Ohio Economists Skeptical of Laws Shielding Firms from COVID-19 Claims

In a survey published by Scioto Analysis this morning, only 8 out of 36 Ohio economists believed laws shielding firms from liability relating to injury and death resulting from COVID-19 will speed Ohio’s economic recovery.

Economists who disagreed that such laws would speed economic recovery in particular emphasized long-term economic impacts, worried that such laws may prolong the pandemic and hurt the economy in the long-term. Multiple economists expressing uncertainty about the economic impacts of such laws said the “devil is in the details,” emphasizing tradeoffs between recklessness caused by too little responsibility and the opposite dangers of exercising too much caution.

Economists were also worried that such laws may increase infection rates, with 22 of 36 economists agreeing that liability laws will incentivize actions by firms that will increase the spread of COVID-19. Economists worried about these sorts of laws emphasized the negative external costs of economic activity on infection rates. Economists who were more conflicted on this question emphasized that firms have incentives in the form of reputation and workforce health to take actions to reduce spread even without liability laws.

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.

If you would like to suggest a question for a future Ohio Economic Experts Panel, email your ideas to panel@sciotoanalysis.com.

Ohio universities tiptoe back into the classroom

With school back in session, Ohio’s eyes are on the state’s 3,600 schools, but also its nearly 100 public and private universities. As universities have scrambled to put together new policies and procedures in place to somehow reconvene an institution that is, in essence, a nine-month string of consecutive mass gatherings, data transparency has become a big piece of the puzzle to both ensuring student safety and convincing the public of the work they are doing.

Ohio State University’s “Safe and Healthy Buckeyes” tracker is a good case study for how transparency is being carried out and gives us some insight into how COVID-19 is spreading at Ohio’s largest university. As of Sept. 4, 5.8% of student tests were coming back positive in the past week. 

Is this positivity rate good? Well, it depends on who you ask. The key performance indicator targets have shifted significantly over the course of this pandemic, with case and hospitalization rates taking center stage early on, a variety of indicators culminating in a county-by-county color code after state restrictions were lifted, and most recently driven by a focus on positivity rates among tests taken. 

The turn to positivity rates kicked off in May when the World Health Organization recommended countries should remain at 5% or lower positivity rate for two weeks before “reopening” their economies. In an interview with “Face the Nation,” however, United States Surgeon General Jerome Adams said that positivity rates of 10% or lower — twice the World Health Organization guidelines — should be what parents should look for in their community as schools reopen.

When it comes to positivity rates, Ohio has fared better than the country as a whole. The United States briefly dipped below 5% positivity rate in June but spiked up to 8.5% positivity in July and has yet to get below 5% positivity since then despite steady progress throughout August. Ohio, on the other hand, has generally been able to keep its positivity rate under 5% since July, reaching a low of 3.1% earlier this month.

What does all this mean for the situation on Ohio campuses? Well, 5.8% positivity rate is not something to necessarily be happy about. Ohio State’s total student positivity rate since August 14th is 3.7%, which is much better than 5.8% and good by anyone’s standards, but the more recent 5.8% rate suggests cases are on the rise among students. 

While it’s easy to dwell on the negatives of school reopenings, it’s important to also ask ourselves what we get from school reopenings. In June, I wrote about the lingering human capital impacts that students will feel from COVID-19 school closures. If current research on the impacts of schooling on child development is correct, however, then older students stand to lose much less than younger students from lost days of schooling, even assuming virtual learning is the same substitute across different age ranges, which it likely is not.

Overall, if viral spread can be controlled, higher education is a valuable investment for the people involved. That being said, higher education institutions have more infrastructure and tools than K-12 institutions to facilitate socially distant learning and less ability to hurt student outcomes down the road. Smart institutions will put this to good use and balance limiting community spread with continued human capital development for students.

This commentary first appeared in the Ohio Capital Journal.

Official unemployment rate doesn’t capture enormity of COVID-19 labor force fallout

We know COVID-19 has hurt workers, especially service industry workers, hard. A recent poll of Ohio economists conducted by my firm found 38 of 40 economists think the economic fallout of COVID-19 will fall disproportionately on low- and middle-income families, with economists citing the labor market structure as the major culprit of this inequitable outcome.

This has been an unprecedented year for Ohio’s labor market. The state unemployment rate tripled in April, jumping from 5.4% in March to a whopping 17.3%, the highest since the Bureau of Labor Statistics started recording unemployment rates in the mid 1970s. In April, almost 1 million Ohioans claimed unemployment, also a record for the state.

The unemployment rate isn’t the whole story for Ohio’s labor force, though. As anyone who has spent some time learning about unemployment measurement knows, the official unemployment rate only tracks the number of people actively seeking work as a proportion of the labor force. This means that people who want to work but have stopped looking for it (discouraged and marginally attached workers) and people who technically have jobs but would like to work more hours (involuntary part-time workers) are not included in the official unemployment measure.

Newer economic measures such as the Genuine Progress Indicator tend to use more comprehensive measures of underemployment to capture the range of impacts underemployment have on the economy. The measurement of choice is the Bureau of Labor Statistics’s “U-6” measure, which includes marginally attached workers and involuntary part-time workers alongside unemployed workers in its measure.

The problem with the U-6 measure is that it is not reported as often as the U-3, the official unemployment measure. While the official unemployment measure is reported on a monthly basis, the comprehensive U-6 measure is only reported on an annual basis, meaning we have incomplete information on what the COVID-19 crisis has done to marginally attached workers and involuntary part-time workers.

Fortunately, the Bureau of Labor Statistics published new annual U-6 numbers last month, covering the 12 months from July 2019 to June 2020. Over this time period, the official unemployment rate was 6.8%, but the comprehensive U-6 unemployment rate was 11.1%, implying that for every three unemployed Ohioans, there were two Ohioans who had given up looking for work even though they wanted to work or who were working part-time who wanted to work full-time.

If that same ratio of marginal/involuntary part-time workers to traditional unemployed held steady in April of 2020, 1.6 million Ohio workers would have been unemployed, marginally attached, or involuntarily part-time that month, leading to a U-6 unemployment rate that would top 28%.

There are reasons to think this number could be lower. Maybe the swift layoffs of March and April 2020 gave little time for workers to become discouraged or employers favored straight-up layoffs over hour cutbacks. On the other hand, maybe programs like Paycheck Protection caused employers to retain workers at lower wages and maybe the quick crush of COVID-19 discouraged workers immediately, meaning the U6 could have been even higher than that. 

Either way, a record of nearly a million Ohioans filed for unemployment in April, but hundreds of thousands more were directly affected by this historic economic event.

This commentary first appeared in the Ohio Capital Journal.

What’s the difference between cost-benefit analysis and cost-effectiveness analysis?

When an analyst approaches a problem and are trying to take an efficiency lens to it, she has two main options at her fingertips: cost-benefit analysis and cost-effectiveness analysis. Understanding how each of these works and the tradeoffs between the two techniques can be the difference between an informative analysis and an analysis that just leaves a policymaker more confused than before.

Cost-benefit analysis and cost-effectiveness analysis are two approaches that sound the same, operate similarly, have similar goals, and are often referred to interchangeably. Despite what these two techniques have in common, they are indeed two distinct techniques that ask different questions and have different approaches to evaluating the efficiency of a program.

At the most fundamental level, cost-benefit analysis and cost-effectiveness analysis are centered on two different questions. While cost-benefit analysis asks whether the economic benefits outweigh the economic costs of a given policy, cost-effectiveness analysis is focused on the question of how much it costs to get a certain amount of output from a policy. Formulas to calculate the two are listed below. 

Cost-benefit = Benefits ($) - Costs ($) (AKA “net benefits”)

OR

Cost-benefit = Benefits ($) / Costs ($) (AKA “benefit ratio”)

Cost-effectiveness = Costs ($) / Outcome

Let’s put this into practice. Say an analyst is conducting a policy analysis on a proposal to expand a school district’s preschool education program by opening free slots for children from low-income families. What would this analysis look like if it focused on cost-benefit outcomes versus cost-effectiveness outcomes?

A cost-benefit analysis would attempt to collate all the benefits of the program such as future labor market earnings benefits for participants, improved health benefits, reductions in crime, and reduction in future social spending, and compare that to program costs and other costs of the program, for instance the potential for increased public-sector spending on higher education. The analyst would then convert all these benefits to dollar figures and then estimate the dollar value of benefits compared to costs as a ratio or difference depending on client needs.

A cost-effectiveness analysis, on the other hand, would focus on a given outcome and see how much spending is needed to bring about that outcome. For instance, if the preschool program was focused on trying to increase high school graduation rates, an analyst could estimate how much would need to be spent on the program, given what we know about how preschool participation impacts high school graduation rates, in order to cause one new person to graduate who would not otherwise. This cost could then be measured against other interventions to improve high school graduation rates to assess how cost-effective opening preschool slots is for improving graduation rates compared to other options the school district may have.

Looking at this example, you can start to see some of the advantages and disadvantages of using one technique versus the other. Cost-benefit analysis is usually considered a more comprehensive analytical technique since the process of monetization (converting all costs and benefits to dollars figures) converts all costs and benefits into a common currency, namely economic benefit.

The drawback of monetization is that it can sometimes fail to give information to policymakers that is all that useful for them. Policymakers are usually interested in outcomes besides economic efficiency, making cost-benefit analysis at best only partially informative to them. Thus, cost-effectiveness analysis can be a good tool for zeroing in on one outcome and comparing alternatives of greater or less cost-effectiveness against one another. The drawback of this strategy, of course, is that it can set you up to leave out “side benefits” that policymakers may also be interested in. For instance, more preschool slots may not be the most cost-effective way to improve graduation rates, but maybe a policymaker would be willing to choose a less cost-effective graduation rate improving program if it had future earnings and crime reduction benefits that other programs didn’t.

Overall, both approaches are powerful tools for a policy analyst, and need to be deployed strategically depending on the client and the project. Because after all, these methodologies are only tools, which means they’re only as good as they are useful for their purpose, which is generating better information for more informed policymaking.

38 out of 40 Ohio Economists Say Mask Mandate Benefits Outweigh Costs

In a survey published by Scioto Analysis this morning, 38 out of 40 Ohio economists said that the economic benefits of mask mandates outweighed the economic costs.

Comments by economists emphasized the low costs associated with mask wearing and the benefits associated with mask mandates in both reduction in risk of death and likelihood mask mandates could prevent further state shutdowns of economic activity in the future.

The survey also found strong agreement among Ohio economists that damage from COVID-19 and its economic fallout will ultimately fall disproportionately on low- and middle-income families, with 38 out of 40 respondents agreeing with the statement. Comments on this question focused on the employment structure of low-income households as well as other subgroups of the population that may be affected by COVID.

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.

If you would like to suggest a question for a future Ohio Economic Experts Panel, email your ideas to panel@sciotoanalysis.com.

Nursing home residents have suffered most from Ohio’s COVID-19 tragedy

As Ohio contends with a historic epidemic, residents of the state’s nearly 1,000 nursing homes are taking the brunt of the damage. As of last week, 2,100 of Ohio’s 2,600 COVID-19 deaths since mid-April — about 4 in 5 deaths — were among nursing home residents.

The extent to which COVID-19’s death burden has fallen on Ohio’s elderly is surprising given what we have experienced with similar diseases. While the CDC reports that 3 in 4 U.S. influenza deaths in the 2018-2019 season were among those age 65 and older, Ohio Department of Health data suggests over 9 in 10 COVID-19 deaths have been among those age 60 and older. 

While ODH doesn’t report individualized age data more fine-grained than the decade level in order to maintain anonymity of COVID-19 patients, by looking at the percentage of deaths that have been among those 60 and older and 70 and older, we can conservatively estimate that 84% of COVID-19 deaths in Ohio have been among those 65 and older, a full nine percentage points higher than nationwide flu deaths last year.

Part of this might have to do with the US’s reliance on long-term care. According to the OECD, the United States has more people in nursing homes than any other developed country. An NBER working paper last month by UCLA and Yale researchers used smartphone data from 30 million phones to find a significant number of cell phones appearing in multiple nursing facilities, probably due to staff working at multiple homes. They estimate that these staff linkages have made nursing home facilities even more dangerous for residents, leading to almost half of nursing home infections via transmission from staff. 

While nursing homes are certainly the right options for some families, many researchers think that the US’s reliance on them lead to worse outcomes for elderly people, even for those with dementia. Assisted living in independent or family settings can potentially lead to more community connectivity and independence for people in their final years at a lower cost than nursing homes require.

Because of these factors, the state of Ohio has dramatically shifted its funding from nursing home care to assisted living over the past ten years. The hope is that this change will save money for the state while improving outcomes for seniors.

One thing is for certain, though: those still in nursing homes have been left most at risk during this global pandemic. And while we didn’t know the extent to which COVID-19 would threaten Ohio’s elderly population, we knew they would be most at risk and that nursing homes would, in particular, be the most important places for mandated social distancing.

COVID-19 didn’t have to ravage nursing homes the way it did this year. Japan has nearly as many people in nursing homes as the United States does but less than 1% of total deaths the US has experienced. Despite their heavy reliance on nursing homes, they were more prepared than we were, and COVID-19 has showed us that there is no substitute for proper preparation when it comes to a global pandemic.

This commentary first appeared in the Ohio Capital Journal.

Announcing the Ohio Economic Experts Panel

By Elizabeth Steffensmeier

On Monday morning, Scioto Analysis announced the launch of the Ohio Economic Experts Panel, a panel of Ohio-based economists weighing in on important public policy problems. The Ohio Economic Experts Panel will answer questions on an ongoing basis relevant to pressing public problems. 

The Ohio Economic Experts Panel has been inspired by the Economic Experts Panel directed by the Initiative for Global Markets (IGM). We plan to replicate the success of the IGM Economic Experts Panel in our state by posing questions of relevance to state policy to economists from colleges throughout Ohio. The goal of this initiative is to help connect policymakers to researchers with information on crucial policy problems.

Each survey will pose statements about pressing public policy decisions. The panelists’ responses will inform the public on the extent that they agree or disagree with the statement. Each question will also have an "uncertain" and "no opinion" option. "Uncertain" is selected when the panelist thinks that the evidence pertaining to the particular question is ambiguous. "No opinion" is selected when a panelist does not feel qualified as an expert in the area. We will be encouraging panelists to use these options when relevant as we recognize that lack of specific subject matter expertise is inevitable--no one can be an expert on everything.

The results of the surveys will be weighted by the respondents’ confidence levels. Both the unweighted and weighted results will be published. In addition, panelists will have the option to leave a brief comment with each question to further elaborate or link relevant research.

The results of the surveys will be informative for policymakers, the media, and the public. We expect that for some questions, there will be consensus among the panelists. If almost all of the panelists strongly agree or agree, knowing that a panel of highly qualified economists expect a particular result from policy enactment is valuable information. However, we also expect that for some surveys, there will not be a consensus. This information is just as important.

Congratulations and thank you to the panelists who will serve on the Ohio Economic Experts Panel:

Dr. Jonathan Andreas: Bluffton University

Dr. Gregory Arburn: University of Findlay

Dr. Bizuayehu Bedane: Marietta College

Dr. David Brasington: University of Cincinnati

Dr. Ron Cheung: Oberlin College

Dr. Jay Corrigan: Kenyon College

Dr. Kevin Egan: University of Toledo

Dr. Kenneth Fah: Ohio Dominican University

Dr. Hasan Faruq: Xavier University

Dr. Vinnie Gajjala- Tiffin University

Dr. Sucharita Ghosh: University of Akron

Dr. Robert Gitter: Ohio Wesleyan University

Dr. Nancy Haskell: University of Dayton

Dr. Paul Holmes: Ashland University

Dr. Faria Huq: Lake Erie College

Dr. Michael Jones: University of Cincinnati

Dr. Fadhel Kaboub: Denison University

Dr. Kristen Keith- University of Toledo

Dr. Bill Kosteas: Cleveland State University

Dr. Charles Kroncke: Mount Saint Joseph University

Dr. Trevon Logan: Ohio State University

Dr. Phillip Mellizo: The College of Wooster

Dr. Diane Monaco: Heidelberg University

Dr. Michael Myler: University of Mount Union

Dr. Joseph Nowakowski: Muskingum University

Dr. Mingming Pan: Wright State University

Dr. C. Lockwood Reynolds: Kent State University

Dr. Martin Saavedra- Oberlin College

Dr. Lewis Sage: Baldwin Wallace University

Dr. Daniel Shoag: Case Western Reserve University

Dr. Dean Snyder: Antioch College

Dr. Olga Standrityuk: Ohio University

Dr. Kay Strong: Baldwin Wallace University

Dr. Albert Sumell: Youngstown State University

Dr. Melissa Thomasson: Miami University

Dr. Thomas Traynor: Wright State University

Dr. Ejindu Ume: Miami University

Dr. Mark Votruba: Case Western Reserve University

Dr. Matthew Weinberg: Ohio State University

Dr. Andrew Welki: John Carroll University

Dr. Kathryn Wilson: Kent State University

Dr. Rachel Wilson: Wittenberg University

Scioto Analysis Releases Cost-Benefit Analysis of AmeriCorps Programs

Tuesday morning, Scioto Analysis released a cost-benefit analysis on AmeriCorps public service programs in Ohio. Co-authors Rob Moore and Noah Stein project that additional funding for state volunteerism programs could result in millions of dollars of economic benefits to the state.

“Public service programs are usually lauded for how they foster civic engagement,” said Stein. “Our analysis builds on that, finding that service programs also boost future wages for participants.”

Overall, the analysts found that every dollar in tax distortion and lost current wage costs due to the program was offset by one to two and a half dollars of benefits in boosted future wages, crime reduction, and increased future volunteering. The paper also touches on distributional impacts, highlighting the immediate cost to participants contrasted against the more larger long-term income gains for program participants.

“Public service programs act much like a year of higher education due to boosts to human capital and general future earning capacity,” said Moore. “Participants in AmeriCorps trade off current labor market earnings in order to boost their earnings down the road.”

Another major impact of public service programs is reduction in probability for participants to conduct crime in the future due to program participation. Career criminality exacts large costs on society, and even marginal reductions in criminality caused by these programs yield large social benefits.

Overall, the analysts estimate that expansion of volunteer options by the state of Ohio would produce $1-30 million in net benefits when balancing the opportunity costs and taxpayer burden with the benefits participants experience from their time volunteering.

This is the third best-practices cost-benefit analysis conducted on a state policy in the state of Ohio in the past decade. The first, Ohio Earned Income Tax Credit Refundability: A Cost-Benefit Analysis, was released by Scioto Analysis in August 2019, followed by a second, Closing Schools for COVID-19: A Cost-Benefit Analysis, which was released in June.

Mask mandates save lives but are a political thornbush

COVID-19 is bigger than ever in Ohio, with the number of new daily cases reaching its highest points of the pandemic in July. As the disease continues to spread throughout the state, Gov. Mike DeWine has struggled to balance public health needs with a growing sentiment against public health measures. 

Before last week’s statewide mask mandate, the main strategy from the administration has leaned on a county-by-county approach that mandates masks in high-spread counties with less resistance to such measures and preserves local authority in areas of lower spread and more antipathy to mandates. This strategy led to a mask mandate for 60% of Ohioans and lighter regulation in places with less spread and less taste for mandates.

Whatever your personal view is on masks mandates, however, available evidence on their use during the COVID-19 pandemic seems to show they slow the spread of disease. Much of the news coverage of the use of masks has focused on very technical questions: What kind of mask needs to be worn to spread disease? How should a mask be worn? Are masks more protective for people wearing them or people around those who wear them?

Whatever the answers are to these questions, the available evidence seems to show us one thing: that mask mandates do slow the spread of disease. A study published in Health Affairs last month by two University of Iowa public health researchers is possibly the most rigorous and applicable study on COVID-19 mask mandates to date. The researchers compared the infection trajectory of 15 states that implemented mask mandates in April and May to the other 35 without mandates to see if mandates showed any measurable impact on infection rates.

The “natural experiment” of variable mask mandates suggests that mask mandates reduced infection rates. The researchers found that growth rates in COVID-19 cases declined by one percentage point in the first couple of weeks after a mandate compared to states without mandates and that case decline rose to two percentage points in the weeks after.

One or two percentage points of growth rate decline might not seem like a lot, but the researchers estimate that the mask mandates prevented at least 230,000-450,000 new infections through the end of May, implying they saved a combined 9,000-17,000 lives over that time period assuming the current 3.7% national COVID fatality rate.

While this study seems to suggest mask mandates can save lives, reduced growth rates might be the effect of other factors. For instance, states that have a larger cultural commitment to reducing the spread might be likely to both adhere to social distancing guidelines and pass mask mandates as well. While the study tries to control for these factors, only so much control can be made in a natural experiment such as this.

These problems present a sticky challenge for DeWine. On the one hand, the evidence available suggests that mask mandates slow spread of disease and save lives. On the other hand, Ohio is a hotbed for anti-mask sentiment and communities need to be on board with masks for mandates to work. DeWine has a difficult path to tread as he tries to convince people to take steps that will slow the spread of COVID-19 and save lives in the process.

This commentary first appeared in the Ohio Capital Journal.

On Balance: New Editor, Same Mission

What a year it has been. Between the unfolding of a global pandemic and nationwide protests around the topic of police brutality, 2020 has already been a jam-packed year for public policy, and we haven’t even made it into election season.

I was appointed the new editor of On Balance in January, taking over for our founding editor Fran Sussman. I had big shoes to fill so figured I’d get a few entries out before posting my own introductory blog post. Then the world turned upside-down in March. I came back from a vacation to see the SBCA board voting to cancel its March conference and then the next few months became about COVID. So forgive this late introduction: there were more important things to attend to.
 
To give you some background about myself, I have a public policy analysis practice based in Columbus, Ohio called Scioto Analysis. Along with doing work for clients, we frequently engage in social impact work focused on improving policy analysis at the state and local level. We have a publication called The Ohio Handbook of Cost-Benefit Analysis and last week we released a best-practices benefit-cost analysis on COVID school closures.
 
I first came across economic analysis of public policy as a lobbyist in the Nebraska state legislature, when I was amazed to see a bill that would have expanded contraceptive coverage unexpectedly gain support from a number of conservative legislators.  The support arose in reaction to a legislative fiscal office report showing the bill would save the state health and safety dollars in the long run. From that experience, I learned the lesson that rigorous analysis presented by a nonpartisan, credible agency can have an impact on the policy conversation.
 
I was also lucky enough to take a class in benefit-cost analysis in graduate school at the University of California, Berkeley, taught by current SBCA board member Dan Acland. It was in that class that I learned that benefit-cost analysis is less about finding that “magic number” of a net present value greater than zero and more about taking apart a policy problem and understanding the real consequences of public action. I am glad I have had the opportunity to stay in touch with Dan because he has consistently given me good advice and thought-provoking commentary on benefit-cost analysis and first introduced me to SBCA in graduate school.
 
At this  moment, benefit-cost analysis is as relevant as ever. In our blog so far this year, we have had posts on COVID-19 policy, response to the opioid crisis, public investment in scientific research, and even the value of a statistical dog life. Benefit-cost analysis forces us to take a step back and understand the economic mechanisms that undergird every policy decision. Whether the problem is environmental, health-related, or even social, public policy is largely about resource allocation and benefit-cost analysis gives us a framework for understanding how scarcity impacts public decision making.
 
I would love to see your words in this blog. Below are some examples of contributions you could make to On Balance.

• Have you published an article in a journal recently with a significant benefit-cost component? Publish an article tie-in summarizing it and putting your research in a broader context.
   

• Read a good book on benefit-cost analysis? Publish a book review with us that tells readers about the content and how it fits into the literature.
   

• Have a perspective on a current event informed by benefit-cost principles? Share it with us in a commentary.
   

• Have a take on how benefit-cost analysis can be better carried out? Submit a practical perspectives piece to us aimed at scholars and practitioners.

These are just a few examples of content you can submit to On Balance: if you have an idea for something creative, I am absolutely open to talking about it. We’re only as good as the content you provide us, so don’t be afraid to step up and submit something so your voice can be heard. As crazy as the world is right now, there is no shortage of need for evidence-based policy decisions. I look forward to seeing this blog continue to be a place for the advancement, exchange of ideas, and dissemination of research related to benefit-cost analysis, all based on submissions from scholars and practitioners.

This blog post first appeared in On Balance, the blog for the Society for Benefit-Cost Analysis. Scioto Analysis Principal Rob Moore is currently serving as the editor for On Balance.