The Economics of Social Distancing

By now I’m sure you know that the phrase of the week is “social distancing”—the act of maintaining distance from others in order to slow spread of disease. Social distancing can mean anything from cancelling of public events to workplace interventions to self-quarantine measures, all with the goal of reducing contact between people to reduce the number of new infections.

Social distancing measures have benefits. The benefit getting the most attention recently is the reduction of strain on the health care system. An epidemic can put strain on capacity of hospitals and clinics, making it harder for the system to prioritize harsher cases and potentially increasing the fatality rate of a disease. The goal of social distancing on this front is to “flatten the curve” of disease outbreak by slowing the spread of disease and spreading new cases out over a longer period of time so the health care system can better manage new cases.

Social distancing can also strategically protect more vulnerable populations and can potentially buy more time for development of treatments and prevention methods.

At the same time, social distancing has costs. Cancelled events means lost experience for attendees and revenues for hosts. Closed workplaces can mean less work for employees and less value for employers. Closed schools and conferences mean reduction in human capital development and exchange of ideas.

In a way, social distancing is different from a lot of interventions analysts study because the benefits are easier to measure than the costs: less people die when social distancing is implemented. That being said, every good analyst needs to ask the question of how much life is preserved through measures as opposed to how much life experience is destroyed through social distancing. After all, social interaction is a cornerstone of much of the theoretical and empirical evidence we have around well-being.

One way to estimate the tradeoffs individuals face when deciding whether to engage in social distancing is to see how individuals mitigate risks to life in other areas. In formal cost-benefit analysis, this is referred to as the “value of a statistical life”, or how willing people are to trade off fatality risks for monetary compensation. The current best practice for determining what people are willing to trade for fatality risk is by comparing compensation of more- and less-risky jobs and how willing people are to take on higher wages for accompanying higher fatality risk in the workplace. Using this technique, the current estimate of how much people value their lives in the workplace is $11 million.

Using this number, we can conduct some conservative individual-level cost-benefit analysis to estimate how people trade off coronavirus infection risk with the costs of social distancing techniques. For instance, if the median household income in Ohio is roughly $55,000, this means two weeks off work unpaid costs the average family about $2,100. Under the most conservative of scenarios using the low fatality rate in South Korea of 0.8%, that means an averted case of coronavirus would be worth about $92,000 to the average household using the standard value of a statistical life. This includes only fatality risk and not other medical costs that are more common with coronavirus, so this should be considered a low-end estimate of the cost to a household associated with coronavirus infection. With fatality rates ranging from 0.8% in South Korea to 6.6% in Italy, the fatality risk reduction value of avoiding a coronavirus infection at the household level could be as high as over $700,000.

Fatality rates as of 6:00am Thursday morning.

Under the conservative relatively low-risk 0.8% fatality rate scenario, if a family thinks taking two weeks off of work unpaid would allow a family member to avoid a 2.3% chance of contracting coronavirus, it makes economic sense for the household to do so on avoidance of fatality risk alone. With as much as one percent of the Ohio population assumed to be infected and coronavirus’s high infection rate, this does not seem to be a ridiculous assumption to make. In the opposite extreme case of an Italian-level 6.6% fatality rate, even avoiding a 0.3% chance of infection is worth the cost of two weeks’ wages.

It also should be noted that a two-week unpaid quarantine is an extreme measure. There may be lower-cost interventions for families such as telecommuting, avoiding mass gathering, and simple sanitary activities that could yield huge savings for households in reduced fatality and medical risks.

That being said, this analysis applies to the average household. Lower-income households might have less ability to mitigate risks and less flexibility in the workplace. Nonetheless, actors like state and local departments of health and broader state and local government want to encourage social distancing in these populations as well. This is where interventions like sick leave funds and even cash transfers could encourage workers to stay home and mitigate the spread of this disease.

Ultimately, social distancing will pay off for households, but public sector actors have tools to create stronger encouragement by reducing the cost of social distancing. Paying attention to both sides of the ledger for households will make interventions to promote social distancing more effective than they would be otherwise.

What Ohio can do to fight coronavirus

Last week, Nancy Messonnier, Director of the Centers for Disease Control and Prevention’s National Center for Immunization and Respiratory Diseases, released a statement on coronavirus’s likely spread to the United States, saying that “It’s not a question of ‘if,’ but rather a question of ‘when’ and how many people in this country will have severe illness.”

Coronavirus is coming to Ohio. The question is what we will do about it as a state. 

While the federal government has done a commendable job of chipping in to fund programs to reduce opioid overdoses as a part of what many are calling the worst drug epidemic in U.S. history, many public health officials have had trouble figuring out how to effectively spend money to reduce deaths.

For infectious disease, though, we have a playbook. Below are four tools policymakers have for reducing the impacts of infectious diseases, roughly in order of cost-effectiveness.

Vaccination

We’ve all heard it: vaccination is the best way to protect yourself and others from infectious disease. The problem we currently have with the coronavirus is that a vaccine will not be created at least for a few months and will not be widely available for as many as a few years. 

When this vaccine is made available, vaccination campaigns and interventions to make them available and cheap will be a cornerstone of the response, but vaccines will be little help to battle the disease in the short term.

Screenings

Screening for illness is an important practice for all kinds of disease, ranging from substance abuse to genetic disease to infectious disease. Screenings can take place during routine or other examinations or can be administered as a standalone service as is commonplace with sexually transmitted infections. 

Ohio Department of Health programs that support targeted screenings, especially in elderly population centers since coronavirus is especially deadly for people older than age 65, could help identify cases early and provide targeted treatment to help the person infected and appropriate social distancing to reduce spread of the disease.

Social distancing

Social distancing interventions such as isolation of suspected cases, school closures, travel restrictions, and cancellation of public events are a quite extreme and costly way to reduce spread of disease. 

While voluntary quarantines can be effective in some cases, it is unlikely a highly communicable disease such as coronavirus that spreads faster than influenza can be stemmed by targeted quarantine. 

That being said, Department of Health officials promoting limited social distancing methods in locations like long-term care facilities could be an effective way to protect the most vulnerable populations from infection.

Treatment

Currently, the CDC recommends no specific antiviral treatment for coronavirus. Doctors can do their best to relieve symptoms and in the most severe cases support vital organ functions. Besides promoting seeking treatment, the state has little it can do on this front compared to a disease like opioid use disorder where interventions like medication-assisted treatment have been shown to be much more effective than alternatives.

All in all, it seems that public information campaigns and building infrastructure for screenings are the top tools the state currently has to fight coronavirus. 

Future medical science progress may lead to effective vaccination and treatment interventions, but social distancing interventions need to be taken lightly, since the personal liberty and economic consequences of such interventions can be severe. 

Like any new disease, the role of the state is to provide support without overshooting the mark and doing harm through its interventions.

This commentary first appeared in the Ohio Capital Journal.

Saving Lake Erie from farm fertilizer runoff: Ohio’s options

Last week, the Ohio Environmental Protection Agency announced via its draft 2020 water quality report that it will be developing a limit on the amount of phosphorus that can be dumped into the Lake Erie watershed.

This was a big announcement for Lake Erie. Four years ago, Ohio signed a pact with Ontario and Michigan to reduce phosphorus runoff into Lake Erie feeding the lake’s toxic algae blooms that are decimating both its ecosystem and its tourism industry. To this point, though, the state has leaned on cleanup dollars and tech incentives to reduce phosphorus runoff, leaving the state well short of the 40% phosphorus reduction goal in the 2016 agreement.

This element of the water quality report signals the state’s first willingness to tackle algae blooms directly. But what options does the state have to finally choke off the flow of fertilizer running into Ohio’s most notable natural resource?

Phosphorus Caps

The most straightforward way to reduce phosphorus runoff would be to enforce a 40% reduction in phosphorus runoff distributed between Ohio’s farms. This approach would mean apportioning limits to phosphorus-based fertilizer to all the farms in the state and enforcing these limits through fines if farms exceed their limit.

While this approach would certainly achieve the goal of reducing phosphorus runoff by 40%, it would also come with two challenges. First would be enforcement. Monitoring and levying penalties cost resources and those would have to be expended in order to give the caps teeth. This would likely be a more cost-effective strategy than current education and cleanup strategies, but could still be costly.

A bigger issue is the economic cost of phosphorus caps. Presumably, larger farms could absorb the costs associated with more expensive or less use of fertilizer more than smaller farms, so caps might need to be set lower for larger farms and higher for smaller farms. It will be hard for the state to equitably and efficiently set caps in light of these complications.

Tradable limits

Enter limit trading. One way to get the benefits of phosphorus reductions at a lower cost than state-mandated caps is to set caps but then allow farms to trade allowances for phosphorus runoff with one another. This will allow less fertilizer-intensive, smaller, and more innovative farms to sell their allowances to more fertilizer-intensive, larger, and less efficient farms, providing incentives for innovation and reductions in pollution while apportioning limits more efficiently. 

While some may balk at allowing a market in pollution, this is the strategy the state of California has taken to reducing carbon emissions and allows regulators to balance the benefits of runoff reduction with the benefits of farm innovation.

Usage Fees

A final strategy would be to attach a usage fee to fertilizer. This would theoretically work similar to tradable limits on phosphorus runoff because it would essentially price the use of fertilizer but would require the state to estimate the correct price needed to bring about sufficient reduction in phosphorus runoff. 

The benefit of a usage fee, however, is that it would greatly reduce monitoring and compliance costs since it would come in the form of a fee paid on each unit of phosphorus fertilizer sold. Fee administration would have a cost, but this cost would likely be much lower than monitoring and enforcing limits for dumping on farms across the state since it would be levied at the retail level. Fees could be spent on cleanup, environmental measures, or even remitted to farmers to reduce their costs incurred while still maintaining the incentive to reduce pollution.

Each approach has costs and benefits, though hard caps may only have political benefits with significant economic costs for farmers without the benefits that tradable limits or usage fees provide. 

It will be up to the EPA and other policymakers to decide which path they want to take to mitigate this significant environmental issue for northern Ohio.

This commentary first appeared in the Ohio Capital Journal.

Should states tax inheritances?

With Iowa and New Hampshire in the rearview mirror, many are predicting a collision course between leftist populist Bernie Sanders and centrist technocrat Michael Bloomberg. Despite their obvious differences, the two candidates share one thing in common: they both have endorsed an expansion of the federal estate tax, a tax on wealth passed from one generation to another. An estate tax is a brand of inheritance tax, designed to reduce inequality by reducing the amount of wealth that can be passed on from one generation to the next and thus limiting intergenerational perpetuation of inequality.

But states that are interested in reducing intergenerational inequality don’t have to wait for the federal government to levy an estate tax. Currently, seventeen states have either an estate tax (assessed on the estate of wealthy people) or a direct inheritance tax (assessed as income for a beneficiary of an inheritance).

Ohio does not currently have an estate tax, since its was repealed in 2013. Ohio’s estate tax was previously a significant source of revenue, raising between $25 and $170 million per year from 1975 to 2014 according to Ohio Legislative Service Commission historical numbers. When comparing the estate tax to Ohio’s GDP, it was fairly stable early on, raising between 0.025% and 0.035% of GDP from the mid 70s to the mid 80s save a spike in revenue in 1983. After dipping to 0.02% of GDP in 1985, the estate tax increased steadily until it hit a localized peak in 2001 at a little over 0.04% of GDP. Estate tax reform at this point cut the relative size of the estate tax by three quarters by 2006, then subsequent reform all but eliminated it by 2015.

Data from the Ohio Legislative Service Commission and the Bureau of Economic Analysis. GDP for 2019 imputed using historical GDP growth trends.

Ohio’s neighbors take a mixed approach to taxation of inheritance. Indiana, Michigan, and West Virginia do not have inheritance taxes, having effectively repealed their own estate taxes in 2013 in Indiana and 2005 in Michigan and West Virginia. Kentucky, on the other hand, has a fairly substantial inheritance tax, which applies to extended family and beyond but covers a large proportion of inheritance transfers. This results in a tax that raises about $50 million per year, which comes out to about 0.025% of GDP, about the rate of Ohio’s estate tax in 1990. Pennsylvania’s inheritance tax is even broader, covering all transfers besides transfers to spouses and parents and raising about a billion dollars per year. With these elements, the estate tax amounted to about 0.128% of state GDP in Pennsylvania in 2018, making it about fives times as large a program as Kentucky’s on a relative scale and twice as large as Ohio’s at its 1983 peak.

If Ohio instituted an inheritance tax at Kentucky’s rate, it could raise about $180 million in revenue in 2020 if the state grows at historical rates. If its inheritance tax were closer to the size of Pennsylvania’s, it could raise revenue of about $960 million. For reference, with $180 million Ohio could fund all of its state Supreme Court and Judiciary services for the year and with $960 million it could fund the entire operations of the Ohio Department of Job and Family Services, in charge of all state job, unemployment, food assistance, and child support services. An inheritance tax for the state of Ohio could potentially reduce inequality and raise revenue for state services at the same time.

The fiscal cost of Ohio’s death penalty

In the spring of 2015, I was living in Omaha, Nebraska, and my friends and I, being the political junkies we are, were all watching as the Nebraska legislature voted one by one to override the governor’s veto to end the use of the death penalty in the state.

It wasn’t the problem of potential misplaced justice that ultimately led to that repeal. It wasn’t the well-documented racial disparities in death penalty sentencing. It was the problem of costs.

“If any other program was as inefficient as this, we would eliminate it,” said Republican state Sen. Colby Coash, a lead sponsor of the Nebraska bill.

Earlier this week, Ohio House Speaker Larry Householder channeled Coash, saying he looked at the issue “from a pure fiscal standpoint,” arguing the law is leading to higher costs without better outcomes.

The weight of the evidence suggests that death penalty cases are more expensive than life without parole cases: a 2016 fiscal analysis reviewed fourteen state and federal studies on the topic and all found death penalty cases to be more expensive than life without parole cases, usually to the tune of $1.2 million more spent on death penalty cases in 2015 dollars. 

The reason for this disparity in cost is that death penalty cases are usually much longer and require more defense attorneys and cases to make final determinations, much of which is due to US.. Supreme Court rulings on the practice.

Unfortunately, we don’t have good data on costs associated with the death penalty in Ohio. A study comparing costs of death penalty cases and life without parole cases in the state of Ohio hasn’t been conducted in over a decade.

Despite this lack of state-specific data, Ohio’s Legislative Service Commission, the research arm of the state legislature, has weighed in on this topic. 

In a 2018 fiscal note on a bill that would have prohibited death penalty sentences for people with “serious” mental illness, the Commission found that studies of the death penalty in other state suggested capital cases could cost $1-3 million more than life imprisonment cases and that capital cases cost 2.5 to 5 times as much as non-capital cases.

The Indiana Legislative Services Agency also conducted an analysis of the death penalty in 2010. In this study, the Agency found that the average death penalty case in Indiana from 2000 to 2007 cost about $170,000 more than the average life without parole case, which comes out conservatively to about $210,000 in 2020 dollars.

The state of Ohio currently has a total of 138 people on death row. This means that if these cases were tried as life without parole, the state could have saved $29 million under the more conservative Indiana Legislative Agency numbers to over $400 million under the higher-end Ohio Legislative Service Commission estimates.

Most people come to their opinion about the death penalty for moral, ethical, or religious reasons. But a low-end estimate of the cost of this seldom-used program could fund state fraud prevention efforts for a year and a high-end estimate could fund all the school construction in the state for a year. Whatever your moral commitments, those are numbers worth paying attention to.

This commentary first appeared in the Ohio Capital Journal.

Medicaid Restrictions Could Impact Infant Mortality

If you’re involved in the public health world in Ohio, you’ve certainly heard about the problems the state has with infant mortality, with the state ranking in the top ten states for infant death rate according to the CDC. At the same time that Ohio struggles with infant mortality, federal regulators have made news by authorizing states to block grant a portion of Medicaid dollars, potentially reducing access to Medicaid coverage by low-income adults. State lawmakers have been skeptical of adopting such a plan at this point, though the legislature does have a track record of restricting access to the program in the past.

It should be noted that federal officials have stated pregnant women will not be impacted by the block-grant scheme. That being said, limiting access to Medicaid for non-pregnant women could make it more difficult for a pregnant woman to understand when she is eligible. Also, there is some evidence that states with Medicaid expansion have shown progress on infant mortality despite pregnant women not being a part of the expansion population, suggesting that either the policy is making it easier for pregnant women to care for their children or that some other factor is at work in expansion states that is leading to reductions in infant mortality rates.

This study is one of the latest in a long list of studies establishing a link between Medicaid coverage and infant mortality. While experimental evidence of Medicaid’s impact on physical health is famously lacking, quasiexperimental evidence of the nation’s largest safety net program’s impact on infant mortality has been mounting for the past twenty-five years. One study suggested higher Medicaid payments have significant impacts on infant mortality rates while another on Medicaid eligibility suggested just being eligible for Medicaid had an impact on child mortality. A later study found higher Medicaid payments could reduce incidence of low birthweight, a significant risk factor for infant mortality. Another study found that expansions of Medicaid access can lead to lower fetal death rates and a more recent study found access to care can reduce racial gaps in infant mortality.

The fact that most of these studies deal specifically with pregnant women as a population suggests that exempting pregnant women from policies that could restrict access could save lives for children down the road. But restrictions for other populations could have spillover effects as well as women find it harder to identify when they are eligible. Care should be taken to craft Medicaid policy that lines up with state public health goals because, as this body of evidence shows, access to care can impact the lives of infants.

Ohio Minimum Wage Hikes Would Likely Increase Wages, Decrease Employment and Poverty

The minimum wage conversation has come to Ohio. Earlier this week, Ohio Attorney General Dave Yost certified ballot summary language for a proposed Ohio constitutional amendment to raise the state’s minimum wage from the current $8.70 to $13 per hour. This proposal, backed by state service worker and teachers unions, comes at the same time that Democrats in the Ohio House are pushing a $15 minimum wage.

So what would happen if Ohio adopted a higher minimum wage? Would it lead to higher wages and reduction in poverty as advocates for the policy claim? Or would it lead to an increase in unemployment as detractors of minimum wage hikes argue?

One place we can look for guidance is an impressive study by the Congressional Budget Office (CBO) on proposals to increase the federal minimum wage. The CBO used Current Population Survey data and available empirical data on employment elasticities to estimate the wage, poverty, and employment impacts of minimum wage hikes.

A quick and dirty way of using this data is to scale the scope of this federal study down to the size of the state of Ohio. There are some reasons this approach is limited: the CBO study includes some states that have higher minimum wages and some that have lower minimum wages than Ohio’s, which falls in the middle of the distribution of states. Ohio is a tad older, a bit whiter, and notably poorer than the country as a whole. A full study replicating CBO’s methodology would be valuable. Unfortunately, I only have the time to write a blog post about this, so I will stick with the imperfect scaling model, reducing CBO’s numbers to 3.6% of projected people affected and assuming linear impacts to extrapolate the impacts of a $13 minimum wage until someone will (shameless plug!) pay me to make more precise estimates. While this approach is not perfect, the direction of the impact is likely to be the same and overall takeaways similar (undermining my plug) to a more thorough replication.

Below is a chart of the projected number of people who would be impacted by the two proposed minimum wage increases based solely on CBO projections scaled to the size of the state of Ohio followed by a discussion of what conclusions we can draw from them.

Data from the Congressional Budget Office and the US Census Bureau.

Raising the Minimum Wage Will Likely…Raise Wages

This may seem obvious, but a higher minimum wage means higher wages for Ohioans—potentially a lot of Ohioans. Using this methodology, we estimate that over 320,000 Ohioans could see their earnings increase in an average week under a $13 minimum wage and almost 610,000 Ohioans could see their earnings increase in an average week under a $15 minimum wage. This represents the number of Ohioans who are currently working under the proposed minimum wage thresholds.

Similarly, 280,000 Ohioans are estimated to be just above the $13 wage threshold and 670,000 are just above the $15 wage threshold, putting them in a range that could be impacted by employers raising wages to comply with the new wage levels. This means that a total of 600,000 to 1 million Ohioans could experience higher wages under a $13 or $15 minimum wage scenario. This total amounts to 11-17% of the total workforce of the state in December 2019.

Wage increases would likely be concentrated among the poor and near-poor as well. Assuming wage increases grow linearly from $12 to $15 and that increases in Ohio would look similar to those reported in the CBO report, a $13 minimum wage would increase an Ohio family in poverty’s wages by 2.7% and a $15 minimum wage would increase its wages by 5.3%, putting hundreds of dollars in the pocket of the average poor family in Ohio. Families in the 100-300% of poverty (making on average $29,000 to $56,000 a year) would also benefit from the increase, families in the 300-600% of poverty range (averaging $95,000 a year) would be marginally impacted, and families making over 600% of the federal poverty level (averaging $230,000 a year) would see wages decrease.

Data from the Congressional Budget Office.

A Minimum Wage Hike Will Probably Reduce Employment

All increases of $12 an hour and higher estimated by the Congressional Budget Office led to a median estimate of a net decrease in employment. While an increase in the minimum wage could bring workers with higher reservation wages into the workforce and could increase employment by correcting for labor market power imbalances slanted towards employers, the CBO projects those impacts would not be as large as the classic supply and demand dynamics that lead to reduced employment in the face of higher wages.

It is worth noting that CBO’s projections all include negligible impacts to employment in its likely range of outcomes, encompassing two-thirds of potential outcomes. It is also worth noting that the high end of this range is quite substantial, with likely estimates skewed towards higher employment impacts. Thus, more extreme employment impacts exist on the high end of the estimate than the low end. Using the same methodology we used to estimate number of workers with wage impacts, we can estimate that a $13 minimum wage would lead to a reduction of 0-75,000 jobs in Ohio with a median estimate of 17,000 lost jobs and that a $15 minimum wage would lead to a reduction of 0-130,000 jobs with a median estimate of 29,000 lost jobs. For reference, Ohio added about 110,000 jobs in 2019, which means that a $13 minimum wage would cost 0-71% of 2019 job growth with a median estimate of 16% and a $15 minimum wage would cost 0-130% of 2019 job growth with a median estimate of 27%. The highest likely impact of a $12 minimum wage would be to lose 1.3% of the total December 2019 workforce size and the highest likely impact of a $15 minimum wage would be to lose 2.4% of the total December 2019 workforce size.

Data from the Congressional Budget Office and the US Census Bureau.

Minimum Wage Increases Will Likely Reduce Poverty

Lastly, the CBO estimates 400,000 Americans would be pulled out of poverty by a $12 minimum wage and 1.3 million Americans would be pulled out of poverty by a $15 minimum wage. Scaling this down to the size of the state of Ohio and assuming a linear trend in poverty reduction from $12 to $15, a $13 minimum wage in Ohio would pull 25,000 Ohioans out of poverty and a $15 minimum wage would pull 46,000 Ohioans out of poverty. In 2018, 1.6 million Ohioans were in poverty, meaning a $13 minimum wage would lead to a 0.2 percentage point decrease in the poverty rate and a $15 minimum wage would lead to a half percentage point decrease in the poverty rate.

It was stressed above and it is worth stressing again: the precision of these estimates could be honed by a more thorough investigation of state-level data. That being said, the takeaways that a minimum wage would likely raise wages for a substantial portion of the low-income workforce, somewhat reduce employment, and have a modest impact on poverty would probably hold even with a more sophisticated analysis. Now it is up to policymakers and the general public to evaluate those tradeoffs and determine their taste for the levels of projected increased wages and decreased poverty in exchange for projected decreased employment.

Scioto Analysis Partnering with UC Berkeley on Genuine Progress Indicator Study

This Spring, Scioto Analysis will be partnering with UC Berkeley’s Goldman School of Public Policy on an update to its November 2018 study of the Ohio state economy. The research team will use the Genuine Progress Indicator framework, a “GDP+” measure that combines traditional economic indicators with environmental and social indicators, as a baseline for economic growth then will explore policy options for policymakers interested in growing the state economy effectively, efficiently, and equitably.

“This partnership is exciting because it will extend the findings of our 2018 study past descriptive statistics and into the territory of policy analysis,” said Scioto Analysis Principal Rob Moore. “The point of this measure is not only to inform policymakers of the state of the economy, but also to provide guidance to policymakers who want to improve it.”

The research team consists of the following researchers, all graduate students in the Master of Public Policy Program at UC Berkeley’s Goldman School of Public Policy.

• Cruz Eduardo Flores Vera, Economic Analyst, who previously worked for the Central Bank of Mexico doing analysis of energy markets.

• Masashi Hamano, Environmental Analyst, who previously worked for the Ministry of Finance in Japan as a budget analyst.

• Isabel Clayter, Social Analyst, who previously worked in financial technology and consumer lending in San Francisco, focusing on consumer finance.

• Ashwin MB, Environmental/Social Analyst, who most recently worked for the Abdul Latif Jameel Poverty Action Lab in New Dehli, India studying governance and transportation interventions.

The research team plans to focus on GPI measurement over the next month then turn to policy analysis in March and April.

Housework and Parenting in Ohio is a $92 Billion Industry

Scioto Analysis estimates that the value of housework and parenting in the state of Ohio in 2016 came out to about $92 billion. For reference, this total amounted to 29% of the state Genuine Progress Indicator (GPI), a “GDP+” measure that includes environmental damage and social indicators such as the value of higher education and the cost of lost leisure time next to traditional economic indicators.

If we compare this estimate to data on other industries in the state of Ohio that are measured to calculate GDP, we can see that housework and parenting would be a larger industry than any other in Ohio besides manufacturing if it was measured as such. The value of housework and parenting is 28% larger than the state professional and business services industry, 33% larger than both the state real estate/rental/leasing industry and the government industry, and is two-thirds larger than both the finance/insurance and health care industries.

Data from the Bureau of Labor Statistics, American Time Use Survey, and Bureau of Economic Analysis

Scioto Analysis calculates the value of housework and parenting using standard methodology for calculation of state GPI. First, we use American Time Use Survey data to estimate how many hours a day the average adult spends on household activities and caring for and helping household members. We then multiply these numbers by average housekeeping and child care wages respectively to determine what the daily value of these activities would be in the form of market wages. These are then multiplied by the number of people in Ohio age 15 and up to reflect the population engaging in these activities and then multiplied by 365 to convert from individual daily value to statewide annual value.

From an efficiency standpoint, this does not tell us a lot. What it says is that $92 billion of activity is generated by workers who are paid nothing for what they do. This sort of analysis does not tell us whether these activities have social benefits (though at least for childrearing it would be surprising to find that that they don’t), just that people are doing a lot of unpaid work.

From an equity standpoint, standard economic theory would suggest that low-income people will be more likely to engage in more hours of housekeeping and caring for household members since they have less opportunity to make high wages in the market. Thus, providing supports for people engaging in these activities may be a way to deal with inequities in human capital. Andrew Yang has made such arguments when championing UBI.

A big reason this number matters, though, is that raising children is central to what many people consider “the good life.” Surveys suggest that child-related activities score higher than all leisure activities besides sex on self-assessed enjoyment among women and men. While economic development strategies that increase employment may lead to higher economic output as measured by GDP, they may be neutral or even harmful to well-being when factoring in the second-largest industry: housework and parenting.

Does Fade-Out Fade Out?

Early childhood education has a problem in the research community, and it’s called “fade-out.” If you work in early childhood research, you’ve heard this term before. If you haven’t, the general problem is that some research suggests that gains from early childhood education programs realized by children as they enter kindergarten “fade away” in elementary years to the point where they are nothing by third grade. If this is true, then early childhood education programs could be expensive programs that ultimately yield little results for children or families.

The savvy policymaker, though, will ask the following question: why should I care about third-grade test scores? Well, third-grade test scores are nice because they allow us to evaluate a program after a couple of years rather than decades. The problem with third grade test scores is that they might be the least predictive results to extrapolate to life outcomes.

Timothy Bartik, a leading economist of job creation, has studied the impact of early childhood education on local wages. In his book From Preschool to Prosperity: The Economic Payoff to Early Childhood Education, Bartik tackles the question of fade-out. He looks at the four most high-profile studies in early childhood education: experimental studies the Abecedarian Project and the Perry Preschool Project, and quasiexperimental evaluations of Head Start and the Chicago Child-Parent Center Program.

Bartik, From Preschool to Prosperity: The Economic Payoff to Early Childhood Education.

As can be seen above, estimates of adult earnings based on third grade test scores are below the estimates of adult earning effects at end of the early childhood program. Thus, Bartik does find evidence for fade-out. However, Bartik also finds evidence of a strong bounce-back from fadeout, with actual adult earnings higher not only than third grade test scores would predict, but also higher than the original end-of-program test scores would have suggested.

These results are also reflected in cost-benefit results reported in a high-profile literature review by the Rand Corporation. The review reported the results of 12 evaluations and meta-analyses of early childhood education programs, some broken out into treatment group subcategories. While the evaluations that only included results from elementary school tended to show negative results, evaluations that followed up with participants in secondary school, early adulthood, and middle adulthood showed increasing net benefits.

Data from Karoly et al., Early Childhood Interventions: Proven Results, Future Promise, pp.xxvi-xxvii

According to Rand,

The largest benefit-cost ratios were associated with programs with longer-term follow-up because they allowed measurement at older ages of outcomes such as educational attainment, delinquency and crime, earnings, and other outcomes that most readily translate into dollar benefits (p. xxv).

This suggests that third grade scores could be missing latent human capital that was built by early childhood scores such as socialization, emotional intelligence, or verbal communication skills that then end up leading to future education, crime reduction, and labor market outcomes.

Ultimately, third grade test scores have little social relevance on their own: they are only a proxy for future outcomes with immense social importance: educational attainment, crime victimization, and labor market earnings. While third grade test scores let us more quickly assess the impact of policies, if these scores are not correlated with socially-relevant outcomes, they are not of much use to policymaking. Maybe the problem isn’t the policies: it’s the limitations of how we test their impacts.