8 Overrated and Underrated Economic Indicators

According to a survey conducted by Pew Research, the state of the economy was the most important concern voters had before this year’s midterms. Because the economy is so important, we should be able to understand whether or not it is doing well, right? 

Unfortunately, “the economy” is a nebulous phrase and there is no definitive way to measure how it is doing. There are a variety of metrics and statistics that policymakers can look at to see how the economy is faring, and in today’s blog we are going to talk about a few of them that are overrated or underrated. 

We should mention that these are just our thoughts on this issue. Certainly there is room for discussion and disagreement, but we hope this offers some new ways of thinking about the economy. 

Overrated - GDP

If you ask a friend how they are doing, you probably wouldn’t expect them to respond by telling you how much stuff they have. While it is generally true that having more things often means that an individual has more access to resources, this is not the whole story of wellbeing. Similarly, because GDP only measures how much stuff is in an economy, it can sometimes miss the bigger picture of how people are doing in the economy. 

Imagine two countries that each have the exact same GDP. Country A has high levels of poverty and extreme levels of pollution. Country B has low levels of poverty, and generates its production without the need for pollution. Clearly country B has a more sustainable economy, but GDP just doesn’t capture that. 

Underrated - GPI

The Genuine Progress Indicator (GPI) is a relatively new alternative to GDP. GPI serves much of the same purpose as GDP, trying to measure how much economic activity is in a country, state, or local area, but it also takes into account things like how sustainable an economy is, or how educated the population is, recognizing that these have economic impacts that are not traded on formal markets.

One of GPI’s biggest advantages over GDP is that it measures the value of non-market activity such as at-home childcare and volunteering. There are lots of extremely valuable ways for people to spend their time that improve the economy even though no dollars change hands to make it happen. 

Overrated - Stock Market

The stock market doesn’t get used as a signal for how the economy is doing as much as it used to, but the past two presidents have both mentioned it so it deserves some discussion here.  

The main difference between the stock market as an indicator and an alternative like the yield curve is that the stock market doesn’t have a neat cutoff point where we can see something is wrong. If the yield curve is inverted, we know that people think the short term is riskier than the long term. If there is a 50% rise or fall in the stock market, that just means there was a change in the prices of stocks. Maybe it was due to variance, maybe it was due to fundamental shifts in the economy, but there is usually no way to know until much later. 

Underrated - Yield Curve

The yield curve is often touted as one of the best recession indicators we have, but since we just talked about GDP being overrated we want to talk more generally about how the yield curve is beneficial outside that context.

The yield curve is the difference between the interest rates for 10-year treasury bonds and 2-year treasury bonds. In normal conditions, we should expect 10-year bonds to have higher interest rates, since it is riskier to hold bonds for longer periods of time where there is more uncertainty. 

When the yield curve is inverted, 2-year bonds have higher interest rates than 10-year bonds, meaning the people who are buying and selling bonds are more nervous about short term economic trends than they are about long term trends. It becomes safer to hold money for 10 years rather than 2. It is always a bad sign when people are more nervous about the short term in the economy than the long term. 

Overrated - U-3 Unemployment

The bureau of labor statistics reports six different unemployment measurements that each include different categories of people in the labor market. U-1 is the most optimistic, only accounting for people in the labor force who have been unemployed for at least 15 weeks. 

In the middle of the spectrum is U-3 unemployment, what is considered the official unemployment rate. U-3 is the most straightforward measure of unemployment, calculating the percentage of the labor force that do not currently have a job. 

U-3 is a useful unemployment measure, but it misses one crucial point that is critical to understanding the state of the labor force, underemployment. 

Underrated - U-6 Unemployment

On the other end of the unemployment measure spectrum is U-6 unemployment. U-6 is often a more useful measure of unemployment than U-3 because it actually does account for underemployment. 

If there is an economic downturn, employers are often faced with the need to cut costs. If they choose to cut costs by reducing the hours people work without laying them off, then other unemployment measures won’t capture the lost economic activity. 

Overrated - Official Poverty Rate

The official poverty rate was created in the mid 1960s by economist Mollie Orshansky as a tool to measure progress in the War on Poverty. At the time, the average family in the United States spent a third of their income on food, so the poverty line was set at three times the cost of a “thrifty food plan” for minimum nutritional intake in the United States and adjusted to family size. This measure is updated every year by the Census Bureau to adjust for inflation.

The Official Poverty Rate has a lot of problems. One is that family budgets have changed significantly over the past half century since the Official Poverty Measure was first adopted. While food cost a third of a family budget in the mid 1960s, agricultural and supply chain advances have dropped that number to about an eighth today. Meanwhile, costs of housing and health care have increased precipitously. Add this to the fact that the Official Poverty Measure does not make geographic adjustments for cost of living and we have a potential for overestimating poverty in some parts of the country and underestimating it in other parts of the country.

Underrated - Supplemental Poverty Rate

Since 2010, the Census Bureau has been calculating an alternative poverty indicator called the Supplemental Poverty Rate. Starting from a basis of two-thirds of average spending, the Supplemental Poverty Rate then counts total income (including both wage income and public benefits) and makes adjustments for geography and work expenses. The Supplemental Poverty Measure gives us a more accurate picture of what poverty looks like in the United States over a half century after the War on Poverty.

Scioto Analysis calculates the Ohio Poverty Measure using a similar methodology to the Supplemental Poverty Measure, but using a larger dataset to allow for more geographic precision.

There is no perfect measure for how well the economy is “doing.” But a dashboard of GPI, the yield curve, U-6 unemployment, and the Supplemental Poverty Rate will give you a more accurate picture of what the economy looks like than a dashboard of GDP, the S&P 500, U-3 Unemployment, and the Official Poverty Measure. So next time when someone talks about “the economy,” don’t be afraid to ask “what do you mean?”

How can a policy analyst define a problem better?

One thing I love about Eugene Bardach’s A Practical Guide for Policy Analysis: The Eightfold Path to More Effective Problem Solving is the guidance Bardach gives to policy analysts to avoid common mistakes they make in the analysis process.

One tool Bardach uses is called “pitfalls and semantic remedies.” The general idea Bardach presents is that there are common pitfalls in how we do policy analysis and if we articulate our problems and approaches in certain ways, we can avoid these pitfalls.

We can find an example of this in problem definition, the first step of the Eightfold Path. One mistake policy analysts can make is to define the solution into the problem. 

Let’s take student loans as an example. If we were to say “students have not had enough student loan forgiveness,” we are assuming that “forgiveness” is the best solution to the underlying problem. By redefining the problem as “too many students are burdened by student loans” or “students with student loans have too heavy burdens,” we open ourselves to solutions besides student loan forgiveness.

As a former undergraduate philosophy major, of course my mind goes to “well, too many students are burdened by student loans” isn’t our final question, right? Isn’t the deeper problem “college graduates do not have enough resources?” Or is it rather “student loans are an unfair burden to place on adults that young people do not understand the financial ramifications of?”

You can see how defining this question takes us in different directions. On the one hand, we could be talking about burden as a utilitarian problem: people aren’t able to get as much of what they want because their resources go toward student loans. On another hand, we could be talking about norms of fairness: students should not be burdened with unfair loans they do not understand the gravity of.

There are a couple of interesting questions this opens up. As analysts, we are usually asked to be utilitarians. This problem can seem like a hammer searching for a nail. Yes, we are (relatively) good at measuring things like consumer surplus, but often questions of fairness are what policymakers are more interested in. Maybe that’s not the place for a policy analyst, though. Questions of fairness are often better handled by the political process, reasoned debate, or by decisions of policymakers.

So where does this leave us as policy analysts? I’ll put forth a few problems that crop up when trying to apply the semantic tip of stripping a problem to mere description.

1. Where do we stop? We can keep digging deeper and deeper and getting to more and more philosophical questions. How do we know when to stop stripping our question down?

2. What do we do when our questions branch? If we end up facing multiple different problem definitions, which one do we choose?

I will offer two pieces of guidance for policy analysts stuck in this muck around stripping down problem definitions.

First, maintain client orientation when defining the problem. While we can push our clients to think about problems in a larger way, we also have to meet them where they are. 

The policymakers who we are working with may be further along in the decision making process than we are as analysts and if we get bogged down in philosophical questions, they can leave us in the dust. So try to define your problem in a way that stretches your client to open their mind to different possibilities, but not so much that the alternative possibilities opened by the problem definition become irrelevant to the client.

For instance, if you are writing a policy analysis on student loan burden for a state senator who ran on a platform of reducing student loans, coming back with a problem definition of “poverty is too high” would likely be astray of what she is looking for. Instead, a problem definition of “too many college graduates are in poverty” could be closer to what she is interested in actually having a policy analysis written around, depending on her political orientation.

Second, embrace the tools of policy analysis. If you are stuck between different ways to define a problem, sometimes it is better to ask yourself which question looks more like a nail. Policy analysts are not philosophers or politicians–they are applied social scientists. Leave the questions about metaethics to the metaethicists and the questions about political feasibility to the political strategists: as a policy analyst, you are best at applying the insights of economics to policy problems.

In this example, a path of “too many people are burdened by student loans” is a more straightforward problem definition than “the student loan system is too unfair.” If a client is ambivalent between the two, you are going to be able to analyze the former better than the latter and give better insight because of your particular technical expertise.

Policy analysis is messy, but by (a) understanding what our clients want, and (b) understanding what you are good at, you will be able to produce analysis that is more relevant to a policymaker. And relevant, insightful analysis is ultimately what we mean when we talk about good analysis.

Ohio economists do not think state loan forgiveness would impact tuition rates, inflation

In a survey published by Scioto Analysis this morning, economists in Ohio did not believe state student loan forgiveness would increase tuition rates or inflation. 

The respondents were split on the question of whether an Ohio student loan forgiveness would help Ohio retain educated workers. Some believed Ohio would retain more workers only if the loan forgiveness was contingent on years spent living and working in Ohio. 

Most economists believed that student loan forgiveness would not affect tuition prices or inflation. Some economists said whether loan forgiveness could be expected again in the future would impact tuition prices more than a one-time forgiveness. Most economists also said such a program would likely have a negligible impact on inflation if any.

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.

Does GDP make states happy?

Earlier this week Gross National Happiness USA, a national grassroots organization for which Scioto’s principal Rob Moore is president, released a landmark survey about happiness in the US. This is the first time a national survey of happiness has been conducted on four questions of happiness that have been asked in the U.K. for a decade and it offers some very valuable insights for those of us in the public policy field. 

One particularly important takeaway is that GDP per capita is not correlated with happiness. The correlation coefficient, the main statistical test for how two datasets relate to one another, between state GDP per capita and state happiness is only 0.09. This constitutes a weak relationship between GDP per capita and state happiness. Many people recognize that GDP is a way of measuring how much stuff is in an economy, not how well people are doing in that economy. Here at Scioto, we recommend the Genuine Progress Indicator (GPI) as a more complete metric to measure economic growth. Still, GDP is the most commonly used measure of the economy, and perhaps this survey is more evidence that broader measures of economic growth are needed in the policymaking world. 

Another interesting finding in the study is that among Americans, there does not seem to be a u-shaped happiness curve. The “u-shaped curve” is a widespread phenomenon in happiness that suggests people are least happy in their 40’s. It has been found in similar surveys across multiple other countries and within the U.S. In this study younger people are less satisfied with their lives than a u-shaped relationship would suggest. 

Although this finding is unexpected, Gross National Happiness speculates that perhaps the Covid-19 pandemic might be responsible, given that younger Americans reported more feelings of loneliness during the pandemic. 

The researchers also found that 45% of respondents reported that family was the most common factor people attributed their happiness to. The next most common topic reported was health, where 6.3% of respondents mentioned it. 

For policymakers interested in the happiness of people in society, this survey is a great tool to find areas for improvement. With more data like this, it will be easier and easier for researchers and analysts to include happiness as a criteria when evaluating public policy options.

How can policy analysts be better storytellers?

By Rob Moore, Principal, Scioto Analysis

For the past year and a half, I have slowly been writing blog posts about the eight steps of Eugene Bardach’s “Eightfold Path.” This eight-step framework is a common framework used for understanding how to conduct public policy analysis and has been very helpful for me to understand what public policy analysis actually is.

This is the final installation in that series, focused on the eighth step in the Eightfold Path: “Tell Your Story.”

As policy analysts, we often focus on the “nuts and bolts” of analysis. We are comfortable with literature review, monetization, statistical tests, sensitivity analysis. These are activities that are certainly used by analysts in an applied context, but are nonetheless safe at home in an academic environment. In order to communicate findings to clients such as policymakers, nonprofits organizations, or the public, we need to be intentional about telling a story they can understand.

One key tool Bardach gives us is the “Grandma Bessie Test.” This is an exercise where you ask yourself if your intelligent but not quite politically-aware Grandma Bessie would understand what you’re talking about if you explained it to her.

I know we don’t all like talking about work in our everyday lives, but I’ve found having conversations with family or friends to be a helpful way to apply the Grandma Bessie Test. If I can explain an insight I’ve had at a party or to my parents or to friends and keep their interest, then I’ve found a way to tell the story of my analysis in an effective way. So next time a family member or someone at a party asks you about your job, don’t focus on the broad strokes of what you do, try to explain a problem you’re working on. It could help you figure out how to tell your story better.

Another piece of advice Bardach gives is to gauge your audience. As policy analysts, our job is to be envoys between the ivory tower of academia and the alabaster dome of policymaking. This means we have to be fluent in two distinct dialects of English, wonkspeak and in politicospeak, along with subdialects relevant to our particular fields of study and policymaking. 

In one recent analysis, I found myself adjusting to a client who spoke a different language than I did. While I saw economists analyzing a policy intervention as a tax and subsidy program, the client bristled at those words, saying that “tax” and “subsidy” have implications among policymakers that are different than those among economists. While I still liked how straightforward those words were in explaining how the policy functioned, I also understood that the words mean very different things to economists than they mean to bureaucrats or legislators. 

Another consideration when telling a story put forth by Bardach is what medium to use when telling it. Below are just a few options for policy analysts.

• Oral presentation, virtual or in-person

• Memo

• White Paper

• Report

• Media Release

• Blog Post

Depending on the resources in your organization, you might need to develop more skills in some of these media versus others. Here at Scioto Analysis, we’re a small shop of two analysts, so we have to be serviceable in all of these forms of communication and spend a lot of time making sure what we present is in the best format for a client.

Bardach also implores you as a policy analyst to give your story a logical narrative flow. I tend to think writing a policy analysis is closer to journalism than it is to formal academic writing. Don’t fixate on your methodology: make it available in appendices at least but in the body of your report at most. Findings should come first so a busy policymaker with possibly only minutes to skim your report will retain the results of your analysis.

Bardach has a great list of common pitfalls to avoid, too.

• Don’t follow the Eightfold Path too closely. Focus on results, not methodology: that is what decision makers want to see.

• Don’t qualify compulsively. Go through your report and strike out double qualifications (e.g. “we find that it may be likely” usually just means “we find that it is likely”). Be straightforward and confident in your findings so they are not lost in a sea of qualification.

• Don’t show off all your work. Don’t give your client a thorough explanation of all the specifications of your Monte Carlo simulation if she is not going to read them. Leave it in the appendix.

• Don’t list without explaining. Don’t include lists of potential policy options or evaluative criteria that do not end up being important to your findings. These are usually not necessary and at best can be relegated to an appendix.

• Don’t spin a mystery yarn. People come to policy analysts for answers, not more questions. Answer the most important questions first rather than burying answers and making clients search for them.

• Don’t inflate your style. Don’t be an academic, don’t be a bureaucrat, don’t be your client’s best friend. Write like a professional trying to provide a clear understanding of what will happen when a policy is put in place.

• Don’t forget that analysis doesn’t persuade—analysts do. A report never stands on its own. As a rule, people trust other people much more than they trust arguments in a vacuum. Your analysis product exists in an ecosystem that includes the credibility of the organization you work for, your own professionalism, and the needs and desires of your client. Keep in mind that all of these will impact what people think of your analysis.

Bardach’s chapter closes with some practical advice.

Have an executive summary unless your report is very short. Use a table of contents if it’s very long (longer than 15-20 pages is a good rule of thumb).

Make sure statistics you use are understandable—always think of Grandma Bessie when using a number. Try to make them as tangible and concrete as possible: use numbers in your narrative that people would understand in everyday life. When tangible, numbers are the most powerful tool we have as analysts. When abstract, they can be the most obscure and impotent.

Reference, reference, reference. Make sure every piece of evidence you use in your analysis has a reference to back it up. At Scioto, we prefer hyperlinks and footnotes. Hyperlinks are nice for less formal products like blog posts, footnotes are good for more formal. While there is a trend toward using endnotes, the world of PDFs makes endnotes overly cumbersome for any reader interested in using them.

As analysts, our job is not just to be accountants, it’s to be communicators. Policymakers need to make important decisions that impact the lives of their residents and others but the collective knowledge of civilization is diffuse, complicated, and sometimes contradictory. It is our job to distill and communicate that information so policymakers can make informed policy decisions. Telling our story is a key part of that work and one that every analyst needs to do.

What can Ohio voters learn from ballot initiatives in other states?

Every election season, the top headlines are around candidates. Which party was able to win, which candidates took office at the federal level, the state level, local government, judicial offices: this is what takes the majority of our headspace.

But because of a gift given to the people during the progressive era, election season is also when people make policy through direct democracy: when we vote for laws at the ballot box.

Ohioans passed two laws last week: a law to require public safety to be taken into account in bail sentencing and a law restricting who local governments could allow to vote. They also allowed a number of bonds and levies across the state.

But what else could Ohioans do at the ballot box? Last week’s election saw pretty substantial issues passed across the country that could be ripe for Ohioans to vote on in 2024.

Abortion

Five states put abortion on the ballot last week, and in each of them, abortion rights won. Probably the most high-profile was Michigan, which codified abortion as a right in their state constitution. Ohio, meanwhile, has a current law banning abortion after five or six weeks of pregnancy.

Ohio looks like Michigan in a lot of ways and anti-abortion initiatives have failed in more red states like Kansas and Kentucky in the past year, so legislation that rolls back the legislature’s fervent anti-abortion bent has the potential to be successful in Ohio.

Minimum Wage

Voters in two states and the District of Columbia raised their minimum wage last week. While the District of Columbia had the most dramatic increase in dollar terms, setting their minimum wage at $16.10, the most surprising was Nebraska. The deep red state raised its minimum wage from $9 to $15 an hour, a much higher rate in real terms when accounting for regional cost of living. 

Those who follow Nebraska politics aren’t quite as shocked as the rest of since Nebraskans passed a $9 minimum wage in 2014—at the time the highest in the country when adjusting for cost of living. What was surprising was the margin—the $15 minimum wage last week passed with 58% of voters in favor. 

While state after state have passed minimum wage increases over the past decade, Ohio has not put an increase on the ballot. A large increase in the state minimum wage could be very competitive on the ballot in Ohio and could be an effective tool for reducing inequality in the state.

Marijuana

Marijuana legalization had more of a mixed reception in 2022. Blue Maryland and red Missouri both passed marijuana legalization measures, but Arkansas and North and South Dakota voters all defeated similar measures. While a handful of small Ohio cities decriminalized marijuana possession on Tuesday, a statewide initiative would need to be thoughtfully crafted to pass in the next couple of years. People still remember the ill-fated 2015 campaign to legalize in the state that failed due to bad design.

In today’s political climate, politics has become sport: people are as fervent about their party winning as they are about their favorite teams scoring touchdowns. But people who vote for a certain party on their ticket will often vote for ballot initiatives that elected officials from their party would never support. The initiative puts policymaking in the hands of the people and Ohio could craft some meaningful policy in 2024 if they take this power seriously.

This commentary first appeared in the Ohio Capital Journal.

Scaling Programs: Trying to Find the Mountain in the Molehill

I am in the process of finishing up a cost-benefit analysis on the value of water quality programs in Ohio. The statewide policy alternatives I am considering are based on the H2Ohio program, which is currently focused in a handful of northwest Ohio counties. Today I wanted to briefly talk about the challenges that come with trying to scale up pilot programs and how policymakers should think about expanding successful programs.

First, let’s recognize why we have pilot programs in the first place. The goal of a pilot program is to test the effectiveness of a proposal before investing a large amount of resources should it fail for some reason. If the trial run is successful, then the next step is to scale up the program to serve a larger population. 

However, pilot programs are often specifically designed to succeed in a way that is harder to replicate on a larger scale. As the economist John List points out in an episode of the Freakonomics Radio podcast, “after researchers in the social sciences do an efficacy test, they forget to tell everyone else that it was an efficacy test.” 

This is not to say that it is wrong for pilot programs to try to achieve good results. It is hard to imagine a researcher getting funding for some intervention where implementation is shoddy and where they plan to target the people least likely to benefit. However, as programs grow it becomes more difficult to ensure implementation fidelity and proper targeting. 

Limiting participation in programs to only those who will receive the greatest benefits also might lead to the most efficient outcome, but it might not be the most equitable outcome. There is no right or wrong way to trade off equity and efficiency. Understanding that the two sometimes compete when resources are limited is the essence of good analysis and policymaking.

In the context of the H2Ohio program, one specific alternative I am considering is making the whole state eligible for the current program. Currently, the program is only open to counties in Northeast Ohio which have large concentrations of farmland and are part of the Lake Erie Basin. Because the rest of the state does not have the same contributions to Lake Erie algal blooms, this program likely won’t have exactly the same results in other counties beyond the current “pilot” counties. 

These insights about problems common to scale-up can inform the sensitivity analysis phase of our cost-benefit analyses. As analysts, we can project what might happen under different conditions, say if scale-up works swimmingly or if scale-up falls far short of the impacts of the pilot program. Exploring different scenarios, especially the ones where things are not quite as effective as they were in the pilot program, often lead to better insights and better decisions.

Four issues voters care about this election season

With elections coming tomorrow, reporters across the state are asking voters what issues matter to them. I saw a recent survey done by 10TV  that caught my eye. 

This survey was a quick, small survey that likely had a convenience sample, but it still gives us some perspective on what voters are talking about. Asking respondents to rank twenty issues, four rose to the top. Looking at it and comparing it to the conversations I have with friends and the news coverage I see, I can see why voters have these on their mind as they prepare to go to the polls this week.

Cost of Housing

As far as places to live in the United States, Ohio is relatively affordable. According to Zillow data, the typical home price in the state is about $210,000—about $100,000 less than the country as a whole. 

That being said, prices are still going up. Median sales prices are up 7.1% over the last year, meaning you’d have to pay about $15,000 more today for a house than you would a year ago. And these are changes that go beyond Ohio’s big cities—small cities like Lancaster, Harrison, and Zanesville are among the top 10 cities in Ohio with fastest growing sales prices, all experiencing price increases of over 30% in the past year.

Home buyers are getting a reprieve with prices starting to slow, but this is the same time the federal reserve is increasing interest rates, making it harder to buy a home. State- and local-level policymakers elected this week will have to decide whether they will promote affordable housing and construction of new housing or allow housing prices to continue to rise.

Abortion

In the wake of the U.S. Supreme Court’s decision to overturn Roe v. Wade, states across the country are restricting abortion in new ways. Governor DeWine signed a ban on abortions after six weeks of pregnancy, which is currently on hold by court order.

The evidence is clear that bans on abortion are much less effective at keeping people safe and reducing the need for abortion than other strategies like increasing access to contraceptives. Policymakers elected this week will decide whether to promote these tools or continue to use abortion bans as a political cudgel.

Cost of food

About one in ten Ohio households are food insecure and about one in twenty are very food insecure, both higher than the national average. Food insecurity is most prevalent among low-income households, single women with children, and black households.

Food insecurity often revolves around lack of access to income, but there are educational interventions that can help reduce food insecurity as well. Policymakers elected this week will have the opportunity to tackle food insecurity through interventions like low-income tax credits and extending the state SNAP-Ed program.

Cost of health care

According to the Health Policy Institute of Ohio, Ohio ranks 47th among the states and D.C. when it comes to health value. Adverse childhood experiences, disparities in health outcomes, and low public health infrastructure investment has led to these problems.

Policymakers elected this week will have opportunities to support programs that prevent health problems before they become expensive private and public liabilities.

These are only a few of the issues that Ohioans are focused on this election season. Let’s hope voters choose the right leaders who have the courage to take on issues like these rather than just talking about them before election day.

This commentary first appeared in the Ohio Capital Journal.

What should a policy analyst know about the normal distribution?

Last year, Scioto Analysis conducted a policy analysis to evaluate alternatives to reduce carbon emissions in the state of Ohio. In order to test our models, we conducted a Monte Carlo simulation, the “gold standard” of sensitivity analysis in cost-benefit analysis and a tool we often employ to see what range of possible outcomes.

Below are the Monte Carlo simulation results for a strong cap-and-trade program. For this Monte Carlo simulation, we ran hundreds of thousands of simulations of alternate policy scenarios, randomly generating different social cost of carbon estimates, discount rates, and price elasticities of demand for electricity. You can see the results of the Monte Carlo simulation below.

If you’ve taken a statistics class, you’re probably familiar with the shape of this distribution. It is one of the most important shapes in statistical analysis and one we end up using a lot when we’re modeling policy outcomes: the normal distribution.

The normal distribution, often called a bell curve because of its shape, is one of the most universally recognized statistical concepts. It is intuitive, broadly applicable, and useful in simplifying complex concepts. Here we will briefly discuss a few important characteristics of the normal distribution and why they are important. 

Parameterization

The parameters of a statistical distribution are the things you need to know to fully understand it. For example, if we consider some binary outcome like flipping a coin (called a Bernoulli distribution), the only parameter we need to know to fully understand the range of outcomes is the probability of an event happening.

The normal distribution is special because it has two parameters, its mean and its variance. We can always calculate the mean and variance of observed data, but the fact that these tell us everything we need to know about the distribution of unobserved data from the same distribution is extremely powerful. 

Symmetry and Outliers

Two other properties we will discuss together are the facts that the normal distribution is symmetric. This means that we should expect to observe values above and below the mean with the same likelihood, and that big outliers are extremely uncommon such that the we should only observe a value only four standard deviations above or below the mean is about 0.001% of the time. These two characteristics often shape how we think about applying the normal distribution to real data.

Consider the distribution of incomes in the US. We know that a few small outliers skew the distribution heavily to the right which makes fitting these data to the normal distribution difficult. If we just calculate the observed mean and variance of all individuals in the US, we would expect there to be extreme outliers in the negative direction as well.

The Central Limit Theorem

Arguably the most important concept in statistics, the central limit theorem is certainly the most useful application of the normal distribution. There is a lot of rigorous math that we will skip over here, but in short the central limit theorem tells us that if we repeatedly take random samples from a population, those sample means will be approximately normal. 

Going back to the income example, if instead of measuring using the mean and variance of all incomes to approximate a normal distribution we took 500 random samples (with replacement) and calculated the means of all of those, we would find that those sample means did in fact follow a normal distribution quite well. 

The normal distribution gives us a way to mathematically describe what we expect to happen with lots of unobserved data quite well. Understanding it at a surface level is valuable for policy analysts and policy makers since it so often works its way into our assumptions, whether we realize it or not.

Conversion therapy bans could prevent hundreds of youth suicides over the next decade

Last Monday, the Akron City Council voted to ban conversion therapy in the city, making it the eleventh city in Ohio to do so. 

The pseudoscientific practice referred to as “conversion therapy” encompasses counseling aimed at children focused on changing sexual orientation. The practice has been condemned by the American Medical Association, the American Counseling Association, the American Academy of Pediatrics, and the American Association for Marriage and Family Therapy.

The Akron ordinance cited a 2019 study by the UCLA Williams Institute that found LGBT+ youth exposed to conversion therapy were twice as likely to consider and attempt suicide than those who hadn’t. Conversion therapy made worldwide news when transgender Ohio teenager Leelah Alcorn posted her suicide note on the social media site Tumblr in 2014, explaining how conversion therapy led to her death.

I still find myself surprised this is a practice that occurs in Ohio today. I’ll admit, I sometimes even think “is this a real problem or are these sorts of ordinances empty gestures?” I have been saddened to find that this problem is indeed very real.

In a survey conducted as a part of Kent State University’s LGBTQ+ Greater Akron Community Needs Assessment, 30 of 701 respondents reported they had received conversion therapy at some point. According to the UCLA Williams Institute, there are 72,000 LGBTQ+ Ohioans age 13-17. 

If Ohio rates of conversion therapy reflect the responses in this survey, that means nearly 3,000 Ohio teenager have been exposed to conversion therapy. The Trevor Project’s 2022 National Survey on LGBTQ Youth Mental Health found 18% of LGBTQ teens made a suicide attempt in the past year. 

If teens exposed to conversion therapy have roughly double the suicide attempt rate of the general population, this means over 1,000 LGBTQ teens in Ohio who have been exposed to conversion therapy attempt suicide every year. If the death rate of suicide attempts reflects the national average, this means 43 LGBTQ teens in Ohio who are exposed to conversion therapy die of suicide every year.

If conversion therapy doubles the suicide attempt rate for youth, this means banning the practice statewide could save over 200 youth lives from suicide in Ohio over the next decade.

Bans on conversion therapy have been enacted so far in the cities of Akron, Athens, Kent, Cincinnati, Cleveland, Cleveland Heights, Columbus, Dayton, Lakewood, Reynoldsburg, and Toledo. The largest cities without bans right now are Parma, Canton, Lorain, Hamilton, and Youngstown, each with total populations over 60,000 people.

City-level bans only go so far, though. Bans can be ducked by people providing conversion therapy across jurisdictional lines only a short drive away, making patchwork municipal bans less effective than a statewide ban. 21 states and the District of Columbia have now enacted statewide bans on conversion therapy, mainly concentrated in the northeast and west. This includes moderate political states like New Hampshire, Pennsylvania, Utah, and Virginia. 

The only state with a conversion therapy ban in the Midwest currently is Illinois, though it is the region of the country most blanketed with city-level bans. If state lawmakers want to reduce teen suicides in Ohio, they have a strong option in front of them.

This commentary first appeared in the Ohio Capital Journal.