What is the “u-shaped curve” in happiness economics?

This week, Scioto analysis released a report on subjective wellbeing in Ohio. One of our key findings was that age was positively correlated with happiness. In other words, older respondents of our survey were happier than younger respondents. 

This result is important in and of itself, but in the context of the broader wellbeing research it is fairly surprising. The reason is because it has generally been accepted for some time now that happiness follows a U-shaped curve over the lifespan. That is, young people are relatively happy, middle aged people are relatively not as happy, and elderly people are once again relatively happy. 

This is not the first time a survey has shown that the U-shaped curve might not exist. The 2022 US Happiness Report from Gross National Happiness USA found a similar trend in their survey, and the most recent World Happiness Report said “happiness among the young (aged 15-24) has fallen sharply in North America – to a point where the young are less happy than the old.” 

When I said earlier that a U-shaped happiness curve has generally been accepted, I should have clarified that I meant among economists. Researchers from other disciplines, particularly psychology, have argued against the U-shaped happiness curve. 

While not all economists accept the U-shaped curve and not all psychologists dismiss it, there are differences in the way acolytes of the two disciplines approach data analysis. In particular, one interesting question is whether or not to include control variables in an analysis. 

Economists include control variables because they want to understand the isolated impact that age has on happiness. In other words, if we took two people who were identical except for their age, what should we expect the difference in happiness to be between them.

This is not the same as just looking at the age of our survey respondents, because there could be some other reason that young respondents were less happy. For example, young people tend to earn less money than older people. 

Psychologists have questioned the use of these control variables, because it leads to a different understanding of the data. As an example, imagine if happiness was very strongly related to income, and age only had a very small impact. If this were true, then young people might report being less happy, but if we control for income then we could still see a U-shaped relationship between age and happiness.

This might lead us to say that young people are more happy than middle aged people, they just have lower incomes. Some would argue that this misses the most important point, which is that young people reported being less happy. 

These two ideas can both be true at the same time. While our survey finds that young people are less happy, it does not necessarily mean that those people will become happier as they age.

What is most interesting to me is the interaction between these two conclusions. I find our evidence very compelling, especially since it is backed up by other researchers. It is extremely concerning however if there still exists a U-shaped happiness curve with respect to age for this cohort. 

That would mean that at this moment, young people are experiencing extremely low levels of happiness, and we should expect them to be even less happy as they approach their forties. We might be staring down an epidemic of low wellbeing. 

As is the case with almost every policy relevant research topic, we benefit from taking an interdisciplinary approach. There is no objectively right or wrong way to interpret wellbeing data, there are just different questions to ask. 

Policymakers should be concerned both with how people are reporting their wellbeing today, and how factors like age will influence future wellbeing. By looking at the complete picture, we can find solutions to improve outcomes today and in the future. 

Subjective wellbeing research is an exceptionally important and woefully understudied topic, particularly at the local level. More detailed information is going to be needed to fully understand exactly how wellbeing changes with age.

How important is Ohio's safety net?

Last week, Scioto Analysis released our updated Ohio Poverty Measure, which is our project to estimate the extent of poverty in the state of Ohio. One of the key inputs in our model is the value of income people receive from public benefits. 

Because our model estimates benefits as a part of people’s incomes, there are two important differences compared to the Official Poverty Measure. First, our poverty lines are generally higher than the official poverty lines. This is because the official poverty line is primarily based on food costs, which are now a lower portion of people’s actual spending than the mix of goods used to determine poverty thresholds for the Ohio Poverty Measure. 

Second, the incomes we compare against the poverty lines are often higher than those used in official poverty calculations. This is because the benefits we account for are often larger than the additional costs (e.g. medical expenses) we account for. 

Because of these adjustments, our model better explains how many people in Ohio have sufficient resources to get by. It also gives us an idea of how much our social safety net is assisting in helping people get by. 

Because of the safety net, we estimate that Ohio’s poverty rate is 8.7%, as opposed to the 12.1% poverty rate reported by the Official Poverty Measure.*

This result is supported by data from Brookings, which found that after adjusting for cost of living Ohio has one of the most generous state safety nets. Ohio is right in the middle in terms of nominal generosity, but because the Midwest is more affordable on average those safety net dollars get stretched further. 

Looking at our data, if we remove the safety net completely in Ohio (state and federal), we find that the poverty rate jumps all the way from 8.7% to 20.5%. This means that in a world without social security, SNAP benefits, housing assistance, or any other public benefit, one out of every five people would not have enough to get by. 

Looking at the geographic breakdown of this impact, we see that while poverty rates are higher across the state, they are significantly higher in areas that already struggled with poverty. Urban city centers and rural Appalachian counties had poverty rates of about 15% ~ 20% when including public benefits. Those can jump all the way up to over 40% if we take that money away. 

In the wealthiest parts of the state, poverty rates went from about 5% to over 10% if public benefits were removed from income. Although these people probably relied much less on benefits like SNAP or housing assistance, many of them still benefited from Social Security, which is by far the largest public benefit program nationally. 

Looking at some of the largest counties in Ohio, we can visually see that poverty rates almost doubled after removing benefits from the calculations. The biggest difference is in Appalachian Athens, Gallia, and Meigs Counties, which had higher poverty rates with benefits than the more urban counties. 

This experiment shows just how important Ohio’s safety net is. Whether it be because of disability, old age, local job market conditions, or need to spend time caring for family members, many people don’t draw enough income from wages alone to support their families.Because of these programs like social security, SNAP, and housing assistance, many people are nonetheless able to put a roof over their heads and food on the table. Public benefits are far more than just a political talking point, they are one of the most important tools the public sector has for making sure people are able to get by.

New survey finds young Ohioans are less happy than older Ohioans

This morning, Scioto Analysis released a report on subjective wellbeing in Ohio. Using a survey of over 600 people from across the state, we explore how people assess the quality of their own lives through questions such as “How happy were you yesterday?”

We find that among our survey respondents, there is a positive correlation between happiness and age, meaning that younger respondents were less happy than older respondents. This challenges the hypothesis that happiness follows a U-shaped curve over the lifespan, where people are the least happy during middle age.

“For years, self-reported happiness across the world followed a ‘U-shaped curve,’ with younger and older adults happier than middle-age adults,” said Scioto Analysis Principal Rob Moore, “this analysis is the newest in a series of studies finding lower levels of happiness for young adults in North America.”

Previous studies that found lower levels of happiness among young people were the 2022 US Happiness Report and the 2024 World Happiness Report.

When asked about what things make people happy, the majority of respondents reported that relationships and social connections were key contributors to happiness. Other important factors were health, and creativity/hobbies. 

Respondents that reported fairly high levels of happiness overwhelmingly indicated that relationships and social connections were drivers of happiness. This could suggest that among younger people, feelings of loneliness are contributing to the disappearance of the U-shaped happiness curve.

Scioto Analysis partnered with Ohio State University’s Environment, Economy, Development, and Sustainability program to conduct this study. Data collection and analysis was conducted by students in the program.

What are “baby bonds?”

Last month, Scioto Analysis released its most recent calculation of the Ohio Poverty Measure. This measure calculates the total income of Ohioans, including benefits and after taxes and unavoidable expenses, and compares it to basic consumption needs.

This is how we usually measure poverty: through a lens of income. A problem that some economists have with this approach is that they believe that escape from intergenerational poverty also has to do with accumulation of wealth than income.

There is reason to believe this. While income disparities in the United States have been increasing, wealth disparities are even more pronounced.

A 2020 Pew Research Center analysis found that while the average high-income household makes about seven times as much as the average low-income household, the average high-wealth household has assets 75 times as large as a low-wealth household. This means the U.S. wealth gap is seven to eight times as large as the U.S. income gap.

Wealth does a few things for families. First, it provides a family safety net for working people. That “emergency fund” you keep in your bank account in case you experience a bout of unemployment? That’s wealth. Wealth helps working people weather the ups and downs of employment endemic to a market economy.

Second, wealth can be a source of income. Large amount of wealth invested can yield dividends that can provide income to a household. It also can be drawn on during retirement as an ongoing source of income.

Wealth also provides the safety that allows people to take risks. Starting a business or investing in a job that is unlikely to have large returns in the short-term is a lot easier to do if you have wealth to fall back on.

But how do we promote wealth, especially for families that are already living paycheck to paycheck and can’t afford to set money aside with pressing needs to pay for now?

Last month, Economist Darrick Hamilton published a commentary in TIME Magazine addressing this topic. His answer is a policy called “baby bonds.”

A baby bond works by paying someone a lump sum once they reach a certain age. This often comes from an investment made at birth that grows in value over time. These can be restricted in use or not.

A great example is Connecticut’s program, which automatically invests $3,200 for any child that qualifies for Medicaid at birth. Between age 18 and 30, the recipient can claim the fund and spend it on buying a home in Connecticut, starting or investing in a Connecticut business, paying for higher education or job training, or saving for retirement. The state of Connecticut estimates a typical bond will have a value of $11,000-$24,000 by the time it is claimed.

By targeting these funds to children who are Medicaid recipients, the Connecticut program focuses on low-income children. But a program like this could be targeted in a number of different ways: by being claimed by low-income households, targeted toward low-income zip codes, or even universal eligibility. Medicaid has the simplicity, though, of being a high-uptake program for low-income families that the state has good data on.

Income interventions are important for fighting poverty today, but wealth-based interventions like baby bonds could be a valuable tool for disrupting intergenerational poverty. State and local lawmakers interested in helping fight poverty in the long-term should consider policies like baby bonds if they are looking for creative ways to fight wealth inequality.

How does a policy analyst impute missing public benefits data?

Last week, Scioto Analysis released our updated Ohio Poverty Measure, a report that we’ve been working on since November. In this measure, we use publicly available data to understand the state of poverty in Ohio. Our methods are based on a wide range of other state and city poverty reports, all of which are heavily influenced by the Census Bureau’s Supplemental Poverty Measure.

To calculate the Ohio Poverty Measure, we primarily used data from the American Community Survey. The American Community Survey is one of the most useful datasets because it has a higher sample size than the Current Population Survey, which is used to calculate the Supplemental Poverty Measure. This makes it the best way to estimate what poverty looks like at smaller geographic resolutions. Though the American Community Survey has such a wide reach, it does have a few important drawbacks.

The most important limitation of the American Community Survey is that it doesn’t ask as many questions as other surveys do. It succeeds in providing detailed information about things like employment and income, but it doesn’t ask about things like medical expenses which we need to know for our poverty report. 

For this information, we turn to information in the Current Population Survey. The Current Population Survey is similar to the American Community Survey, but it asks a smaller number of people a larger number of questions. Here, the tradeoff is sample size for more detailed responses. 

While we could have used the Current Population Survey as the base data for our analysis like the Supplemental Poverty Measure, we’d be relying on a much smaller sample to make claims about all of Ohio. Since we performed our analysis at the Public Use Microdata Area level (the smallest identifiable geographic area in these datasets), this would subject our results to sampling error. 

So how do we use data from the Current Population Survey to fill in the missing data from the American Community Survey? Formally, this process is called “data imputation,” and there is a great deal of statistical research on the topic. 

There are many ways to conduct data imputation. One simple example is simply assigning every person the average value of a missing variable. In our context, this would be bad since something like medical expenses will be zero for many people and quite large for a small portion of people, though it does have the desirable characteristic that the imputed data will have the same mean as the original data. 

For the Ohio Poverty Measure, we follow the same steps for imputing missing data that other poverty reports before us have. We use a two-step modeling process to first determine who is likely to have non-zero missing values, and then isolating that group we try to determine what the value is. 

To do this, we build two regression models from the Current Population Survey data. The first is a binary outcome regression that predicts the probability of an individual response having a non-zero value. The second looks only at those responses that have non-zero values and predicts the size of the missing variable. 

We then take these two regression models and use the American Community Survey data to get predicted values for the probability of a non-zero value. We then estimate the total size of the missing variable. 

Then, we rank the American Community Respondents by their predicted probability of having a non-zero missing value. We want to make sure that the same percentage of people in the American Community Survey have non-zero values as in the Current Population Survey, so we only count the most likely people until the proportion in the American Community Survey matches that in the Current Population Survey.

Making predictions is one of the most important parts of policy analysis. We often think that the predictions are the outputs of our work, not part of the input. However, with some clever statistical thinking, we can give ourselves access to really amazing data like the American Community Survey, even if it doesn’t have exactly all the information we need. As long as we can find a good way to impute it, we can take advantage of everything else it has to offer.

Is it “cost-benefit analysis” or “benefit-cost analysis?”

At Scioto Analysis, we are doing a multi-year project where we are demonstrating how a good cost-benefit analysis is conducted. 

As part of this series, we have conducted cost-benefit analyses on the state Earned Income Tax Credit, school closings for COVID-19, AmeriCorps, urban canopy programs, water quality programs, an Ohio child tax credit, legalization of medical marijuana, and daylight saving time. We are currently conducting a cost-benefit analysis of a minimum wage increase for Ohio.

We also are members of the Society for Benefit-Cost Analysis, the international association of analysts across the public, private, and academic sectors working to improve the theory and practice of benefit-cost analysis and support evidence-based policy decisions.

That’s not a typo: we conduct “cost-benefit analysis” and are members of the Society for “Benefit-Cost Analysis.”
So what is the difference between “cost-benefit analysis” and “benefit-cost analysis?”

Nothing.

These two phrases are used interchangeably in the world of cost-benefit analysis and are often used by different people, but refer to the same phenomenon.

The main differences between the two phrases are where they are used. “Benefit-cost analysis” is common in academia and in federal regulatory decision making. “Cost-benefit analysis” is more common outside of these sectors in the United States and in non-U.S. contexts.

But why do two phrases refer to the same practice? Below are some of the explanations I have heard over the years. I will be clear: I can’t vouch for any of these. I don’t know how true any one of these are, but they have nonetheless been offered to me as explanations for why people say “benefit-cost analysis.”

To represent “professionalized” practice

One explanation relayed to me by my colleague Michael Hartnett from the most recent Society for Benefit-Cost Analysis conference was that cost-benefit analysis had a push for mainstream acceptance in the 1970s, before Ronald Reagan required cost-benefit analysis of all federal regulations. Economists were trying to standardize the practice and promote it as a systematic form of applied economic analysis. In order to differentiate the practice from more sophisticated approaches to evaluating policy, “benefit-cost analysis” was put forth as a way to refer to the systematic practice.

To emphasize the importance of benefits

“Cost-benefit analysis” seems to focus on costs before benefits because…it comes first in the phrase. By placing the word “benefit” first, “benefit-cost analysis” assuages the fear of people who think conducting this analysis is overly focused on costs policy to the detriment of its benefits.

This explanation sounds a little silly, but it does fit with some worries people have. The loudest voices against cost-benefit analysis are often advocates who are afraid costs of policies they champion will outweigh their benefits. This theory is that by placing benefits first, those people will have their fears assuaged.

To reflect the formula of “benefits minus cost”

The central formula of cost-benefit analysis is calculation of net present value, or 

Present Benefits - Present Costs = Net Present Value

By placing “benefit” first in the phrase, we capture that central formula in the technique. This explanation is similar to the previous one: it is about trying to get people to understand how the system works. Seems a little weak for the confusion created, though.

Linguistic cadence

This is an especially interesting one: that the phrase “cost-benefit analysis” rolls off the tongue better than “benefit-cost analysis,” so “cost-benefit analysis” will persist no matter how much people try to get others to use the latter. The argument has to do with word emphasis within the phrase. I don’t know how true this is, but it is interesting.
Overall, the battle between “benefit-cost analysis” and “cost-benefit analysis” seems a lot like the battle between the phrases “this data” and “these data,” classic linguistic squabbling, sometimes between elites and mundane use, rarely important. While I will not be soon to give up my membership at the Society for Benefit-Cost Analysis, we’ll probably continue using “cost-benefit analysis.” Why? Because that’s what policymakers tend to use, and we’d rather have them understanding the analysis than reading articles like this.

Ohio Poverty Measure finds over 260,000 Ohioans pulled out of poverty by Social Security

Today, Scioto Analysis released its updated Ohio Poverty Measure, an indicator specifically tailored to estimate the extent of poverty in the state of Ohio. Using this measure, we find that in 2021, 8.7% of Ohioans lived in poverty. This is lower than the 12.1% poverty rate according to the Official Poverty Measure and higher than the 8.1% poverty rate according to the Supplemental Poverty Measure, the two main poverty measures calculated by the United States Census Bureau.

Among public benefit programs, we estimate social security has the largest impact of any public benefit program in Ohio, lifting over 260,000 Ohioans out of poverty in 2021. The measure also finds SNAP benefits, formerly known as “food stamps,” had a substantial impact on poverty, reducing the statewide poverty rate by nearly two percentage points.

The Ohio Poverty Measure is the most accurate measure of poverty in the state, using methodology inspired by the California Poverty Measure, New York City Poverty Measure, Oregon Poverty Measure, and Wisconsin Poverty Measure. The Ohio Poverty Measure was first calculated by Scioto Analysis in 2021, using data from 2018. This report constitutes the first comprehensive update of that data, giving estimates of poverty from 2021.

The Ohio Poverty Measure estimates the impacts of government assistance, the tax system, and expenses based on geographic cost-of-living differences. Including these adjustments makes the Ohio Poverty Measure more precise than both the Official Poverty Measure and the Supplemental Poverty Measure.

According to the Ohio Poverty Measure, Black Ohioans are 75% more likely than White Ohioans to be experiencing poverty, with 14% of Black Ohioans experiencing poverty compared to only 8% of White Ohioans. 

Additionally, we also find stark geographic disparities in poverty rates. Ohio residents living in urban core geographic areas and rural Appalachian communities experience poverty at much higher rates than those across the state as a whole. Ohio residents living in suburbs surrounding Ohio’s largest cities experience poverty at much lower rates than residents across the state as a whole. 

Child care in Ohio must promote both equity and quality

At Ohio Gov. Mike DeWine’s State of the State address earlier this month, he announced a voucher program to help low-income Ohio families pay for child care.

DeWine’s current proposal is to provide vouchers for families making up to 200% of the federal poverty level, $60,000 or less for a family of four. This follows in the footsteps of other proposals to target child care assistance to low-income families.

I’ve written before about how child care is a double-edged policy problem. Often when we talk about child care, especially from an economic development perspective, we think about it as a way to free up time for parents and allow them to work. But child care arrangements are also the place where children spend substantial time during some of their most important years of development. 

Because of the amount of time spent in child care, the quality of child care arrangements can have significant impacts down the road for child development.

A key problem with the child care system is that parents often need to prioritize child care that fits their work needs. Young children get no say in the child care arrangements they take part in and don’t have the capacity to evaluate which child care arrangements will lead to better outcomes for them down the road.

This is a central problem with child care Ohio State Professor Emeritus David Blau writes about in his book, “The Child Care Problem: an Economic Analysis.” Since parents are making decisions for children, they often underconsume high-quality child care.

This is why Blau recommends a system in this book that does two things: provides subsidies to low-income families to handle the equity problem of child care while giving higher subsidies to high-quality centers to handle the above efficiency problem.

Quebec had an experiment that shed some light on what happens when only one of these two levers is pulled. In the late 90s, the Canadian province enacted a universal child care program that charged families just $5 a day for child care. Policymakers declined, however, to tie subsidies to child care quality.

An evaluation of this program after 20 years conducted by well-regarded economists found the program led to a sizable shock in outcomes for children. Not only did this program lead to negative effects on non-cognitive outcomes that persisted into school years, it also led to worse health, lower life satisfaction, and higher crime rates for these children later in life.

Ohio has been bandying about different proposals around child care quality for years now. The current law of the land is the Step Up to Quality program, a five-star child care program that provides higher subsidies to higher-quality programs. Last month, the Ohio Department of Children and Youth and Ohio Department of Job & Family Services proposed a new rule to reduce this five-tier rating system to a three-tier rating system which could reduce payments for quality.

Whatever the state settles on when it comes to payments for low-income families and centers, Blau’s recommendations still ring true: subsidies should be targeted toward low-income households to advance equity concerns and should be targeted toward high-quality centers to support child development. This ensures both low-income families today and children tomorrow are benefiting from public investment in the child care system.

This commentary first appeared in the Ohio Capital Journal.

How do we incorporate animals into cost-benefit analysis?

A book I’m particularly excited to read that came out earlier this year is Dog Economics: Perspectives on our Canine Relationships. This book, written by two of the leading economists in the public policy field, applies a range of economic concepts to dogs and their relationships with human beings.

I first was exposed to David Weimer’s work on dog economics when I was serving as editor for On Balance, the blog for the Society for Benefit-Cost Analysis. Weimer had recently won the Society’s Journal Article of the year award for an innovative analysis he had done to estimate the value of a statistical life for dogs.

Why calculate the value of a statistical life for dogs? Well the reason is because federal agencies sometimes promulgate regulations that will directly impact canine well-being. Weimer was drawn to this idea because the Food and Drug Administration had conducted a benefit-cost analysis on approval of a new type of dog food. A glaring omission from the analysis? The risk to health for dogs who consumed the product.

But how do we calculate the value of safer dog food? When human beings participate in the labor market, they trade off risk of death for more pay. Using these tradeoffs, we estimate how much people value marginal reductions in risk of death, which allows us to come up with the value of a statistical life.

Dogs don’t participate in the labor market. In a literal sense, dogs don’t participate in markets. They don’t have income, they don’t have assets, they don’t purchase goods and sell their services. So how can we estimate a dollar value they put on reductions of their risk of death?

Weimer’s answer is to ask their owners. Weimer conducted a “contingent valuation” study to see how much value people put on their dogs’ lives. He did this by posing questions about hypothetical vaccinations for dogs that would reduce dogs’ likelihood to contract potentially deadly diseases. By eliciting the willingness to pay dog owners have for these vaccinations, Weimer was able to derive a value of statistical life for dogs.

This is certainly a step in the right direction for incorporating the value of public policy to animals into cost-benefit analysis. But this approach still falls short of fully incorporating animals into cost-benefit analysis. Why? Because it defines the benefits that accrue to animals through their owners’ altruism rather than on their own terms.

This would be sort of like if you surveyed people on how much less income they’d be willing to take on as a family in order for their spouses to have the opportunity to take on less dangerous jobs. It should be a good proxy for the value of a statistical life, but a more accurate measure would come from seeing how much people actually take on themselves, not relying on a report from someone who cares about them.

This approach is taken often when assessing benefits to children. But even here we see problems. In his book The Child Care Problem: An Economic Analysis, economist David Blau talks about the market failure caused by parents underconsuming high-quality child care. This happens because parental demand for child care that will nurture their children and lead to better outcomes for them and society falls short of the optimal social benefit. If children could rationally decide for themselves what quality child care to consume, they would likely be willing to pay more for quality child care than parents do.

For now, though, it is difficult for us to divine how much of a value of risk of death dogs take on themselves. Some cutting-edge researchers are conducting studies with chickens, seeing how much feed they are willing to give up in order to live in more free-range environments. The results are promising: initial studies have found chickens exhibit a rational demand curve for more open space.

Non-human animals are not as different from humans as we act like they are. They have to operate under conditions of scarcity just as much as humans do. And they are often subjected to the impacts of public policy just like human beings are. Hopefully as we innovate new ways to study public policy, we will also find ways to incorporate their interest into our benefit-cost models.

How do immigrants impact the labor market?

It’s presidential election season again, which means that debates around the most politically divisive issues are front and center in the national news. Immigration is one of the most commonly debated topics, and according to data from Pew Research, 57% of Americans think that it is a top priority for the President and Congress. 

Political narratives aside, what actually happens when immigrants come to the United States? You will get extremely different answers to this question depending on who you ask, so let’s instead use this opportunity to explore some of the data surrounding immigration. Specifically, what happens to the labor market when immigrants move in. 

Before we talk about actual data, it will be useful to visit some of the basic economic theory behind this question. If a wave of immigrants move into a community and begin participating in the labor market, then theory says we should see a growth in the labor supply. Assuming that nothing else changes, this would have two main effects.

First, unemployment should rise. Assuming that the demand for labor is unchanged, then there would just be more people competing for the same number of jobs. 

Second, wages would go down. Competition among potential workers should cause a race to the bottom for wages. 

These two outcomes only happen in a perfectly competitive labor market, where all workers are competing for the same jobs and wages can fluctuate with supply and demand. In the real world, the labor market functions very differently. 

To see just how differently this plays out in real life, we can look at the example of the Mariel boatlift, a mass migration event where thousands of Cubans moved to Florida in 1980. This event was the subject of a landmark 1990 paper by the economist David Card, who explored the labor market impacts of such a sudden shock.

Card’s study was a pioneering deployment of “difference-in-differences” economic evaluation. Card looked at unemployment and wages in Miami before and after the Mariel Boatlift then compared these to changes in unemployment and wages in comparable metropolitan areas. These comparable metropolitan areas served as “synthetic controls,” mimicking an experimental design and allowing Card to see what the actual impact of the sudden influx in workers had on employment and wages

Card found that, when compared to comparable metropolitan areas, the sudden wave of immigration actually had no effect on unemployment or wages in low-skill industries. Instead, the Miami labor market was able to immediately grow and absorb these new workers. 

Why was this able to happen? The dynamics of labor markets are extremely difficult to understand, but I think there are a few main reasons.

One reason is that in a healthy economy, there should be room for growth. Many economists consider there to be “full employment” not when the unemployment rate is 0%, but rather when everyone who wants to work is able to with relative ease (the rule of thumb I learned is that full employment is equivalent to an unemployment rate of about 3% - 4%). There are far more technical definitions of full employment, but the idea is we should always expect there to be some job openings. 

The second reason is that immigrants don’t just come here to work, they come here to live. They spend money in their new communities, they start their own businesses, they grow the local economy. By moving to Miami, the immigrants from the Mairel boatlift created the opportunity for employers to expand their operations. 

The political discourse around immigration isn’t very data-driven these days, and that can be an issue. Hopefully this example helps shed some light on what actually happens when immigrants move to the United States.