What's the matter with ALICE?

A few years ago, I first heard about a research project conducted by United Way affiliates called ALICE.

ALICE stands for Asset-Limited, Income-Constrained, and Employed. The goal of the ALICE project is to estimate the number of people in every community who fit into this category. Why does this category matter? Because people with low levels of assets, limited income, and employment still often cannot afford what we call “necessities.”

How do we know if someone lives in an ALICE household? Well, it’s very similar to how we determine if someone is below the federal poverty level: researchers set a threshold based on some conception of what qualifies as sufficient income. If a household has income below that threshold, they are an ALICE household. If their income is above that threshold, they are not.

The way ALICE researchers set the threshold is by adding up the local cost of a range of goods. This list comprises housing, child care, food, transportation, health care, technology, taxes, and other miscellaneous goods. This differs from the methodology of the federal poverty threshold, which uses the cost of food times three as its threshold. It also departs from the supplementary poverty threshold methodology, more popular among poverty researchers, which uses average spending amounts to anchor its threshold.

According to the 2022 ALICE report, 54 million of the United States’s 129 million households fell below the ALICE threshold. This means more than three times as many households are ALICE households than there are households in poverty.

Some have claimed that the ALICE framework should be a replacement for the federal poverty level. This means that policymakers should shift their focus from the 12% of households struggling the most to the 42% of households who do not have income that meets the ALICE threshold.

Policymakers have been making this shift for decades now. At last month’s Association for Public Policy Analysis and Management Fall Research Conference, a number of sessions focused on the changing nature of the federal social safety net. One of the biggest takeaways I took from these sessions is (1) that the social safety net is larger now than it was a few decades ago, and (2) that the social safety net has shifted from supporting people at 0-50% of the federal poverty line to supporting people at 100-150% of the federal poverty line.

A big reason for this is welfare reform in the 1990s. In 1993, a household claiming Assistance for Families with Dependent Children (AFDC, colloquially known as “welfare”) could bring their total income over the federal poverty level with wage income at 0% of the federal poverty level.

Let the implications of that sink in: before welfare reform, the United States had guaranteed income for households with children.

That changed with welfare reform. With Assistance for Families with Dependent Children transformed into Temporary Assistance for Needy Families (TANF), the program was block granted and significantly reduced. What took its place was the Earned Income Tax Credit, a program that primarily gave cash to households with children with working parents.

The growth in the social safety net since then has been documented by Dr. Katherine Michelmore of the University of Michigan, who recently won the David N. Kershaw Award for this work. The growth in support for families has been on the backs of expansions of the Earned Income Tax Credit and Child Tax Credit in the decades since welfare reform.

This has led to a change in overall support for families. Overall, families are receiving more support today than they did in the early 90s. But this support has shifted from families at the lowest level of the income distribution to families who are at or a little bit above the federal poverty level.

This is because Assistance for Families with Dependent Children did not require a household to work to receive support. If a household had children, they needed support, so Assistance for Families with Dependent Children gave it to them. Today’s social safety net does not work that way. Both the Earned Income Tax Credit and the Child Tax Credit require wage income to claim. This means our current social safety net currently supports low-income people who are near or above the federal poverty level, not people who are in deep poverty.

Despite the current structure of our social safety net, the fixation in safety net policy is still on people who are low-income but not below the federal poverty level. Policymakers’ attention to the “benefits cliff” is a great example of this. In Ohio, eligibility for food assistance was recently expanded significantly. Who was it expanded to? You guessed it–people at 130-200% of the federal poverty level. This likely means hundreds of millions of dollars of new annual support for low-income families, but none for families below the federal poverty level.

Helping low-income people should be no crime from a social welfare perspective. What worries me is who is left out of that conversation. ALICE purports to widen the scope of our safety net by demonstrating how 42% of families are struggling rather than only the 12% of families who are below the federal poverty level. But it sets its ALICE threshold for a family of four in Franklin County, Ohio with two parents, a preschool-age child, and an infant at over $91,000. The federal poverty level for that same family is about $28,000. Can we seriously say that a family of four making $90,000 should be lumped into the same category as a family of four making $27,000? Or that a policy that helps the family making $90,000 is just as socially beneficial as a policy that helps the family making $27,000?

This isn’t to say that ALICE is a bad framework: it helps us learn something, especially about the cost of goods that many families need to survive. But it also lumps a wide swath of the population together that are dealing with varying problems and can make us lose sight of those who are struggling most.

Great 20th Century Political Philosopher John Rawls put forth his famous “difference principle” in his 1971 classic A Theory of Justice. He argued that social and economic inequalities “are to be to the greatest benefit of the least-advantaged members of society.”

Helping people who are struggling is good. Supporting people who are dealing with problems like benefits cliffs is socially beneficial, but losing sight of people who are struggling the most is a huge problem in our current safety net. Our fixation on the population of low-income people above the federal poverty line has come at the expense of people in deep poverty. The longer we continue to follow this path, the more people who are the least-well-off will continue to suffer.

Does paid family leave pay off for society?

Earlier this week, I wrote about the differences between academics and policy analysts and how policy analysis needs to be more comprehensive than research to be relevant to policymaking decisions. I used the example of the papers I heard presented at the APPAM Fall Research Conference about paid family leave and how those were narrowly focused on labor market outcomes. I may have left that session wanting to hear more about the whole range of benefits and costs associated with paid family leave, but there are people out there who are doing the kind of work I find most interesting. 

In February of this year, Prenatal-to-3 Policy Impact Center released a cost-benefit analysis of a paid family leave proposal in Pennsylvania. They looked at all the different ways this program could impact people’s wellbeing, and condensed it into an incredibly readable report. Here are some of the key takeaways they found: 

Positive Net Benefits

The Pennsylvania proposal would offer a 20-week paid family leave program funded by a 1% payroll premium split between employers and employees. These researchers found that it would generate an estimated $379 million in annual net benefits. Over a lifetime, the net benefits per birth year reach $1.7 billion. When compared to the costs of this program, this represents a benefit-cost ratio of $18 of benefits for every $1 of costs. 

Positive Economic Impact

The program is projected to increase maternal employment, boost earnings for families with infants, and reduce infant care costs. Additionally, businesses would benefit from decreased job turnover, increased productivity, and more female leadership. This reinforces the idea that paid family leave is beneficial not just for families but for the broader economy as well.

Public Health Benefits

The analysis links paid leave to improved food security, reduced infant hospitalizations and mortality, increased breastfeeding rates, better postpartum health for mothers, and a decrease in severe child maltreatment. These public health benefits underscore the policy's potential to promote public health and reduce long-term healthcare costs.

Reduced Societal Costs

The program is expected to lead to significant cost savings in healthcare, special education, criminal justice, and subsidized child care. By supporting families during critical periods, paid family leave can help mitigate long-term societal costs. We’ve seen in other contexts how this kind of short-term assistance can help bridge the gap for some people and enable them to achieve better long-term economic stability.

—

Any policy that can return hundreds of million dollars in annual net benefits is worth considering for policymakers. This is not to say that paid family leave is a silver bullet that will solve all our problems by any means, but it does seem to make things better for a lot of people. 

Some people may still be negatively affected by a paid family leave program. I’ve written before about how different firms feel the costs and benefits of these programs differently. Policymakers who are excited about the topline benefit number should also be thinking about how they can use these social dividends to help support the people and businesses that might be individually worse off as a result of this policy. 

This type of comprehensive policy analysis provides the most useful information to policymakers. Doing this type of analysis, however,  would be impossible without the contributions of academic research. Analysts need academics to create this rich literature so we can understand all the impacts a policy might have, and policymakers need analysts to wade through all that research and pull out these comprehensive insights to guide their decisionmaking process.

Good policy analysis reveals the big picture

Last month, I attended the Association of Public Policy Analysis and Management’s annual conference. This was a meeting of people from seemingly every academic discipline whose research was at all related to public policy. More than many other conferences, APPAM is a great opportunity to learn about new developments in some of the fields that impact our work as policy analysts.

The first session I attended was focused on the impacts of paid family leave programs. Paid family leave is the policy that allows new parents to take paid time off work when they give birth to or adopt a child. Usually people who take paid leave do not earn 100% of their paycheck while on leave, but it still puts more money into the pockets of people to take time off work to bond with their children.

All of the presenters asked slightly different questions about paid leave policies, but there was one conclusion that was consistent across all three papers: paid family leave policies do not do a good job of keeping new mothers attached to the labor force. 

Viewed through the narrow lens of labor economics, it seemed as though paid family leave was an ineffective policy. These papers touched on some ancillary impacts, but these presentations were largely focused on the labor market. 

This is one of the most important differences between academic research and policy analysis: academics can focus on getting the most accurate results possible on a single aspect of a policy, while analysts need to synthesise all the information they can find about a particular policy to look at it holistically. 

In the case of paid family leave, we as analysts should not just be interested in the labor market impacts, but in every way this could change peoples lives. I would have loved to hear more discussion about the poverty impacts of paid family leave, or more about how paid family leave impacted the firms that had to respond to this new policy.

Another important consideration is how this might lead to intergenerational effects. With any policy that impacts children, especially newborns, we need to consider not just what the effects are today but what sort of investment we are making in the long run. A robust paid family leave policy could potentially contribute to better developmental outcomes for children, improved health for both parents and children, higher wages in the future, and other positive impacts that come from a small boost delivered early on. 

Additionally, policy analysts need to keep our research focused on policies that could reasonably exist. For example, it isn’t feasible for policy makers to implement a paid family leave program that would pay individuals 300% of their salary for a full year. It’s obviously not sustainable, so policy analysts shouldn’t waste time exploring it. It might be an interesting question for an academic to approach though, just to see if there are any interesting insights we can glean from such an outlandish policy idea.

Policy analysis is inherently interdisciplinary. To see the whole picture on certain issues, analysts need to account for information from multiple perspectives. I enjoyed all the papers I heard about paid family leave at this conference and I feel much better prepared to account for the labor market impacts of paid family leave. Still, my analyst brain was left wanting a much broader discussion about all of the potential impacts.

Ohio economists: mass deportations could undermine economy

In a survey released this morning by Scioto Analysis, 14 of 20 Ohio economists surveyed thought that mass deportations of undocumented immigrants would significantly reduce Ohio’s gross domestic product. President-Elect Donald Trump has stated that he intends to carry out mass deportations of undocumented immigrants across the country soon after taking office. If this policy is carried out, local police will be responsible for detaining and holding immigrants in conjunction with state and federal law enforcement agencies. 

Rachel Wilson from Wittenberg University agreed that Ohio’s economy would shrink, writing “Not only will it be reduced by immigrants production but because of their missing demand. Immigrants do not come and work in a vacuum. They spend the money they make creating additional demand for goods and [services]. They often have a high marginal propensity to consume from each dollar they earn.”

Of the remaining six economists, three were uncertain about the impact on gross state product and three disagreed deportations would significantly reduce gross state product.

“The share of undocumented workers is small in Ohio,” said Bob Gitter of Ohio Wesleyan University, “Deportations would not have a significant impact.”

In addition to significantly reducing Ohio’s economy, 15 out of 20 economists disagreed that mass deportations would ease the burden on Ohio’s safety net. Some policymakers argue that undocumented immigrants take advantage of publicly funded social services while not paying taxes to support those services, but economists in the survey disagreed.

Kathryn Wilson from Kent State says “Estimates are that undocumented immigrants pay more than $250 million in taxes within the state of Ohio per year. There would be some reduction in education expenses within Ohio if there were mass deportation, but most social safety net programs are not available to undocumented immigrants. I expect that the loss in tax revenue would more than offset the reduction in costs within Ohio.”

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.

3 recommendations to improve federal policy analysis

Earlier this month, the Office of Management and Budget released its draft report to Congress on the many cost-benefit analyses the federal government performed during fiscal year 2023. The report covers a lot of topics, touching on major regulations federal agencies analyzed over the past year and their impacts at the federal and state levels. 

Later on in the report, there is a chapter that includes recommendations to reform the regulatory process. Below are the three recommendations that we at Scioto Analysis are paying the most attention to in the coming years. 

A Government-Wide Approach to Improve Benefit-Cost Analysis

Currently, government agencies do much of their analytical work independently. However, as our methods for identifying impacts improve, we are seeing that regulations in one agency might impact outcomes in another. Because of this, the authors of this report suggest that federal regulators begin to take a more collaborative approach to analyzing federal regulators. By working across agencies, analysts can overcome common analytical challenges, share valuable insights, and enhance the overall quality of regulatory decisions.

Quantifying the Social Cost of Greenhouse Gases Using the Best Available Evidence

In the realm of environmental policy, understanding the true cost of greenhouse gas emissions is crucial. The report advises that agencies should rely on the most up-to-date scientific evidence when estimating the social cost of greenhouse gas emissions. This recommendation is timely, considering the rapid advancements in climate science and the evolving understanding of how these emissions impact the planet.

The 2023 memorandum from the Interagency Working Group on Social Cost of Greenhouse Gases serves as a guiding document, encouraging agencies to exercise professional judgment in selecting the most appropriate estimates for their analyses. By grounding regulatory decisions in the best available evidence, we can ensure that policies are both scientifically sound and effective in mitigating the adverse effects of climate change.

We’ve seen with the first Trump administration and the Biden administration how quickly measures like the social cost of carbon can change. We might expect another dramatic change in this value come January. Hopefully agencies can cut through the politics and rely on the best evidence with these measures.

Distributional Analysis Under Incomplete Information

Public policy often involves navigating tradeoffs, particularly when it comes to balancing distributional equity with maximizing net benefits. At Scioto Analysis, we talk about the equity-efficiency tradeoff every time we do an analysis. In such cases, agencies are encouraged to consider statutory provisions, public input, and their own historical practices in making these decisions.

Consistency is key. Maintaining a standardized approach in how distributional concerns are addressed can lead to fairer outcomes. Additionally, the report suggests exploring compensatory measures, such as grants or spending programs, to mitigate undesirable distributional effects. This approach not only promotes equity but also ensures that the benefits of regulation are broadly shared across society.

—

These recommendations reflect some of the most important challenges facing policy analysts going forward. Understanding how to collaborate with experts from other fields, using the best available evidence, and developing solid empirical methods to perform distributional analysis are key topics that are going to drive this discipline forward. These conversations are happening at the federal level, and hopefully we can apply these same techniques to state and local policies as well.

Stop using acronyms!

How do you feel when you are reading a story you find very interesting, only to hit a snag? It’s a word you don’t know. No, it’s not a new word. It’s a jumble of letters that certainly means something to someone, but not to you. You only know one thing about this jumble of letters: they stand for a phrase that would mean a lot more if it was just written in full.

Yes, you have come across an acronym: the scourge of comprehensible writing everywhere. In a place where an author could have given you a phrase, instead she gave you a vocabulary exercise.

Some technical writing takes the acronym to the extreme. I have seen government or think tank reports that include an entire glossary at the beginning that can be referenced by readers who are not fluent in their particular dialect of Bureaucratese. The author then wipes her hands and continues to write with any range of esoteric acronyms, knowing that the reader can “simply” scroll to the top of the document and locate the abbreviation she doesn’t understand before scrolling back down to the portion of the text she is reading.

The best practice of acronymizing within a report is to write out the full word when using it for the first time, then adding a parenthetical that includes the acronym you intend to use throughout the rest of the document. Theoretically, this allows the reader to internalize the acronym and then recognize that usage throughout the rest of the report.

There are two problems with this approach.

First, if you have to include a parenthetical to explain what an acronym refers to, you are effectively introducing a reader to jargon. If a reader does not fully process the acronym the first time, she will find herself confused as she continues to read and will either have to turn back to locate the first use of the acronym to see what it is referring to or will plow through reading, hoping the acronym isn’t important. If multiple acronyms are used, this gets even worse.

Second, if a reader only reads part of a report, book, or essay, she will not have the context of the first use of the acronym. This is likely to happen in lots of technical writing. If someone is consuming a study, report, or any other type of nonfiction, she often does not have the time to read every word of it and is best served by picking out the parts of it that are most useful to her. If she skimmed over the first establishment of an acronym, later uses of it will just lead to more confusion.

But we do like acronyms. Some of them have infiltrated our language to the point that they have become words in their own right. Some acronyms are commonly used or actually are more clear than the full phrase. For instance, wouldn’t you rather read “DNA” than “deoxyribonucleic acid?”

So what is a writer to do? At Scioto Analysis, we conduct technical writing for a general audience and we write in a field, public policy, that looooves acronyms. So how do we handle these stumbling blocks of clear writing? Below are some tips for guidance for using acronyms in a way that is productive and facilitates the primary goal of writing: clear communication.

1. Know your audience

The very most important consideration when using an acronym is audience. If you are writing primarily for an audience that is very familiar with how the tax code impacts people in poverty, you might be good to use the phrase “EITC” in place of “earned income tax credit.” If you think a minority of writers might not be familiar with the phrase, it is probably better to spell it out.

Remember that writing is not the same exercise as speaking. Sure, you may say “EITC” exclusively when you are referring to the earned income tax credit, which is okay when people you are speaking to can clarify the meaning of the term. When writing, the reader cannot ask for that type of clarification from you. So unless you want that reader to do extra work to understand the meaning of what you are writing, spelling out the phrase will make it more clear than using an acronym.

2. Understand the acronym

Another important consideration is which specific acronym you want to use. An acronym like “CDC” or “EPA” are commonly used in public policy and many people outside of the public policy world understand what they mean. Using them is probably less frustrating for a reader than an acronym like the “OECA,” which stands for the Office of Enforcement and Compliance Assurance, or even worse, the “AOC” when it stands for “Architect of the Capitol,” a federal agency in charge of stewarding public landmarks on Capitol Hill.

Probably the worst offense of all is coining your own acronym. Forcing a reader to learn new vocabulary that did not exist until they read your paper is a capital offense for an analyst who is trying to make concepts more understandable for the readers, not less. 

3. Avoid the acronym whenever possible

It is okay to repeat phrases. I repeat: it is okay to repeat phrases. If you say “Department of Transportation" throughout your report instead of “DOT,” even many people who are employed by the Department are not likely to be offended by hearing its name. Most people will not think twice hearing a phrase that is important to your report, analysis, or research over and over again: that is what they expect when they read something on that topic. What people will be offended by is if they have to read a phrase that they do not understand and flip (or more commonly “scroll”) from page to page to figure out what you are talking about.

People in government love to hate acronyms. People outside of government love to hate acronyms in government. Give these people one less thing to hate. Stop using acronyms.

What would “mass deportations” do to Ohio’s economy?

The dust has settled on the 2024 presidential election and we now know that Donald Trump will once again be President of the United States.

Trump has promised many things for his second term in office: deregulation, tax cuts, an end to Russia’s war with Ukraine, tariffs on all goods from other countries. The step he could take that could have the most immediate impact on both human rights and Ohio’s economy, however, would be on immigration.

Trump has promised to conduct mass deportations of unauthorized migrants, rounding up immigrants in workplaces, schools, homes, and places of worship to send them back to their countries of origin. Local law enforcement will be a key player in determining how “mass deportations” will be carried out in the state of Ohio. 

Municipal police departments, county sheriffs offices, and the state highway patrol will have to decide how much to defer their work from policing violent crimes and property crimes to carry out federal immigration policy. What decisions local law enforcement make around prioritization could have a significant impact on Ohio’s economy.

Earlier this week, Ohio Capital Journal Reporter Marty Schladen wrote about the important role immigrants play in Ohio’s economy. Immigrants in Ohio are taxpayers, consumers, business owners, doctors, software developers, professors, cooks, health care workers, and college students.

An analysis done by researchers at the American Enterprise Institute, Brookings Institution, and Niskanen Center released before the election shines some light on what the new administration’s immigration policy could do to immigration. Trump’s immigration plan is estimated to reduce both authorized and unauthorized immigration, increase removals from the interior, increase adjudication of current cases leading to more removals, and encourage others to leave on their own. 

These researchers estimate this would mean as many as 740,000 fewer immigrants in the United States in the first year of Trump’s presidency. Weighted for Ohio’s foreign-born population as reported in the American Community Survey, that could mean as many as 9,700 fewer immigrants in Ohio in about a year. 

The AEI/Brookings/Niskanen study reports this massive reduction in the number of immigrants in the United States would cost the country 0.1 to 0.4 percentage points in GDP in 2025. In Ohio, weighted for Ohio’s foreign-born population, that would mean somewhere between $330 million and $1.3 billion in lost gross state product. 

For comparison, the Ohio Department of Development estimates 21 counties in Ohio have a gross domestic product of $1.3 billion or less. So if these policies are carried out as planned, Ohio could lose a small county’s worth of its economy in fewer consumers, business owners, and workers. On a per capita basis, this means a cost of $28 to $110 per person in the state. So you can consider this a head tax of $28 to $110 per person to pay for having fewer immigrants living in this state.

Just because something shrinks the economy doesn’t mean it is bad. We might decide it appropriate to institute policies that trade off economic growth for reductions in poverty and inequality, improvements in environmental quality, or more time for people to spend with their children or elderly parents. But what exactly are we buying for this immigration crackdown? After all the national conversation on this topic, I still don’t have an answer to this question.

This commentary first appeared in the Ohio Capital Journal.

Columbus Foundation releases 2024 Benchmarking Central Ohio report

Yesterday, The Columbus Foundation released a new community research report, Benchmarking Central Ohio 2024. Commissioned by The Columbus Foundation and developed in partnership with the Columbus-based firm Scioto Analysis, this year’s report examines five key areas: population vitality, economic strength, personal prosperity, lifelong learning, and community well-being. Within these five key areas are dozens of indicators, ranging from housing affordability and small business ownership to pre-K enrollment and air quality. The 2024 report is the latest in a series of community benchmarking reports commissioned by The Columbus Foundation, starting in 2007.

 “This report, now in its eighth edition, shows our commitment to providing information about and for our community. It is essential to regularly examine the region’s health, economic competitiveness, and quality of life so that we as a community can respond to pressing challenges and emerging opportunities,” said Doug Kridler, President and CEO of The Columbus Foundation.

Similar to an annual physical—during which a physician examines a patient’s overall health by gathering health information and monitoring changes over time—the Benchmarking Central Ohio reports track and assess how the Columbus region is doing across indicators over time and how the region compares to other metropolitan areas in the United States. The 2024 study compares the Columbus metropolitan region to 22 other metropolitan areas, including peer communities of similar size and geography and high-performing communities.

The Columbus Foundation has long invested in community research to help enhance community knowledge across a range of important issues, such as mental healthcare, digital equity and inclusion, youth services, aging populations, and homelessness. This research serves to educate the community, inform regional priorities, identify local assets and pain points, and help local nonprofits determine where to focus their programming.

“Research is an extremely valuable resource that, when used strategically, can help us better understand our community’s strengths, weaknesses, inequities, and possibilities,” said Matt Martin, Director of Community Research at The Columbus Foundation. “By creating a shared understanding of our community’s challenges and opportunities, research can help galvanize impactful actions, policies, and investments that ultimately improve people’s lives.”

The Benchmarking Central Ohio 2024 report shows that the Columbus metropolitan region ranks towards the top across several indicators, including preschool enrollment, women in corporate leadership, and rates of volunteerism and library usage. However, the report also raises areas of concern, such as high poverty rates and public health challenges like infant mortality and overdose deaths. Viewed holistically, the data in the report reveal a multifaceted landscape of the community’s strengths, challenges, and potential.

“The power that comes from having all of this data compiled in one place goes beyond seeing how Columbus performs on individual metrics,” said Rob Moore, the study’s principal author. “The most valuable insights come from how these metrics interact with each other. For example, how can we leverage Columbus’ strong workforce and relatively low cost of living to address its challenges with poverty and health?”

What are marriage bonuses and penalties?

In July of 2023, my cousin got married. This was an exciting time for my family because she was the first of our generation to get married. But she didn’t legally get married in July–that’s just when she had the ceremony. As far as the state of Missouri is concerned, she had been legally married since April.

You may have been able to guess this based on when she chose to get married, but my cousin and her husband wanted to file their taxes jointly in 2023. She was just finishing up her time in med school, so they were living almost exclusively off of her husband’s salary at the time. This meant that by filing jointly, they ended up paying less income tax.

This phenomenon is often referred to as the marriage bonus, and it comes from the fact that in the eyes of the government, marriage is an important financial contract. Because married couples share all of their resources, they get to add their income together when filing taxes and are treated as a single unit. A married couple where both partners earn $50,000 a year would pay the same amount of taxes as one where a single earner makes $100,000 a year. 

The main reason this can lead to such a big decrease in taxable income is because we have progressive income tax brackets. Married couples essentially double the size of those income brackets and end up paying less as a result.

On the other hand, some couples can incur a marriage penalty if combining their incomes makes them ineligible for certain tax credits. Take the Earned Income Tax Credit for example. Below is a table from the IRS showing the income eligibility thresholds:

Under the wrong circumstances, married couples can lose thousands of dollars in tax credits by filing their taxes together. As the number of children increases, the threshold for joint filers to receive the Earned Income Tax Credit becomes relatively closer to the threshold for single filers. This means that families with more children face even more severe marriage penalties than those with fewer children. 

Some couples can also see marriage penalties if both partners are high earners and have similar incomes. The 2017 Tax Cuts and Jobs Act lessened this penalty for middle- and high-income families, though in some cases they could lower their tax liability by filing separately. 

A 2018 analysis from the Tax Policy Center found that 43% of married couples receive a marriage bonus, and 43% of married couples receive a marriage penalty. The average bonus was $3,062 compared to if those couples filed separately, and the average penalty was $2,064.

Last year at the Association for Public Policy Analysis and Management annual conference, I listened to a roundtable discussion about tax policy. In one of the opening remarks, a presenter talked about how tax policy is often viewed as an accounting problem, with little attention paid to how it shapes the way we live.

Marriage bonuses and penalties reward couples that are able to live off the salary of one individual. This implicitly punishes couples who rely on two incomes to get by. While this is technically an avoidable problem (couples could file their taxes separately), it’s extremely difficult to know whether or not that would actually lead to savings for some people. It definitely will lead to more work and confusion that comes with filing taxes in the first place. 

This quirk of the tax code is another example of how interconnected all aspects of public policy really are. A seemingly innocuous decision like allowing married couples to file their taxes together can reward people for tying the knot–or punish them for it.

Are state politics dead in Ohio?

In 2018, I conducted an analysis of state House races. In this analysis, I found that Hillary Clinton’s 2016 election results explained 90 percent of the variation in state House races and 98 percent of the variation in state Senate races.

This week, we got new evidence about why local elections might be dead in Ohio. In 2022, Republican lawmakers voted to make state Supreme Court races partisan. While Democrats have won state Supreme Court seats in nonpartisan elections, the three candidates this year with “R” next to their name won with 55.7 percent, 55.2 percent, and 55.1 percent of the vote, nearly identical to Donald Trump’s 55.2% of the statewide vote.

Sherrod Brown, who had won his first election to Senate against then-Senator Mike DeWine by 12 percentage points in 2006, reelection in 2012 by six percentage points, and a third time in 2018 by about seven points, finally lost to Trump-backed challenger Bernie Moreno by about four points. Moreno ran about five points behind Trump, but the “R” next to his name was certainly crucial.

The function of a federal system is to offer different services at different levels of government. At the federal level, national defense and basic human rights are ensured. At the state level, provision of health care and education are provided. At the local level, public safety and infrastructure is provisioned. Voters are given power to elect people at different levels of government who will ensure they get the services they need at each level of government.

This approach breaks down if state and local government is just seen as an extension of national politics. The past two speakers of the Ohio House of Representatives have left office after investigations by the FBI for corruption. One is now serving a 20-year prison sentence after being convicted in the largest racketeering case in Ohio history. What has that cost the party in power? Zero statewide offices and maybe a few state legislative offices that would have changed hands due to redistricting anyway. They still hold a supermajority in both houses. But the nationalization of politics has made the “R” next to their names more important than the actions of their members.

When I lived in Nebraska a decade ago, I saw a state government that had strong institutions built on nonpartisan elections. State legislators were elected without party affiliations on the ballot. There was no party caucusing in the capitol building: the speaker and all committee chairs were elected by a secret ballot. Even though only 17 registered Democrats were in a House of 49 members, nine of them held committee chairs the first year I was there. This was because people were elected on the merits of their trust from other members, not their partisan affiliation.

I don’t know what the cure is for Ohio’s state politics becoming subsumed by national politics. But I do think that politicians making all elections more partisan is not helping. Ballot boards, redistricting commissions, courts: what do any of these roles have to do with partisan politics? The answer is nothing. Nothing, that is, except trying to fool and manipulate the public into believing that state government doesn’t matter.

This commentary first appeared in the Ohio Capital Journal.