Quick analysis: Is there a relationship between income and physical exercise?

This is going to be a quick analysis where I attempt to answer a question in what basically looks like the first pass I’d do for a larger research project. The goal is not to conclusively answer the question I pose, but rather to show how you could go start exploring a question like this and get at least some evidence and see if this is worth exploring further. I’ve previously done a similar analysis looking at commute times and housing prices.

Research question: how do income and exercise relate to one another?

Hypothesis: There is a non-linear relationship between income and physical activity levels. As with my previous quick research blog, I got this idea from a newsletter that was looking at physical activity in different countries. One thing that jumped out at me when I saw the rankings was how there didn’t seem to be any relationship between how rich a country was and how active it was. My intuition based on living in the U.S. would be that income and exercise would be much more closely related.

The question that follows for me is whether this trend holds at the national level. Do we see a similar non-linear trend between income and physical activity if we just look at data from one country, or are there indeed broader cultural trends that make some low-income countries more active than some wealthy countries? 

Methods

I will be looking at the Center for Disease Control’s Behavioral Risk Factor Surveillance System (BRFSS). This is the largest health survey conducted in the U.S. each year, and in particular it asks respondents some basic demographic questions (including their household income) as well as a few relevant questions about the amount of physical activity they report.

There are two outcome questions we will look at: a binary variable for whether or not a respondent reported exercising enough to meet the recommendation for the amount of aerobic exercise they should be doing and a categorical variable for whether a respondent is classified as highly active, active, insufficiently active, or inactive. Both of our dependent variables are calculated based on respondents' answers to other questions about their type and frequency of their exercise. These are not self-reported measures, so we don’t need to worry about how people interpret things like “highly active.”

We will use an ANOVA (analysis of variance) model to test whether there is some impact. The reason we are going to use ANOVA to start is because I am curious if there is a non-linear relationship between income and exercise. By treating each income category separately rather than assuming that physical activity increases or decreases at a constant rate with income, ANOVA allows us to identify differences between income groups that a simple linear regression might miss.  

Results

The first thing we see when we look at the responses is that if there is an effect, it is almost certainly linear. Table 1 reports the percentage of respondents in each income group that meet their aerobic exercise recommendation, as well as the average response category for the categorical variable about total activity level. This variable is coded such that one is associated with highly active and four is associated with inactive, so smaller numbers mean more physical activity on average.

Table 1: Higher income respondents are more active than lower income respondents

The ANOVA results back for both variables both conclusively suggest that there is a relationship between these income categories and both of the response variables, with both p-values coming out to essentially zero. We can in both cases reject the null hypothesis that there is no relationship between income and exercise level. 

Limitations

I have not really gone far enough to fully understand the relationship between income and amount of physical activity. We found by looking at these data that there is some relationship, and the clear order of these averages suggests that it is quite likely linear. More work is needed to demonstrate more strongly that the relationship is indeed linear, and what the strength of the relationship is. 

Preliminary conclusions and next steps

There certainly is some relationship between income and the amount of physical activity respondents participate in, and we have plenty of evidence to dispute the original hypothesis that it is non-linear. The connection between income and exercise appears to be positively correlated, meaning higher income is correlated with higher likelihood of being physically active, though the exact strength of this relationship is still open to further research. The next steps to fully understand this relationship would be to look for potential confounders and account for those as well.

Data center debate exposes Ohio’s uneven energy landscape

In the battle over data center siting in Ohio, one of the flashpoints is how data centers impact electricity prices.

Data centers are indeed energy-intensive and the worry about their impact on local energy prices has moved most developers to work to get their energy generated in behind-the-meter projects that don’t draw from the electrical grid.

So if you are a data center developer, what kind of power are you going to put behind the meter?

Looking at the top sources of power in the United States, you can whittle the options down pretty quickly.

Coal, once the heavyweight for energy in the United States, has seen its economics turn against itself and the United States has only begun construction on one coal-fired power plant in the past 13 years.

For all the talk of small modular reactors, nuclear power is still not viable in Ohio due to the massive up-front costs and decades of regulatory hurdles to clear.

Hydropower and geothermal power demand specific topographic conditions, and biomass has economies of scale that don’t make it competitive with other technologies.

This leaves developers with three choices: solar, wind, and natural gas.

The state has put its thumb on the scale when it comes to choosing between these technologies. A range of state decisions have made siting solar, wind, and natural gas projects very different from one another.

Solar and natural gas projects over 50 megawatts must be approved by the Ohio Power Siting Board. For reference, this would be large enough to power all the homes in Canton with a little bit of energy left over.

Wind projects, on the other hand, only need to be 5 megawatts to face Ohio Power Siting Board scrutiny. That is only enough to power about half the homes in Athens.

The state has also given considerable latitude to county governments to ban solar and wind projects in unincorporated areas, a barrier gas-powered plants do not have to overcome.

For projects that are not outright banned, two local representatives get a vote on Siting Board decisions for wind and solar projects, a requirement not faced by natural gas projects.

Solar and wind projects also face a regime of siting rules that do not apply to gas plants.

Solar facilities face specified setbacks, landscaping, fencing, stormwater, noise, and vegetation requirements. Wind facilities face turbine setbacks and shadow-flicker, ice-throw, blade-failure, communications-interference, noise, and aviation requirements.

Solar and wind projects also face decommissioning planning requirements that gas-powered plants are not subject to.

Gas-powered plants do have one requirement that solar and wind projects do not: they must submit a range of operational air-quality analyses. This makes sense to a certain extent given solar and wind generation is emissions-free.

In an ideal world, technologies can compete against each other on a level playing field.

If there are specific costs associated with outcomes like public health, environmental sustainability, or even aesthetics, these can be captured through fees and taxes specifically designed to internalize these costs into the market.

Creating separate regulatory regimes for different technologies, on the other hand, makes legislators the arbiters of technological superiority rather than the market.

This commentary first appeared in the Ohio Capital Journal.

Taxes are necessary. Which ones do the least harm?

When I first enrolled in graduate school, I was pretty high on taxes.

Maybe it was the “contrarian” in me. Maybe it was the excitement of Bernie Sanders suddenly making “socialism” a mainstream word in the political lexicon. Or maybe it was just the fact that I was going to policy school and I knew programs were funded by taxes so I liked them. Suffice it to say, I was quick to dismiss anything nasty people had to say about taxes as political posturing.

This made it hard for me to accept the assurances from my economics professors that taxes did indeed destroy social value.

This does not mean that government necessarily destroys social value, but many taxes do destroy social value in one place, even if it creates it in another place.

The main way taxes do this is through distorting markets. By tagging an extra price on transactions, consumers and producers self-select out of markets they would otherwise be competitive in. This increases prices and reduces quantities traded at the same time.

Not all taxes are created equal, though. Some taxes are incredibly distortionary. Some barely distort markets at all. Some are negatively distortionary–they actually make markets work better.

How do we know which taxes are more distortionary and which are less? We mainly find this through empirical study. That being said, there is a general hierarchy of taxes that can help you have a reasonable idea of which are most or least distortionary.

Most distortionary: Narrow-based taxes on elastic goods

The taxes that distort the economy most are narrow taxes on single goods. These end up being distortionary because they shift spending decisions so wildly. To understand the intuition behind this, imagine a tax on hamburger buns. This would lead to a reduction in purchases of hamburger buns because they are more expensive, but would also lead to less consumption of hamburger patties, more consumption of hotdog buns, and more consumption of hotdogs, the latter two of which serve as substitutes for hamburger buns and hamburgers.

Analyses of federal taxes in Australia lend support to this claim. House sale taxes have tax burdens that exceed their value of revenue raised. New car taxes destroy nearly a dollar in value for every dollar they raise in revenue. Since purchasers of new cars can substitute to purchasing used cars and buyers of property can instead become renters of property, these taxes end up shifting preferences from ideal purchases to secondary purchases and reshuffling markets.

Less distortionary: broad-based taxes

So if assessing taxes on narrow goods leads to distortion, shouldn’t we instead levy taxes on broad sets of goods? Well this has been the logic of economists for years now: levy taxes on a broad range of goods so they have less of an impact on the broader economy. The logic here is that if you tax something broad like labor, purchases, or property, which people cannot avoid, then their purchasing decisions will not change as much as if you tax something narrow.

The federal Office of Management and Budget estimates marginal excess tax burden at 25 cents of value destroyed for every dollar of revenue raised, much lower than the above estimates for the cost of narrow taxes. Researchers have put the cost of a broad-based sales tax at 13 cents on the dollar and a broad-based property tax at 14 cents on the dollar. Estimates on value added taxes, which spread a tax throughout the stages of production in an economy, should theoretically be even lower but land in the same territory as these estimates in the empirical evidence.

Not distortionary: user fees

Some taxes and fees like gas taxes are designed to mimic market mechanisms. While roads are public goods constructed and maintained with tax dollars, a major source of their funding is through gas taxes. Levying a gas tax and earmarking these funds for road maintenance means that people and companies who buy more gas (and presumably use roads more) pay more taxes. So the people who are paying for the roads are the people who are using the roads.

Ideally, a perfect user fee would have a social cost that equals its social benefit, netting a marginal excess tax burden of zero. This is not always the case, however. The rise of electric vehicles means that many people who use roads are not paying gas taxes, which makes gas taxes a less perfect user fee than they would be otherwise.

You can imagine other user fees that would have no distortion, like fees for water, sewage, and waste disposal conducted at the city level. If these are made in proportion to the volume of service, then these fees provide no distortion to the economy: their social benefits can theoretically equal their social costs.

Negatively distortionary: Pigouvian taxes

A final category of taxes are taxes that actually enhance market efficiency: Pigouvian taxes. Named after the father of welfare economics Arthur Pigou, this is a category of taxes that are deployed when a market has total social costs that exceed the private benefits realized from the transaction. Examples of these are carbon taxes and cigarette taxes. Since future generations impacted by climate change and breathers of secondhand smoke are not willing participants in markets in carbon and cigarettes respectively, the marginal social costs of these transactions exceed the marginal private costs, representing a failure in the current market. Taxes on carbon and cigarettes bring private costs in line with social costs, making the markets more efficient than they would be otherwise. 

These are all just rules of thumb: there can be some narrow-based taxes that have low elasticities that are not very distortionary. There are also some user fees that do not operate efficiently and cause large distortions. And distortion is not the only policy-relevant dimension of tax policy. Often we are willing to trade off efficiency for equity outcomes, like we would do with a graduated income tax or a corporate tax. But I hope this framework at least gives you an idea of the logic behind marginal excess tax burden and a framework to approach tax policy and design.

Is money really fungible?

Recently, I came across a new working paper that looks at some of the impacts that came about as a result of recent changes to the Supplemental Nutrition Assistance Program, otherwise known as SNAP (formerly “food stamps”). In some states, SNAP benefits became more limited and are no longer able to be used on the purchase of certain unhealthy items including sugary drinks. 

The whole paper is extremely interesting and definitely worth a read, but today I wanted to dive in more closely to one of the key questions the paper raises, is money fungible?

What does it mean for money to be fungible?

If something is fungible, that means it can be interchanged with something equivalent. It shouldn’t matter whether someone gets paid in cash, a check, or via direct deposit. As long as the value is the same, (aside from some minor inconvenience) those differences don’t matter.

Two of the core functions of money are that it is a unit of account and a medium of exchange. In practice, these two characteristics should suggest that all money is equal. In a broad sense, money is certainly fungible (i.e. when considering how people trade money).* However, it is more interesting to think about this in terms of how people make decisions about their personal spending. 

Is money fungible across a budget?

This is where the paper comes in. The researchers looked at changes to the SNAP program that banned the purchase of sugary drinks and food. This is an interesting change, because according to the paper over 80% of SNAP households spend more on food each month than their SNAP benefits cover. According to economic theory, this implies that SNAP essentially acts as a cash transfer, since we’d expect monthly food budgets to remain constant with or without these benefits. 

If this is true, then we should expect these SNAP changes to have little to no effect on specific spending decisions. The benefit size isn’t changing, these are just new restrictions on how it can be spent. If these households are already spending more than their SNAP benefits on food, then they could just switch around what items they buy with their SNAP cards and what items they buy with cash after. 

The main takeaway from the paper is that money isn’t necessarily fungible across budgets. People who receive SNAP benefits do not appear to be changing their non-SNAP grocery spending to compensate for the fact that their SNAP-supported grocery spending changed. While there may be situations where a one-to-one change isn’t possible for some reason, economic theory would suggest that the fungibility of money should make it so this transition was much smoother than it ended up being. 

What does this mean for policymakers

While many of us will find it inherently interesting that we have another concrete counter-example to a basic principle of economic theory, we should ask ourselves how this is actually relevant to policymakers in the real world. 

One takeaway is simply that policies such as these SNAP restrictions can actually reduce consumption of certain goods. This can be both a blessing and a curse, as it gives policymakers another tool for changing behaviors which can be difficult, but it also highlights how some policies might have unintended consequences.

Another takeaway is that household budgets play an important role in how people make spending decisions. In this SNAP example, nothing about the market for sugary drinks was impacted by this policy change. There were no new taxes or subsidies, nothing impacted a substitute or complimentary good, from a theoretical perspective the equilibrium price and quantity should have remained constant. The fact that we saw an observable change in the amount consumed suggests that policies such as these can have indirect effects that shape markets.

One important difference that makes SNAP different from other tax-and-transfer programs is that SNAP benefits come preloaded on an electronic benefits transfer card (essentially a debit card). This is functionally quite different from programs like Social Security or the Earned Income Tax Credit that are distributed as cash. Those programs are the most similar to SNAP given their size and scope, but it may not be possible to achieve a similar impact because their administration doesn’t create a separate pool of resources that can be budgeted separately.

One argument against this kind of policy intervention is that it is an overly paternalistic decision made by the government, and that people should have the ability to make decisions about their food intake more freely. That is a tradeoff that policymakers should be wary of, since overly restrictive policies can reduce the overall economic benefit that comes from improved health outcomes. Whether these policies end up becoming more widespread, it is fascinating to see how people react to changes like these.

* There are plenty of examples where money can have different values. Someone who wants to buy something from a vending machine might have a higher value for five $1 dollar bills relative to a single $5 bill. Similarly, most people I know don’t like carrying around $100 bills, preferring the easier to use $20 denomination.

Which cities have the most downtown employment?

Earlier this month, I wrote a blog post about which cities are next for a congestion fee. So far, the only city in the United States that has implemented a congestion fee is New York City, and we estimate in our cost-benefit analysis of Manhattan’s congestion fee that in 2025 alone, the zone produced $2 billion in net benefits.

One of the patterns we noticed in other cities that have implemented congestion fee zones across the globe is that they are almost always centered on the city’s central business district. This makes sense: the central business district is typically the most consistently busy part of a city. People commute there to work, retail and dining naturally cluster around those workers, and freight trucks have to enter daily to deliver food and supplies to local businesses

To try to predict which cities might implement congestion fee zones next, we looked at employment by central business district estimates from Demographia. The results were about what you would expect: New York City, San Francisco, Washington D.C., and Chicago were the top four cities with the highest proportion of their workforces in the central business district. 

However, Demographia’s last published breakdown used 2012-2016 data, and a lot has changed since then (the COVID-19 pandemic and the rise of remote work, for instance). Today, I decided to replicate Demographia’s methodology to update these numbers using more recent data.

What data is available?

We use American Community Survey data a lot at Scioto Analysis. The American Community Survey is an annual survey by the federal government on social, economic, demographic, and housing information, and it helps inform a lot of our analysis. While standard American Community Survey data is helpful for telling us where people live, it’s not designed to tell us where people work.

However, every five years, the Census Transportation Planning Products program releases five-year estimates of where people work, where they live, and how they commute between the two using pooled microdata from the American Community Survey. The most recent dataset is 2017-2021. The most recent dataset covers 2017-2021, capturing early post-pandemic shifts in commuting and remote work.

The Census Transportation Planning Products data includes total jobs by census tract, city, and metropolitan area, but it doesn’t explicitly label which census tracts belong to central business districts. The United States Census Bureau used to regularly publish reports defining central business districts, but they stopped doing this decades ago. As a result, to identify central business districts, we have to start with the Census Bureau’s 1982 central business district boundaries. 

Central business districts have obviously evolved since 1982. To match Demographia’s methodology, I made a couple of adjustments to the central business districts (like expanding districts in New York City and Chicago), but because there’s no current official designation, it’s possible that some districts are inaccurate. Finally, because tract boundaries shift over time, we run the historical tracts through census tract crosswalks to accurately link 1982 district boundaries to 2021 workplace data.

What cities do people work downtown the most?

Using the data sources above, I calculated estimates for the cities where people work in the central business district the most among the top 50 metropolitan areas in the country. The table below shows the top ten metropolitan areas with the most workers in the central business district.

Top Cities for Central Business District Employment
Metropolitan Area	Central Business District Employment	Metro Employment	Central Business District Share of Metro Employment
New York, NY	1.6 million	6.9 million	24%
Chicago, IL	240,000	3.6 million	6.6%
San Francisco, CA	230,000	1.6 million	14%
Philadelphia, PA	210,000	2.3 million	9.4%
Dallas-Fort Worth, TX	200,000	1.8 million	11%
Boston, MA	180,000	1.9 million	9.6%
Washington, D.C.	170,000	2.2 million	7.6%
Minneapolis-St. Paul, MN	160,000	1.3 million	12%
Los Angeles, CA	130,000	4.1 million	3.3%
Atlanta, GA	100,000	910,000	11%

The top ten cities for central business district employment aren’t very surprising. Most of the biggest cities in the country also have the largest central business district workforces. All ten of these metropolitan areas, other than Dallas-Fort Worth and Minneapolis-St. Paul, saw their central business district employment decrease in the 2021 data compared to 2016. 

One obvious explanation for this trend is the COVID-19 pandemic. A lot of workers moved to hybrid or remote work environments during stay-at-home orders. Because of this, some companies may have closed their downtown offices. Even as businesses reopened, a lot of workers remained online, and some companies may have moved outside of the central business district amidst rising prices for property.

Los Angeles ranks ninth in central business district employment, which is surprisingly low given that its metropolitan area employment is only second to New York City. The proportion of workers in the Los Angeles metropolitan area that work downtown is a measly 3.3% compared to New York City’s 24%. However, the public transit system in New York City is superior to Los Angeles’s, and the population density in Los Angeles is about one-third of New York City.

To better understand which cities have the highest concentration of workers in their central business districts, the table below shows the top ten metropolitan areas with the highest proportion of downtown workers.

Top Cities for Central Business District Employment as a Share of Metro Employment
Metropolitan Area	Central Business District Employment	Metro Employment	Central Business District Share of Metro Employment
New York, NY	1.6 million	6.9 million	24%
Louisville, KY	76,000	410,000	18%
Indianapolis, IN	97,000	530,000	18%
New Orleans, LA	60,000	350,000	17%
Richmond, VA	58,000	400,000	15%
San Francisco, CA	230,000	1.6 million	14%
Austin, TX	48,000	380,000	12%
Minneapolis-St. Paul, MN	160,000	1.3 million	12%
Charlotte, NC	64,000	560,000	11%
Atlanta, GA	100,000	910,000	11%

Some of these top metropolitan areas don’t surprise me, and a lot of them are the same cities with the highest central business district employment: New York City, San Francisco, Minneapolis-St. Paul, and Atlanta. 

Other than these areas, it appears that mid-sized cities like Louisville, Indianapolis, New Orleans, and Richmond have some of the highest downtown job concentrations. This might be because these metropolitan areas are smaller geographically and have smaller total workforces. If the central business districts in these cities are fairly large, it’s easier to take up a larger proportion of the metropolitan area.

Another interesting trend within these cities is that five of them are state capitals: Indianapolis, Richmond, Austin, St. Paul, and Atlanta. It’s possible that for these cities, a lot of the labor force is concentrated in the public sector. Oftentimes, government offices are located close together or even in the same buildings as each other. This could be part of the explanation for why some of the cities with large labor forces–like Chicago, Philadelphia, Dallas, and Boston–are missing from this list. If more of the jobs in these cities are private, they aren’t as compelled to be concentrated in a certain area.

Some of these metropolitan areas–like New York City, San Francisco, Minneapolis-St. Paul, and Atlanta–have high-ranking public transit systems. The other cities may be candidates for public transit improvement. If a lot of workers are travelling to the central business district regularly for work, high-quality public transit is important for accessibility, reducing congestion, and reducing emissions.

Ohio General Assembly members propose sweeping bill to regulate data center development

Last week, a group of legislators in the Ohio House of Representatives introduced a far-reaching bill to regulate data center development across the state of Ohio.

In the face of concerns about the environmental, aesthetic, and public finance ramifications of Ohio’s rapid growth in data center development, Ohio House Bill 983 introduces a range of interventions designed to insert the public into data center development decisions.

The bill will require voter approval for every new construction or expansion of a data center with peak electric load over one megawatt for every municipality and township within five miles of the project.

Permits issued without voter approval would be considered void.

It would also impose new air emission and water discharge standards on a range of substances including PFAS, glycols, metals, and other organic compounds. These standards would apply to existing data centers after eighteen months.

Owners of data centers would also be held financially responsible for water supply and water pressure impacts associated with their centers.

On the fiscal side, the legislation would ban local governments from offering property tax incentives for data centers and power plants associated with them. It would also require public disclosure of all data center agreements associated with development and supply.

This legislation would significantly slow down the development of data centers in Ohio, if not stop it altogether.

The voter approval requirement in particular could lead to dozens of required communitywide votes across the country to authorize data center developments.

This would have a substantial impact on Ohio’s economy.

Developers are set to invest $40 billion in data centers across the state of Ohio over the next four years. While this legislation would prevent some wasteful incentive spending, it would also likely cost the state economy tens of billions of dollars in investment.

One of the major concerns people have with data center development in Ohio is strain on public utilities, particularly electricity and water.

If data center development drives up demand for each of these, it could drive prices for electricity and water up for local residents.

This could fall more heavily on low-income residents because they spend more of their income on utilities than high-income residents. This bill’s proposed interventions could mitigate some of these cost concerns.

There are other spillover effects people worry about with data center developments.

Will incentives leave less money available for schools? Will data centers lead to wastewater, air emissions, and noise pollution?

These are concerns that could be bluntly dealt with through bans on incentives and community votes, which are likely to torpedo most projects.

Overall, though, it seems like this bill is a hammer for an issue that likely needs a scalpel.

Communitywide votes to authorize new developments would likely be tantamount to a total ban of data center development across the state. This could slow Ohio’s economy to the tune of tens of billions of dollars over the next few years.

Making sure that Ohio’s electricity and water systems keep up with new development could probably be achieved with less economic pain than a de facto permanent ban on development would create.

This commentary first appeared in the Ohio Capital Journal.

What assumptions underlie cost-benefit analysis?

Over the past year, I have had some correspondence with Aidan Vining, one of the co-authors of a leading textbook on public policy analysis and the “bible” of cost-benefit analysis. This has been exciting for me because few people have had a larger hand shaping guidance for this generation of public policy analysis than Dr. Vining. His perspectives on policy analysis broadly and cost-benefit analysis specifically have been incredibly influential to both the profession and me.

Last month, Vining emailed me a journal article he just published in the Journal of Benefit-Cost Analysis titled “Ten Principles of Cost-Benefit Analysis and Five Barriers to Its Wider Use.” I thought it would be valuable to share these ten principles on this blog because they are helpful to understanding how cost-benefit analysis works.

1. Cost-benefit analysis measures efficiency, not overall social welfare.

Vining argues that cost-benefit analysis is a tool for analyzing “allocative efficiency,” not “social welfare.” What does this mean?

Basically, what he is saying is that cost-benefit analysis helps us understand whether a policy will make the economic “pie” bigger–whether it will increase the amount of value in society, measured in dollars. This is not the same as measuring whether society is better, because distribution of that value may matter. My graduate school benefit-cost professor Dan Acland thinks that it is appropriate to fold utility measures into cost-benefit analysis. Vining argues against this, saying it is better to present those measures alongside traditional willingness-to-pay results.

This is a matter of controversy in cost-benefit analysis, but my inclination, to the chagrin of my mentor and friend Dr. Acland, is still to say dollars should be presented as dollars and weights should be presented alongside them. This transparency allows the policymaker to get the full analysis rather than obscuring standard willingness to pay beneath weighted willingness to pay.

2. The individual is the basic unit of analysis.

I find this to be a very interesting element of economic analysis in general. In economic analysis, the whole is always equal to the sum of its parts. Vining argues this is a “minimalist meaning of methodological individualism,” but it is indeed philosophically weighty. What we value about being parts of social units seems to transcend what we gain from them individually, at least when done right. A marriage is not just the benefits it bestows on both parties, it is a good in itself. It is useful to understand, though, that cost-benefit analysis has little value to understanding that sort of good and how social organization interacts with it.

3. People’s choices are usually treated as rational.

Vining says that cost-benefit analysts generally assume that people make their decisions based on their preferences and that they are rational and well-informed. This is an underlying assumption throughout economic analysis as a whole, with some exceptions. In our cost-benefit analysis we conducted on cigarette taxes last year, we used a model developed by cost-benefit researchers to estimate how much of cigarette consumption is “rational,” treating irrational consumption as deadweight loss. Vining’s frequent collaborator David Weimer has also written a book on how to incorporate economic research findings on systematic irrationality into cost-benefit analysis models.

4. Existing property rights usually provide the starting point for analysis.

Cost-benefit analysis is usually conducted to evaluate a policy that could, will, or has been implemented. Since a policy requires a change from a status quo, cost-benefit analysis evaluates a change from a certain status quo. This can have methodological impacts, like whether to use “willingness to pay” or “willingness to accept” measures. It also means cost-benefit analysis is not a good policy for telling you who has the rights to certain goods and services, just whether a policy will increase or decrease the amount of value people get from them given the current resource distribution.

5. Each affected person counts equally.

Traditionally done, cost-benefit analysis treats each individual’s costs and benefits as equally important. Even Dan Acland’s equity weighting scheme is ultimately about trying to bring costs and benefits closer to being in line with utilitarian impact, thus treating each individual equally. Vining says this makes economic impact and “stakeholder-specific” analysis fundamentally incompatible with cost-benefit analysis. I have written in the past about how “personhood” may even be too narrow of a definition for standing in the use of cost-benefit analysis.

6. Value is primarily measured through willingness to pay or accept.

Cost-benefit analysis is ultimately the project of evaluating the satisfaction of preferences throughout an economy. This means that the criteria for a cost-benefit analysis has to be the value people place on goods created by (or destroyed by) a given policy. Using revealed preference, survey, time use proxies, estimation of value of statistical life, or other strategies that focus on how people themselves value outcomes is key for effective cost-benefit analysis.

7. Future impacts must be discounted.

People prefer present benefits to future benefits and future costs to present costs. Vining argues that some of this is irrational, but some is not. Vining lays a stake in the discounting debate, noting a discount of two to three percent, though acknowledging many other governments go much higher. We have written a lot about discount rates in the past.

8. CBA follows a “no-envy” rule.

While there is some evidence that benefits that accrue to some people cause psychological costs to others (think “keeping up with the Joneses”), cost-benefit analysis tends to stay away from factoring these impacts into the analysis of the total value of those benefits. This helps cost-benefit analysis stay in the lane of estimating allocative efficiency and not smuggling equity measures into the analysis, thus making the analysis more obscure.

9. The nation is ordinarily how standing is determined.

This is an interesting claim and a new one for me. Not the result of the claim, but the logic that gets us there. Vining argues that constitutions can be treated as “efficient” since members all gain the benefit of society. This makes constitutional government a legitimate basis for cost-benefit analysis, which could be national, but also can apply to some states. Vining argues that municipal governments do not have constitutions, though does not make it clear why city charters and “home rule” provisions do not qualify in the same way a state constitution does. But this is his argument for why economic impact analysis does not qualify as cost-benefit analysis: people have standing outside of the analysis area unless some sort of constitution draws that line.

10. International agreements can sometimes expand that boundary.

Vining argues that contracts between states can increase standing. This comes up when we do analysis of carbon emissions, which usually have small local costs and large global costs. Vining’s argument implies that an international agreement to curb emissions could lead to full global valuation of carbon emission reductions for national projects.

Vining’s paper provides a good overview of some key underpinnings of cost-benefit analysis. Good exercise of cost-benefit analysis means understanding what it is doing, understanding how it works, and understanding its limits. If analysts can do these things well, cost-benefit analysis will continue to be a valuable tool for making more efficient public policy.

How do you measure happiness?

I spent last week in Lexington, Kentucky at the annual conference of the International Society for Quality-of-Life Studies, a global interdisciplinary society dedicated to advancing research and knowledge on quality of life (QOL), wellbeing, and happiness studies. For the past decade, I have served on the board for Gross National Happiness USA, a grassroots organization committed to changing measures of progress and success in the United States. The first Scioto Analysis study was released in conjunction with Gross National Happiness USA so we go back a while.

Why should we care about happiness? When I was at the think tank that I worked at before starting Scioto Analysis, I was excited to tell a colleague that I had joined the Gross National Happiness USA board. Her response to me was only to say “fun.” I saw this work as crucial work in the public policy space, but for a lot of people who consider themselves “serious” policy analysts and researchers, the concept of happiness is treated like a little treat rather than an important public policy undertaking.

Two hundred fifty years ago, Jeremy Bentham first argued “It is the greatest happiness of the greatest number that is the measure of right and wrong.” While Bentham widely popularized the idea, he was paraphrasing Irish Philosopher Francis Hutcheson, who argued that the best action produces “the greatest Happiness for the greatest Numbers” fifty years earlier. Suffice to say that people have been explicitly making the argument that morality is based on improving happiness for hundreds of years.

Despite this longstanding charge for morality and public policy, research on happiness has continued to be a fringe subfield in the world of public policy. 

Part of this is due to a lack of data. Gross Domestic Product is calculated quarterly, and employment numbers are released monthly. Due to tax data and survey data, researchers have many places to turn to see what the impacts of public policy are on traditional economic indicators. Environmental data from the Environmental Protection Agency and the Energy Information Administration give researchers data to see how policy impacts the environment. The Centers for Disease Control and Prevention publishes deep research on health, giving researchers opportunities to study health and public policy.

The imbalance between traditional economic indicators and well-being indicators is replete throughout government. The U.S. Federal Government has 36 “principal indicators,” none of which are well-being indicators. The U.S. Federal Reserve releases quarterly projections of GDP growth, unemployment, inflation, and interest rates. The Bureau of Labor Statistics’s monthly jobs report draws on approximately 60,000 households and 121,000 employers representing about 631,000 worksites. Its employer survey covers one-third of all nonfarm payroll jobs. Meanwhile, the CDC asks a life satisfaction question to only two-thirds of states.

While people in the United States do not often focus on statistics around happiness and well-being, progress has been made in other countries, especially in Europe. For over a decade, the United Kingdom’s Office of National Statistics has been asking the question “Overall, how satisfied are you with your life nowadays?” to representative samples of people across the country. The United Kingdom has gone as far as to incorporate well-being measures into their cost-benefit analysis, though there is legitimate question whether this confuses both the practice of benefit-cost analysis and well-being research.

The United Kingdom is not alone in asking this question. Statistics Canada asks its residents “How do you feel about your life as a whole right now?” Germany’s Socio-Economic Panel asks “How satisfied are you with your life, all things considered?”

You can see that many of these formulations follow a similar pattern. The Organisation for Economic Co-operation and Development (OECD) recommends the question “Overall, how satisfied are you with life as a whole these days?” as the core life satisfaction question for its member states. You can see this echoed throughout most of these questions.

Notably, the most common, headline-grabbing happiness report in the world does not use this question.

The World Happiness Report, known for its annual national happiness rankings, uses data from the Gallup World Poll. Respondents are asked to imagine themselves somewhere on a ladder, with the bottom rung being their worst possible life and the top rung being their best possible life. They then answer the question “On which step of the ladder would you say you personally feel you stand at this time?”

In general, this “Cantril Ladder” question is treated as interchangeable with the “how satisfied are you with your life nowadays” question. Some are starting to question this assumption though.

At this year’s conference, I attended a talk where a researcher shared data from the Global Flourishing Study. One of the big findings she shared was that two questions people have about life satisfaction ended up having very different correlations with other questions, even though they are often treated interchangeably in happiness research.

The Global Flourishing Survey asks both the life satisfaction and the Cantril Ladder question, so researchers were able to compare how these questions interacted with other questions using the same respondents and the same survey. What they found was that the life satisfaction question correlated strongly with interpersonal connections and relationships. The Cantril Ladder question, on the other hand, was much more determined by income than the life satisfaction question was.

This intuitively makes sense to me. The Cantril ladder is more of a comparative question, where you are explicitly challenged to imagine your life as better or worse. If I think about my life in those comparative terms, I often think about having more or less money. On the other hand, if I think about how well my life is going nowadays, I think much more about how I spend my time, including who I get to see on a day-to-day basis.

It seems like both of these questions are illuminating and we probably should do some more research to find out how these two compare to one another. But whichever direction we go, well-being deserves a place at the table. Policymakers should have information on how policy impacts well-being. That is, after all, one of their most important jobs: to promote the general welfare and to help their citizens pursue happiness.

What’s the difference between a carbon tax and cap-and-trade programs?

In environmental policy, reducing carbon emissions generally means putting a price on pollution or limiting the amount of pollution that can occur. Two of the most common approaches are carbon taxes and cap-and-trade. While both use market-based mechanisms to reduce emissions, they create different incentives and have different tradeoffs for policymakers and businesses. 

Carbon Tax

A carbon tax works by setting a price on carbon emissions, and allowing firms to emit as much carbon as they can afford. This functions as a Pigouvian tax which if set would lead to an efficient decrease in carbon emissions. 

In order to effectively set a carbon tax, policymakers need to understand what the social cost of carbon is, in other words what the global externality of carbon emissions costs. If policymakers arrive at a solid estimate for this number, then economic theory tells us that markets with any amount of carbon emissions should respond accordingly and shift their production/consumption to a socially optimal level. 

Cap-and-trade

Sometimes branded as “cap-and-invest,” this policy works by setting a limit on the total amount of carbon emissions that occur in a given jurisdiction, then setting up a market that enables firms to buy and sell the legal ability to emit carbon. For example, every factory in a state might be given a certain allotment of carbon credits that determine how much total carbon they are allowed to emit. Factories that emit more than that will have to buy credits from factories that emit less than their allotment, acting as a de facto subsidy for firms that reduce emissions. 

This provides a market framework that essentially allows firms to determine among themselves where the most cost-effective emission reduction opportunities are. Because firms can buy and sell these carbon allowances, firms that have opportunities to reduce their emissions can finance these changes by selling their allowances to firms that can’t change as easily. 

Which is better: carbon taxes or cap-and-trade?

In terms of reducing emissions, both can be effective if implemented correctly. The tradeoffs are more about what signals firms are receiving and how the policies react to sudden changes in economic conditions. 

A carbon tax offers price certainty to firms. This means that they can more easily plan their response to the policy, and it is clear based on the price of carbon whether or not some pollution reducing investment would be worthwhile financially. 

The main drawback of a carbon tax is that a sudden change in economic conditions might make the policy less effective at reducing emissions. An economic boom in a heavily polluting industry might lead to producers absorbing the carbon tax and polluting through it, or burdensome regulations on carbon-free electricity sources might make fossil fuels relatively more competitive. 

In contrast, the cap-and-trade framework does away with price certainty and instead limits overall emissions in a given region. There can be more price volatility for firms, but there is more certainty surrounding total emissions. 

Cap-and-trade can also adjust more naturally to changes in the cost of reducing emissions. If reducing emissions becomes unexpectedly cheap, firms have an incentive to reduce more pollution and sell their excess allowances. If emissions reductions become more expensive, firms can instead purchase allowances from other firms, preventing the overall cost of meeting the emissions target from becoming unnecessarily high. 

Ultimately, there is no single policy that is always better. Both carbon taxes and cap-and-trade can be effective tools for reducing emissions. As long as both are designed properly, policymakers can focus on which areas of certainty/uncertainty they feel more comfortable with.

Ohio economists split on the impacts of permanent daylight saving time

In a survey released this morning by Scioto Analysis, 7 of 15 economists agreed that the twice yearly clock change harms Ohio’s economy.

In July, the Sunshine Protection Act was passed in the United States House of Representatives and is currently awaiting approval from the United States Senate. The Sunshine Protection Act would make Daylight Saving Time, which is traditionally observed from the second Sunday in March to the first Sunday in November, permanent across the United States. Proponents of the bill argue that more sunlight in the evening would provide more usable time for shopping and recreation during the winter. Opponents argue that less daylight in the morning would create safety concerns for schoolchildren and productivity losses for agriculture and construction workers.

7 of 15 economists agreed that the twice yearly clock change harms Ohio’s economy. According to Bill LaFayette of Regionomics, “The big impact comes from throwing off people’s circadian rhythm when the time changes. There are more traffic crashes, more strokes and heart attacks, and errors at work that could have financial implications.” Of the remaining economists, 6 disagreed and 2 were uncertain.

7 of 15 economists were uncertain if permanent Daylight Saving Time in Ohio will increase consumer spending on leisure, dining, and retail. Of the remaining economists, 4 agreed that consumer spending would increase and 4 disagreed. Jonathan Andreas of Bluffton University agreed, explaining, “The morning hasn’t been as much of a time for spending as the evening, so retailers like this proposal!” However, according to Kevin Egan of the University of Toledo, “If there is any small increase in spending in the extra hour at night during the winter, it will be offset by less spending somewhere else.”

8 of 15 economists were uncertain if permanent Daylight Saving Time will improve the health and quality of life for Ohio residents. According to Kathryn Wilson of Kent State University, “There are conflicting results that I would expect. Research shows that the time change disrupts sleep patterns. [...] However, when permanent daylight savings was implemented in the 1970s, there were a number of children killed on their way to school in the morning.” Of the remaining economists, 2 agreed that health and quality of life would improve for Ohio residents, and 5 disagreed. 

The Ohio Economic Experts Panel is a panel of over 30 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.