Why carbon prices matter

Last week, New Republic Staff Writer Kate Aronoff penned a commentary arguing against approaches to climate action like carbon taxes and cap-and-trade programs. Her core argument in the commentary is that proper climate action is a question of trust and that public sector actors are more trustworthy than private actors when it comes to addressing climate change.

Aronoff makes some good points throughout her commentary. She hits on a core problem with climate change: left to their own devices, households have a strong incentive to emit carbon because their private benefits are much higher than their private costs. This is classic “tragedy of the commons” problem–a stable climate is a common resource. If no one is managing that resource, individuals will deplete it until it no longer exists.

This is a problem that was pioneered by Nobel Prize-Winning Economist Elinor Ostrom. Ostrom did groundbreaking work describing how common property can be successfully managed by groups that use it.

In an analysis Scioto Analysis conducted a few years ago, we looked at options for the state of Ohio to reduce carbon emissions. The tools we analyzed were a renewable portfolio standard (a mandate for utilities to generate a certain percentage of energy from carbon emissions), a cap-and-trade system (a limit to carbon emissions managed by the state), and a tax on carbon. Ultimately, when estimating the impact of each of these interventions, we found that any of these interventions would be highly preferable to the status quo, each with the ability to abate hundreds of billions of dollars worth of carbon emissions compared to the status quo.

So you have some policy analysts like us who have found compelling evidence that a cap-and-trade or carbon tax program would have a significant impact on carbon emissions in the state of Ohio. Why, then, does Aronoff say “[c]arbon pricing has thankfully fallen out of favor among wonks and lawmakers?”

There are two elements of Aronoff’s claim that are worth exploring. First, is the empirical claim that wonks and lawmakers do not favor carbon pricing.

It has been a few years since the IGM Forum has polled its national panel of economists on carbon pricing, but last time it did, the opinions were overwhelmingly positive. In a March 2021 survey, 91% of economists agreed increasing the price of carbon was sound policy, with 79% “strongly agreeing” and only 2% uncertain. No economists disagreed with the statement. In a November 2021 survey, 75% of economists agreed a global price floor on carbon emissions would be an effective tool for achieving sharp reductions in global carbon emissions. I am not aware of any evidence that this overwhelming economic consensus has deteriorated.

Meanwhile, 53 countries and 40 subnational jurisdictions have implemented some sort of carbon price. These range from 12 US states in the Northeast and West Coast to high-income countries like Canada, France, Germany, and the United Kingdom all the way to middle-income countries like China, Kazakhstan, Mexico, and South Africa.

What Aronoff may be referring to is some high-profile conflicts that have emerged over carbon pricing. Hedge Fund Executive Brian Heywood has been leading an effort in Washington State to repeal their cap-and-trade program through a ballot initiative. Canada’s Conservative Party has made their country’s carbon tax a rallying cry in their current effort to take back their government for the first time in a decade.

So yes, carbon pricing has its critics among lawmakers, though I don’t think Heywood and Canadian conservatives were ever particularly fans of carbon pricing mechanisms. But if we were in this universe where experts were fleeing from the consensus that carbon pricing is an effective tool to reduce emissions and lawmakers across the world were repealing these laws, why would we be thankful for this?

Aronoff’s argument against carbon pricing is that economics is wrong. The core argument she lands on at the end of her commentary is that “Only governments are equipped to make the kinds of plans that will keep people safe as temperatures rise.”

There is a bit of a conflation of two ideas here. Aronoff spills a lot of digital ink arguing against the idea of carbon pricing, which is a climate mitigation strategy, not adaptation strategy. In layman’s terms, this means carbon pricing is put in place to reduce the rise in temperatures. Adaptation strategies, like deciding where to live as climate changes, are strategies we deploy in light of temperatures rising.

What Aronoff claims throughout her commentary is that people are not capable of making decisions that are in line with the common good. She argues that key decisions are best left up to government because if people consume energy as they like or more to where they would like, it will lead to depletion of these common resources that we are talking about.

What she misses in this analysis is that government always relies on individuals to carry out its mandates. Tackling climate change is not something government can do on its own: it is going to take action by individuals to make sure that our stable climate resource is not depleted and that we take the necessary steps to manage infrastructure and our economy in the face of what climate change does occur.

On the other hand, individuals, left to their own devices, have little reason to act altruistically. Given the chance to help their family by purchasing electricity or have a marginal reduction in carbon emissions, by and large they will put their family first.

That is why carbon pricing matters. If people don’t see the cost of carbon on price tags, they will not change their consumption habits. The strange irony of arguing government needs to throw the kitchen sink at carbon emissions is that carbon pricing is the kitchen sink. It’s an impediment to actors throughout the economy contributing to a carbon-intensive economic system that will spur a host of economic adaptations that will reduce emissions.

So no, government can’t fight the climate on its own. And neither can markets. Only a market regulated by the public sector will be able to do that.

How to count non-human animals in cost-benefit analysis

Earlier this year, former OIRA Director and leading regulatory scholar Cass Sunstein published an article in the Journal of Benefit-Cost Analysis on why regulators should consider the impact of policy on non-human animals.

This is a topic we have written about at Scioto Analysis before. Non-human animals are often left out of cost-benefit analysis, with impacts of regulations on the lives of non-human animals getting a de facto value of zero in regulatory analysis.

Dave Weimer, a leading scholar of cost-benefit analysis, wrote a prominent article in 2019 on the “value of statistical dog life.” In this article, Weimer conducted a contingent valuation study where he surveyed dog owners on their willingness to pay for vaccines that would marginally decrease the mortality risk for their pets. Using this method, Weimer estimated a value of statistical dog life of $10,000.

While this is a step in the right direction for valuation of non-human animal life, this approach still derives the value from human markets using the value human beings place on non-human animal lives. This makes sense if human beings are the only people who participate in markets and economize their resources. And this is the general argument analysts make who exclude non-human animal costs and benefits from cost-benefit analysis: non-human animals do not take part in markets, so it makes little sense to designate market values to their welfare. Instead, the welfare of non-human animals should be treated as a consideration separate of cost-benefit analysis.

The problem with this reasoning is that non-human animals clearly manage scarce resources and engage in market-like behaviors with one another. Chimpanzees have been observed to engage in grooming and food sharing that mimics markets, with grooming serving as a form of currency. Capuchin monkeys have been able to intuit currency in laboratory settings, even demonstrating behaviors like price sensitivity and demand elasticity. A 2017 Bloomberg article detailed the many ways markets have arisen among non-human animals, ranging from wasps engaging in childcare markets to cleaner fish deciding who to eat dead skin and parasites off based on quality choices.

Sunstein says nonhuman animals pose an important challenge and problem for analysts conducting cost-benefit analysis. Last year, the Office of Management and Budget conducted the largest overhaul of Circular A-4, the federal regulatory guidance document for cost-benefit analysis, in a generation. Unfortunately, the question of how to incorporate benefits and costs of nonhuman animals into federal cost-benefit analysis did not receive any treatment in this revision. Sunstein calls this a “missed opportunity” and I am inclined to agree.

Sunstein goes back to the history of the value of statistical life to illustrate the complexity of valuing the benefits and costs of nonhuman animals. Value of statistical life began as a measurement used to estimate the cost of retraining bombers during the Korean War. Later iterations focused on future wages as a way to estimate the value of life. Most recently, value of statistical life is estimated by using labor market data. Economists are able to estimate the value people place on minute reductions in risk of death by seeing how different occupations at different skill levels are paid. This has led to a value of statistical life estimate that hovers around $10 million.

An analogue to the problem of estimating costs and benefits for non-human animals is the problem of estimating the value of statistical life for children. Children do not participate in the labor force, so we do not have the datasets we have to estimate the value of statistical life for children that we do for adults. This leaves us with options to either assume the value of statistical life for children is the same as it is for adults or find another way to estimate the value of statistical life for children. Often, researchers will use contingent valuation or revealed preference studies of parents to estimate the value they put on children to try to get at the value of statistical life for children as estimated by parents.

The problem with this approach is the assumption of perfect altruism, which we know is not the case with parents or pet owners. There also could be an opposite problem: pets or children could (in a theoretical perfectly rational state) value other things more than minute reductions in risk of death. In this case their guardians could overestimate the value of statistical life for their wards. There is a mismatch between the preferences of pets and children and their caretakers.

Putting aside these methodological problems, many laws have been put in place specifically to protect the welfare of non-human animals. Sunstein raises the examples of the Endangered Species Act, the Animal Welfare Act, and the Marine Mammal Protection Act as three laws that were specifically adopted to protect the welfare of non-human animals. Leaving their welfare out of the calculation of costs and benefits–especially in light of the information that nonhuman animals absolutely engage in market behavior–seems to be fundamentally missing the point of this legislation and leaving key information out of analysis that is supposed to guide policymakers in implementation of the legislation.

Sunstein says in his article that the problem with incorporating non-human animals into cost-benefit analysis is not a problem of quantification, but a problem of monetization. We can create estimates for how many non-human animals will be impacted by a regulation or other policy change. The problem is trying to figure out what the value of those benefits or costs are. Sunstein grapples with the same problems we lay out here. We can use stated preference or other strategies for estimating the value of impacts on non-human animals. Ultimately, though, these strategies depend on human assessment of the value of benefits and costs to non-human animals, not assessment by those who receive the benefits and costs themselves.

With these methodological problems in place, though, what can we do? Sunstein does not proffer much within his article: he mainly focuses on the problem rather than the solution. He does put forth one tool for consideration, though: break-even analysis. Basically, see what the value of non-human animal benefit or cost would need to be to tip the scales in the benefit-cost calculation. Then we can use our intuition to see if that is a reasonable value for the given benefit or cost and apply that reasoning to policymaking.

We consider break-even analysis to be a form of sensitivity analysis, and sensitivity analysis is an excellent tool in any benefit-cost analysis where we don’t know a key input. Maybe we don’t have the answers for the economic value of benefits and costs accrued by regulation to nonhuman animals. In the meantime, sensitivity analysis gives us a technique for incorporating consideration of non-human animals into benefit-cost analysis in light of a dearth of valuation information.

What is the Marginal Excess Burden of Taxation?

Next month is the presidential election, which means it is the time of year when everyone starts to have extremely strong opinions about taxes. Depending on who you talk to, taxes might be the backbone that keeps our country functioning or the heaviest weight preventing us from achieving our true potential as a society. 

From an economics perspective, taxes are a uniquely interesting way to move money from one household to another. When conducting a cost-benefit analysis, we are agnostic about transfers of money from one place to another. While there has been a recent push to pay more attention to the distributional effects of transferring money around, classical economic theory tells us there is no net economic benefit to moving money from one person to another.

If you give your neighbor $20, the economy hasn’t grown or shrunk, a small part of it has just changed hands. In terms of the value those $20 create, it is unlikely that your neighbor will use it in a dramatically more efficient way than you. $20 in your hands is equal value to $20 in anyone else’s hands.

This logic changes when we talk about transferring money to the government (i.e. taxes). There are two main reasons why:

1. People change their behavior in response to changes in their taxes.

2. The government can spend its money on providing public goods.

Say the government wants to fund an expanded Child Tax Credit and plans on raising income taxes to finance it. Individuals can see that their labor is relatively less valuable to them since more of the income will be paid in taxes. This gives them good reason to work fewer hours, instead spending time on other activities that have become relatively more valuable. 

This results in a distortion of the economy that economists call the “marginal excess burden of taxation.” When we do a cost-benefit analysis, this marginal excess tax burden is what we estimate as the social cost of raising taxes. Again, we are agnostic about transfers because a dollar in one person’s hands is no better than the dollar anywhere else. What we care about is how this policy change impacts the way people spend their time, which then changes the size of the economy. This distortion is also the justification for the “all taxes are bad” crowd. 

However, taxes don’t just sit in a vault somewhere, they often get used to fund important public programs. Public infrastructure, our social safety net, public schools, all of these need money to function. Additionally, because these are public goods they suffer from the free rider problem. Essentially they can’t be funded privately, because there are no incentives for people to pay for these goods. 

So, how can we tell if a tax policy change is worth it or not? We should figure out whether the dollars brought in by that tax are going to fund something that creates more economic value than the marginal excess burden of taxation we are incurring. 

There is another key tradeoff of public policy: equity vs. efficiency. Yes, in a vacuum taxes are an inefficient way to collect money because they create a drag on the economy. However, not collecting any taxes would lead to massive gaps in equity as people with fewer resources would not be able to access all sorts of things that are publicly provided by the government. 

We still have a month to go before the election is over, so we should expect the rhetoric about taxes to stay extremely polarized for the time being. Hopefully, once we know who the next president is we can begin to have more productive conversations about tax policy and its implications on the economy. 

Ohio tackles the “benefits cliff”

Earlier this month, the Ohio Department of Job and Family Services announced it was expanding eligibility for SNAP benefits, the program formerly known as “food stamps.”

This may come as a surprise to those who follow state politics in Ohio, which I assume most who read the Ohio Capital Journal do. But the expansion is aimed toward fixing a problem that people across the political spectrum are worried about: the “benefits cliff.”

The benefits cliff is the buzzword (“buzzphrase?”) used to describe a prevalent problem in policy design. A means-tested program focuses its funds on low-income households. This often means limiting eligibility based on income. For instance, a cash assistance program could limit eligibility to households with incomes under 150% of the federal poverty line.

The problem with a strict cutoff, however, is that it creates incentives on either side of the cutoff that can significantly impact work decisions. For instance, if your family gets $250 a month in means-tested cash assistance and you are offered a $100 raise that will make you ineligible for that assistance, you have a good reason to turn down that raise.

Similarly, if you earn income just over the eligibility threshold, this provides an incentive for you to cut hours to gain eligibility. If you are at the cliff, you don’t want to jump off. If you are at the bottom of the cliff, you want to get back on top.

When I was in graduate school, I did my capstone project on this problem. I recommended nonprofits aiming to ameliorate the problem of the benefits cliff provide cash to families that eases off as families make more income. Unfortunately, the now-closed research firm I worked with declined to share this information with the client it was prepared for, saying this analysis was not relevant to the client’s work.

Luckily, the Ohio Department of Job and Family Services is now proving them wrong. With their new expansion of the SNAP program, the Department is providing what they are calling a “sliding scale” of benefits that extend from the previous cliff of 130% of the federal poverty line to 200% of the federal poverty line. This means basically all low-income households will be eligible for SNAP benefits, with households closer to 200% of the federal poverty line receiving lower amounts than those at 130% of the federal poverty line and below.

The benefits cliff is a policy problem of our own making. Well-designed policies can eliminate cliffs. This does not eliminate incentives: households will still have some incentive to not take raises, promotions, or more hours due to lower benefits being provided as income increases. But eliminating the cliff will make the incentives much less drastic.

This is a great example of government working. People in the business community and poverty advocates both saw this as a problem with the system. Policymakers were willing to come to the plate and make the resources available to solve much of the problem. Ohio’s benefit system will be more efficient because of this. In a time when it is easy to be cynical about what government can do, let’s applaud a clear win when we have one.

This commentary first appeared in the Ohio Capital Journal.

Federal government takes the lead in removing lead pipes

On Monday, the Biden administration issued an updated Lead and Copper rule that requires drinking water systems across the country to identify and replace all lead service lines in the next 10 years. The rule also requires more rigorous testing of drinking water systems and increased communication about the risks associated with lead in drinking water.

I’ve written about the EPA’s lead and copper rule before, mostly focusing on this paper which talks about the immense benefits that this rule could create. That same paper recently influenced our own analysis of a proposal to replace lead service lines in Ohio. 

The biggest takeaway from both of these analyses is that removing lead service lines from our drinking water systems could be one of the most valuable ways to spend public dollars we have. The damage done by lead in our drinking water is immense, resulting in a wide range of economic and health problems for everyone exposed. Our analysis found that removing every lead pipe in Ohio could result in 650 fewer infant deaths and nearly 10,000 avoided deaths from heart disease in the first 15 years. 

Not only would replacing lead service lines prevent deaths, but it would also significantly improve the quality of life for Ohioans in many other ways. The analysis revealed that over 290,000 children in Ohio would avoid losing an average of 1.25 IQ points, a benefit that would lead to $8.4 billion in future earnings over the next 15 years. This is just one example of how the removal of lead service lines is an investment in Ohio’s future, an investment that pays dividends in better health, higher productivity, and stronger communities.

Our study also highlights the mental health benefits of replacing lead pipes, estimating that 3,800 fewer cases of depression and 520 fewer cases of dementia would occur over the 15-year period. We’d also see fewer cases of ADHD, anemia, and coronary heart disease that would result in substantial economic benefits in terms of medical costs and lost productivity.

From an environmental and economic perspective, the benefits are equally as striking. Replacing lead service lines would reduce water waste, with an estimated $82 billion saved over 15 years. Lead service lines, many of which are decades old, no longer function efficiently and contribute to unnecessary water loss. Updating this aging infrastructure is a key part of making Ohio’s water systems more sustainable and cost-effective in the long run.

This is especially important for Ohio, which faces a disproportionate burden when it comes to lead service lines. The state accounts for over 8% of the nation’s lead pipes despite making up only 3.6% of the U.S. population. This makes the issue even more pressing for Ohioans, and efforts to replace these pipes are extremely likely to be positive investments for the state.

Ultimately, the data from this cost-benefit analysis underscores that replacing lead service lines isn’t just about addressing a health crisis, it’s about making a smart financial investment in Ohio’s future. 

Does testing teachers help students?

In May of this year, I wrote a blog post talking about the unintended consequences of taking standardized test scores out of the college admission process. In that post, I highlighted a paper written by economists from the Federal Reserve Bank of Philadelphia.

Many people who advocate for colleges and universities to not look at test scores in their admission process point to the fact that test scores are not a perfect indicator of future academic success, and they could be biased in favor of students who have more resources for expensive practice materials and tutors. 
However, this paper found that in practice, test scores could reduce bias in the admission process. This is because when schools did not have access to the information about test scores, they instead relied on information that introduced even more bias such as whether or not a student has family members who attended the school. 

I thought about this paper this week when I was reading a new article in the Journal of Public Economics talking about the impact of using test scores to hire teachers in Colombia. This paper focuses on a new merit-based hiring system for teachers nationwide. The goal of the program was to increase the quality of teachers, which in turn should lead to better student achievement. 

Despite the well-intentioned goals of this policy,  the authors of the study found student performance actually declined in the wake of its implementation. According to the authors, students' test scores dropped by 8.2% of a standard deviation and both college enrollment and graduation rates decreased significantly after merit-based hiring was implemented.

This finding parallels the earlier debate about college admissions: test scores, while useful in some contexts, are not a perfect measure of quality. Just as test scores in college admissions do not fully capture a student's potential for success, teacher test scores do not completely  reflect their effectiveness in the classroom. The Colombian policy overemphasized one narrow measure—cognitive ability—at the expense of other important factors, such as teaching experience.

The decline in student test scores and matriculation outcomes can be partly explained by an influx of inexperienced teachers driven by merit-based hiring. The share of teachers with little to no experience in Colombia increased from 10% to 30%. The literature shows that teacher quality tends to be lower during the first five years of teaching, and this reform exacerbated that issue by bringing in a large number of new teachers.

This case study highlights an important lesson: teaching experience matters for student outcomes. While cognitive ability is important, it is only one component of what makes a teacher effective. Experienced teachers have had time to develop classroom management skills, learn how to adapt their teaching methods to different student needs, and build relationships with their students—all of which are critical to fostering a productive learning environment.

The study offers an important policy takeaway for education systems worldwide: teacher hiring criteria need to be multifaceted. Instead of relying on a single metric like test scores, hiring systems should consider a broader set of criteria, including both ex ante (before hiring) and ex post (after hiring) measures of teacher effectiveness. Experience, interpersonal skills, and classroom performance are equally valuable indicators that should inform not only hiring but also decisions about retention and promotion.

Just as in the case of college admissions, where removing test scores has been found to inadvertently increase bias by relying on less objective measures, focusing too narrowly on test scores in teacher hiring can also lead to unintended consequences. This suggests that policymakers should aim for a balanced approach that integrates multiple dimensions of teacher quality, combining cognitive abilities with real-world teaching experience and performance data.

Ultimately, the lesson from both college admissions and teacher hiring reforms is clear: while test scores provide valuable information, they are far from perfect. Effective policy design requires a comprehensive approach that considers all elements of a person's abilities, whether they are a student or a teacher. Only by doing so can we create systems that both promote fairness and improve outcomes.

New Report: Replacing Ohio’s lead pipes will improve public health and provide up to $185 billion in economic benefits

A new cost-benefit analysis reveals that replacing all of Ohio’s estimated 745,000 lead water service lines will result in fewer deaths, better physical and mental health outcomes, less water waste, and significant economic benefits for Ohioans and their communities. 

The study, commissioned by the Ohio Environmental Council (OEC) and completed by Scioto Analysis, demonstrates that for every dollar invested in lead service line removal in Ohio, the state will see a public health and economic benefit of $32 to $45. The complete replacement of lead pipes that carry water into Ohioans’ homes and buildings will grow the state’s economy between $145 and $185 billion over the next 15 years. 

Water service lines transport drinking water to Ohioans’ homes and businesses. Pipes that are made of lead release low levels of the toxin into drinking water that lead to chronic health issues for adults and children. According to the U.S. EPA drinking water can make up 20% or more of a person’s exposure to lead. 

“Getting water delivered to your home through a lead service line is like drinking your water through a lead straw. Lead in water can cause serious health problems, especially for children,” said Annalisa Rocca, Drinking Water Manager for the Ohio Environmental Council. “We need to get the lead out now.” 

This is the first study to quantify the health and economic benefits of lead service line replacement for Ohioans. Specifically, the report found that full replacement of lead services over the next 15 years will lead to: 

• 650 fewer infant deaths, leading to a monetized benefit of $4.4 billion in reduced risk of death over the 15-year time period.

• Preventing the loss of an average of 1.25 IQ points each for over 290,000 children in Ohio over the next 15 years, leading to a total benefit of $8.4 billion in future earnings over the same time period.

• 9,700 lives saved from heart disease over the first 15 years of its implementation, generating a total of $66 billion in reduced risk of death over the 15-year time period.

• 3,800 fewer cases of depression, resulting in a benefit of over $290 million in reduced medical costs and productivity cost savings over the same time period.

• 7,300 fewer cases of anemia over the next 15 years, leading to a benefit to Ohio of $22 million in decreased morbidity and mortality costs.

• 2,400 cases of coronary heart disease would be avoided, a benefit of over $52 million over the 15-year time period.

• 520 fewer cases of dementia across the state, resulting in a benefit of over $13 million in reduced caregiver, family disruption, and medical costs over the 15-year time period.

• 150 fewer cases of ADHD in Ohio children, resulting in a benefit of $1.5 million in reduced medical, caregiver, and family disruption costs over the 15-year time period.

Additionally, there are significant economic and environmental benefits to updating an aging infrastructure system. Because all lead service lines are 38 years old or older, they no longer work as efficiently as possible when transporting water to Ohioans’ homes. Replacing lead lines will reduce water waste and save Ohioans an estimated $82 billion over the next 15 years. 

Unfortunately, Ohio ranks as one of the top states in the country for lead service lines. As many as 8.1% of lead service lines in the country are located in Ohio, while only 3.6% of the U.S. population is in Ohio — meaning this infrastructure issue has an outsized impact on our health and economy. 

There are multiple efforts underway to eliminate lead water lines that carry drinking water into our homes and buildings across Ohio. At the federal level, the Biden-Harris Administration has invested a historic $15 billion through federal programs like the Bipartisan Infrastructure Law with an estimated $735 million coming to Ohio for lead line replacement through 2026. At the state level, the DeWine Administration has invested $4.5 million in lead line mapping and replacement through the H2Ohio program.

Recently, Rep. Dontavius Jarrells introduced House Bill 534, Ohio‘s Lead Line Replacement Act,  which would require all public water systems to fully replace lead service lines within 15 years. The legislation includes other key provisions to advance water affordability and workforce development and to support water utilities in meeting the new requirements. 

“We knew that replacing lead service lines is critical for the health of Ohioans, especially Ohio’s kids, but this study shines a light on the tremendous economic benefits of doing so,” said Rocca. “The faster utilities and customers replace lead service lines, the sooner Ohioans will realize the health and economic benefits.” 

Funding for Replacing Ohio’s Lead Lines: A Cost-Benefit Analysis, was generously provided by the Environmental Policy Innovation Center. For more information about lead service line replacement in Ohio, please visit OEC’s blog or check out this video.

How will AI impact the economy?

Recently, I’ve been incorporating generative artificial intelligence into my day-to-day work pretty often. Specifically, I like to ask ChatGPT to help me write Excel functions. I’m not super familiar with all the syntax of Excel, so this can save me a lot of time when I’m trying to work on an extremely large dataset. 

As a busy policy analyst who finds excel functions tedious to write sometimes, this is an amazing way to increase my efficiency. I can outsource some of the things I am inefficient at and spend more time on more valuable things. 

Of course, there’s a catch. Writing excel functions may be a small part of my job, but it might be most of the work for another person’s job. That person might not be so thrilled that their extremely valuable skill can now largely be automated. 

Computers are better than humans at a lot of things. Often we assume that computers only excel at things with certain outcomes like mathematical calculations, but are incapable of performing creative tasks. 

I’d argue that this is not actually true, and that computers have actually been able to perform extremely creative tasks for decades now. For example, Stockfish and Google’s AlphaGo have been playing Chess and Go respectively at levels that no humans can match for quite some time. Chess and Go are far too computationally complex for these algorithms to “solve” the games, and some would argue that they find unique and creative ways to approach their games. 

Instead of computers being incapable of creativity, I’d argue that computers have lacked flexibility. The algorithms that play complex strategy games are immensely powerful and creative, but they could never be used for anything other than the games they were designed for. 

What makes the new era of AI models so fascinating is that they are extremely flexible in what they can do. Writing excel functions used to be difficult for a computer because it is context dependent. Now AI models can take a description written by a human and output working code for nearly any problem. 

As we begin to think about how AI might fundamentally change the labor force as it continues to develop, we need to understand from a theoretical perspective how the labor force is currently constructed. 

A macroeconomic model for a labor force with AI is presented in a new paper by Anton Korinek from the University of Virginia. Currently, economists describe our economic output using a simple production function

Y = A · F(K, L)

In this function, Y is the total output, A represents the level of technology, K stands for capital and L stands for labor. Essentially, our economic output is a function of capital and labor that is scaled by our level of technology. In a world with extremely sophisticated AI models, Korinek suggests that we be exposed to a new production function:

Y = A · F(K, L+M)

Here, M stands for machines, which represents AI and robots that can replace labor. Korinek assumes that at some point of technological advancement, machines will serve as a perfect substitute for human labor. 

Whether or not AI can actually become a perfect substitute for human labor is still an open question. In particular, I wonder if AI will be able to replicate the social dynamics that are extremely important in some jobs like teaching. Regardless, policymakers are going to have to grapple with a new reality where labor can in many cases be easily replaced.

While the efficiency gains from AI can lead to increased productivity, the reduction in employment is going to require a change in the way our economy works. Currently, most people get some slice of the total output of our economy as a reward for contributing to its creation, often in the form of wages for labor. 

An AI-driven economy will theoretically be able to generate far more output, but we need to come up with a new way of distributing that assuming that most people won’t be directly contributing to its creation in the same way. Finding a way to do this that is equitable and efficient is going to be an essential challenge of an AI-driven economy. 

How can we make child care work for kids?

Before I started working at Scioto Analysis, I worked for a think tank here in Columbus, Ohio. My new policy area focus in this position was on child care, a topic I did not know much about at the time.

To dive into this topic, I did what I usually do when I am trying to learn about a new public policy area. I went onto Amazon and I searched the phrase “economics of child care” so I could find a book to get me started.

Economics is a key tool in the public policy world. Earlier this week, Planet Money Newsletter published an article about the waxing and waning of the influence of economists on public policy in D.C. It followed the skepticism of mainstream economics in the middle of the 20th century to the rise of economics in policymaking with the appointment of Paul Volcker as chair of the federal reserve to quell runaway inflation in the 70s. It then followed through the rise of the Council of Economic Advisers until President Donald Trump “demoted” the chair of the Council by not putting its chair on his cabinet. Today, both Harris and Trump are supporting policies like exempting tips from taxes that most economists do not think either promotes economic efficiency or supports equity goals.

Despite some moves against the use of economics in policymaking at the federal level, economics is still one of our best tools for finding the answers to key questions about public policy. Economics is especially good at helping answer the following questions: does a policy work, does it grow the economy, and how does it impact different types of people?

Picking up a book about the economics of child care, I found answers to these questions. The book I read, David Blau’s The Child Care Problem: An Economic Analysis helped expose the problems with child care markets.

Child care markets are heterogeneous and hard to pin down. They are a mixture of people substituting informal markets (care for children at home or by volunteer family members) for less formal markets (paid home care in the neighborhood) or more formalized markets (free-standing child care centers). Each of these options have a range of costs and a range of quality as well, giving different experiences to children who are cared for.

And the experience for the child is something that really matters here. One thing Blau’s book opened my eyes to is that child care is the other side of the coin for another key public policy issue: early childhood education. While with child care we are often focused on trying to find some place to occupy a child while the parent can go work, the quality of this occupation matters since young children are at the point in their life where differences in care can have substantial impacts on their development.

The folly of focusing on child care to the detriment of early childhood development and education was demonstrated with the Quebec child care experiment in the late 90s. The province of Quebec made child care available for all residents for $6 a day. Subsequent evaluation of the impacts of the policy found Quebec’s universal child care program found children who took part in the program had worse health, lower life satisfaction, and higher crime rates later in life than those before the program took place.

The explanation researchers have given for this trend is that the universal child care program provided for universal child care availability but not universal child care quality. Without quality controls in place, children ended up in low-quality care that led to negative non-cognitive outcomes down the road.

In The Child Care Problem: An Economic Analysis, Blau argues there is a market failure in the child care market. If children were fully rational and in control of their own decisions about where to go for child care, they would likely, on average, choose child care that was more expensive and less convenient than their parents choose for them.

The natural answer to this problem is to subsidize child care that is higher-quality relative to child care that is lower-quality. By making high-quality child care cheaper, it will encourage parents to put their children in higher-quality care, closing the gap between the social cost and the private cost of this child care.

This entire argument, however, hinges on a crucial claim: that high-quality child care improves lifetime outcomes for children. Our strongest evidence that this is the case comes from two studies conducted in the 1960s and 1970s: the Perry Preschool Project and the Abecedarian Project. Both of these were randomized controlled trials that placed children in high-quality early childhood programs and followed them and the control group for decades afterwards to see what the impacts of the program were to life outcomes.

While many positive outcomes have been recorded from these studies, they have also come under some criticism. The studies were small, with just a few hundred participants between the two studies. They also focused on a certain subgroup of the population, in particular African-American children in specific parts of the United States (specific communities in Michigan and North Carolina). Additionally, they occurred in the 60s and 70s, under the specific social and economic conditions of that era and the decades to follow. All of these considerations cast doubt on whether the results of these studies can be extrapolated to the current day. 

On top of these limitations, skeptics also point to a negative result from studies of early childhood: fadeout. While the children enrolled in the program had better outcomes in kindergarten, these results were found to fade out in the subsequent years until their evaluations were the same as their peers later in elementary school.

A working paper released this month and co-authored by Nobel Prize Winner James Heckman responds to many of these criticisms. In this paper, Heckman and his co-authors argue that criticisms are too narrowly focused on specific methodology. They argue that IQ fadeout does not occur in the Perry Preschool program and that long-term outcomes like improved educational attainment, higher earnings, reduced crime rates, and better health outcomes are demonstrated by Perry Preschool. All of these lead to a high benefit-cost ratio for the program.

They argue that the lesson of Perry Preschool is not “design a program exactly like Perry Preschool and you will see children succeed.” It is “involve parents and adults in children’s lives and you will see children succeed.” And that’s ultimately what we see in Blau’s work, in Quebec, and in the evidence we have on early childhood development: parental involvement is what sets children up for success later in life.

Would raising the tipped minimum wage close racial and gender pay gaps?

One of the most surprising topics of policy agreement in the current presidential election cycle is proposals by the Harris and Trump campaigns to exempt tips that workers receive from income taxes. 

We recently asked our Ohio Economic Experts Panel about this question, and the consensus among economists was that while this will likely help a subset of high earning tipped workers (e.g. servers in high-end restaurants), it likely won’t be an effective anti-poverty policy since many tipped workers do not end up paying very much income tax. 

An alternative proposal to exempting tips from income tax could be to instead make it so the minimum wage for tipped workers is the same as the overall minimum wage. Currently, employees who “customarily and regularly” make more than $30 per month in tips only need to be paid a small portion of the minimum wage by their employer, so long as the tips they receive get their total earnings to at least the minimum wage amount. The employer needs to make up the difference if that person doesn’t receive enough tips in a given pay period. Essentially, customers directly pay the salary for these tipped workers.

But would this change help the people it’s supposed to? A new paper from David Neumark and Emma Whol suggests that increasing the tipped minimum wage may not have the desired effect on reducing wage disparities or boosting earnings. Specifically, this paper focuses on the effects of increasing the tipped minimum wage for women and for racial minorities in the restaurant industry.

Not all restaurant workers are affected equally by changes in the tipped minimum wage. Data shows that Black and Hispanic workers are more likely to be employed in non-tipped positions within the restaurant industry, such as kitchen staff, where they do not benefit directly from policies aimed at tipped workers. As a result, raising the tipped minimum wage might not significantly improve earnings for these groups, which limits its effectiveness as a tool for addressing racial wage gaps in the industry.

Increasing the tipped minimum wage has been shown to actually widen the hourly pay gap between minority and White workers in some cases. While it does reduce the gender pay gap in hourly wages, it doesn’t necessarily translate into increased weekly earnings for women due to potential reductions in working hours. This suggests that while the policy could help in reducing gender disparities to some extent, it doesn’t provide a comprehensive solution for all workers in the restaurant industry.

In contrast, raising the regular minimum wage appears to have a more significant impact on reducing wage disparities and improving overall earnings for both minority and female workers in the restaurant industry. Regular minimum wage increases help non-tipped workers, where many minority employees are concentrated, leading to higher hourly and weekly earnings. However, this comes with a caveat: while increasing relative earnings, raising the regular minimum wage can also reduce employment opportunities. Policymakers need to balance the benefits of higher wages with the potential risks of job losses.

Given these nuances, only raising the tipped minimum wage may not be the best approach to reducing economic disparities in the restaurant industry. A more effective strategy could involve focusing on raising the regular minimum wage and implementing additional measures to protect employment levels. For instance, policies that provide targeted support for training and career advancement opportunities for minority and female workers in the industry could help address some of the root causes of wage disparities.