spread a deadly virus is laughable at best and extremely dangerous at worst. Let’s hope that cooler heads prevail and that these fringe ideas don’t come to embarrassingly define public policy in Ohio to the rest of the world.

This commentary first appeared in the Ohio Capital Journal.

Most Ohio economists don't think state pandemic spending cuts helped the economy

In a survey published by Scioto Analysis this morning, only 5 of 24 Ohio economists agreed state spending cuts during the pandemic will lead to more economic growth for Ohio in the long run.

Among those who disagreed with the statement that pandemic spending cuts will lead to greater economic growth in the long run, multiple economists cited the value of state spending during a recession to offset a private sector economic slowdown. Another topic that multiple economists touched on was the long-term impact of short-term reductions in K-12 and higher education spending.

Those who agreed with the statement emphasized the role that federal funds played in stabilizing the state budget and how those likely offset spending cuts made by the state. They also focused on the role of fiscal stability in state economic stability in the long run.

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.

Cost-benefit analysis is a tool, not a decision rule

Public policy analysis is an inescapably multicriteria undertaking. What do I mean by that? I mean that there are very few if any public policy problems where you can translate “what is the best decision?” into a single question. Nearly every public policy question is about answering multiple questions and then trying to find balance between the answers to these questions.

For instance, say a state government is faced with the public policy problem “should the state implement a universal early childhood education program?” We may be tempted to turn to the policy analyst’s trusty companion: cost-benefit analysis. Using this tool, we can come up with a number that represents the net benefits to society of implementing the policy as opposed to the next best policy.

Sounds good enough, no? We were able to use cost-benefit analysis to translate the question of “should the state implement a universal early childhood education program?” to “would implementing a universal early childhood education program yield net social benefits?” We’ve been able to take a difficult question and make it more specific and thus come up with an answer that can be analyzed.

The problem with this approach is that the net present value of a social cost (the technical term for the number we derive from a cost benefit analysis) tells us the impact of a policy on society as a whole. Policymakers are probably also interested in the distributional impacts of a policy. E.g. “does the policy reduce poverty and inequality?”

A straw man argument against the use of cost-benefit analysis in public policy analysis says that some policies are desirable that yield net social costs. Another way to state this objection is to say policies cannot be reduced to economic costs and benefits and that this makes cost-benefit analysis an unworthy pursuit.

These critiques are actually correct at their core. Their mistake is in assuming that any policymaker uses cost-benefit analysis as a decision rule.

Even in federal regulatory decisionmaking, the part of the government that utilizes rigorous cost-benefit analysis the most, policies are not reduced to only their net present value when judged by regulators. Cost-benefit analysis is only one consideration used along with a number of other analytical tools, including distributional analysis, sensitivity analysis, administrative burden, and others. 

The beauty of cost-benefit analysis is that we can use it to put a range of different policies that are seemingly incommensurable on the same scale. We can compare education, health, infrastructure, really any type of policy to another using the criteria of net present value.

This does not, however, mean that cost-benefit analysis gives us a final word on the desirability of a policy. As a matter of fact, for some policies, cost-benefit analysis can distract from the policy goal. This is especially true for redistributional policies, which may reduce economic growth but improve the income distribution.

Does this mean cost-benefit analysis is worthless for evaluating redistributional policies? I don’t think so: it is valuable for policymakers to understand what the extent of economic impact will be, even if that is not the central goal of the policy. Does this mean the policy becomes worthless if it has a negative net present value? Not a chance: it’s just one consideration of many in the crafting of good public policy.

How much does Ohio lose when it turns down federal funds?

A story that has been roiling over the past weeks is Gov. Mike DeWine’s decision to cancel the federal pandemic unemployment program. This decision came months before the federal expiration date on Sept. 6.

News coverage of the decision focuses on $300 checks the governor’s decision is making unavailable to unemployed Ohioans. $300 does not sound like a lot, but when considering that over half a million Ohioans may be impacted by this decision, the numbers start to add up. 

A fact sheet released by the Century Foundation in May estimated benefit reduction in Ohio could cost the state $3.7 billion in federal funds. This amounts to half a percentage point of Ohio’s 2020 GDP.

The logic behind reduction of unemployment benefits is straightforward. Reducing benefits will reduce the cost for those who are currently not working to take jobs, which means more people will enter the labor force. If the labor force grows, the economy grows.

The economic picture is a little more complex than this, though. Services such as child care exist in formal and informal markets. So an unemployed mother may be spending time caring for a child at home but then with reduced benefits find herself having to place a child in formal child care. 

This means the formal economy grows (child placed in formal child care, GDP goes up), while new value is not actually created. In the long-term, this could even hurt the economy as low-quality child care centers can be worse for child development than home care, resulting in worse education and wage outcomes for children down the road.

The other side of the coin is even more straightforward: $3.7 billion of federal funds not in Ohio’s economy means… $3.7 billion not in Ohio’s economy. Unemployment benefits that come from the federal government are used to purchase goods and services within the state of Ohio. Reducing Ohio’s economy by half a percentage point will mean billions of dollars aren’t being used to stimulate our state economy and thus will have countervailing impacts that will reduce job growth in the state. 

In short, less money in the state means less consumer spending which means less ability for firms to make new hires.

Maybe in the past year and a half we have become accustomed to massive state intervention in the economy, but this decision should not be short of mind-blowing for someone who is following state policy and interested in Ohio’s economy. While the news coverage of this decision by the governor often reduces it to a squabble between “some” workers who are losing benefits and employers who want cheaper labor, there is a larger story here.

With the stroke of a pen, the governor was able to reduce the state’s gross domestic product by half a percentage point. Yes, the actions taken last year likely had a larger impact. After all, Ohio’s economy shrunk by nearly three percentage points from 2019 to 2020. But those interventions were made to reduce loss of life. The alleged benefits of turning down nearly four billion dollars in federal funds depend on a much muddier economic argument.

This commentary first appeared in the Ohio Capital Journal.

New research: Broadband policy is jobs policy

It’s no secret that Ohio’s Appalachian counties have the most sluggish labor markets of the counties across the state. According to last month’s Ranking of Ohio County Unemployment Rates from the Ohio Department of Job and Family Services, eight of the ten highest-unemployment counties this month are in Appalachian Ohio.

Meanwhile, Appalachian Ohio also suffers from the worst access to the internet of any region of the state. According to BroadbandNow, the twelve counties with the most households without broadband access in Ohio are all in Appalachian Ohio, with anywhere from 32% to 69% of households having no access to 25 mbps internet.

These two facts about southeast Ohio are likely the result of a number of geographic, economic, and cultural factors. It can be hard to tell whether one drives the other: a weak labor market can hurt demand for services like internet connectivity. Conversely, lack of access to the internet can make it hard for employers to find workers and vice versa.

Some new research from the University of Maryland, however, sheds light on how internet connectivity and employment interact with one another.

Economist George Zuo recently published a study in the American Economic Journal that analyzed the impact of Comcast coverage on employment rates and earnings. This study looked at geographic variation in Comcast coverage, individual variation in eligibility for Comcast services, and the timing of rollout of coverage to estimate what the impact of Comcast coverage has been in the United States on employment rates and earnings.

The estimated impact of Comcast coverage on employment rates and earnings in the study is substantial. Zuo found that households that received broadband has employment rates 10-12 percentage points higher after getting broadband coverage than before. He also found the typical household earned $2,200 more after getting hooked to the internet than they did before.

According to Zuo, earnings impacts are driven by higher likelihood families with internet will enter the labor force and lower unemployment rates for these families. This seems to make sense: higher broadband connectivity should lead to easier job searches, more ability to work at home, and more opportunities for learning at home.

Zuo’s research also suggests broadband connectivity improvement advances equity goals. Employment impacts for families at 150 percent of the federal poverty level (who we can consider “low-income”) are strong while employment impacts for families at 300 percent of the federal poverty level (who we can consider “middle-income”) are nearly nonexistent.

The research has some reasons to curb expectations for Appalachian Ohio, however. In particular, the study finds that employment impacts associated with broadband connectivity were much stronger in urban areas than in non-urban areas.

In light of this information, the state budget’s $250 million grant program for broadband has the potential to provide substantial benefits to households in southeast Ohio and other parts of the state without broadband access. If Zuo’s research can be extrapolated to other settings, broadband development is indeed economic development and high-speed internet is a jobs program. We will see if this pans out as this grant program rolls out over the next couple of years.

This commentary first appeared in the Ohio Capital Journal.

Ohio economists split on Vax-a-Million program

In a survey published by Scioto Analysis this morning, 12 of 27 Ohio economists agreed Ohio's "Vax-a-Million" program is a cost-effective program for encouraging vaccination in the state. Another nine expressed uncertainty about the statement and six disagreed with it.

Among those who agreed the program was cost-effective, comments focused on the low costs of the program. Kevin Egan of the University of Toledo said “an equally expensive advertising-only campaign about benefits of vaccination would likely be less effective than this lottery program which resulted in much free publicity about it.” Michael Myler of the University of Mount Union focused on the value of a statistical life, saying “it appears to be in the 7-10 million-dollar range for one ‘average’ human.”

Others who agreed, though, were skeptical of the number of vaccinations that came about as result of the Vax-a-Million program and some had comments about the design of the program.

Among those uncertain about the program’s cost-effectiveness, some wanted to see more evaluation of the program while some were disappointed with the results they have seen so far. Bluffton University’s Jonathan Andreas said “I was hopeful about the creativity of the strategy, but in hindsight it doesn't look like it worked as well as I would have hoped. It was worth a try and now we know that the results turned out to be mediocre at best.”

Those who disagreed the program was cost-effective pointed to evidence gleaned by a study conducted by Boston University researchers that was unable to detect any increase in vaccinations caused by the program. Most vehement in disagreement was Denison University’ Fadhel Kaboub, who said “Vax-a-Million is a gambling technique that should be used by casinos rather than state institutions.”

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 is "Cost-Free Evaluation?"

What’s the most amazing thing about evaluation?

Is it that it lets us know if something works or not? That’s pretty amazing, no doubt. Understanding if a program has the effects people claim it does is a huge part of understanding the value of the program.

Is it that evaluation helps us understand how much a program works? This is almost more amazing than knowing if the program works. For instance, maybe a graduation-focused tutoring program works because it increases the number of people who graduate each year at a high school by one. Another program is found to work by increasing the number of people who graduate by ten. Knowing how much a program works can be even more valuable than just knowing if it works in the first place.

So what’s even better than knowing both if a program works AND how much a program works? Knowing how much a program works—in respect to its resources. Maybe the second tutoring program above graduates 10 new people at a cost of $10,000. But the first tutoring program graduates 1 at $100. This means program 1 is ten times as cost effective as program 2. While the first program may be more effective on an absolute basis, the second is more effective per dollar…which can be very important for a cash-strapped school. 

So if a school was running tutoring program 2 for ten people and it was costing them $10,000 but then switched those ten people into tutoring program 1, the school would save $9,000…and still graduate the same number of students. 

Theoretically, the school could have evaluated both programs for up to $9,000, implemented the recommendations that came from that evaluation, and ended up on top, the evaluation effectively paying for itself.

In the evaluation world, this concept is called “cost-free evaluation.” If evaluation focuses on this question of cost-effectiveness, it can be a strong tool for saving money and effecting better outcomes at the same time.

In Results for America’s book Moneyball for Government, members of the Bush and Obama administrations put forth a number of recommendations for improving the efficiency and effectiveness of government by being more results-oriented and understanding the interaction between costs of programs and results brought about by those programs.

One of their recommendations is for every federal agency to have 1% of their discretionary budget set aside for evaluation. This means that they can focus their evaluation efforts on programs that may be expensive or ineffective and potentially make that money back for the American people. Then money can be spent on better programs or even rebated back with less spending overall.

There are of course wrinkles to evaluation. Just because a program is cost-effective when small does not mean it will be cost-effective as it grows. Often programs have trouble when they try to scale up. But this is why evaluation needs to occur during the scale-up process, too. If anything, evaluation could be more important here because there are more resources to be lost here. But the promise rings true: evaluation, when done right, can be free. And that is truly amazing.

White Male Workers Still Out-Earning Women and Non-White Workers in Ohio

In 2019, the average non-white male worker in Ohio made $12,000 less than the average white male worker in the state. The average white female worker made $19,000 less and the average non-white female worker made $23,000 less than the average white male worker in the state.

This is all according to American Community Survey data from the United States Census Bureau. Considering the number of workers in each category, the gap in earnings between white male workers and the other three categories came out to $560 million in 2019.

This gap has persisted over the past five years. While the earnings gap fell to $470 million in 2011, it ballooned 21% from 2011 to 2015 on the backs of higher earnings for white male workers. Since then, the earnings gap has persisted in the $560 million to $570 million range every year.

The total gap is largely driven by the earnings gap between white male workers and white female workers. This is because of both the large difference in earnings between white male workers and white female workers and the fact that for every non-white worker in Ohio there are more than three white female workers.

This is despite the fact that white women became a smaller portion over the workforce and grew their earnings more than any other category over the past decade. While Ohio’s non-white male workforce grew by 23% from 2010 to 2019 and its workforce of white males and non-white females grew by 11% each, only 5% more white women were working in Ohio in 2019 than in 2010. At the same time, they were earning 11% more after adjusting for inflation in 2019 than in 2010, higher than the 8% increase for white male workers and the 5% increase for non-white males and females.

Despite the earnings growth white female workers experienced in the past decade, the gap between white male earnings and white female earnings continued to grow due to the high baseline earnings white male workers enjoyed in 2010. The 4% ten-year increase in the white male/white female earnings gap pales in comparison to the 14% ten-year increase in the gap between non-white female and white male earnings and the 25% increase in the gap between non-white male earnings and white male earnings.

Secondary data collection for this analysis was conducted by Masashi Hamano. Analysis conducted by Rob Moore.