How does raising the minimum wage impact small businesses?

Over the past year, we’ve been doing a lot of research on the impacts of raising the minimum wage in Oklahoma. We’ve looked at the impacts on housing, public safety, health, and the labor market.

Throughout all of these reports, we’ve tried to balance the increased wages people would be exposed to with the economic rationale that imposing a binding price floor in the labor market would theoretically lead to some reductions in employment. Overall, the literature suggests that while employment may go down in the face of a minimum wage increase, the effect often isn’t large enough to offset the higher wages.

Our minimum wage model analyzes the impact of a minimum wage on the state as a whole and tries to measure the average effects of wage changes across the state. However, individual firms will respond to this policy change differently from one another. Opponents of raising the minimum often point to the idea that minimum wages are particularly burdensome for small businesses that often have very tight budgets and operate in less stable markets. 

This idea is challenged by a new paper published in the Quarterly Journal of Economics by Nirupama Rao and Max Risch. 

These researchers constructed a new panel data set linking business, their owners, and their employees with their tax returns. This allows them to carefully track business and employee changes at the firm level across states. They use their new dataset to conduct a difference-in-differences analysis to see how state minimum wage changes impact independent firms. 

The first outcome they find is that minimum wage increases do not lead to independent firms laying off employees. Instead, they find a modest reduction in future hiring, particularly among teenagers. Their research suggests firms don’t have to lay off their employees because they’re able to increase revenue by enough to fully offset the additional costs.

While minimum wage increases don’t seem to have significant impacts on existing firms, they do create distortions in the labor market in other ways. In states that raised their minimum wage, industries with high shares of impacted workers saw fewer new firms enter the market in the following years. The effect is small, new firm entry decreases by about 2%, but still this may lead to less competitiveness.

The silver lining of fewer firms entering is that those that do choose to enter are more productive on average. Past research has found that when states raise their minimum wage, workers become more productive. 

In fact, there is an overall trend in markets with higher minimum wages that tends to favor more productive firms. The end result is that despite lower firm entry, industries in states with higher minimum wages tend to have the same aggregate revenue as those in states that don’t raise their wages. 

In other words, minimum wage increases do not have any negative impacts on overall economic activity. There is a shift that excludes some less productive firms in favor of more productive firms, but this nets out to a null effect. 

This new evidence shows that minimum wage increases tend to reshape markets rather than weaken them. Existing firms largely maintain employment by raising revenue and benefiting from more productive workers, while new firm entry declines slightly as less productive businesses are filtered out. Overall economic activity is not significantly impacted since the firms that stick around tend to be more productive and cover the difference that new firms would make.

What would it cost to end child poverty in the United States?

In 2021, a quiet revolution in child poverty policy took place in the United States.

A provision in the American Rescue Plan expanded the federal child tax credit by increasing the maximum amount of the credit and making all families, even those with small amounts of income, to claim the credit. This led to over $100 billion being funneled to low-income households with children and millions of children lifted out of poverty.

The expanded child tax credit died in late 2021 when Senator Joe Manchin declined to continue the program, publicly worrying about the cost of the program and privately worrying poor families would use the money from it to pay for “drugs.”

Reducing child poverty is a priority for policymakers for a number of reasons. While many still find ways to blame adults for their poverty, they often have a harder time justifying poverty for children. On top of this, investing in children has broader economic benefits. A study in Social Work Research estimated that child poverty costs the United States $1 trillion a year (about 5% of gross domestic product) in economic productivity, increased health and crime costs, and increased costs as a result of child homelessness and maltreatment.

Reducing child poverty has been a priority for developed countries across the world over the past decade. Canada’s 2016 expansion of its Child Benefit cash program has been credited with lifting nearly half a million Canadian children from poverty. Poland’s “Family 500+” cash transfer program has led to substantial reductions in child poverty.

What would it look like if the United States tried to end child poverty? How much would such a program cost?

The 2019 Roadmap to Reduce Child Poverty produced by a consensus team from the National Academies of Science, Engineering, and Medicine had strategies ranging from an $8.7 billion package of work supports that would reduce child poverty by 19% to a $110 billion universal supports package that would cut child poverty in half.

But what would it cost to end child poverty altogether?

According to responses to the American Community Survey, 11 million children in the United States were living in households with incomes below the federal poverty level in 2024. Eight million of these children were age five to 17 and three million were below age five.

Let’s say we paid out a poverty threshold income to each household in poverty based on the number of children in the household. This would cost about $16,000 per child per year (about $1,300 per month), totaling about $180 billion per year.

Strangely enough, this number is only $70 billion more than the National Academies’s proposal to cut child poverty in half. This is surprising–reducing the first case of child poverty is usually cheaper than reducing the next case of child poverty, which is usually cheaper than reducing the next one. We see this in the National Academies proposals, where the cheaper work-focused proposal reduces child poverty at about half a billion dollars per percentage point of reduction while the universal supports approach reduces poverty at about $2.2 billion per percentage point reduction.

The generous $1,300 monthly child allowance would reduce child poverty at $1.8 billion per percentage point, more cost effective than the universal supports approach. This is because the universal supports package is made up of earned income tax credit reforms, child care subsidies, a more modest child allowance of $225 per month, a higher minimum wage, and expansion of social safety net provisions to more legal immigrants, all policy interventions that are less targeted than a cash transfer to households with children with income below the federal poverty level.

While $180 billion sounds like a lot of money, there are policy options for providing a benefit like this while keeping the federal budget revenue-neutral.

According to the Committee for a Responsible Federal Budget, replacing the payroll tax with an employee compensation tax that covers all employee compensation including health insurance and stock options would raise $330 billion per year, more than financing such a program. Raising the payroll tax rate by 1% would raise $172 billion per year, nearly enough to cover the entire cost of the benefit.

Repealing the One Big Beautiful Bill tax cuts would raise $640 billion per year, paying for this benefit and a whole lot more along with it. Even just repealing the One Big Beautiful Bill Act tax cuts for income over $400,000 would raise about $250 billion per year, enough to fund the benefit and more.

There are other tax changes that could finance a revenue-neutral or a deficit-reducing child allowance to end child poverty. One would be enacting a 2-3% tax on wealth, which would raise about $350 billion per year. Another option would be to enact a 5% value added tax on goods, excluding goods such as groceries, education, health care, new housing, and financial services. This would raise about $330 billion per year, more than enough to pay for this generous child benefit.

In reality, the minimum dollars needed to end child poverty would be lower than this since most households already have income. My colleague Michael Hartnett used American Community Survey data to estimate that paying each household enough to get over the federal poverty line would cost about $168 billion. If it would cost $168 billion to end overall poverty, then by definition child poverty could be abolished at that cost or lower. Using that data and just looking at households with children, my colleague Michael Hartnett has estimated that ending child poverty would cost about $135 billion. This means that the interventions detailed above or some that are even cheaper than them could end child poverty and be revenue neutral.

There are likely other options to end child poverty, but these calculations show it is possible and within the realms of policy options at the disposal of the federal government. While it is an option, it is ultimately up to Congress and the President to decide if this is a priority.

How much would it cost to provide tuition-free public college?

According to a Gallup study from last year on the state of higher education, better employment and earning opportunities are still one of the main reasons people pursue education past high school. Unfortunately, the cost of attending college is a barrier too steep to overcome for many people. According to that same study, the cost of a degree was the reason most commonly cited for people to drop out of programs. 

Even among those who do go to college, over 48% took out federal student loans. This debt delays the income related benefits many graduates expect to receive as they spend their early professional years just getting back to even. 

All of this can be summed up by saying college is very expensive right now, and in many cases it is prohibitively expensive. If we accept that having more people attend college is good for our overall economic wellbeing, then we might even say that college is too expensive, and that the public sector should intervene.* 

How much would it cost the federal government to make public colleges and universities free to attend? 

Before we can answer that question, we first need to determine what it even means to make college free to attend. If just the tuition is free, that means students are still on the hook for textbooks, living expenses, etc. 

To see how some of these proposals might look in practice, we can follow a 2020 study by Georgetown University’s Center on Education and the Workforce looking at some of the free college plans proposed by the Biden campaign. They analyzed three proposals, the first two were tuition-free programs while the third was a complete debt-free program. 

Tuition free programs still leave students with debt after college, but fully cover the costs of the program while they are attending. One option would be a “first-dollar” tuition free program, where the government fully covers the cost of tuition at each school, and students are free to take other forms of financial aid to cover expenses like room and board. 

A less generous version of this is a “last-dollar” tuition free program, where students receive whatever financial aid they would otherwise be eligible for, and the government covers whatever tuition remains left over. 

Last-dollar programs naturally cost less than first-dollar programs, but they end up giving less resources to people who need them more. If someone is eligible for more financial aid because they have less money to begin with, then they end up with less free money from the government. 

The most generous option would be a complete debt-free program. This would mean the government covers the full cost of tuition, as well as any other school related expenses such as books or lodging. 

According to that study from 2020, these programs would cost $27.8 billion, $58.2 billion, and $75 billion in the first year for the last-dollar, first-dollar, and debt free programs respectively. 

If we adjust those for inflation, that would be $35 billion, $73 billion, and $92 billion. 

If we compare these costs to other parts of the federal government, they are roughly similar to the budgets for the Department of Transportation ($38 billion) and the department of agriculture ($70 billion).

No matter which specific program the government went with, a free college program would cost a lot of money upfront. However, the good thing about getting more people to go to college is that in the long run we expect these people to have higher wages. That means greater tax revenue for the federal government over time, to the point where these costs would at least be partially offset. The Georgetown Study estimates that by year 10 of President Biden’s proposal, the additional tax revenue would fully offset the costs of the program. 

The real benefit comes from realizing that people pay taxes for far longer than they attend college. By getting more people in the door, we can build up a larger tax base to cover each following year of college students. 

Certainly any program with a price tag this big requires careful thought from policymakers. However, if we decide we can handle the short term costs, the long term benefits are clear. 

*Obviously student loans are one such intervention, but they don’t lower the cost of a degree, just defer it.

Original Analysis: Is Franklin County, Ohio Still Affordable?

This morning, Scioto Analysis released an analysis of the cost of living in Franklin County, Ohio. This county price parity analysis compared Franklin County to sixteen comparison counties across the Midwest, South, and West regions of the United States. 

The analysis answered whether Franklin County is still affordable in the wake of post-pandemic inflation across the United States. We find that Franklin County is distinctly more affordable than its peers, ranking third in affordability, trailing only Cleveland’s Cuyahoga County and Cincinnati’s Hamilton County by a slim margin.

Inflation outpaced wage growth in Franklin County along with the rest of the United States between 2019 and 2023. Despite trends of inflation, Franklin County continues to be relatively affordable, especially for essential goods like health care, groceries, and housing. 

Franklin County has a relatively high tax burden in terms of income tax, sales tax, and property tax with the fifth highest tax rate in all three of these categories compared to the sixteen comparison counties. However, after adjusting for differences in cost-of-living between these counties, Franklin County ranks fourth in adjusted post-tax income for low- and high-income households and fifth for middle-income households. The relative affordability in Franklin County despite a high tax burden is primarily driven by an overall progressive tax structure and the low cost of goods and services.

Across the entire nation, low-income households are burdened disproportionately more by property taxes than high-income households. Franklin County uses property taxes to fund mandated human service programs, presenting a challenge for policymakers in Franklin County to balance tax policy with social program requirements.

Ultimately the key drivers of affordability across our seventeen counties are region and housing. The top five most affordable counties are exclusively located in the Midwest, while the South and West comprise the seven least affordable counties. Housing accounts for an average of 18% of total household spend across our seventeen counties. In Franklin County, the average monthly cost of housing is around $1,100, which is relatively more affordable than its peers. This is partially due to a steady growth of housing supply in Franklin County, compared to counties like Austin’s Travis County where average housing costs soar alongside a volatile housing supply.

While no county is immune to rising prices in the status quo, Franklin County has proven to be particularly resilient during a time of rising unaffordability. 

The full report can be accessed here.

Could a higher minimum wage be an answer to Ohio’s affordability challenges?

Last month, the state of Michigan’s minimum wage increased to $13.73 per hour, $2.73 higher than Ohio’s minimum wage of $11 an hour.

Ohio’s minimum wage grows every year due to a constitutional amendment passed by Ohio voters in November 2006 that tied the state minimum wage to the inflation rate.

Despite being lower than Michigan’s minimum wage, Ohio’s minimum wage is higher than West Virginia’s, which is currently $8.75, and Indiana, Kentucky, and Pennsylvania, which are each set at the federal minimum wage of $7.25

Minimum wage increases have benefits that go beyond worker income.

Economic security from higher wages lead to lower suicide rates, lower rates of firearm homicide, and lower infant mortality rates.

In a 2024 cost-benefit analysis Scioto Analysis conducted on a $15 minimum wage for Ohio, we estimated a minimum wage increase of that magnitude would save about 4,000 lives in its first ten years from fewer suicides, homicides, and infant deaths.

What should the minimum wage be in Ohio?

For about a decade now, the “Fight for $15” movement has championed a $15 minimum wage nationwide. But $15 when the movement began in 2012 would equal over $21 today.

Is $15 too low now?

University of Massachusetts Amherst Economist Arindrajit Dube proposes state and local governments tie minimum wages to median wages, making subnational minimum wages equal to half the median area wage.

This has some reason to it since half the median income is a common international benchmark for poverty in developed countries and is used by the Organisation for Economic Co-operation and Development as its poverty benchmark.

Statewide, that benchmark would put the minimum wage at $10.83, half the median annual wage of about $45,000, suggesting the current minimum wage could be a good benchmark for the state if labor markets are competitive.

But for the Columbus Metropolitan Statistical Area, that number is closer to $50,000, leading to a median wage of about $12.

For my home town of Bexley, the median wage is closer to $73,000, meaning half the hourly wage would be closer to $17.50.

There can be problems with making minimum wages too localized.

Having a $17.50 minimum wage in Bexley next to a $10.30 minimum wage in Columbus and its median wage of $43,000 could cause some strange incentives for businesses to locate on the other side of city lines to skirt wage laws.

But if I am looking at Dube’s guidance and Ohio’s current policy, a moderate minimum wage reform would empower local governments to increase their local minimum wages as they see fit.

This would allow communities to bring their minimum wages closer in line with local conditions.

One limitation of the Dube approach is that sometimes wages are kept artificially low due to market power by employers.

In oligopsonistic labor markets where a small number of employers employ most of the workers, employers can keep wages lower than the market would set them as if it was competitive.

In these cases, minimum wages can bring the prevailing wage closer in line with the market-clearing wage, both increasing wages for workers and increasing employment by encouraging workers to enter the labor market who were staying out of it due to low wages and tradeoffs inherent with working such as transportation costs, child care costs, and loss of time to care for children.

All in all, minimum wages can be a tool for meeting affordability challenges since empirical research on minimum wages finds a $100 increase in minimum wages only leads to a $4 increase in grocery prices.

They also can support public health and reduce poverty and inequality.

In this era of affordability challenges, it might be time for Ohio to revisit its minimum wage.

This commentary first appeared in the Ohio Capital Journal.

What drives population growth across the United States?

In my previous blog posts, I’ve written a lot about how we can use American Community Survey data to learn about patterns and trends within the economy. Last month, I looked at who is moving out of Ohio and where they’re going. The month before that, I looked at affordability measures in Ohio, specifically focusing on the costs of housing across different counties in Ohio.

Analyzing trends of data is interesting, but we have to be careful not to equate correlation to causality. For example, last month, I found that older Ohioans tend to frequently move to warmer states like Florida and Texas, while younger Ohioans tend to move to bustling states with big cities like New York and Illinois. But without doing more in-depth analysis, it’s hard to say that people are moving to New York and Illinois because they’re young.

Another thing we can use American Survey data for is comparing population growth against different factors such as poverty, household income, educational attainment, and life expectancy. First, I wanted to look at how population growth trends compare to poverty rates across the United States.

The figure above shows population growth rates compared to poverty rates across the United States in 2024.  In 2024, there appears to be a negative correlation between poverty rates and population growth across the United States. That is, the lower the poverty rate is within a certain state, the higher we expect the population growth rate to be in that state. This would imply that as poverty rates improve within a certain state, residents are more likely to move to and remain in that state. This makes a lot of sense, especially given the current levels of discourse around affordability across the nation. While we can’t completely associate poverty with lack of affordability, we would expect people who are suffering from affordability across the nation to be driven to states with low poverty rates. 

Alternatively, we could look at the relationship in the inverse direction. More people moving to a state could result in poverty rates going down. On one hand, this could simply be a matter of numbers: if the people moving to new states are already well-off, the rates of poverty could appear to be improving even if the actual number of people experiencing poverty isn’t changing. On the other hand, more people moving to a new state can stimulate local economies and increase state tax revenue which might be correlated with lower poverty rates. We should also consider that states with stronger economies tend to have lower poverty rates. People often want to move to these states not solely because of low poverty rates, but more often because of employment opportunities due to strong economies.

Some outliers to this trend are Vermont, which has a relatively low poverty rate of 9% while sustaining negative population growth, and the District of Columbia, which has one of the highest poverty rates of 17.3% and a stable population growth rate of 1.28%.

Another important metric we can look at is how median household income compares to population growth rates, shown in the figure below.

There appears to be a moderate correlation between median household income and population growth rate across the United States in 2024. This suggests that people are motivated to move to a new state if they have opportunities to earn a higher income. In the past, we have frequently found at Scioto Analysis that employment opportunities are one of the largest drivers of population growth. Better employment opportunities give potential movers a tangible benefit of them moving: a higher income in their pocket each month. However, there is an interesting trend present in the graph: most of the states that have the highest population growth rates are clustered near the middle of states ranked by median household income. These are states like Texas, South Carolina, and Florida, where it could be the case that new graduates are flocking to these states for a relatively higher starting wage.

The third metric I compare to population growth rates is educational attainment, which is shown in the figure below.

The story we get from this graph is similar to the last one– the more educated a state is, the higher its population growth rate is. The first thing this trend suggests to me is that the majority of people moving states are people with a bachelor’s degree or higher. As the population across different states increases, the percentage of the population with a bachelor’s degree or higher increases too because those are the kinds of people who are moving to different states to begin with.

There could also be some sort of intrinsic argument relating educational attainment to quality of life. States with higher educational attainment often have higher life expectancies and economic prosperity. Perhaps due to a higher quality of life, more people are driven to more highly educated states. I would expect reality to fall somewhere in between these two explanations: people with higher educational attainment are likely moving to states with good employment opportunities and higher wages.

The last variable I compare to population growth rates is life expectancy, shown in the figure below.

Life expectancy is the first of our variables to have little to no correlation with population growth rates. If anything, there is a small positive correlation between life expectancy and population growth rates across the United States, though it is too small to draw any major conclusions from. I find the lack of correlation between life expectancy and population growth rates to make sense. Poverty rates, household income, and educational attainment are all variables that potential movers can observe with relative ease. If a state has low poverty rates, high median income, and high educational attainment, I would expect people to be drawn to those states– they can benefit from those sorts of positive metrics in the short-run. 

On the other hand, life expectancy is more abstract. It’s harder to say for sure what actually causes differences in life expectancy between states, and it’s even harder to believe that moving to a new state would have a drastic impact on an individual person’s life expectancy.

It could also be the case that life expectancy numbers are skewed from the COVID-19 pandemic– perhaps if we looked at life expectancy compared to population growth rate trends ten years ago, the results would look completely different.

More in-depth analysis is necessary to establish causality between any of our variables and population growth, but analyzing scatterplots and correlation can provide an understanding of the direction of different variables within the economy. People seem to be moving to states with lower poverty rates, higher median wages, and higher rates of educational attainment, all things we would expect from people looking to move to a new state seeking betterment.

How do we determine “food insecurity?”

Recently, I’ve been thinking a lot about the Supplemental Nutrition Assistance Program (SNAP, formerly known as food stamps). Both myself and my colleague Rob have written about the topic, and I think that once state programs have to start making adjustments, this is going to become an even bigger topic. 

SNAP is one of the most important anti-poverty programs in the country. In addition to its anti-poverty impact, research has also shown that it has led to massive reductions in severe hunger and malnutrition. Looking beyond the most severe cases, we also know that SNAP has an impact on food insecurity.

Food insecurity is a topic that I think many people understand intuitively, but don’t understand formally. The Department of Agriculture defines the state of food insecurity as when “[a household’s] ability to acquire adequate food is limited by a lack of money and other resources,” but this doesn’t tell us how we actually determine if a household is food insecure. It’s not like the Poverty Line where there is a defined cutoff point for income. With data on a household’s income and the number of people in it, we can easily calculate if someone is considered in poverty.

Since we are interested in calculating consistent statistics over time instead of just feeling out what counts as food insecurity, USDA relies on survey questions as conducted in part with the Current Population Survey. The Current Population Survey along with the American Community Survey is one of the most important annual data collection programs run by the Census Bureau. While the American Community Survey has a larger sample size and provides us with good demographic data, the Current Population Survey is focused on getting a smaller number of people to answer a larger set of more detailed questions. That is why the CPS is used to get more precise labor market data, and includes supplements that enable researchers to ask questions about topics such as food insecurity. 

To determine whether a household is food insecure or not, surveyors ask a series of 18 questions they are asked about their food habits. Questions like “‘We worried whether our food would run out before we got money to buy more.’ Was that often, sometimes, or never true for you in the last 12 months?,” or “In the last 12 months, did you ever eat less than you felt you should because there wasn’t enough money for food? (Yes/No).”

While the exact questions and thresholds depend on whether or not a household has children in it, the general rule is that if a household responds in the affirmative to 3 of these questions, then they are considered “food insecure.” If they respond in the affirmative to 6 of these questions (or 8 with children) then they are considered “very food insecure.” 

Taken together, these measurement tools give us a consistent metric to use to track food insecurity over time and evaluate how different policies interact with household wellbeing. One thing we have learned from this research is that SNAP participation leads to reduced food insecurity. 

SNAP doesn’t eliminate food hardship entirely, but it does make a big difference. Especially when we consider the more severe outcomes such as extreme hunger and malnutrition. As states adjust their programs in the coming years, this data will remain essential for understanding how policy choices translate into real changes in household food conditions.

Why do we care about emergency savings?

When we talk about economic wellbeing, the conversation often gravitates toward familiar indicators: poverty rates, food insecurity, housing cost burdens, or income growth. These are all important, but they tend to capture only one dimension of a household’s financial life. Lately, I’ve been thinking more about a different indicator—one that people intuitively understand and care deeply about, even if it doesn’t show up as often in policy debates: whether individuals have any emergency savings. 

One reason I find the focus on emergency savings so fascinating is that the metric is a function of both income and consumption. Most of our indicators of wellbeing focus solely on one side of that equation: poverty or food insecurity for example. Emergency savings as a metric for well being is a bit closer to something like the Supplemental Poverty Measure which takes into account differences in cost of living,

So, why do we care about whether or not people have some emergency savings at all? What does it tell us about people’s wellbeing that we don’t get from knowing their poverty status or whether or not a person is housing cost burdened?

One reason to care about emergency savings is research that ties emergency savings to financial stress. Households without any emergency savings report significantly higher levels of anxiety about their finances. When a car breaks down, a child gets sick, or hours at work are cut, families without savings have few options. They may turn to credit cards, payday loans, or simply fall behind on bills. This stress doesn’t even require an emergency to occur, instead the stress is the result of households knowing they wouldn’t be able to respond if something came up. 

Economists sometimes say that saving is just deferred consumption. This implies that saving money doesn’t provide excess value to people, but rather it is a tool for smoothing out our consumption over time. However, if having some amount of savings leads to its own tangible benefits, then that means it does provide its own unique benefit.

Another reason is that we know how bad situations can become if households are unable to pay for certain necessities because of an emergency. A car problem might force someone to choose between losing their job because they can’t get to work anymore or paying their rent in a given month. These events not only negatively impact the people who experience them, but they create additional strain on the social safety net.

Emergency savings offer a useful lens into how households manage uncertainty. They reflect whether families have even a small buffer to handle unexpected expenses without immediate disruption. When that cushion is missing, the result is often heightened stress and a greater risk that a single setback can cascade into larger problems. 

Looking at emergency savings alongside other measures of wellbeing helps us understand the financial challenges households face. Even a small amount of emergency savings can make the difference between a temporary challenge and a lasting hardship.

Immigration slowdown threatens Ohio’s future

For one reason or another, I have been writing and thinking a lot about population growth lately. An important contributor to population growth in Ohio over the past ten years has been international immigration, which also happens to be on my mind a lot these days.

According to American Community Survey responses, Ohio’s population grew by about 290,000 from 2014 to 2024, an increase of only about 2.5% over that time period. At the same time, Ohio’s foreign-born population grew by 170,000 from a baseline of 480,000, a 36% increase. This means that three out of every five people added to Ohio’s population over the last 10 years was born in another country.

This matters because new people in the state have an impact on the economy. These new residents are buying products in stores and are working in companies. The American Immigration Council estimates that foreign-born residents spend $20 billion in Ohio every year and fill hundreds of jobs, particularly in the transportation and warehousing; manufacturing; and professional, scientific, administrative, and waste services industries.

Foreign-born workers are key to Ohio’s medical, tech, and higher education industries, with more than 1 in 5 Ohio doctors, 1 in 5 Ohio software developers, and about 1 in 6 Ohio college instructors born outside the United States.

Foreign-born Ohio residents are also starting businesses. According to the American Immigration Council, 5.1% of Ohio residents were born in another country, but 8.2% of its entrepreneurs were. This matters because new and small businesses make up a disproportionate amount of job creation in the economy, meaning that foreign-born residents are driving job creation in the state.

Foreign-born residents also contribute to the tax base in Ohio. The council estimates immigrants contribute $2.5 billion in state and local taxes per year. That is enough to fund Ohio’s departments serving agriculture, children and youth, developmental disabilities, health, public safety, and transportation, and still have $100 million left over.

All this is not limited to documented immigrants. The council found Ohio’s 120,000 undocumented immigrants spend about $2.6 billion in the state every year and pay about $740 million in taxes every year.

And because they exist, all of this could be in trouble.

Due to aggressive deportation, enforcement policy, and rhetoric by policymakers at the federal level, net immigration into the United States dropped from 2.7 million in 2024 to 1.3 million in 2025, according to U.S. Census Bureau data released last week. The report says this number will drop to about 300,000 in 2026 if current trends continue. The bureau estimated that the U.S. foreign-born population dropped by 1.5 million from January to June 2025. The bureau also said the country might see more people moving out of the United States than moving into it within the next few years.

Ohio is already set to lose population by mid-century. If Ohio has trends like the rest of the country, loss of immigrants will cause Ohio’s population to drop more quickly than previously expected among a segment of the population that is younger, more entrepreneurial, and more likely to fill jobs than the population as a whole.

So for those of you who want fewer workers, entrepreneurs, taxpayers, inventors, doctors, educators, tech workers, and consumers, raise a glass and rejoice. You are getting just what you wanted.

This commentary first appeared in the Ohio Capital Journal.

What is the Gini Coefficient?

Back in 2022, we released a study on inequality in Ohio and a few policy options that could impact it. This was one part of our series studying different ways to understand the economy, as well as reports on human development, poverty, subjective wellbeing, and the economic growth. 

We are currently working on updating the inequality study, and as part of that we are calculating the Gini Coefficient for Ohio. 

The Gini coefficient is a numerical measure of inequality, most commonly used to describe how evenly income or wealth is distributed within a population. It ranges from 0 to 1, where 0 represents perfect equality (everyone has the same amount of income) and 1 represents perfect inequality (one person has all income, and everyone else has no income). The Gini coefficient is a useful summary statistic to assess income inequality, though it can be a little esoteric and hard to understand by itself. That’s why today I wanted to explain what the Gini Coefficient is and why it’s so important to understand inequality. 

To understand the Gini Coefficient, we first need to take a detour and talk about the Lorenz Curve. The Lorenz Curve was introduced in 1905 by economist Max Lorenz as a way to represent income or wealth inequality graphically. The Lorenz Curve plots the cumulative income held by the bottom X% of a population. For example, we might be able to say the bottom 50% of the population cumulatively has 25% of the total income in some region. 

Below is an example of what a Lorenz Curve looks like in practice. The straight line shows what perfect equality would look like in theory, while the curved line shows the empirical Lorenz curve.

When a Lorenz curve is shown, it’s often plotted alongside a straight line that shows what perfect equality looks like. If this chart is difficult to understand, it may be easier to consider the straight line case first. In this case, if you have say the bottom 25%* of the population and add up all their income, then those people cumulatively have 25% of the total income of the whole population. The curved line represents the real data in some place. Above we see that the bottom 60% or so of the population has 25% of the total income. The closer the curved line is to straight, the more equal the income distribution is.

This curve is the basis for the Gini Coefficient, which was first developed by Corrado Gini in 1912. The Gini Coefficient is defined as the ratio of the area between the perfect equality curve and the Lorenz Curve divided by the total area under the perfect equality curve. In the image above that is equal to A / (A + B).

This term has many nice mathematical properties. It’s straightforward to calculate, it is bounded between 0 and 1, and it allows us to easily compare Lorenz curves from different populations. Without the Gini Coefficient, you’d have to stare at two charts and try to determine minute differences between curves. Instead, we can just look at this one metric and see whether one area has more or less income inequality than another.

The Gini Coefficient is not the only important factor when studying inequality, but it does provide a lot of information. As we progress with our updated inequality study for Ohio, we will dig deeper into what’s driving these patterns and how they’ve shifted over time. By pairing the Gini Coefficient with other measures and exploring the policies that shape economic outcomes, we hope to offer a clearer sense of what might actually move the needle on inequality in Ohio.

*In cases where people have the same income, their order can be determined randomly in order to calculate the cumulative income percentage.