Secondary Market Effects in Cost-Benefit Analysis

According to a recent paper published in the Journal of Benefit Cost Analysis, most policy analyses underestimate the costs of interventions by ignoring the effects they might have on secondary markets.

For context, policy often impacts secondary markets that are for goods that are substitutes or complements to the market we are making a direct policy change in. An example given by the researchers is how a tax on coffee consumption might impact the market for tea.

In most cost-benefit analyses, these effects are largely ignored. From a practical perspective, they are quite difficult to measure, and from a theoretical perspective this exclusion has been justified by the assumption that because these markets are linked, our initial primary market analysis will inherently account for secondary market changes. 

Mathematically, we can think of the total effects of a policy as an equation:

Net Effects = Primary Market Effects + Non-Market Effects + Secondary Market Effects

In most cases, the primary market effects and the non-market effects have different signs (e.g. a tax on cigarettes that improves public health). If the assumption that the secondary market effects are accounted for in the primary market effects holds, then we can ignore that part of the equation.

Unfortunately, most cost-benefit analyses do not take the required steps to satisfy this assumption. Instead, they often underestimate the second market effects and miss a key cost.

Fortunately, the researchers have developed a solution to this problem. By using fairly simple to calculate elasticities, they found a way to estimate the impact of a primary market change in a secondary market. 

If analysts incorporate these simple elasticity-driven analyses into their cost-benefit analyses, they will be able to determine an estimate for the impact of a policy change on a secondary market. This means that these future cost-benefit analyses will be more accurate and can better inform policy decisions. 

One interesting note the authors highlight at the end of their paper is that these effects are often quite small in practice. In the examples they provide, secondary market effects are small enough that ignoring them entirely likely would not affect a policy maker's decision. 

In fact, in both examples provided by the authors the inclusion of the secondary market effects failed to move the point estimate outside the confidence interval for the initial primary market effects. In other words, there was no statistically significant difference between the model that included secondary market effects and the model that did not.

Still, these small improvements on the margins add up to better policy analysis. Although the examples presented by the authors could have potentially ignored these secondary effects, their inclusion increases the accuracy of the model. Additionally, some policymakers may be interested in the impacts on secondary markets. For instance, a Congressmember who represents a district that produces tea would be interested in the impacts of a tax on coffee, even if they look small on a national scale.

I imagine that given the size of these effects, many analysts will continue to ignore them. In a world where limited resources can go into performing these analyses, spending those resources on such a small effect will often not be deemed valuable.

This advancement illustrates the value that academics researching policy analysis can add. As cost-benefit analysis becomes more widely used, these small marginal adjustments will become more and more important. Hopefully, policymakers will be able to make use of the information that comes from these changes to the process too. 

Is the value of a statistical life the same for everyone?

In an essay published earlier this year in the Journal of Benefit-Cost Analysis, former OIRA director Cass Sunstein grapples with a tough question: should the value of a statistical life vary for different populations?

The value of a statistical life is a key tool analysts use to understand the economic implications of regulatory actions at the federal level. Sunstein calls the value of a statistical life (VSL) “the workhorse of cost-benefit analysis,” saying “it is the principal driver of benefits in multiple domains, whether we are speaking of highway safety, road safety, food safety, cigarettes, or pandemics.”

The value of a statistical life has a long history. The statistic was first an estimate of how much it would cost to replace a worker. It later evolved into the sum of future earnings of an individual. Its current approach is drawn from risk of death reductions based on the relative safety of workplaces. 

The modern method is focused on labor market data. Economists use the relative danger of different workplaces combined with wage data to see what wage premium is paid to people to take on additional risk of death. For instance, a welder who works on skyscrapers is usually paid more than one who works in a shop, all other things being equal. This extra payment represents a market for risk of death reduction, where workers will take lower pay for lower risk of death and will require higher pay for higher risk of death.

One complication of this approach is that lower-income people have less money, so they are willing to pay less to reduce their risk of death than upper-income people. This is because minute reductions in risk of death could cost the same as a meal or even a bill payment, something low-income people would not want to trade off.

This unearths an insight about regulatory policy: it can in theory be quite paternalistic, forcing poor people to spend their limited resources on minute reductions in risk of death that they would never make on their own.

Despite all this, federal agencies and most analysts in general use a uniform value of a statistical life. This means that low-income, middle-income, and upper-income people’s willingness to pay for risk of death reduction is simply assumed to be the same.

Sunstein’s article talks about the implications of this assumption. He first tackles the question of subsidies, saying that low-income people, all other things being equal, will benefit from subsidies allocated according to this principle, though less than they would from a cash transfer or some alternate uses of these funds. This is because they are likely to receive some benefit from the subsidy, even if it is not as much as middle- or upper-income people.

Note that this is only true if they are not the ones paying the bill for the subsidy. If low-income people are footing the bill, they could end up less well-off than they would if the program were not in place.

As for regulations, Sunstein argues that it all depends on incidence. If low-income people accrue much of the benefits of a regulation and little of the costs, they will of course benefit. But if they pay a large proportion of the costs and accrue little of the benefits, they will do poorly under a situation where their value of a statistical life is assumed to be higher than it is.

Overall, what I take from this essay is that decomposition of benefits and costs matters. We should know who benefits and who will pay for a regulation not only for equity reasons, but for efficiency reasons, too. This will give us a better idea of what really happens when we put a regulation in place.

How can we do tax cuts better?

Currently, the Ohio House and Senate are in negotiations over the Ohio state budget. The budget for the Ohio House included broad-based income tax cuts while the budget for the Ohio Senate focuses on business and income tax cuts that largely benefit upper-income residents.

A recent analysis by Richard C. Auxier and David Weiner of the Urban-Brookings Tax Policy Center looks at an alternate strategy for tax cuts and its impact on the income distribution in Ohio. Rather than approaching tax cuts through reductions in income tax rates, this analysis looks at how a child tax credit would impact household incomes. The analysis looks at a proposal for a child tax credit that is cheaper at $550 million per year than the current Senate tax cut proposal of $780 million per year.

Using the TPC state tax model, they find the Ohio Senate’s tax reduction would lead to no additional income for households making under $50,000, less than $100 annually for households in the $50,000 to $75,000 range, a few hundred dollars annually for families in the $75,000 to $200,000 range, and over $800 annually to the average family making more than $200,000.

As we have written before, this sort of distribution of public funds can be undesirable from a welfare standpoint because households with more income have less use for additional dollars than low-income households.

A $250 tax credit for households with children, on the other hand, would have benefits distributed throughout income categories. Their analysis projects that the average household across nearly every income bracket would save $100 under the proposal. The results are even more dramatic for families who would receive the credit, with them receiving an average of about $400 per household across all income brackets. This impact comes 30% cheaper than the Senate proposal, suggesting the intervention could be more equitable and cost less than the current proposal.

Why does the child tax credit offer benefits to low-income individuals that overall tax cuts do not? This is because upper-income households pay more in income taxes than lower-income households currently, so they have more to gain from an income tax cut. A flat child tax credit, on the other hand, applies to all households with children.

Their analysis also puts forth a number of policy alternatives that can be adopted by policymakers in Ohio. This includes a couple of cheaper policy options than the $550 million child tax credit proposal including a $440 million proposal that limits benefits to young children and a $315 million proposal to make the CTC nonrefundable, limiting its benefits to middle- and upper-income households. They also offer a $640 million proposal to make the state earned income tax credit refundable, which would help lower- and middle-income households. All three of these alternatives are more affordable than the current $780 million income tax cut proposed by the senate.

Analysis like this is important because it challenges policymakers to get creative. If policymakers want to reduce taxes, they can do that in a way that also promotes equity goals. It just means taking potential policy options seriously.

How does inequality impact well-being?

Our most recent published study looking at GPI in Ohio has received some coverage recently. As a result, I wanted to revisit it one more time to cover one of the most significant differences between GPI and GDP: the income inequality adjustment. 

In Ohio, we estimate that income inequality costs the state over $100 billion annually, by far the largest cost measured by GPI.

Taking a step back, we should think about why we want to measure the cost of income inequality. People have very different opinions about how much inequality is acceptable. Most people tolerate some of the inequality that comes with more efficient growth (e.g. employees might get small raises when their company’s profits double), but find extreme inequality undesirable.

From a theoretical perspective, this ties into the idea of the marginal utility of money, in other words how much our money is worth to our well-being. If your first thought is that every dollar is the same, consider this example. 

Imagine two people. Person A spends $300 a month on food, never goes hungry, and budgets carefully. Person B spends $3,000 a month on food and goes to expensive restaurants regularly. Is person B really getting 10 times the value from their money as person A?

We should expect person B to have some added value from their extra $2,700 a month in food spending: you don’t have to prepare your own food at a fancy restaurant and it is a form of recreation that is valuable. Still, it is unlikely that person B is getting 10 times as much value from that spending as person A.

Another way to think about it, imagine instead of person B living lavishly, they adopted the same budget as person A, and used the leftover money to feed 9 additional people on the same budget who would otherwise have to skip meals or stretch their budget to eat. Surely those people would receive more value for that money than person B otherwise would.

So how do we factor this intuitive insight into an economic model?

When calculating GPI, we follow the approach used by John Talberth and Michael Weisdorf. They propose a logarithmic adjustment for the diminishing marginal utility of income. Essentially, $100 is worth less to a rich person than a poor person, and is worth almost nothing to the people with the highest incomes.

There are a few key factors in this adjustment. First, we only adjust incomes above the median. This assumes that people earning less than or equal to median income in a given area are receiving the full economic benefit of their income.

Additionally, because of the logarithmic reduction in utility, very high earners contribute very little additional benefit to the economy with their consumption. The natural extension of this idea is that these individuals would contribute more to the society if they didn’t consume all of their resources, but rather distributed them throughout the income distribution.

A final consideration with the income inequality adjustment is that more than any other indicator, it measures the efficiency of our economy rather than the effectiveness of it. Other costs like pollution and underemployment are designed to shine a light on tangible economic activity that GDP ignores, but this adjustment is an inherently distinct concept.

Its inclusion is certainly relevant, there is research showing that including measures for inequality is important to public policy. Despite the fact that it is the most unique of all the measures in GPI, accounting for income inequality helps us better understand how our economy truly is faring.

Economists agree subsidies will improve housing affordability

In a survey released this morning by Scioto Analysis, the majority of economists agreed that a tax credit for low-income housing developers would significantly lower housing prices for low-income renters.

This policy has been a point of contention between the Ohio House of Representatives and the Ohio Senate. House lawmakers supported spending $500 million on tax credits for low-income housing developers in their most recent budget proposal, but the Senate removed this item.

Among economists who agreed, questions remained about whether this policy would be an efficient use of government resources. As Jonathan Andreas of Bluffton University wrote, “housing costs (particularly for starter homes) are higher now than they have been historically because of a supply problem… I doubt this is the most cost-effective way to increase the supply of housing, but the best way is often politically infeasible and this should increase the supply of housing.”

Charles Kroncke from Mount St. Joseph university strongly disagreed that this policy would lower prices for low-income renters, writing “only if government mandates specific rental prices. Even then, I would expect developers to have a preference for market priced developments.”

Economist opinions were mixed on the prospects of these subsidies for economic growth or reducing income inequality.

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.

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.