Suicide is on the rise in Ohio

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Five Ohioans die of suicide every day.

This is just one of the many data points released in a new publication released last week by the Health Policy Institute of Ohio. This data snapshot focuses on the prevalence of suicide in Ohio and how incidence has changed over time.

Below are some of the top findings from the release.

Suicide is a leading cause of death for working-age Ohioans.

Over 1,400 Ohioans died from suicide in 2022, the most recent year we have data for. This makes suicide the fifth-leading causes of death for working-age Ohioans, behind unintentional injuries like drug overdose and motor vehicle crashes, cancer, heart disease, and COVID-19.

Ohio’s suicide death rate is 15 deaths per 100,000 people, just slightly above than the national rate of 14.5 deaths per 100,000 people.

Suicide victims are disproportionately white, male, working-age, and Appalachian.

In 2022, 17 white Ohioans died from suicide per 100,000 population, higher than the rate of 12 for Black Ohioans, 10 for Hispanic Ohioans, and 7 for Asian Ohioans. Men were also four times likely to die from suicide than women. This is despite the fact that women attempt suicide at a rate 70% higher than men.

Suicide rates were highest in 2022 for working-age adults, higher than the rate for young adults, retirement-age adults, and children. Suicide was most common in Appalachian counties, with 15 of Ohio’s 22 counties with the highest suicide rates located in Appalachia.

Suicide is on the rise–for nearly everyone.

Since 2007, suicide rates have increased for men and women, white, Black, and Hispanic Ohioans, and Ohioans in every age group. The only major demographic group that has seen a flat suicide trend are non-Hispanic Asian or Pacific Islander Ohioans.

Risk factors for high school students are also becoming more common.

Compared to 2019, female Ohio high school students were more likely in 2021 to feel sad or hopeless, seriously consider suicide, make a plan to commit suicide, or attempt suicide. While more male high-school felt sad or hopeless and seriously considered suicide over that time period, fewer made a plan or attempted suicide. The increase in suicide plans and attempts among female students was much larger than the decrease among male students.

The increase in suicide rate is driven by firearms.

Suicide deaths involving a firearm increased 60% from 2007 to 2022. This accounted for 75% of the total increase in suicides over that time period. The remainder of the increase was driven mostly by an increase in deaths by suffocation and other causes. Deaths by poisoning decreased over that time period.

Suicide is a hard social problem to make progress against. That being said, the Health Policy Institute of Ohio suggests interventions to improve mental health to prevent suicide attempts.

A 2016 evidence review published in the American Journal of Psychiatry concluded legislation reducing firearm ownership lowers firearm suicide rates. It also acknowledged, however, that court interpretations of the second amendment to the U.S. Constitution have made most legislative options for reducing firearm ownership politically unfeasible in the United States.

The researchers however, say targeted initiatives like gun violence restraining orders, smart gun technology, and gun safety education may be able to reduce risk for current gun owners. These sorts of approaches do not have a strong evidence base yet, but they at least give us something to tackle this difficult problem.

This commentary first appeared in the Ohio Capital Journal.

What is Cantril’s Ladder?

There is a disconnect between how the general public is doing and how policymakers assess how the general public is doing. If a friend asked “how are you doing today,” it wouldn’t make sense to respond by telling them how much stuff you have.

That doesn’t mean how much stuff you have is a useless piece of information. It might be correlated with how you are doing, but it’s not the whole picture. But this is what policymakers are appealing to when they use the standard slate of economic indicators to determine how well society is doing. 

At Scioto Analysis, we want policymakers in the United States to start directly asking their constituents how they are doing. In the academic world, we call these types of questions subjective wellbeing measures. 

The goal of subjective wellbeing research is to find a way to scientifically measure how people believe their lives are going, and determine what factors influence their own subjective assessment. A good example of this is the United Kingdom’s Office for National Statistics’ wellbeing research.

One of the earliest examples of a well defined subjective wellbeing measurement is Cantril’s Ladder, first proposed by Hadley Cantril in his 1965 book “The Pattern of Human Concerns.” Cantril’s ladder is an example of an evaluative measure. Evaluative measures are designed by researchers to try to generally understand life satisfaction among a population. 

Below is the adaptation of Cantril’s ladder used in the Gallup World Poll:

• Please imagine a ladder with steps numbered from zero at the bottom to ten at the top. Suppose we say that the top of the ladder represents the best possible life for you and the bottom of the ladder represents the worst possible life for you.

• If the top step is 10 and the bottom step is 0, on which step of the ladder do you feel you personally stand at the present time?

Cantril’s Ladder was an important addition to wellbeing research because it allowed respondents to define the upper and lower rungs of the ladder themselves. This means that by combining the results from the ladder with outside data, researchers can see what factors associate with people rating their wellbeing highly. 

This is different from simply looking at income or health directly because it allows the respondents to indirectly say how valuable those things are. This information allows us to better understand what actually makes people happy.

One addition to the ladder that Gallup uses in their polling is the addition of a future wellbeing question. Specifically, they ask “on which step do you think you will stand about five years from now?”

This additional question can be useful for fully understanding how well our society is doing. For example, if people are reporting high levels of current wellbeing, but low levels of expected future wellbeing, we might be more concerned than if many people are going through a rough patch, but expect to be doing much better soon. 

Subjective wellbeing data can be an extremely useful tool for policymakers in the United States to have. In 2022, the grassroots organization Gross National Happiness USA released the US happiness survey, a first of its kind look at subjective wellbeing across the US. Currently, We are working with a group of students from Ohio State to conduct a wellbeing survey in Ohio. Hopefully this kind of research can encourage policymakers to seek out this data.

How can Ohio kick its tobacco habit?

According to the Centers for Disease Control and Prevention, 20,200 Ohioans die from smoking-related illnesses each year.

Ohio is going through the worst overdose crisis in its history, with 5,300 Ohioans dying of drug overdose in 2021. That means tobacco is killing more than three times as many Ohioans as drug overdoses.

Ohio’s smoking rate is one of the highest in the country, coming only behind West Virginia, Kentucky, and Louisiana in the percentage of residents who smoke. So people are dying of tobacco use, and they are continuing to smoke.

To combat this pervasive public health program, local governments, spurred by public health activists, have been working to reduce access to tobacco products. On Jan. 1, the City of Columbus enacted bans on flavored tobacco and menthol cigarettes, two types of tobacco that appeal to children and kickstart lifetime addiction habits.

To prevent this ban from going into effect, the Ohio General Assembly included a provision into the state budget bill to reverse bans like this. Ohio Gov. Mike DeWine, who has made protecting children and public health big parts of his policy priority over his years as governor, vetoed the measure. Republicans in the General Assembly then overrode that veto.

Senate President Matt Huffman has said he wants to engineer a compromise between big tobacco and the governor’s office on tobacco regulation in the state.

If we want to be honest about what the real policy rub is here, it’s obvious: public health versus economic growth. Those defending the tobacco companies are worried that regulations will be bad for the economy.

So let’s say we wanted to reduce unnecessary death and help the economy…is that possible?

According to the Washington State Institute for Public Policy, it is. This institute has conducted cost-benefit meta-analyses on hundreds of programs states can sponsor, estimating their economic benefits and costs and confidence in their results. The Institute has conducted analyses of a number of programs that could reduce tobacco use and grow the economy.

One of the most effective interventions is providing access to tobacco quitlines. This is a relatively low-cost intervention that has the potential for large benefits. While reductions in health care costs associated with smoking pay for the program on its own, it also has been found to increase wages for participants and reduce chance of death. All these lead to tobacco quitlines being good tools for saving lives and growing the economy.

Another very effective program is anti-smoking media campaigns, which also reduce health care spending, increase wages, and save lives. These campaigns are even cheaper per participant than quitlines, leading to very large benefits for society compared to the costs of the program.

A more tailored program for children is the Model Smoking Prevention Program, a classroom program for older elementary and middle-school children. This program is similarly very cheap while leading to higher wages, lower health care spending, and lives saved.

This is only a few of the many options available in the Washington State Institute for Public Policy’s database. The Ohio General Assembly has options if it wants to reduce the impact of tobacco on children and the public.

This commentary first appeared in the Ohio Capital Journal.

How can state EITCs be better?

Now that the calendar has turned to 2024, it’s time for people to start thinking about filing their taxes. For low-income individuals, tax season is an opportunity to access benefits that are tied to tax returns, most notably the Earned Income Tax Credit.

Although the federal Earned Income Tax Credit is an extremely important anti-poverty policy, it has some noticeable gaps in who it serves. For example, the credit amount is extremely small for people who don’t have any dependent children. Similarly, the phase-in rate for the credit is fairly slow, meaning the lowest earners don’t receive the maximum benefit. 

The good news is that states can design their own tax policy, and they aren’t obligated to follow the federal government's lead when promulgating state taxes and tax credits. In 2014, the California Legislative Analyst’s Office put together a report on some different choices states could make with their Earned Income Tax Credits. 

Currently, the most common policy for states is to make their credit a percentage of the federal credit. This puts more money in the pockets of low-income individuals, but it does nothing to address any design shortcomings of the federal Earned Income Tax Credit outside of the size of the benefit. 

One alternative policy is to target Earned Income Tax  Credit benefits toward lower income individuals. As the EITC currently exists, people don’t receive full benefits until they’ve made over $10,000 of earned income. This notably doesn’t include things like social security or supplemental security income, meaning many people who need support the most are not receiving it. 

In theory, this encourages people to continue to seek work in order to maximize their benefits. However, people who are unable to find adequate work get left with a noticeably smaller tax return. If states took it upon themselves to increase the rate at which their EITC phases in, they could provide a decent benefit to the lowest earners without compromising the incentive created by the federal EITC, potentially even increasing work incentives for low-income earners.

It is also worth recognizing that the importance of labor supply can sometimes be overstated in the discourse surrounding poverty policy. Labor supply considerations are important, but participating in the labor market is not the only way individuals can provide benefits to society. 

Another option laid out by the California Legislative Analyst’s Office is for the state to target its Earned Income Tax Credit at individuals who do not have any dependent children. Currently, the maximum credit for an individual with no children is $600, and benefits phase out after earning $15,000. The benefit jumps to almost $4,000 with a single dependent, with benefits continuing to be paid for households over $45,000 of earned income.  

States could either increase the benefits for people without children, or they could lengthen the income window where those people are able to receive benefits. Both options would make the EITC a much more effective anti-poverty policy.

Considering the labor supply angle, this change would likely increase the incentive to work in most cases. That is because the current EITC for individuals without children is so small that there isn’t much benefit to working a job just to qualify for it. Simply increasing maximum income where people could claim this credit would encourage more people to find work.

State policymakers have a lot of options when it comes to defining their state’s tax policy. Despite the fact that this report is almost a decade old, it highlights the fact that creative and thoughtful policy choices can help fill in the gaps created by federal policy.

Universal Basic Income Won’t Fix Everything–But It Will Fix Poverty

In 2017, I moved back to my home state of Ohio after spending a few years organizing in Nebraska then attending graduate school on the west coast.

When I returned, I was determined to work to alleviate what I saw as an unacceptable condition in Ohio: poverty.

One of the advocates for poverty alleviation I met early on was Jack Frech. For over three decades, he was the director of the human services department for Athens County, an Appalachian Ohio county that has the distinction of being the poorest county in the state.

Frech was part of a coalition in the 90s called “Give People Money.” Their radical idea? That the most direct way to reduce poverty is to get cash in the hands of people who need it.

In Frech’s words, giving people money won’t solve all of their problems. But it will solve the problem of people not having enough money.

I think this is what rubs me the wrong way about some people’s defeatism around cash transfers to alleviate poverty. There is an implication that cash transfer programs like basic income either solve all problems or are a failure. Yet we don’t expect that high of a threshold for success for other programs. Why do we expect this from basic income?

In her recent Governing opinion piece, State Policy Network Communications Director Erin Norman argues basic income is an unworthy public investment because it does not solve public problems like, in her example, loneliness. She also references some studies from the late 1960s and early 1970s that found evidence of reduction in labor supply driven by cash transfer programs.

There are a couple problems with this approach to evaluating basic income. The first is empirical. An analysis of 16 recent trial basic income programs found 93% of outcomes found no meaningful impact on labor supply. In some trials, we have even seen increases in labor supply, driven by income reducing barriers to work by providing resources for transportation and child care.

A second is an inherent tension between the goals stated by Norman. Increases in labor supply will not necessarily reduce loneliness: it may even increase it. Pushing people to spend more of their time working means less time spent with friends and family, which is a more sustainable way to reduce loneliness than expecting coworkers to fill this gap for people.

I am glad Norman raised the question of loneliness in her opinion piece. This is certainly a problem of public policy import, but it is also a difficult thing for the public sector to solve. The state can’t package a friend in a box and send it to you. But it can easily cut you a check and make sure it ends up in your mailbox.

Norman also points to alternatives to basic income, in particular occupational licensing reform. This is a worthy undertaking that could create opportunities for minority and immigrant workers and business owners while also reducing the price of goods and services for people who want them. This reform, however, is likely to have more marginal impacts on poverty than a direct program like guaranteed income.

Overall, the policy analytic trap Norman falls into in her piece is one that has gripped analysts for decades now: the elevation of labor supply maximization to the pinnacle of policy analytic attention. Basic income challenges this framework. It says that supporting people to do things like caring for children or elderly or disabled family members, pursuing education, or starting businesses is in the public interest, even if it decreases labor supply in the short-term.

So I’ll agree with Norman on one point: maybe basic income won’t fix all problems in society. But maybe it doesn’t have to. Maybe ending poverty is enough.

What’s the difference between an entitlement and a block grant?

In the United States, not all social safety net programs are the same. The wide array of different eligibility requirements and funding mechanisms can make it difficult for the public to understand how our safety net functions. 

One of the most important differences between federal safety net programs is whether they are designed as entitlements or block grants. If you’re like me and you’re new to the public policy world, it might be surprising to find out that not every safety net program is an entitlement. This is only something I’ve learned doing work on an updated version of Scioto Analysis’ Ohio Poverty Measure.

An entitlement program is not just another way of saying a safety net program, instead it is an important financial designation. It means that everyone who is eligible for the program will receive benefits as long as they claim them. The largest entitlement program is social security. 

Block grant programs, on the other hand, have a fixed amount of money allocated by Congress. Often, the federal government hands out cash to the states and state-level policymakers decide how these funds get allocated to the people who need it. One example is housing subsidies. Once the money allocated for housing subsidies runs out, no more people get benefits until more money comes in. 

Block granting an entitlement program can have major consequences for how many people are helped by the program. Consider the Temporary Assistance for Needy Families (TANF) program. TANF is a Clinton-era version of a program initially established in 1935 as the Aid to Families with Dependent Children (AFDC) program under the social security act. In 1996, the Personal Responsibility and Work Opportunity Reconciliation Act replaced the entitlement program AFDC with the block grant TANF.

The immediate result was a sharp decline in the number of people receiving benefits. The Center on Budget and Policy Priorities estimates that TANF reached 2 million fewer people than AFDC would have in 2019. 

What are the advantages of entitlement vs block grant design? A major tradeoff policymakers should consider when entertaining the idea of block granting an entitlement program is cost certainty vs. program reach. One fact about block grants is that they are much less sensitive to changing economic conditions. If there is an economic downturn, the program’s budget won’t immediately balloon. Instead, policymakers will be able to pull the lever more precisely to ensure the budget doesn’t get overwhelmed. 

However, this inherently means that people who need benefits to get by probably won’t get them immediately. As we saw when AFDC became TANF, block grant programs reach far fewer people than entitlements. It also means that block grant programs are less effective as automatic stabilizers. Entitlement programs kick in automatically to provide support to individuals and the macroeconomy during recessions, while block grant programs are too rigid to respond without additional policymaker action.

With entitlement programs, the risk of underestimating costs can be offset by better economic projections. Still, there is the risk that policymakers don’t allocate enough resources to a program and it fails as a result.

Recently, Minnesota made free school lunches an entitlement program in their public schools. The program proved to be adopted much more widely than policymakers projected, causing it to already be over budget. There doesn’t seem to be any risk of the program getting shut down, but policymakers are going to have to make budgetary adjustments going forward. The most obvious example of an entitlement program potentially growing too large is social security. 

Block grants can be an effective tool for policymakers to control the costs of safety net programs at the outset. For state and local governments that have balanced budget requirements, this can be an important consideration. However, policymakers should be aware of the tradeoffs. A safety net that doesn’t reach the people who need it the most is not very effective at its central goal: cushioning the impact of market economies on workers and families.

Is pornography a public health problem?

According to the U.S. Department of Health & Human Services, if you live in Ohio, you are 15% more likely to die of heart disease than the average American.

You are also 11% more likely to die of cancer than the average American.

Accidents are even worse. Ohioans are 40% more likely to die of an accident than the average American.

Respiratory disease, cerebrovascular disease like stroke, Alzheimer’s disease, diabetes, kidney disease, suicide, homicide, pneumonia, sepsis: Ohioans die of all of these conditions at higher rates than the average American. The only categories highlighted by the National Institutes of Health that Ohioans don’t die of at higher rates than the general U.S. population are liver disease (2% lower) and the flu (exactly the same).

Given the public nature of this information, public health-minded policymakers would be crafting a strategy to address this rampant mortality. There are so many fronts on which progress could be made, where valuable political capital could be spent to save lives.

Earlier this week, a bipartisan coalition of legislators joined with the governor to introduce their big public health push for 2024. Was it a package to tackle heart disease? Cancer? Are policymakers going to craft a plan to tackle respiratory disease, strokes, diabetes? Maybe suicide or homicide?

No. Public enemy #1 in Ohio is porn.

Executive and legislative leaders joined together to introduce legislation to require all viewers of pornographic material in the state to share personal information such as a state ID online to do so. Some legislators have already been trying for years to declare a “public health crisis” around consumption of pornography in Ohio.

If Ohio passed this legislation, they would join a handful of bible belt states, Montana, and Utah in requiring residents to share their personal information to access pornographic material.

There seems to be some evidence that pornography could have impacts on the health of some individuals and could have some impacts on social norms around sex and sexuality.

But why has pornography risen to such a fixation of policymakers across the United States? 

Emily F. Rothman, Professor of Community Health Sciences at the Boston University School of Public Health, is a foremost researcher on the impact of pornography on public health. She wrote the 2021 textbook Pornography and Public Health.

In this book, Rothman outlines how out of step policymakers are with public health leadership.

The professional public health community is not behind the recent push to declare pornography a public health crisis. One might think that if pornography is a public health menace, “destroying the lives of millions,” public health entities and professional societies must have a viewpoint on the topic, perhaps a clearly outlined health-promotion agenda related to the problem, and a strategic plan. At least one of the National Institutes of Health (NIH) must have named it as a priority, the Centers for Disease Control and Prevention (CDC) must have a branch devoted to putting a stop to it, and the World Health Organization must have at least one infographic on its harms. But none of these things exists or has happened. In fact, there is no public health professional presently in any position of public health leadership or authority who has gone on record to say pornography is a public health topic of interest–let alone a public health crisis. In 2016, in a written statement to CNN, the CDC said it “does not have an established position on pornography as a public health issue. Pornography can be connected to other public health issues like sexual violence and occupational HIV transmission.” But if public health entities are not behind the movement to declare pornography a public health problem, who is? And why are they using the language of public health for their cause?

Maybe policymakers are way ahead of the public health community on this one. But I’ll say this: if they spent as much time trying to reduce cigarette consumption as they spent on pornography, they would probably save a lot more lives.

Should I use a linear probability model or logistic regression?

Over the last month, I’ve been working on updating the Ohio Poverty Measure. The Ohio Poverty Measure is a poverty measurement tool calculated by Scioto Analysis and based on similar measures in California, New York City, Wisconsin, and other states. 

One of the biggest hurdles to overcome on this project is imputing information about specific additions to income not available in the American Community Survey, the main dataset used for calculating the Ohio Poverty Measure. Specifically, to calculate the Ohio Poverty Measure, we need to impute data for which poverty units receive housing subsidies and free school lunches. 

To work around this, we use answers from the Current Population Survey to impute recipiency of housing and school lunch benefits to families who respond to the American Community Survey.

The Current Population Survey asks a larger number of questions to a smaller subset of the population compared to the American Community Survey. Our goal is to use Current Population Survey data to build a model that predicts the probability a poverty unit receives one of these benefits, then use that model to determine which poverty units in the ACS data get the benefits and estimate the size of those benefits.. 

This approach will not get the answer exactly correct for each individual family, but assuming the Current Population Survey population is similar to the American Community Survey population, this approach should give us a useful approximation of these benefits. 

So, let’s talk about how to best build this type of model. 

The simplest approach would be to define the outcome of benefit recipiency as a numeric variable, (e.g. one for people who receive the benefit and zero for those who don’t) and use a regular linear regression approach to estimate recipiency. With binary outcome data, we call this a linear probability model. 

Unfortunately, linear probability models have two main drawbacks. First, linear regressions assume continuous outcome variables. This means that we can make predictions that go below zero or above one. Since our outcomes are supposed to represent probabilities, this is undesirable. There is no such thing, after all, as a -25% or 125% probability of housing subsidy receipt.

Second, linear probability models, as the name suggests,  linearly increase or decrease in one direction or the other. This is closely related to the first problem, since as we approach extremely likely or unlikely outcomes we actually don’t expect to see linear changes in the probability. It makes much more sense that our model should asymptotically approach one or zero in those cases.

The solution to this problem is to use a generalized linear model. For binary outcomes, the logistic or probit regression models are the most common choices. These models bound our outcome nonlinearly between zero and one, with our predictions asymptotically approaching those values. 

Because in our data, we are dealing with some extreme probabilities (people with high income should be ineligible for these benefits and therefore have probability zero), the linear probability model is a poor choice for estimating recipiency. Linear probability models perform best when looking at situations where the outcome is almost always close to 50/50. Near the middle, all of these models are fairly close. It’s as you get further into extreme probabilities that the shortcomings of the linear probability model really begin to show themselves.

For this project, I chose to use a logistic regression model. Still, this only allows us to say what the probability of a poverty unit receiving some benefit might be, we still need to figure out who receives benefits in our ACS data. 

The solution to this is quite simple. First, we look at the CPS data and see what percentage of those respondents receive this benefit. Because the CPS is a random sample (after weighting it), we can assume that this is the proportion of people that actually receive these benefits. Next, we rank every person in the ACS data by their probability of receiving these benefits. Finally, we give benefits to people in the ACS data with the highest probabilities until the same percentage of people receive benefits as in the CPS data.

By using a logistic regression instead of a linear probability model, we more accurately determine the probability of receiving benefits for people at the extremes of our survey. Because we are looking specifically at people near the ends of our predictions, it’s important that our model functions correctly in those places. For most binary outcome data, the choice is simple.

What would a productive Ohio General Assembly look like?

Last year, the Ohio General Assembly made fewer new laws than it had in any year since 1955. The sixteen laws passed in 2023 beat out the previous record holder, 2009, when Democrats controlled the Ohio House and were only able to agree with the Republican Ohio Senate on 17 new laws.

There are a lot of political explanations for why so few bills became laws in Ohio last year. It may be because of the fractured Republican leadership in the Ohio House. It may be because so much of policymaking happens in the state budget rather than in individual bills. It may be that polarization has led to less common ground between legislators.

When I hear about a statistic like this, the question that comes to mind to me is this: why does it matter? The most straightforward answer to this question is that Ohio is still facing many issues that public policy could be the answer for. 

Whether it’s the opioid epidemic, food insecurity in both urban and Appalachian Ohio, low water quality in Ohio’s streams and rivers, or economic prospects in post-industrial cities, Ohio has no shortage of problems. And public policy could be the answer to these problems.

The nice thing about “number of bills enacted into law” is that it is a specific, measurable statistic. It definitely tells us something about the body and its political tenor, though it is debatable what exactly it is telling us.

But if we want to evaluate the question of how much good the general assembly is doing for the state of Ohio, we’d want to evaluate it differently. Theoretically, the state could cram a bunch of policy into a single budget bill that improves Ohioans’ lives considerably, passing one law but doing lots of good for its residents.

At the federal level, regulations are evaluated using cost-benefit analysis. Every regulation that may have a $100 million impact on the national economy is subject to cost-benefit analysis to assess the impact of the regulation on the public. These cost-benefit analyses are released in a federal report every year that talk about the overall impact on regulations on the U.S. economy.

In the spirit of this approach, I’ll put forth some metrics that we could ideally use to assess the effectiveness of the Ohio General Assembly.

How much will new laws grow the statewide economy? Normally, we’d measure this as a change in gross domestic product. Ideally, we would measure this using a comprehensive economic indicator like the Genuine Progress Indicator that captures the external impacts of economic activity, informal markets that aren’t captured in standard measures, and time markets where people trade off their valuable time for things they want.

How much will new laws reduce poverty and inequality? Impacts on poverty could be measured using more comprehensive, mainstream poverty indicators like the Supplemental Poverty Measure, which makes geographic adjustments for cost of living and includes the value of benefits. Impacts on inequality can be assessed by seeing how laws change the distribution of income.

How much will new laws foster human development? This means using measures like income, years of schooling, and life expectancy to see how much laws are improving people’s abilities to live the lives they want to live.

Finally, are laws improving how people evaluate their own lives? This means utilizing surveys of people’s well-being such as used in the World Happiness Report to evaluate public policy changes.

Ultimately, the way to evaluate policymakers is not on how many laws they pass, but on how these laws improve the lives of their residents. Having think tanks, analysts, and journalists focusing on these questions will give us the best picture of how well the Ohio General Assembly is performing.

This commentary first appeared in the Ohio Capital Journal.

Ohio economists expect benefits from evidence-based reading curriculum

In a survey released this morning by Scioto Analysis, 15 of 16 Ohio economists agreed that implementing evidence-based early childhood literacy curriculum in Ohio public schools would improve human capital in the long run. In recent months, Governor DeWine has reinforced his desire to require these practices, commonly referred to as the “science of reading,” in Ohio’s public schools. 

As Kevin Egan (University of Toledo) wrote: “Children are our future workers, investing in high quality education for all children grows the future economy more and equalizes opportunity for everyone.” 

Despite the consensus opinion from respondents, some economists identified challenges that this program could face. 

“My answer is based on my understanding that research shows the current reading curriculum leads to disparities, hence the switch to ‘evidence-based’ (new evidence),” said Curt Reynolds of Kent State University. “If these new strategies help close gaps in education that would be very important.” 

“There may be challenges faced in effective implementation of the programs and schools may need additional support to provide teacher training, " said Faria Huq of Lake Erie College.

Assuming these techniques are implemented well and teachers are prepared, Ohio economists are optimistic about some of the secondary effects this program might have. 15 of 16 respondents agreed that these practices could grow the economy in the long run, and 14 of 16 respondents agreed that these practices could reduce inequality 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. Individual responses to all surveys can be found here.