Ohio Senate budget will affect poor people on food assistance, affordable housing, and other programs

The Ohio Senate Republican budget passed last week put forth a new vision for social safety net spending in Ohio. 

The proposal suggests reduced spending on food banks, housing for pregnant women, affordable housing, and school meals for poor children. It also proposes making it harder for low-income people to get access to Medicaid, SNAP (formerly known as “food stamps”), and other public benefits.

These changes to the budget are used to fund income and commercial activity tax cuts.

When Senate President Matt Huffman was asked about this range of cuts to social services, his explanation was that he is trying to “stimulate” a conversation about sustainability of the Temporary Assistance for Needy Families fund. He expressed worry that the fund would be insolvent in five years if spending and revenue continues at current levels for years into the future.

So let’s talk about it.

Temporary Assistance for Needy Families (often shortened to “TANF”) is a relatively small program that mainly provides income to very poor Ohioans. It is the successor to the Aid for Families with Dependent Children (AFDC) program. This was the program that had gained the dreaded label of “welfare” in the early 90s.

Many politicians did not like AFDC because it gave cash to low-income families. It became a massive dog whistle punching bag for the Reagan administration, who was able to vilify it to such effect that it was ultimately the Clinton administration that finished the program, following up on a campaign promise to “end welfare as we know it.”

And he did. The new TANF program was a block grant given out to states to not only provide cash assistance, but also to pilot a range of different programs focused on getting people to work.

Early on, this change was seen as a success. Poverty abated and employment, especially among single mothers, increased. But this was the 90s–a period of economic expansion. 

The subsequent recession of the early 00s followed by the Great Recession of 2007 to 2009 exposed how these changes to the social safety net had made it less resilient and kicked out many of the supports previously in place to hold struggling families up. 

While new programs from the 90s like the earned income tax credit are a good tool for supporting families who have work, they fail when structural problems make work unavailable on a massive scale.

All this is to say Huffman has a little bit of a point here. Block granting TANF took one of the most straightforward and effective income support programs in U.S. history and capped it, limiting its potential effectiveness greatly. Now the dollars available for supporting low-income families need to come from somewhere else.

Is the answer to this problem to cut social spending left and right and use that as a tool to fund income tax cuts and cuts to commercial activity? Probably not. If the plan put forth in the Senate is adopted, it will represent a massive transfer of income from the most needy Ohioans to those with the most resources already. Seems like a big cost to try to make a point.

What makes a good economic indicator?

Earlier this week, Scioto Analysis released an updated Genuine Progress Indicator (GPI) calculation for Ohio. GPI is an alternative measure to GDP that tries to still measure how productive an economy is, while accounting for other things that either provide or take away value like leisure time and air pollution.

We found that GPI is consistently a bit lower than GDP, and over the past five years has grown by less. This seems to explain why despite fairly healthy GDP growth during the pandemic recovery, many people still seem to be struggling in our economy.

At Scioto Analysis, we believe that GPI is a more useful economic indicator than GDP, but this comes with an assumption about what the goal of an economic indicator is. So, I’d like to talk about that assumption, and try to help explain what the goal of measuring our economy even is.

I think about economic indicators like GPI and GDP as having two main goals: they should accurately describe the current state of the economy such that growth of the indicator correlates with increased well-being and they should help policymakers identify areas for improvement.

In other words, a higher score for the indicator should mean society is more well-off and the indicator should help point policymakers trying to improve well-being in the right direction.

Let’s apply this framework to another indicator to understand it better. For example, unemployment. Unemployment does a fairly good job of describing the state of our economy, and usually as unemployment goes down well-being goes up. However, as we’ve talked about before, underemployment is a major issue that goes unnoticed by our typical unemployment figure, U-3 unemployment.

Additionally, U-3 unemployment only counts people who are actively searching for jobs and ignores those who are not even trying. This is good because it means we don’t accidentally count people who aren’t working for reasons such as being a full time student, but it also means one policy option for reducing unemployment would be to discourage people from trying to work.

If we incorporate underemployment into our measure as U-6 unemployment does, then we do a better job of checking both boxes. The indicator is better correlated with well-being, and unemployment/underemployment numbers are fairly straightforward in suggesting policies to improve our society.

A similar situation arises between GPI and GDP. GDP is certainly correlated with well-being, but maybe not as much as we’d think. Additionally, GDP growth could come at the cost of decreased well-being. For example, if public health improves and fewer people end up going to the doctor then there would be less economic activity. Our society might be more well off, but GDP might not rise.

Before we can say that GPI is more correlated with well-being than GDP, we’d need to increase our sample size. Anecdotally, it seems to better represent the state of our economy, but more calculations are certainly needed before we can say for certain.

Where GPI really shines through is that it suggests much more practical policy decisions than GDP does. For example, if a policymaker is debating between two policies, GDP would always prefer the one that would lead to more growth in the formal economy. But, if that growth is only in the short term (GPI takes into account the current value of past investments) and it comes with a large non-market cost (e.g. pollution) then it might actually not be so good for our society.

By looking at a set of conditions that better reflects our understanding of what makes an economy “good,” GPI is often a more useful economic measure than GDP. It is far from perfect as it currently stands, but hopefully more policymakers will begin to ask for GPI calculations so that we can have more informed discussions about our economy.

Food insecurity in Ohio as bad as ever

Back in 2016, I conducted a policy analysis on food insecurity in Ohio.

I was drawn to this question because I saw a report from the Center for American Progress rating states on a variety of different indicators. Ohio, as it still tends to do, fell near the middle of the pack on nearly every indicator. The exception was food insecurity.

In my analysis, which I eventually published with Innovation Ohio, I looked at a few different interventions to reduce food insecurity, focusing on cost effectiveness. I settled on a program called SNAP-Ed – a nutritional financial literacy program that has been shown to have significant results at reducing food insecurity in treatment populations and has been verified by randomized controlled trials.

A lot has changed since that 2016 analysis but one thing has remained the same — food insecurity is still a significant problem in Ohio.

The COVID-19 pandemic created new challenges for families struggling with food insecurity in Ohio. On the front end, typical patterns of food consumption were upended by the drastic economic effects of large-scale economic shutdowns across the world. This led to more of a need for food bank services across the state and increased usage significantly.

The abatement of the pandemic did not lead to a corresponding abatement in food insecurity. This is because the supply chain and labor market changes accompanying the drawdown of the pandemic led to rapid increases in prices across markets. Food markets were hit disproportionately, with many food staples rising in price by double digits on a yearly basis.

This means food banks have continued to strain even after pandemic restrictions have been lifted.

We see this reality reflected in a recent survey by the Ohio Association of Foodbanks, which found that over two-thirds of food bank clients had to choose between paying for food and paying for transportation, gas, or utilities and over half had to choose between paying for food or health care or housing.

Food banks have been our plug-and-chug answer to poverty in the United States. As we’ve slowly reduced cash payments, access to benefits like SNAP (formerly known as “food stamps”), and put work requirements on benefits like government-supplied health insurance, a refrain from policymakers has been “let them have food banks.” While giving people food benefits such as SNAP to shop in grocery stores is the more straightforward policy approach, food banks appeal to a sense of charity among policymakers.

But food banks are only as useful as they are resourced. If food banks are to take the place of a more robust safety net as envisioned during the War on Poverty or that exists in most other developed countries, the state needs to sufficiently resource them so they can provide food for clients.

A problem with the food bank model is that if demand for services outstrips supply, the only adjustment they can make is increase wait times. This is because the limit on food bank services does not only come from lack of food, but also from labor to distribute the food. So low-income people have time they could spend looking for work, developing skills, or caring for family members waiting in line so they can eat. This is not a productive use of time.

In an ideal world, we have a robust safety net that provides income support for people who fall into poverty and that integrates people into society so poverty does not persist across the generations. In absence of this, sufficient funding for food banks is the bare minimum we could provide to give people a chance to escape poverty.

This commentary first appeared in the Ohio Capital Journal.

Ohio’s Genuine Progress Indicator helps explain economic anxiety

This morning, Scioto Analysis released an updated Genuine Progress Indicator (GPI) calculation for the state of Ohio over the past five years. GPI is an alternative to GDP for measuring the wellbeing of an economy, including consideration for environmental and social conditions. 

In recent years, a shortcoming of GDP is that it has overstated the strength of the post-covid economic recovery. Many Ohioans are still feeling the negative effects of the pandemic, despite the fact that state GDP has grown significantly over the past two years. 

Conversely, GPI reveals a less optimistic picture of the wellbeing of the economy during this time – more in line with what people are actually experiencing. Since 2018, GPI has only grown by 8% compared to almost 24% for GDP. 

One of the most significant reasons GPI has been lower than GDP in recent years is the cost of income inequality in Ohio. In 2022, income inequality was valued at almost $150 billion. This comes after a large spike in inequality during the pandemic. 

This is counterbalanced by some positive trends in GPI as well. Personal spending on durable goods and other forms of investment stayed fairly constant despite the pandemic. This meant that the multi-year services people receive from big purchases like cars and homes has continued to provide steady value to the economy during this time. 

“GDP fails to explain people’s lived experience in the economy today. Productivity is correlated with wellbeing, but it’s not the whole story,” said Michael Hartnett, the study’s co-author. “Our study helps demonstrate that in the United States, GPI can help get our indicators more closely aligned with what we actually value in society.”

The Launch of the RISE Together Innovation Institute

Since the beginning of this year, we have been working on a project with the RISE Together Innovation Institute, a new poverty alleviation center in Franklin County. The institute was created as part of a strategic plan led by Franklin County to help improve conditions for people in poverty.

RISE began in 2019 when the Franklin County board of commissioners convened community leaders to assemble a roadmap for reducing poverty. The roadmap settled on 13 goals to strive for, covering the categories of work, health, housing, and youth. 

The RISE Together Innovation Institute (formerly known as the Innovation Center) was charged with being the body that worked with key stakeholders and community members to put the roadmap into action. 

Today, RISE is led by CEO Danielle Sydnor, an advocate and community leader. Danielle began her career in the banking industry, but has for many years been working in community organizations working to improve people's lives. In addition to her work in Columbus, Danielle is the most recent past president of the Greater Cleveland Branch of the NAACP.

Scioto’s work with RISE began with a project to gather data and develop statistics for their website which launched earlier this month. We created a snapshot of what poverty looks like in Franklin County to better contextualize what problems exist and help dispel some of the most common misconceptions about poverty.

Some of the key takeaways from our report have already been published, but the full document is still going through some final edits before publishing.

All of this research culminated last week in the second annual Poverty Innovation Summit. At the Summit, participants discussed some potential policies that RISE could focus on trying to implement in the county. The three policies they discussed were paid parental leave, medical debt forgiveness, and accessory dwelling units. 

These policies were all chosen because they already have some local traction in policy circles and they help advance the original roadmap’s goals of improving work, health, and housing respectively. 

Another reason these policies are exciting is because they are all fairly well-researched. One of our core beliefs at Scioto is that there needs to be more evidence-based policy in state and local government, so it’s exciting that organizations like RISE are stepping up and helping make that a reality. 

Going forward, we are excited to continue to work with RISE and research new ways to help alleviate poverty in Franklin County, as Scioto’s principal Rob Moore is the new policy analyst in residence with RISE.

This commitment to evidence-based policy is one of the many reasons the RISE Institute is an extremely exciting organization. Their commitment to community engagement and well-researched policies and support from the Franklin County government means they could have a strong possibility of improving conditions for people in poverty. 

There is still lots of work to be done in Franklin County. Poverty rates are higher here than in other parts of the state. However, as more resources are invested into understanding poverty and the policies that can alleviate it, we can get closer to a poverty-free future.

How can we measure poverty better in 2023?

The way we measure poverty is not the best way to do it.

The current poverty measure–known as the “Official Poverty Measure”--was originally developed by economist Mollie Orshansky during the War on Poverty in the 1960s. At the time, the average family spent about a third of their income on food. Orshansky then surmised that a family that had three times the income necessary to pay for a “thrifty food plan” would have the resources necessary to survive. Thus, the official poverty measure was born.

Since then, the economy has changed. Due to advances in agricultural technology, the average family now spends about one-eighth of their income on food. Meanwhile, essentials like health care and housing have gone up in price over time.

Due to these changes in the economy, economists have proposed an update to the official poverty measure. This was first put forth in a 1995 National Academies study to modernize the U.S. poverty measure. The proposal put forth a number of recommendations to modernize poverty measurement in the U.S., but sat on a shelf for over a decade before being implemented.

In the late 00s, New York City calculated the New York Poverty Measure, a new measure of poverty based on the recommendations from the National Academies. Soon after, the Census Bureau calculated the first Supplemental Poverty Measure, a new measure for the United States that incorporates the recommendations of the National Academies study into a new national poverty measure.

The findings of the Supplemental Poverty Measure were a little surprising. Overall, the measure found a nationwide poverty rate very close to the official poverty measure. The real departure, though, comes when the data is disaggregated. 

For instance, child poverty is lower and elder poverty is higher in the Supplemental Poverty Measure than in the Official Poverty Measure. This is because the Supplemental Poverty Measure counts a lot of benefits programs that help families with children as income that the Official Poverty does not. It also subtracts the cost of medical out of pocket expenses from family income, which makes elderly people look poorer than the Official Poverty Measure. 

The Supplemental Poverty Measure also has big impacts on regional poverty. Because it includes a cost of living adjustment based on housing costs. This leads to poverty being much higher on the West Coast and much lower in the Midwest in the Supplemental Poverty Measure compared to the Official Poverty Measure.

The Supplemental Poverty Measure made what was possibly its biggest policy splash yet last year when the Census Bureau released its annual poverty numbers and found that the Child Tax Credit lifted two million children out of poverty in 2021.

All this matters because recently, a researcher at the conservative American Enterprise Institute published a working paper recently decrying use of the Supplemental Poverty Measure in federal policymaking. His argument is that changing from the 1960s Official Poverty Measure to the more modern Supplemental Poverty Measure would automatically increase federal spending on SNAP (formerly known as “food stamps”) and Medicaid.

What we know about the Official Poverty Measure is this: it is outdated and no longer reflects how policymakers or the public think about poverty. The Supplemental Poverty Measure comes much closer to what poverty looks like in 2023. If this is a better path forward to addressing poverty in 2023, there is no reason the U.S. should hesitate from taking it.

What do I do when data is missing?

Recently, I’ve been working on calculating the Genuine Progress Indicator (GPI) for Ohio. GPI is an alternative measure to GDP that tries to capture what is going on in an economy while adding things like the value of having an educated workforce and subtracting things like the social costs of crime.

One addition GPI makes to GDP is that adds the value of leisure time and time spent on non-market work. The unpaid time we spend doing housework or caring for children, for example. The reason we want to include these indicators is that we know these things provide value to our economy, but because money never exchanges hands they don’t get measured by  GDP.

To estimate the value of these things in the economy, we use data from the American Time Use Survey produced by the bureau of labor statistics. This survey tells us how much time Americans over the age of 15 spend on different activities.

Unfortunately, the American Time Use Survey wasn’t conducted in 2020 because of the pandemic. To make matters worse, the pandemic also led to dramatic changes in what activities people spent their time on day-to-day.

Normally with missing data, we can use the observed data we have to make some estimate for what the missing value is. We might do this by assigning the missing value as the average of our observed data.

But in this case we know that the average of the observed data is not representative of the missing data. We know that because of the shutdowns, people spent way more time at home.

In statistics, we call this type of missing data missing not at random (MNAR). Specifically, data is MNAR if it is missing because of some unobserved condition.

MNAR data is extremely hard to work with as a statistician. It essentially guarantees that there will be some bias in the final results.

One of the most common ways to deal with MNAR data is to perform sensitivity analysis. We can test what our results look like if the missing data is more or less similar to the observed data we have. This way, we can at least get an idea of what the range of reasonable results might be.

However, as is always the case with sensitivity analysis, it relies heavily on our assumptions as researchers. It is important to make those assumptions as clear as possible and to communicate how they affect the results.

In the context of the GPI study, I chose to extrapolate the data from 2020 using the other years of data. I know this is going to lead to biased results, but in the context of this particular report the single estimate isn’t as important as the overall trend.

Another reason I took this approach was because of what GPI is trying to measure. Specifically with leisure time, we are assuming that leisure time during work days could be replaced with additional work for a wage, and that people are choosing to take that leisure time instead. 

In the context of the pandemic, a lot of people weren’t really choosing to use that time for leisure necessarily. This means that not only would we have to make some assumptions about the additional time people spent at home, but we would have to adjust the way we valued that time. 

In total, I chose to acknowledge that we don’t have data for 2020 and that those particular indicators are flawed for that year. The overall story of GPI vs GDP remains unchanged, and I minimized the additional assumptions I had to make. Hopefully as more research about the pandemic becomes available, there will be a more rigorous way to address this specific problem.

The Value of a Statistical Life--for children

Earlier this year at the Society for Benefit Cost Analysis conference, I had the opportunity to listen to a representative from the Consumer Product Safety Commission (CPSC) talk about how they were approaching the idea of the Value of a Statistical Life (VSL) for children.

To be clear, VSL is not a measure of how valuable human life is. VSL is an estimate for how much we are willing to pay for reductions in the risk of death. For example, we require seat belts in cars because they are relatively low cost and reduce the probability of death quite substantially, but we do not have traffic lights at every single city intersection because that cost is too high for not enough risk reduction. For CPSC, having an accurate estimate of VSL is important for deciding whether new regulations are efficient.

VSL represents how much an average person would be willing to pay for a reduction in the risk of their own death. An individual with limited resources has to make decisions about how to spend those resources, and VSL quantifies how people make these tradeoffs using labor market data.

This is where issues arise when trying to figure out VSL for children. Children don’t have the same autonomy when it comes to decisions about their safety or how they spend resources. This makes it impossible to calculate their VSL the same way we do for adults.

One way we could approach this problem would be to ignore it and just assume children have the same VSL as adults. The issue with this is that most people would agree that we value risk reductions for children higher than we do for adults–we are willing to pay more to save a child’s life than we would to save the life of an adult.

The question then becomes the following: how much higher do we value risk reduction for children?

By reframing the question this way, we can use the same methods we use to calculate the adult VSL. The key difference is we are figuring out how much adults are willing to pay to reduce the risk of death for children.

One way we can do this is by measuring things like how much more an extra safe car seat is worth compared to an average car seat. This will tell us how much more people are willing to spend on childproofing that reduces risk of death for a child.

Estimates from the economic research on the topic have suggested that the range for child VSL is between 1.5 and 3 times the adult VSL. For the time being, CPSC has decided to use twice the adult VSL as their estimate for child VSL. Given that they just chose a round number in the middle of the range, this might be subject to change upon further research.  

There are still plenty of remaining questions about child VSL. Should there be a sliding scale between 0 and 18 years old? Is there a better way to estimate it than the willingness to pay of adults? 

It is an open topic of research, and one that is extremely important to get right. Overvaluing VSL means wasting resources on regulations that are largely ineffective, while underestimating it means living in a riskier world than we would prefer. Hopefully as more researchers begin exploring this topic, we can arrive at a well-thought-out and accurate consensus.

A debt ceiling breach would be a disaster for the states

As President Joe Biden and House Speaker Kevin McCarthy spar over the debt ceiling, the fate of state economies hang in the balance.

Earlier this month, Moody’s Analytics released an analysis of what a prolonged debt ceiling breach would do to state economies.

The biggest impacts Moody’s estimated were around jobs. With the federal government not making payments, large states could lose hundreds of thousands of jobs. If the federal government runs out of spending authority, it won’t be able to pay federal workers such as agency professionals, military, or staff at national laboratories.

Moody’s estimates these impacts could be large in big states. According to the firm, California would lose over 800,000 jobs, Texas over 500,000 jobs, Florida and New York about 400,000 jobs, and Ohio, Pennsylvania, and Georgia over 200,000 jobs.

These would lead to massive unemployment problems in states across the country. Moody’s projects Michigan’s unemployment rate would reach 10.8% under a sustained debt ceiling breach, up from 4.1% today. California’s unemployment rate would reach 8.7% and Ohio’s would approach double digits at 9.5%. All this would lead to a pronounced recession in most states.

What would this mean for state policy? One major impact would be that revenues would crash compared to expectations. States that rely on income taxes to fund state programs would have direct impacts on state revenues as massive job losses lead to much lower income tax collection rates than expected. States that rely more heavily on sales taxes would also have a significant reduction in revenue. This is because if people have less income to spend, they are less likely to make purchases that are subject to sales taxes.

This would lead to states needing to plug the gaps in their budgets with “rainy day” funds. These are funds put in place to help continue government operations in the case of a revenue shortfall. Among states, there are a range of different sizes of “rainy day” budget stabilization funds. 

According to an analysis by Pew, Wyoming’s budget stabilization fund could fund the government for nearly an entire year without taking in any additional revenue. On the opposite end, New Jersey would be unable to finance state spending tomorrow if forced to rely on its budget stabilization fund. The average state could finance state spending for a little under a month and a half with their budget stabilization funds.

Another major impact to a debt ceiling breach on state government would be strain on the state social safety net. A massive spike in unemployment caused by a debt ceiling breach would put states with weak unemployment trust funds in a bind. As of January, California, Connecticut, Illinois, and New York had $0 balances in their unemployment trust funds. States like Michigan, Ohio, and especially Pennsylvania also had very low balances that put them at risk of insolvency.

These sorts of situations are even worse in the case of a recession triggered by debt ceiling breach because the federal government cannot step in to fill the gaps in state social safety nets. During the COVID recession of 2020, the federal government passed legislation that bailed a lot of state governments out of some tough fiscal situations to fund unemployment, school lunch, and SNAP and to stimulate the economy with cash payments to individuals. A federal government hitting its debt ceiling would not be able to do this.

The silver lining is that Moody’s report only gives a 10% chance of the debt ceiling actually being breached. But playing chicken with state economies has become commonplace in federal policymaking. Let’s hope policymakers come to an agreement that brings us further from what would be a disaster for the states.

Nudges or taxes and subsidies: what’s better?

Much hay has been made over the use of “nudges” in public policy over the past decade or so. This trend may have come to its apex in 2017 when Richard Thaler, co-author of the popular book Nudge, won the Nobel Prize in Economics.

Nudges are behavioral interventions designed to encourage certain behaviors without impinging on the range of choices available to someone. A classic example is the design of food options at a cafeteria. If fruit options are placed at eye level at checkout and pastry options are placed at knee level rather than vice-versa, people are more likely to choose to add fruits to their meals rather than pastries without being deprived of a choice of options.

Nudges are exciting because they allow us to engineer choice architecture to design choices to improve overall well-being while still allowing people to choose different options. It shares this result with another major tool at the disposal of government: taxation and subsidization. These are the bread-and-butter tools we have for reeling in negative externalities and increasing production of goods that have positive spillover effects. By taxing social bads and subsidizing social goods, governments can encourage consumption of goods that benefit society and discourage consumption of goods that harm society without banning choices.

A recent study in the National Bureau of Economic Research’s working paper series compares nudges to more traditional tax and subsidy schemes to assess the relative effectiveness of one strategy over another under different contexts. The three contexts the researchers study are cigarettes, flu vaccinations, and household energy consumption.

For cigarettes, the researchers look at the relative impact of warning labels on cigarette packaging and cigarette taxation, two interventions designed to increase cessation of cigarette use. They find that warnings are more economically efficient than taxation because they are most effective at deterring problem smokers. They even find that warning labels combined with taxes are only marginally more effective than warning labels alone. Point nudging.

The researchers also look at the impact of public campaigns to increase flu vaccination. The researchers find that the optimal subsidy for flu vaccination would be to make them free for the public. They also found that under the most likely scenarios, this would be a more efficient intervention than public vaccination campaigns. By making the flu vaccine free, people would be more likely to get vaccinated than simply seeing ads or other marketing materials encouraging them to get vaccinated. There were some slim scenarios the researchers were not able to rule out where public campaigns could be just as efficient as subsidization, but this still ends up being a point for subsidies.

Lastly, the researchers looked at social comparison nudges to reduce energy consumption versus taxes like a carbon tax. These types of nudges send information to consumers showing how much their neighbors are using energy in order to encourage them to reduce energy use. These nudges have been shown to be effective in experimental studies in the past. Under this scenario, taxes were often seven to eight more efficient than the social comparison nudges. Strong point for taxation/subsidization.

What this study found overall is that under certain circumstances where the population has large differences in internal biases such as cigarette use, nudges are a more efficient way to correct market failure. Under situations where the public generally is biased in the same way, taxes and subsidies are the more efficient tool. This can be a useful rule of thumb for policymakers interested in rooting market-based solutions in the available evidence.