What’s the difference between an entitlement and a block grant?

In the United States, not all social safety net programs are the same. The wide array of different eligibility requirements and funding mechanisms can make it difficult for the public to understand how our safety net functions. 

One of the most important differences between federal safety net programs is whether they are designed as entitlements or block grants. If you’re like me and you’re new to the public policy world, it might be surprising to find out that not every safety net program is an entitlement. This is only something I’ve learned doing work on an updated version of Scioto Analysis’ Ohio Poverty Measure.

An entitlement program is not just another way of saying a safety net program, instead it is an important financial designation. It means that everyone who is eligible for the program will receive benefits as long as they claim them. The largest entitlement program is social security. 

Block grant programs, on the other hand, have a fixed amount of money allocated by Congress. Often, the federal government hands out cash to the states and state-level policymakers decide how these funds get allocated to the people who need it. One example is housing subsidies. Once the money allocated for housing subsidies runs out, no more people get benefits until more money comes in. 

Block granting an entitlement program can have major consequences for how many people are helped by the program. Consider the Temporary Assistance for Needy Families (TANF) program. TANF is a Clinton-era version of a program initially established in 1935 as the Aid to Families with Dependent Children (AFDC) program under the social security act. In 1996, the Personal Responsibility and Work Opportunity Reconciliation Act replaced the entitlement program AFDC with the block grant TANF.

The immediate result was a sharp decline in the number of people receiving benefits. The Center on Budget and Policy Priorities estimates that TANF reached 2 million fewer people than AFDC would have in 2019. 

What are the advantages of entitlement vs block grant design? A major tradeoff policymakers should consider when entertaining the idea of block granting an entitlement program is cost certainty vs. program reach. One fact about block grants is that they are much less sensitive to changing economic conditions. If there is an economic downturn, the program’s budget won’t immediately balloon. Instead, policymakers will be able to pull the lever more precisely to ensure the budget doesn’t get overwhelmed. 

However, this inherently means that people who need benefits to get by probably won’t get them immediately. As we saw when AFDC became TANF, block grant programs reach far fewer people than entitlements. It also means that block grant programs are less effective as automatic stabilizers. Entitlement programs kick in automatically to provide support to individuals and the macroeconomy during recessions, while block grant programs are too rigid to respond without additional policymaker action.

With entitlement programs, the risk of underestimating costs can be offset by better economic projections. Still, there is the risk that policymakers don’t allocate enough resources to a program and it fails as a result.

Recently, Minnesota made free school lunches an entitlement program in their public schools. The program proved to be adopted much more widely than policymakers projected, causing it to already be over budget. There doesn’t seem to be any risk of the program getting shut down, but policymakers are going to have to make budgetary adjustments going forward. The most obvious example of an entitlement program potentially growing too large is social security. 

Block grants can be an effective tool for policymakers to control the costs of safety net programs at the outset. For state and local governments that have balanced budget requirements, this can be an important consideration. However, policymakers should be aware of the tradeoffs. A safety net that doesn’t reach the people who need it the most is not very effective at its central goal: cushioning the impact of market economies on workers and families.

Is pornography a public health problem?

According to the U.S. Department of Health & Human Services, if you live in Ohio, you are 15% more likely to die of heart disease than the average American.

You are also 11% more likely to die of cancer than the average American.

Accidents are even worse. Ohioans are 40% more likely to die of an accident than the average American.

Respiratory disease, cerebrovascular disease like stroke, Alzheimer’s disease, diabetes, kidney disease, suicide, homicide, pneumonia, sepsis: Ohioans die of all of these conditions at higher rates than the average American. The only categories highlighted by the National Institutes of Health that Ohioans don’t die of at higher rates than the general U.S. population are liver disease (2% lower) and the flu (exactly the same).

Given the public nature of this information, public health-minded policymakers would be crafting a strategy to address this rampant mortality. There are so many fronts on which progress could be made, where valuable political capital could be spent to save lives.

Earlier this week, a bipartisan coalition of legislators joined with the governor to introduce their big public health push for 2024. Was it a package to tackle heart disease? Cancer? Are policymakers going to craft a plan to tackle respiratory disease, strokes, diabetes? Maybe suicide or homicide?

No. Public enemy #1 in Ohio is porn.

Executive and legislative leaders joined together to introduce legislation to require all viewers of pornographic material in the state to share personal information such as a state ID online to do so. Some legislators have already been trying for years to declare a “public health crisis” around consumption of pornography in Ohio.

If Ohio passed this legislation, they would join a handful of bible belt states, Montana, and Utah in requiring residents to share their personal information to access pornographic material.

There seems to be some evidence that pornography could have impacts on the health of some individuals and could have some impacts on social norms around sex and sexuality.

But why has pornography risen to such a fixation of policymakers across the United States? 

Emily F. Rothman, Professor of Community Health Sciences at the Boston University School of Public Health, is a foremost researcher on the impact of pornography on public health. She wrote the 2021 textbook Pornography and Public Health.

In this book, Rothman outlines how out of step policymakers are with public health leadership.

The professional public health community is not behind the recent push to declare pornography a public health crisis. One might think that if pornography is a public health menace, “destroying the lives of millions,” public health entities and professional societies must have a viewpoint on the topic, perhaps a clearly outlined health-promotion agenda related to the problem, and a strategic plan. At least one of the National Institutes of Health (NIH) must have named it as a priority, the Centers for Disease Control and Prevention (CDC) must have a branch devoted to putting a stop to it, and the World Health Organization must have at least one infographic on its harms. But none of these things exists or has happened. In fact, there is no public health professional presently in any position of public health leadership or authority who has gone on record to say pornography is a public health topic of interest–let alone a public health crisis. In 2016, in a written statement to CNN, the CDC said it “does not have an established position on pornography as a public health issue. Pornography can be connected to other public health issues like sexual violence and occupational HIV transmission.” But if public health entities are not behind the movement to declare pornography a public health problem, who is? And why are they using the language of public health for their cause?

Maybe policymakers are way ahead of the public health community on this one. But I’ll say this: if they spent as much time trying to reduce cigarette consumption as they spent on pornography, they would probably save a lot more lives.

Should I use a linear probability model or logistic regression?

Over the last month, I’ve been working on updating the Ohio Poverty Measure. The Ohio Poverty Measure is a poverty measurement tool calculated by Scioto Analysis and based on similar measures in California, New York City, Wisconsin, and other states. 

One of the biggest hurdles to overcome on this project is imputing information about specific additions to income not available in the American Community Survey, the main dataset used for calculating the Ohio Poverty Measure. Specifically, to calculate the Ohio Poverty Measure, we need to impute data for which poverty units receive housing subsidies and free school lunches. 

To work around this, we use answers from the Current Population Survey to impute recipiency of housing and school lunch benefits to families who respond to the American Community Survey.

The Current Population Survey asks a larger number of questions to a smaller subset of the population compared to the American Community Survey. Our goal is to use Current Population Survey data to build a model that predicts the probability a poverty unit receives one of these benefits, then use that model to determine which poverty units in the ACS data get the benefits and estimate the size of those benefits.. 

This approach will not get the answer exactly correct for each individual family, but assuming the Current Population Survey population is similar to the American Community Survey population, this approach should give us a useful approximation of these benefits. 

So, let’s talk about how to best build this type of model. 

The simplest approach would be to define the outcome of benefit recipiency as a numeric variable, (e.g. one for people who receive the benefit and zero for those who don’t) and use a regular linear regression approach to estimate recipiency. With binary outcome data, we call this a linear probability model. 

Unfortunately, linear probability models have two main drawbacks. First, linear regressions assume continuous outcome variables. This means that we can make predictions that go below zero or above one. Since our outcomes are supposed to represent probabilities, this is undesirable. There is no such thing, after all, as a -25% or 125% probability of housing subsidy receipt.

Second, linear probability models, as the name suggests,  linearly increase or decrease in one direction or the other. This is closely related to the first problem, since as we approach extremely likely or unlikely outcomes we actually don’t expect to see linear changes in the probability. It makes much more sense that our model should asymptotically approach one or zero in those cases.

The solution to this problem is to use a generalized linear model. For binary outcomes, the logistic or probit regression models are the most common choices. These models bound our outcome nonlinearly between zero and one, with our predictions asymptotically approaching those values. 

Because in our data, we are dealing with some extreme probabilities (people with high income should be ineligible for these benefits and therefore have probability zero), the linear probability model is a poor choice for estimating recipiency. Linear probability models perform best when looking at situations where the outcome is almost always close to 50/50. Near the middle, all of these models are fairly close. It’s as you get further into extreme probabilities that the shortcomings of the linear probability model really begin to show themselves.

For this project, I chose to use a logistic regression model. Still, this only allows us to say what the probability of a poverty unit receiving some benefit might be, we still need to figure out who receives benefits in our ACS data. 

The solution to this is quite simple. First, we look at the CPS data and see what percentage of those respondents receive this benefit. Because the CPS is a random sample (after weighting it), we can assume that this is the proportion of people that actually receive these benefits. Next, we rank every person in the ACS data by their probability of receiving these benefits. Finally, we give benefits to people in the ACS data with the highest probabilities until the same percentage of people receive benefits as in the CPS data.

By using a logistic regression instead of a linear probability model, we more accurately determine the probability of receiving benefits for people at the extremes of our survey. Because we are looking specifically at people near the ends of our predictions, it’s important that our model functions correctly in those places. For most binary outcome data, the choice is simple.

What would a productive Ohio General Assembly look like?

Last year, the Ohio General Assembly made fewer new laws than it had in any year since 1955. The sixteen laws passed in 2023 beat out the previous record holder, 2009, when Democrats controlled the Ohio House and were only able to agree with the Republican Ohio Senate on 17 new laws.

There are a lot of political explanations for why so few bills became laws in Ohio last year. It may be because of the fractured Republican leadership in the Ohio House. It may be because so much of policymaking happens in the state budget rather than in individual bills. It may be that polarization has led to less common ground between legislators.

When I hear about a statistic like this, the question that comes to mind to me is this: why does it matter? The most straightforward answer to this question is that Ohio is still facing many issues that public policy could be the answer for. 

Whether it’s the opioid epidemic, food insecurity in both urban and Appalachian Ohio, low water quality in Ohio’s streams and rivers, or economic prospects in post-industrial cities, Ohio has no shortage of problems. And public policy could be the answer to these problems.

The nice thing about “number of bills enacted into law” is that it is a specific, measurable statistic. It definitely tells us something about the body and its political tenor, though it is debatable what exactly it is telling us.

But if we want to evaluate the question of how much good the general assembly is doing for the state of Ohio, we’d want to evaluate it differently. Theoretically, the state could cram a bunch of policy into a single budget bill that improves Ohioans’ lives considerably, passing one law but doing lots of good for its residents.

At the federal level, regulations are evaluated using cost-benefit analysis. Every regulation that may have a $100 million impact on the national economy is subject to cost-benefit analysis to assess the impact of the regulation on the public. These cost-benefit analyses are released in a federal report every year that talk about the overall impact on regulations on the U.S. economy.

In the spirit of this approach, I’ll put forth some metrics that we could ideally use to assess the effectiveness of the Ohio General Assembly.

How much will new laws grow the statewide economy? Normally, we’d measure this as a change in gross domestic product. Ideally, we would measure this using a comprehensive economic indicator like the Genuine Progress Indicator that captures the external impacts of economic activity, informal markets that aren’t captured in standard measures, and time markets where people trade off their valuable time for things they want.

How much will new laws reduce poverty and inequality? Impacts on poverty could be measured using more comprehensive, mainstream poverty indicators like the Supplemental Poverty Measure, which makes geographic adjustments for cost of living and includes the value of benefits. Impacts on inequality can be assessed by seeing how laws change the distribution of income.

How much will new laws foster human development? This means using measures like income, years of schooling, and life expectancy to see how much laws are improving people’s abilities to live the lives they want to live.

Finally, are laws improving how people evaluate their own lives? This means utilizing surveys of people’s well-being such as used in the World Happiness Report to evaluate public policy changes.

Ultimately, the way to evaluate policymakers is not on how many laws they pass, but on how these laws improve the lives of their residents. Having think tanks, analysts, and journalists focusing on these questions will give us the best picture of how well the Ohio General Assembly is performing.

This commentary first appeared in the Ohio Capital Journal.

Ohio economists expect benefits from evidence-based reading curriculum

In a survey released this morning by Scioto Analysis, 15 of 16 Ohio economists agreed that implementing evidence-based early childhood literacy curriculum in Ohio public schools would improve human capital in the long run. In recent months, Governor DeWine has reinforced his desire to require these practices, commonly referred to as the “science of reading,” in Ohio’s public schools. 

As Kevin Egan (University of Toledo) wrote: “Children are our future workers, investing in high quality education for all children grows the future economy more and equalizes opportunity for everyone.” 

Despite the consensus opinion from respondents, some economists identified challenges that this program could face. 

“My answer is based on my understanding that research shows the current reading curriculum leads to disparities, hence the switch to ‘evidence-based’ (new evidence),” said Curt Reynolds of Kent State University. “If these new strategies help close gaps in education that would be very important.” 

“There may be challenges faced in effective implementation of the programs and schools may need additional support to provide teacher training, " said Faria Huq of Lake Erie College.

Assuming these techniques are implemented well and teachers are prepared, Ohio economists are optimistic about some of the secondary effects this program might have. 15 of 16 respondents agreed that these practices could grow the economy in the long run, and 14 of 16 respondents agreed that these practices could reduce inequality in the long run. 

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. 

What would it cost to end homelessness in America?

On Wednesday, I went to a luncheon hosted by the Columbus Metropolitan Club on the topic of homelessness. The panelist posed a question to one of the panelists: what would it cost to end homelessness in America?

The panelist Bob Weiler, a real estate developer in Columbus, said the money exists, but the political will doesn’t. The response was useful and rang true to me, but I’m a policy analyst. I wanted to place my hand into the proverbial wound: I wanted to know how much it would cost.

So let’s do some math.

According to the U.S. Department of Housing and Urban Development’s 2023 Annual Homelessness Assessment Report to Congress, 653,104 people experienced homelessness during the annual point in time count in January 2023.

Let’s start with an expensive way to estimate this: comparing it to the cost of prison. Incarceration is an expensive form of housing due to the high costs of security. 

The Federal Bureau of Prisons estimated in their 2021 Annual Determination of Average Cost of Incarceration Fee that the 2019 cost to imprison one person for a year was $39,158. If we adjust this cost for inflation to December 2023 prices, then this comes out to $46,114.24 per person in current dollars.

This means the cost to house every homeless person in the United States using this estimate of the cost to provide shelter for federal inmates would be about $30 billion. This should be considered a high-end estimate due to the high costs of providing shelter in this way.

What if we just paid for the average cost of housing across the country? According to the Apartment List National Rent Report for January 2024, the nationwide median rent in January 2024 was $1,379. This means that paying for the rent for each of these 650,000 people experiencing homelessness would cost about $11 billion.

There should be cheaper ways to house people than simply paying the median rent, but these figures suggest that on the high end, eliminating homelessness in the United States should cost somewhere from $11 billion to $30 billion per year.

How does this compare to other social spending by the federal government? Let’s look at the the top antipoverty programs in the country in the 2023 Census Bureau annual report on poverty in America to assess this question.

The top antipoverty program in the United States is Social Security, which pulled 29 million people out of poverty in 2022. The United States spent $1.5 trillion on Social Security in FY2023. This means eliminating poverty using the most expensive method above would cost about 2% of what we currently spend on social security retirement payments.

The second largest antipoverty strategy in the United States is refundable tax credits, which pulled 6.4 million Americans out of poverty in 2022. The federal government distributed about $57 billion in cash to families through the earned income tax credit in 2023. So the least efficient method we have for ending homelessness would cost a little over half as much as the federal earned income tax credit.

The third-largest anti-poverty program is the Supplemental Nutrition Assistance Program (SNAP), formerly known as “food stamps.” This program pulled 3.7 million Americans out of poverty in 2022. The University of Missouri’s Food & Agricultural Policy Research Institute estimates SNAP cost the federal government $112 billion in FY 2023. That means our most expensive method for ending homelessness would cost about a quarter of the annual federal spending on SNAP.

It looks like Bob was right. The money is there, the question is whether the will is there to use that money to end homelessness in America.

Can school lunches boost student achievement?

Over the past month, I’ve been working on updating the Ohio Poverty Measure, a report published by Scioto Analysis that uses local data to provide the most accurate picture of poverty across Ohio. This measure is based on other poverty measures like the California poverty measure, and the New York City poverty measure, all of which are based on the Supplemental poverty measure.

These measures improve on the official poverty measure by accounting for non-wage income that people have like SNAP benefits, subtracting unavoidable costs like work or medical expenses, and making a geographic adjustment for cost of living. 

During this research, one thing that stuck out to me was the inclusion of free and reduced breakfast and lunch at schools as an addition to income. What surprised me about this was just how inexpensive these meals were, usually only a few dollars per meal. 

This made me wonder, how much good did these free meals actually provide for students? Would increasing the accessibility of these meals be a low-cost way to substantially improve outcomes for low-to-middle income students? 

Overall, the research supports the idea that free lunch is beneficial for students. In the short run, one study found that universal free meals improved test scores for both poor and non-poor students. This suggests that there might be an effect beyond offering meals to students who otherwise might not be able to afford eating lunch. Since test scores are correlated with future earnings, this means offering school lunch could be a long-term intervention for improving human capital and fostering economic growth.

Another study found that the quality of a school lunch can have an impact on test scores. More nutritional meals in California public schools were associated with better performance on standardized tests. This effect could potentially help explain why test scores increased among non-poor students who do not receive free lunch as well as those who receive subsidized lunch. On the margins, it is likely that some students could have improved the nutritional content of their lunches by switching to the meals offered by the school. 

One study from Sweden found that the adoption of free school lunches in the 1960’s led to higher lifetime earnings for students. This is unsurprising, as we know that adolescent academic performance is associated with higher earnings as an adult. 

What are the implications of this for policymakers? Universal free lunch for students on the surface seems like it could be a valuable investment. To know for sure, a full cost-benefit analysis would be needed. The evidence we have now is enough to make the idea credible.

Still, it is important to remember that this program can be expensive. In Minnesota, where universal free lunch recently became law, lawmakers initially underestimated the costs of the program by almost $100 million, causing some to argue that the program needs to be scaled back. Other policymakers who consider free lunch programs should recognize that the demand for lunch goes well beyond poor students. 

Hopefully, Minnesota can continue their free lunch program and other states can look to it to see its effectiveness. Maybe one day, this program will enable us to measure the long-run effects that free lunch has on these students' adult outcomes.

The science behind Ohio Gov. DeWine’s gender-affirming care ban veto

In late December, Ohio Gov. Mike DeWine vetoed House Bill 68, a bill to ban gender-affirming care practices for minors.

This came as a surprise to many as DeWine has often found himself embracing social conservative positions throughout his long career. And indeed, DeWine framed his decision as a conservative one in his public statements on the veto, saying his decision is “about protecting human life” and rejecting the idea that “the government knows what is best medically for a child rather than the two people who love that child the most, the parents.”

In DeWine’s statements, he says repeatedly that it was the testimony of parents and people who received gender-affirming care that this care saved their lives that swayed him. That young people who felt trapped in bodies that weren’t theirs were able to find solace in this care that turned them away from the impulses of self-destruction that gender dysphoria can spark.

What is gender-affirming care? According to the U.S. Department of Health and Human Services’s Office of Population Affairs, gender-affirming care comprises four general practices.

The most simple practice is social affirmation. This includes practices such as adopting gender-affirming hairstyles, clothing, name, gender pronouns, and use of gender-appropriate restrooms and other facilities. These signal to someone experiencing gender dysphoria that they are being socially accepted for their gender expression.

Another is puberty blockers. According to a review of gender-affirming care published in the Annual Review of Medicine last year, these are hormones that can pause pubertal development,  prevent otherwise permanent development of secondary sex characteristics that are not aligned with a person’s affirmed gender identity, and allow time for further gender exploration.

Next is hormone therapy: testosterone therapy for biological females and estrogen therapy for biological males. This sort of therapy can help development of gender-affirming characteristics and is partially reversible.

The final is gender-affirming surgery. This includes operations to change chest shape, genitals, or reproductive organs to affirm gender.

DeWine’s focus seems to be on puberty blockers and hormone therapies. He says explicitly in his public statements that he does not support gender-affirming surgery for minors. But the bill went further than that, prohibiting physicians from “prescribing a cross-sex hormone or puberty blocking drug to a minor” according to analysis of the bill by the Ohio Legislative Service Commission.

The best evidence available tells us transgender and gender diverse youth have higher risk for mood disorders, anxiety, depression, suicidal ideation, and suicide attempts. It also tells us that these youth tend to have better mental health and well-being outcomes later in life if they receive gender-affirming care earlier rather than later.

We should acknowledge that we are still gathering data on this sort of treatment. But the early data is promising. Short- and medium-term studies have found higher sense of well-being, resolution of gender dysphoria, and even lower rates of suicidal ideation.

Like any type of treatment, there are side effects to consider. Some early evidence of impacts to bone density, growth, blood pressure, neurocognitive development, and fertility need to be taken seriously.

Overall, it seems that in this case Governor DeWine listened to the current scientific consensus: gender-affirming care can save lives, and for that reason is a viable option for families that care about the mental health and safety of their children.

This commentary first appeared in the Ohio Capital Journal.

How can we reduce the federal debt?

Coming into 2024, the current U.S. federal debt is roughly $34 trillion. That is over $100,000 of debt per person, and more importantly it is roughly 1.2 times as large as the nation's economic output. Near the end of 2023, this became a more prominent problem as the credit rating firm Moody’s lowered their evaluation of the federal debt from “stable” to “negative.”

At Scioto Analysis, we mostly spend our time talking about State and Local governments, which are not capable of financing anywhere near the same amount of debt as the federal government due to the ubiquity of balanced budget amendments. Still, the federal debt has big implications for lower forms of government. 

Safety net programs like social security and Medicaid are extremely important federal programs that could be candidates for spending cuts should the political winds blow the right way. If these programs got diminished or went away, there would be massive gaps in the state safety net that state and local government could be saddled with.

According to the Committee for a Responsible Federal Budget’s Debt Fixer interactive tool, the federal government would need to reduce expenditures/raise taxes to generate over $6.7 trillion by 2034 in order to get the debt to GDP ratio back to 98%. Their interactive tool lets users select different policy options that have been analyzed by the Congressional Budget Office to see what certain changes would have on the overall budget (this was the inspiration for Scioto Analysis’ own State Budget Tool). 

One takeaway I have from some time with this tool is that a balanced federal budget requires both tax increases and spending cuts. It sounds obvious to say, but once you start looking at these options more closely it becomes clear why this is politically an extremely difficult option. 

Of all the options laid out by the CRFB, the most significant debt reduction policy is a wealth tax on the ultra wealthy. This would account for roughly $3.1 trillion in new revenue, less than half of the gap needed to reduce the debt to GDP ratio to 98% by 2034. Some other examples of tax policy are enacting a Value Added Tax ($2.9 trillion), increasing the corporate tax rate ($1.3 trillion), and taxing financial transactions ($1.1 trillion).

Looking at the spending side of the equation, we see that the three biggest options laid out are replacing the Affordable Care Act with state credits ($800 billion), limiting the growth of annual defense spending ($560 billion), and making Social Security benefits a flat amount ($530 billion). 

Although it is theoretically possible to reduce the debt solely by increasing taxes, it is pretty clear that this is politically infeasible. If policymakers are serious about reducing the budget deficit, there is going to need to be a mix of budget cuts and tax increases. 

Perhaps federal policymakers can learn from state and local governments at this moment. States almost universally are required to have balanced budgets, which means finding creative compromises. The drawback of requiring a balanced budget comes in periods of recession, where federal government intervention is often essential to preventing deep economic turmoil.

Although it is still unlikely that the current federal debt will result in a catastrophic economic collapse, this is still an extremely important issue. The day will eventually come where spending needs to get under control before the risk becomes too much bigger. The longer we wait before figuring the budget out, the more painful the change will be. 

Four policy stories you may have missed in 2023

2023 was a big year for policy research. We saw an update of the federal cost-benefit analysis guidelines, new evidence of the importance of state safety nets, and more. Below is a quick selection of some of the most important research from the past year. 

Office of Management and Budget Circular A-4

We’ve already written about the importance of Circular A-4 on the cost-benefit analysis world, but as arguably the most consequential new written report of the last year it is worth going over again. Circular A-4 is the Office of Management and Budget’s official guidance on how to perform regulatory analysis.

This is the most important guidance for cost-benefit analysis in the United States. Broadly speaking, many of the changes to this document make it so that regulatory analysis will put more emphasis on how policies affect future generations. Not all regulatory analysis is concerned with such long timelines, but those that are will not be as short-term focused as they have been. 

Brookings Research on State Safety Nets

Just last month, Brookings released a report comparing the generosity of social safety nets in each state. By generosity, they specifically mean how eligibility changes across states (it is important to note that uptake for these programs is a separate problem). They find that safety net programs are often more generous on the East Coast, while the Deep South and Southwest regions are lacking slightly.

Additionally, this report looks at how safety net programs change over time. Unsurprisingly, the COVID-19 pandemic was responsible for the largest one-year increase in safety net generosity. The largest increase historically was after the 2008 financial crisis. 

Flint Water Crisis Shows Economic Impacts

In 2015, Flint, Michigan experienced a health crisis due to a contaminated public water system. This event garnered national attention, and the problem was eventually resolved in 2016. The health effects of the poor drinking water have been explored previously, but this year a team of researchers looked at how this event has impacted housing prices in Flint.

They found that housing prices in Flint fell by as much as 20% following the crisis and have yet to recover, despite the fact that water quality has been stable for many years. Because most Americans accumulate wealth by owning homes, this is a major economic blow to these residents. This research highlights the importance of public policy in the aftermath of major crises. The long term effects of major events are not always obvious, but they can still be extremely severe. 

Diversity among teachers shows positive long-run effects

A new study from researchers at American University has found that increasing racial diversity in the teaching workforce can lead to positive long run effects, especially for students from historically marginalized communities. Past research has shown that increased diversity can lead to many short term benefits, but this paper is the first of its kind that looks at long run benefits like college attendance.

These authors found that Black students who had at least one Black teacher in grades K-3 were 13% more likely to graduate from high school than their peers, and 19% more likely to go to college. White students experienced no long-term changes in educational attainment. 

I am looking forward to seeing what new things we learn in 2024.