Does testing teachers help students?

In May of this year, I wrote a blog post talking about the unintended consequences of taking standardized test scores out of the college admission process. In that post, I highlighted a paper written by economists from the Federal Reserve Bank of Philadelphia.

Many people who advocate for colleges and universities to not look at test scores in their admission process point to the fact that test scores are not a perfect indicator of future academic success, and they could be biased in favor of students who have more resources for expensive practice materials and tutors. 
However, this paper found that in practice, test scores could reduce bias in the admission process. This is because when schools did not have access to the information about test scores, they instead relied on information that introduced even more bias such as whether or not a student has family members who attended the school. 

I thought about this paper this week when I was reading a new article in the Journal of Public Economics talking about the impact of using test scores to hire teachers in Colombia. This paper focuses on a new merit-based hiring system for teachers nationwide. The goal of the program was to increase the quality of teachers, which in turn should lead to better student achievement. 

Despite the well-intentioned goals of this policy,  the authors of the study found student performance actually declined in the wake of its implementation. According to the authors, students' test scores dropped by 8.2% of a standard deviation and both college enrollment and graduation rates decreased significantly after merit-based hiring was implemented.

This finding parallels the earlier debate about college admissions: test scores, while useful in some contexts, are not a perfect measure of quality. Just as test scores in college admissions do not fully capture a student's potential for success, teacher test scores do not completely  reflect their effectiveness in the classroom. The Colombian policy overemphasized one narrow measure—cognitive ability—at the expense of other important factors, such as teaching experience.

The decline in student test scores and matriculation outcomes can be partly explained by an influx of inexperienced teachers driven by merit-based hiring. The share of teachers with little to no experience in Colombia increased from 10% to 30%. The literature shows that teacher quality tends to be lower during the first five years of teaching, and this reform exacerbated that issue by bringing in a large number of new teachers.

This case study highlights an important lesson: teaching experience matters for student outcomes. While cognitive ability is important, it is only one component of what makes a teacher effective. Experienced teachers have had time to develop classroom management skills, learn how to adapt their teaching methods to different student needs, and build relationships with their students—all of which are critical to fostering a productive learning environment.

The study offers an important policy takeaway for education systems worldwide: teacher hiring criteria need to be multifaceted. Instead of relying on a single metric like test scores, hiring systems should consider a broader set of criteria, including both ex ante (before hiring) and ex post (after hiring) measures of teacher effectiveness. Experience, interpersonal skills, and classroom performance are equally valuable indicators that should inform not only hiring but also decisions about retention and promotion.

Just as in the case of college admissions, where removing test scores has been found to inadvertently increase bias by relying on less objective measures, focusing too narrowly on test scores in teacher hiring can also lead to unintended consequences. This suggests that policymakers should aim for a balanced approach that integrates multiple dimensions of teacher quality, combining cognitive abilities with real-world teaching experience and performance data.

Ultimately, the lesson from both college admissions and teacher hiring reforms is clear: while test scores provide valuable information, they are far from perfect. Effective policy design requires a comprehensive approach that considers all elements of a person's abilities, whether they are a student or a teacher. Only by doing so can we create systems that both promote fairness and improve outcomes.

New Report: Replacing Ohio’s lead pipes will improve public health and provide up to $185 billion in economic benefits

A new cost-benefit analysis reveals that replacing all of Ohio’s estimated 745,000 lead water service lines will result in fewer deaths, better physical and mental health outcomes, less water waste, and significant economic benefits for Ohioans and their communities. 

The study, commissioned by the Ohio Environmental Council (OEC) and completed by Scioto Analysis, demonstrates that for every dollar invested in lead service line removal in Ohio, the state will see a public health and economic benefit of $32 to $45. The complete replacement of lead pipes that carry water into Ohioans’ homes and buildings will grow the state’s economy between $145 and $185 billion over the next 15 years. 

Water service lines transport drinking water to Ohioans’ homes and businesses. Pipes that are made of lead release low levels of the toxin into drinking water that lead to chronic health issues for adults and children. According to the U.S. EPA drinking water can make up 20% or more of a person’s exposure to lead. 

“Getting water delivered to your home through a lead service line is like drinking your water through a lead straw. Lead in water can cause serious health problems, especially for children,” said Annalisa Rocca, Drinking Water Manager for the Ohio Environmental Council. “We need to get the lead out now.” 

This is the first study to quantify the health and economic benefits of lead service line replacement for Ohioans. Specifically, the report found that full replacement of lead services over the next 15 years will lead to: 

• 650 fewer infant deaths, leading to a monetized benefit of $4.4 billion in reduced risk of death over the 15-year time period.

• Preventing the loss of an average of 1.25 IQ points each for over 290,000 children in Ohio over the next 15 years, leading to a total benefit of $8.4 billion in future earnings over the same time period.

• 9,700 lives saved from heart disease over the first 15 years of its implementation, generating a total of $66 billion in reduced risk of death over the 15-year time period.

• 3,800 fewer cases of depression, resulting in a benefit of over $290 million in reduced medical costs and productivity cost savings over the same time period.

• 7,300 fewer cases of anemia over the next 15 years, leading to a benefit to Ohio of $22 million in decreased morbidity and mortality costs.

• 2,400 cases of coronary heart disease would be avoided, a benefit of over $52 million over the 15-year time period.

• 520 fewer cases of dementia across the state, resulting in a benefit of over $13 million in reduced caregiver, family disruption, and medical costs over the 15-year time period.

• 150 fewer cases of ADHD in Ohio children, resulting in a benefit of $1.5 million in reduced medical, caregiver, and family disruption costs over the 15-year time period.

Additionally, there are significant economic and environmental benefits to updating an aging infrastructure system. Because all lead service lines are 38 years old or older, they no longer work as efficiently as possible when transporting water to Ohioans’ homes. Replacing lead lines will reduce water waste and save Ohioans an estimated $82 billion over the next 15 years. 

Unfortunately, Ohio ranks as one of the top states in the country for lead service lines. As many as 8.1% of lead service lines in the country are located in Ohio, while only 3.6% of the U.S. population is in Ohio — meaning this infrastructure issue has an outsized impact on our health and economy. 

There are multiple efforts underway to eliminate lead water lines that carry drinking water into our homes and buildings across Ohio. At the federal level, the Biden-Harris Administration has invested a historic $15 billion through federal programs like the Bipartisan Infrastructure Law with an estimated $735 million coming to Ohio for lead line replacement through 2026. At the state level, the DeWine Administration has invested $4.5 million in lead line mapping and replacement through the H2Ohio program.

Recently, Rep. Dontavius Jarrells introduced House Bill 534, Ohio‘s Lead Line Replacement Act,  which would require all public water systems to fully replace lead service lines within 15 years. The legislation includes other key provisions to advance water affordability and workforce development and to support water utilities in meeting the new requirements. 

“We knew that replacing lead service lines is critical for the health of Ohioans, especially Ohio’s kids, but this study shines a light on the tremendous economic benefits of doing so,” said Rocca. “The faster utilities and customers replace lead service lines, the sooner Ohioans will realize the health and economic benefits.” 

Funding for Replacing Ohio’s Lead Lines: A Cost-Benefit Analysis, was generously provided by the Environmental Policy Innovation Center. For more information about lead service line replacement in Ohio, please visit OEC’s blog or check out this video.

How will AI impact the economy?

Recently, I’ve been incorporating generative artificial intelligence into my day-to-day work pretty often. Specifically, I like to ask ChatGPT to help me write Excel functions. I’m not super familiar with all the syntax of Excel, so this can save me a lot of time when I’m trying to work on an extremely large dataset. 

As a busy policy analyst who finds excel functions tedious to write sometimes, this is an amazing way to increase my efficiency. I can outsource some of the things I am inefficient at and spend more time on more valuable things. 

Of course, there’s a catch. Writing excel functions may be a small part of my job, but it might be most of the work for another person’s job. That person might not be so thrilled that their extremely valuable skill can now largely be automated. 

Computers are better than humans at a lot of things. Often we assume that computers only excel at things with certain outcomes like mathematical calculations, but are incapable of performing creative tasks. 

I’d argue that this is not actually true, and that computers have actually been able to perform extremely creative tasks for decades now. For example, Stockfish and Google’s AlphaGo have been playing Chess and Go respectively at levels that no humans can match for quite some time. Chess and Go are far too computationally complex for these algorithms to “solve” the games, and some would argue that they find unique and creative ways to approach their games. 

Instead of computers being incapable of creativity, I’d argue that computers have lacked flexibility. The algorithms that play complex strategy games are immensely powerful and creative, but they could never be used for anything other than the games they were designed for. 

What makes the new era of AI models so fascinating is that they are extremely flexible in what they can do. Writing excel functions used to be difficult for a computer because it is context dependent. Now AI models can take a description written by a human and output working code for nearly any problem. 

As we begin to think about how AI might fundamentally change the labor force as it continues to develop, we need to understand from a theoretical perspective how the labor force is currently constructed. 

A macroeconomic model for a labor force with AI is presented in a new paper by Anton Korinek from the University of Virginia. Currently, economists describe our economic output using a simple production function

Y = A · F(K, L)

In this function, Y is the total output, A represents the level of technology, K stands for capital and L stands for labor. Essentially, our economic output is a function of capital and labor that is scaled by our level of technology. In a world with extremely sophisticated AI models, Korinek suggests that we be exposed to a new production function:

Y = A · F(K, L+M)

Here, M stands for machines, which represents AI and robots that can replace labor. Korinek assumes that at some point of technological advancement, machines will serve as a perfect substitute for human labor. 

Whether or not AI can actually become a perfect substitute for human labor is still an open question. In particular, I wonder if AI will be able to replicate the social dynamics that are extremely important in some jobs like teaching. Regardless, policymakers are going to have to grapple with a new reality where labor can in many cases be easily replaced.

While the efficiency gains from AI can lead to increased productivity, the reduction in employment is going to require a change in the way our economy works. Currently, most people get some slice of the total output of our economy as a reward for contributing to its creation, often in the form of wages for labor. 

An AI-driven economy will theoretically be able to generate far more output, but we need to come up with a new way of distributing that assuming that most people won’t be directly contributing to its creation in the same way. Finding a way to do this that is equitable and efficient is going to be an essential challenge of an AI-driven economy. 

How can we make child care work for kids?

Before I started working at Scioto Analysis, I worked for a think tank here in Columbus, Ohio. My new policy area focus in this position was on child care, a topic I did not know much about at the time.

To dive into this topic, I did what I usually do when I am trying to learn about a new public policy area. I went onto Amazon and I searched the phrase “economics of child care” so I could find a book to get me started.

Economics is a key tool in the public policy world. Earlier this week, Planet Money Newsletter published an article about the waxing and waning of the influence of economists on public policy in D.C. It followed the skepticism of mainstream economics in the middle of the 20th century to the rise of economics in policymaking with the appointment of Paul Volcker as chair of the federal reserve to quell runaway inflation in the 70s. It then followed through the rise of the Council of Economic Advisers until President Donald Trump “demoted” the chair of the Council by not putting its chair on his cabinet. Today, both Harris and Trump are supporting policies like exempting tips from taxes that most economists do not think either promotes economic efficiency or supports equity goals.

Despite some moves against the use of economics in policymaking at the federal level, economics is still one of our best tools for finding the answers to key questions about public policy. Economics is especially good at helping answer the following questions: does a policy work, does it grow the economy, and how does it impact different types of people?

Picking up a book about the economics of child care, I found answers to these questions. The book I read, David Blau’s The Child Care Problem: An Economic Analysis helped expose the problems with child care markets.

Child care markets are heterogeneous and hard to pin down. They are a mixture of people substituting informal markets (care for children at home or by volunteer family members) for less formal markets (paid home care in the neighborhood) or more formalized markets (free-standing child care centers). Each of these options have a range of costs and a range of quality as well, giving different experiences to children who are cared for.

And the experience for the child is something that really matters here. One thing Blau’s book opened my eyes to is that child care is the other side of the coin for another key public policy issue: early childhood education. While with child care we are often focused on trying to find some place to occupy a child while the parent can go work, the quality of this occupation matters since young children are at the point in their life where differences in care can have substantial impacts on their development.

The folly of focusing on child care to the detriment of early childhood development and education was demonstrated with the Quebec child care experiment in the late 90s. The province of Quebec made child care available for all residents for $6 a day. Subsequent evaluation of the impacts of the policy found Quebec’s universal child care program found children who took part in the program had worse health, lower life satisfaction, and higher crime rates later in life than those before the program took place.

The explanation researchers have given for this trend is that the universal child care program provided for universal child care availability but not universal child care quality. Without quality controls in place, children ended up in low-quality care that led to negative non-cognitive outcomes down the road.

In The Child Care Problem: An Economic Analysis, Blau argues there is a market failure in the child care market. If children were fully rational and in control of their own decisions about where to go for child care, they would likely, on average, choose child care that was more expensive and less convenient than their parents choose for them.

The natural answer to this problem is to subsidize child care that is higher-quality relative to child care that is lower-quality. By making high-quality child care cheaper, it will encourage parents to put their children in higher-quality care, closing the gap between the social cost and the private cost of this child care.

This entire argument, however, hinges on a crucial claim: that high-quality child care improves lifetime outcomes for children. Our strongest evidence that this is the case comes from two studies conducted in the 1960s and 1970s: the Perry Preschool Project and the Abecedarian Project. Both of these were randomized controlled trials that placed children in high-quality early childhood programs and followed them and the control group for decades afterwards to see what the impacts of the program were to life outcomes.

While many positive outcomes have been recorded from these studies, they have also come under some criticism. The studies were small, with just a few hundred participants between the two studies. They also focused on a certain subgroup of the population, in particular African-American children in specific parts of the United States (specific communities in Michigan and North Carolina). Additionally, they occurred in the 60s and 70s, under the specific social and economic conditions of that era and the decades to follow. All of these considerations cast doubt on whether the results of these studies can be extrapolated to the current day. 

On top of these limitations, skeptics also point to a negative result from studies of early childhood: fadeout. While the children enrolled in the program had better outcomes in kindergarten, these results were found to fade out in the subsequent years until their evaluations were the same as their peers later in elementary school.

A working paper released this month and co-authored by Nobel Prize Winner James Heckman responds to many of these criticisms. In this paper, Heckman and his co-authors argue that criticisms are too narrowly focused on specific methodology. They argue that IQ fadeout does not occur in the Perry Preschool program and that long-term outcomes like improved educational attainment, higher earnings, reduced crime rates, and better health outcomes are demonstrated by Perry Preschool. All of these lead to a high benefit-cost ratio for the program.

They argue that the lesson of Perry Preschool is not “design a program exactly like Perry Preschool and you will see children succeed.” It is “involve parents and adults in children’s lives and you will see children succeed.” And that’s ultimately what we see in Blau’s work, in Quebec, and in the evidence we have on early childhood development: parental involvement is what sets children up for success later in life.

Would raising the tipped minimum wage close racial and gender pay gaps?

One of the most surprising topics of policy agreement in the current presidential election cycle is proposals by the Harris and Trump campaigns to exempt tips that workers receive from income taxes. 

We recently asked our Ohio Economic Experts Panel about this question, and the consensus among economists was that while this will likely help a subset of high earning tipped workers (e.g. servers in high-end restaurants), it likely won’t be an effective anti-poverty policy since many tipped workers do not end up paying very much income tax. 

An alternative proposal to exempting tips from income tax could be to instead make it so the minimum wage for tipped workers is the same as the overall minimum wage. Currently, employees who “customarily and regularly” make more than $30 per month in tips only need to be paid a small portion of the minimum wage by their employer, so long as the tips they receive get their total earnings to at least the minimum wage amount. The employer needs to make up the difference if that person doesn’t receive enough tips in a given pay period. Essentially, customers directly pay the salary for these tipped workers.

But would this change help the people it’s supposed to? A new paper from David Neumark and Emma Whol suggests that increasing the tipped minimum wage may not have the desired effect on reducing wage disparities or boosting earnings. Specifically, this paper focuses on the effects of increasing the tipped minimum wage for women and for racial minorities in the restaurant industry.

Not all restaurant workers are affected equally by changes in the tipped minimum wage. Data shows that Black and Hispanic workers are more likely to be employed in non-tipped positions within the restaurant industry, such as kitchen staff, where they do not benefit directly from policies aimed at tipped workers. As a result, raising the tipped minimum wage might not significantly improve earnings for these groups, which limits its effectiveness as a tool for addressing racial wage gaps in the industry.

Increasing the tipped minimum wage has been shown to actually widen the hourly pay gap between minority and White workers in some cases. While it does reduce the gender pay gap in hourly wages, it doesn’t necessarily translate into increased weekly earnings for women due to potential reductions in working hours. This suggests that while the policy could help in reducing gender disparities to some extent, it doesn’t provide a comprehensive solution for all workers in the restaurant industry.

In contrast, raising the regular minimum wage appears to have a more significant impact on reducing wage disparities and improving overall earnings for both minority and female workers in the restaurant industry. Regular minimum wage increases help non-tipped workers, where many minority employees are concentrated, leading to higher hourly and weekly earnings. However, this comes with a caveat: while increasing relative earnings, raising the regular minimum wage can also reduce employment opportunities. Policymakers need to balance the benefits of higher wages with the potential risks of job losses.

Given these nuances, only raising the tipped minimum wage may not be the best approach to reducing economic disparities in the restaurant industry. A more effective strategy could involve focusing on raising the regular minimum wage and implementing additional measures to protect employment levels. For instance, policies that provide targeted support for training and career advancement opportunities for minority and female workers in the industry could help address some of the root causes of wage disparities.

How can we end hunger in America?

Last week, I attended the Columbus Metropolitan Club’s forum on ending hunger in central Ohio.

I will admit: often when I go to public forums on these sorts of topics, I can be disheartened. Ohio is an individualistic state, which means that much of the public discourse revolves around interest groups advocating for why they should “get theirs” when it comes to public programs.

What impressed me so much about the conversation at this forum was how much the panelists were focused on concrete, root-cause solutions to hunger.

Matt Habash, President and CEO of the Mid-Ohio Food Collective, has been a passionate advocate for public involvement in hunger issues for years. He has talked about the massive number of missed meals by people in central Ohio and how food banks will never have the resources to combat this on their own. This leads naturally to saying the public sector needs to be involved.

This led Habash to talk about important public programs like the Supplemental Nutrition Assistance Program (SNAP), formerly known as “food stamps,” free school breakfast and lunch programs, and the child tax credit.

Even more surprising to me, though, were the words from Dr. Mysheika Roberts, who has served as Health Commissioner for the City of Columbus’s Public Health Department for the past seven years. When asked what can be done to reduce hunger, she had three words that received applause from the audience: “universal basic income.”

I was surprised not only at the boldness of her opinion, but also at the reception from the audience. A proposal that seemed like a pipe dream five years ago has sprung into the forefront of conversations about poverty even in the public discourse social circles of a fairly moderate city.

But I still had questions. SNAP, free lunch, child tax credits: these were all policies that could only be addressed at the state level and above. What can local leaders do to address hunger?

I posed this question to the panel. Dr. Roberts’s answer? “Universal. Basic. Income.”

Habash tacked on his agreement, saying it was income issues that were driving hunger in central Ohio.

Amartya Sen famously argued that a famine has never occurred in a functioning democracy. His explanation for this observation is that famines are not technological problems, they are social problems. Yes, there are food shortages in democracies, but in a government that is built to respond to the needs of its citizens, democracies find ways to get food to people who need them. In autocracies, this does not happen.

In a way, Habash and Roberts are making a similar argument. That it is lack of resources that drives hunger, and that by solving the resource problem, we can solve the hunger problem.

Habash argues this is partly because food budgets are actually more elastic than other budgets like rent or transportation. If you don’t pay rent, you will lose your home. If you don’t pay for gas, you won’t get to work. If you skip a meal or two? Well, you kick the consequences down the road. Resources-constrained families will do what they need to do in this case.

So what is our shortfall? Feeding America estimates there are 44 million food insecure people in the United States. They estimate that the nationwide food budget shortfall (at a per-meal cost of $3.99 based on spending by food-secure households as reported in the Current Population Survey conducted by the Census Bureau) is $33 billion.

$33 billion is not a small amount of money. But it’s also not a completely ridiculous amount of money to spend compared to what we already spend on hunger. The USDA estimates the federal government spent a little over $110 billion on SNAP benefits in 2023. So this would represent a 29% increase in SNAP spending if that was how the federal government decided to bridge that gap.

How could we find $33 billion? Well, let’s go to the Committee for a Responsible Federal Budget’s Debt Fixer tool. This is a fun tool that can be used to create your own federal budget using policy options estimated by the Congressional Budget Office. So lo and behold: here are 33 options that could be used to create a balanced-budget (or revenue-positive) end to hunger in the United States according to Debt Fixer.

1. Limit annual defense spending growth to 1% (average savings of $51 billion per year)

2. Limit annual nondefense spending growth to 1% (average savings of $41 billion per year)

3. Eliminate farm subsidies (average savings of $39 billion per year)

4. Replace Obamacare with state grants (average savings of $80 billion per year)

5. Increase Medicare premiums for all beneficiaries (average savings of $76 billion per year)

6. Reduce Medicare advantage costs (average savings of $53 billion per year)

7. Reform Medicare provider payments (average savings of $36 billion per year)

8. Allow private plans to compete with Medicare (average savings of $36 billion per year)

9. Eliminate Medicaid provider taxes (average savings of $59 billion per year)

10. Block grant Medicaid and grow per-person spending with medical inflation (average savings of $60 billion per year)

11. Set social security benefits to a flat amount (average savings of $42 billion per year)

12. Raise the payroll tax cap to cover 90% of earnings (average savings of $89 billion per year)

13. Subject earnings greater than $250,000 to the payroll tax (average savings of $161 billion per year)

14. Raise payroll tax rate by 1% (average savings of $68 billion per year)

15. Limit highway spending to current revenue (average savings of $46 billion per year)

16. Rescind Inflation Reduction Act climate tax credits (average savings of $71 billion per year)

17. Devolve K-12 education spending to the states (average savings of $81 billion per year)

18. Repeal the Tax Cut and Jobs Act of 2017 (average savings of $52 billion per year)

19. Increase taxes on capital gains and dividends (average savings of $34 billion per year)

20. Enact a 25% ultra-millionaire tax on unearned income (average savings of $58 billion per year)

21. Impose ultra-millionaire wealth tax (average savings of $308 billion per year)

22. Eliminate the mortgage income deduction (average savings of $39 billion per year)

23. Limit the charitable deduction (average savings of $43 billion per year)

24. Eliminate the state and local tax deduction (average savings of $151 billion per year)

25. Institute a cap on the health insurance tax exclusion (average savings of $72 billion per year)

26. Increase corporate tax rate to 25% (average savings of $67 billion per year)

27. Increase corporate tax rate to (average savings of $117 billion per year)

28. Enact a financial transactions tax (average savings of $112 billion per year)

29. Enact a carbon tax (average savings of $91 billion per year)

30. Enact a value-added tax (average savings of $259 billion per year)

31. Restore estate tax to 2009 levels (average savings of $37 billion per year)

32. Increase taxes on foreign-earned business income (average savings of $63 billion per year)

33. Cap the pass-through business deduction for high earners (average savings of $33 billion per year)

Maybe you don’t agree with all of these policy changes. But do you agree with one of them? Do you agree one of them could be worth ending hunger in the United States? If anything, this list tells me something. We have no excuse to say that hunger is an inevitability in the United States.

Ohio economists optimistic about Ohio minimum wage increase

In a survey released this morning, 10 out of 19 economists disagreed that a $15 minimum wage would result in a large increase in unemployment in Ohio. Kevin Egan from the University of Toledo wrote, "There is lots of evidence now from prior minimum wage increases that the impact on employment is small." 

One economist who believed that this minimum wage increase would lead to a spike in unemployment was Curtis Reynolds from Kent State. He wrote in his comment “This would be almost 50% higher than the current minimum wage of $10.45.  While I would love to see people being paid a higher wage, this almost certainly causes unemployment, at least in the long run.  Perhaps that could be offset by phasing it in slowly over time but this is a very large increase.  Smaller increases likely would have small unemployment effects and would be justified as research has shown that labor markets are not very competitive (meaning that wages are held below what they would be in a competitive market).”

Additionally, 11 of 19 economists agreed that increasing the minimum wage to $15 per hour would significantly increase the wellbeing of low-income workers. As Faria Huq from Lake Erie College wrote, “Some groups might be hurt due to unemployment, but those that are employed are likely to see an improvement in well being. Increased productivity would also benefit employers.”

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. 

How can Ohio support preventative care?

From a health care perspective, I am a fairly young and healthy member of the system. My standard interaction with doctors is an annual visit in January where I find out I’m a little vitamin D deficient.

I am surprised, however, that year after year I end up with a bill for hundreds of dollars from my health care provider, Mount Carmel. Despite the Affordable Care Act’s requirement that all health insurance plans in the United States cover a preventative care visit each year, I still end up with “extra” fees tacked on to my visit.

“New patient processing.” Fees for blood work. All these seem to be an essential part of preventative care, but the health care system claims they are additional services past preventative care and my insurance companies consistently agree with them.

In a poll released last month by the American Cancer Society, a majority of Ohioans say a candidate’s position on access to affordable, comprehensive health coverage is very important for their decisions to vote.

You would think focusing on preventative and primary care would be a priority for a country struggling with keeping health care costs low. Preventative and primary care is cheaper than dealing with complications of diseases down the road. And in a state like Ohio where residents get one of the worst “bang for their buck” health care outcomes for how much they spend, you would think it would be particularly important.

While a lot of health care policy is decided at the federal level, states also have significant latitude in deciding health policy. Medicaid spending, which makes up a significant portion of state spending, gives the state a significant lever for influencing health care markets in the state.

A number of states have been using their authority to influence the provision of primary care for residents. In 2022, the Oklahoma State Legislature enacted legislation to require Medicaid managed care companies to spend at least 11% of their medical expenditures on primary care. This mandate creates an incentive for companies to shift spending from expensive treatment to more affordable prevention.

Other states have taken exploratory first steps to reform beyond the Medicaid sphere. In 2022, Nebraska enacted legislation to create a council to study the rate of primary care spending in the state and make recommendations about an appropriate level of spending in the state and steps toward achieving this goal.

Other states are working with private research firms to study ways to improve primary care for residents. In 2022, Virginia enacted legislation to contract with a private research firm to study the financing, quality, and delivery of primary care to Virginians.

Other states are paving the way for better primary and preventative care for their residents, which will lead to both better outcomes and lower costs for patients. While I don’t know the relative effectiveness of these policies, I do know one thing. I can keep coming back for preventative care visits year after year and spending on bills I shouldn’t have to pay. But for someone at the federal poverty level, the bill I got hit with is half a paycheck. She has a lot more reason to skip next year’s appointment. And next year’s appointment might be the important one.

This commentary first appeared in the Ohio Capital Journal.

Rent supports or eviction moratorium: which works better?

Last month, I wrote a blog post about how a strong safety net and temporary assistance can provide strong returns on investment to an economy. One of the examples I brought up was the COVID-19 eviction moratorium, which essentially acted as temporary rent assistance for low-income people. 

Evictions are a major issue in the housing market, especially in low-income neighborhoods, where they can lead to social and economic instability. There is a lot of research on the effects of evictions, but there has been very little policy analysis done on potential solutions. A new working paper aims to address this gap by creating an economic model of evictions to explore what the impacts of these potential policies might be. 

One of the key findings of the study is that evictions are inherently suboptimal from a social welfare perspective. In the competitive equilibrium, evictions occur when landlords, facing the risk of non-payment, decide to terminate a rental agreement rather than negotiate a lower rent. However, a benevolent social planner—who is focused on maximizing overall welfare—would not evict tenants who could potentially continue to contribute positively to the rental market. The social planner would allow these tenants to remain housed, recognizing that evictions result in significant negative externalities that impact not only the evicted families but also the broader community.

The study highlights how the current market dynamics lead to lower housing quality and supply for renters facing high eviction risk. This is because landlords anticipate lower future profits from units with high eviction probabilities and respond by reducing investment in these properties. The resulting lower-quality housing further exacerbates inequality and reduces overall welfare.

To address these inefficiencies, the researchers evaluate two types of policies: eviction restrictions and rent support. While eviction restrictions can reduce the number of evictions, they also lead to unintended consequences. For instance, severe restrictions can discourage landlords from investing in housing quality or even from providing rental units in the first place, leading to a reduction in overall housing supply. 

On the other hand, rent support—where the government provides financial assistance to cover the rent of unemployed tenants—emerges as a more effective policy. Unlike eviction restrictions, rent support directly addresses the issue of non-payment without discouraging landlords from participating in the rental market or reducing their investment in property quality. The model shows that rent support can potentially eliminate evictions and even increase the supply of housing, as it provides landlords with guaranteed income during periods of tenant unemployment. Additionally, because rent support raises overall housing quality, it can have positive spillover effects on renters who are not directly at risk of eviction but who benefit from a more stable and well-maintained rental market.

The researchers also demonstrate that, in times of crisis, such as during the COVID-19 pandemic, temporary eviction moratoriums can raise welfare by preventing a sharp rise in evictions when job-finding rates are low and unemployment is high. However, even in such situations, the introduction of rent support alongside eviction moratoriums can better ensure that both housing stability and supply are maintained.

The model presented in this research provides compelling evidence that rent assistance is a more effective public policy than eviction moratoriums in both normal economic conditions and times of crisis. Rent support not only prevents the immediate social costs associated with evictions but also promotes a healthier and more equitable rental market by maintaining housing quality and supply. Policymakers aiming to reduce evictions and enhance overall welfare should prioritize policies that provide direct rent assistance to those facing temporary financial hardship, ensuring both tenant stability and a robust housing market.

What are “wraparound services?”

One program that I have heard coming up in the social services sector over the past few years is a suite of interventions called “wraparound services.”

The basic concept of "wraparound services" is to provide seemingly unrelated services to someone who needs help with a specific issue. For example, providing employment and housing assistance to someone who seeks out behavioral health services.

Wraparound services can be a range of different services including case management, counseling, crisis management, education, family support, psychiatric care, health care, legal services, recreational therapy, long-term care, group therapy, and transportation.

When I first heard about “wraparound services,” I was admittedly skeptical about the approach. The kind of wide-eyed excitement they aroused in people I heard talking about them smacked of a snake-oil intervention. All in all, they felt like a firehose approach to social problems: if we throw enough interventions at someone, something is going to stick and we are going to help them improve their lot in life.

I also was skeptical because I hadn’t heard of any evaluation of these interventions. It seems like a lot of the faith in the “wraparound approach” was built on faith in the current social services system. The idea is that we have the right programs in place to help people, we just need to get the right people connected with the right programs and they will see improvement. I hadn’t seen any evidence that this is indeed the problem with our social system and I was skeptical of that logic model. 

I needed to see more evidence.

A working paper published by the National Bureau of Economic Research this month finally gives some credence to the claims of wraparound programs. Researchers from the Rochester Institute of Technology and the University of Notre Dame conducted a randomized controlled trial of a wraparound program.

Participants were randomly assigned to intensive wraparound services provided flexibly and intensively over a number of years. Participants designed their own goals and then were supported by these programs.

The topline finding of the researchers was that participants in the program were employed at rates ten percentage points higher than control group members after a year of receiving services and that the effects persisted past the conclusion of the program. The researchers did not find evidence that wraparound services helped participants achieve other goals besides employment, though, even when participants had identified non-employment goals as their primary goals.

What does this tell us about the program? The first thing I take away from this study is that wraparound programs are effective on one dimension. A ten percentage-point increase in labor force participation rate is a big deal. For context, if the state of Delaware, which is 39th in the country in labor force participation rate, had a 10 percentage point increase in labor force participation, it would tie North Dakota for the highest labor force participation rate in the country.

The second thing I take from this is that the impact of wraparound programs is limited. This is not a panacea of a program that is able to solve every aspect of social life for those who are struggling. It will help them improve their chances of employment, though.

This finding does lead to a clear question: why wraparound at all at this point? If wraparound programs are just a good jobs program, why provide all the other services? Why don’t we cut our costs by funding all these superfluous programs and focus on what is actually helping people?

One of the findings from the researchers helps explain a problem with this approach. In their research, they found some evidence that participation in the wraparound program increased hopefulness and agency among participants. The researchers hypothesize that it is the hopefulness and agency that caused the increase in employment. So some of the wraparound services, in particular counseling, might be what is driving the employment effect.

I am happy to see there is some evidence that wraparound services are working. I also think these programs need to be evaluated and ineffective pieces of them need to be trimmed back. The goal of social services is to make sure that people get the support they need to thrive, and resources spent on programs that don’t do that are resources that are withheld from programs that do. More research will help us further cut through the noise of a program like this.