Where do the beneficiaries of the Affordable Care Act live?

Posted on April 7, 2015May 15, 2018 by David Evans

The three interactive graphics below detail the most salient points about where the beneficiaries of the Affordable Care Act live, by state and county, in terms of access to expanded Medicaid coverage, new subsidies for health insurance, and how a ruling by the Supreme Court in the case of King vs. Burwell against the Obama Administration would affect ACA coverage.

The first map contains estimates for the relative rank of counties by the share of families eligible for Medicaid. Families making less than 138 percent of the Federal Poverty Level—about $33,000 for a family of four—are eligible for Medicaid if their state has elected to expand the program. The states opting out of the Affordable Care Act’s Medicaid expansion (those on the tan spectrum) are among those that would benefit the most. The counties in darker green are greatly benefiting from the expansion of Medicaid. Many of the counties that either are or would be major beneficiaries of Medicaid expansion are rural. (See Figure 1.)

Figure 1

The second map contains estimates for the relative rank of counties by the share of families in the income range that qualifies them for subsidies on the exchange. Families making between 138 percent and 400 percent of the Federal Poverty Level—between $33,000 and $97,000 for a family of four—are eligible for exchange subsidies if they do not have access to employer coverage. The beneficiaries of the subsidies are spread across the country with higher concentrations in suburban and middle-income counties. (See Figure 2.)

Figure 2

The third map contains estimates for the relative rank of counties by the share of families in the income range that qualifies them for subsidies on the exchange. Families making between 138 percent and 400 percent of the Federal Poverty Level are eligible for exchange subsidies if they do not have access to employer coverage. If the Supreme Court rules in the King vs Burwell case that states using a federally run exchange are ineligible for subsidies, then families in those states on the tan spectrum will lose their subsidies while those in gray will be largely unaffected. The counties that would be hurt the most from the loss of subsidies are disproportionately in the South. (See Figure 3.)

Figure 3

Download a PDF of the Technical Appendix.

What Do Americans Think Should Be Done About Inequality?

Posted on March 26, 2015April 18, 2018 by Casey Schoeneberger

A new online survey of some 10,000 Americans’ reaction to growing income inequality offers novel insight into public perceptions of inequality and what—if anything— should be done about it. The survey first presents some respondents with information about the extent of inequality—for example, by displaying how much more income a respondent would earn if increases in economic growth since 1980 had been more evenly distributed—and then assesses their attitudes toward inequality and policies aimed at ameliorating gaps between rich and poor, compared to other respondents who did not see the information. The survey shows that while respondents who view information about inequality are more likely to believe that inequality is a serious problem, they show no more appetite for many government interventions to reduce inequality— with the notable exceptions of increasing the estate tax and the minimum wage.

View full pdf here alongside all endnotes.

Our working hypothesis is that those surveyed alighted on the estate tax because it applies to many fewer Americans than respondents had assumed. And respondents favored increasing the minimum wage because doing so does not necessitate heavy government involvement (unlike, say, the Supplemental Nutrition Assistance Program, or food stamps for low-income Americans). The survey reveals a deep mistrust of the federal government’s ability to administer programs effectively and efficiently even after confronted with the importance of these programs in alleviating poverty among those Americans at the bottom of the ladder.

There are a number of nuances, of course, to the findings from the survey, which are detailed in our forthcoming paper, “How Elastic Are Preferences for Redistribution? Evidence from Randomized Survey Experiments.” Our conclusions bear directly on public policy debates in Washington, D.C. and in statehouses across the country as the U.S. public grapples with what, if anything, to do about a wealth and income gap now as wide as just before the onset of the Great Depression in 1929 (See Figure 1.)

Figure 1

The survey

Over 10,000 respondents completed the surveys we designed for this project. The mix of respondents gives us some confidence that the results we find would mirror the attitudes of typical Americans. While online surveys do disproportionately draw from certain groups such as younger adults, our sample compares favorably with both the CBS News election survey from 2011 and the Rand Corporation’s American Life Panel online survey.

For our study, respondents were randomly assigned to a treatment group who viewed a short online presentation conveying information about income inequality, or a control group who did not view this presentation. This customized approach was made possible by an online platform that enabled us to gather detailed income data on the respondents and in turn inform them interactively about where they fell in the U.S. income distribution.

Respondents were also asked to self-report their political preferences using a five-point scale, from very liberal to very conservative. Then, both treatment and control groups answered a series of questions about their views on inequality and which policies, if any, they favored to address it. We call the difference between the percent of liberal and the percent of conservative control group respondents agreeing on these various issues the “political gap”—and we examine how our treatment might “close the gap” between liberals and conservatives on these various issues.

The findings

There are several novel findings that emerge from our survey. When respondents are given the actual data on the growing income gap in the United States, their concern about the problem increases by a staggering 35 percent—an effect equal in size to roughly 36 percent of the liberal-conservative gap on this question. Moreover, viewing information about inequality also significantly influences attitudes toward two redistributive policies: the estate tax and the minimum wage (See Figure 2).

Figure 2

When respondents in the treatment group learn the small share of estates subject to the estate tax (roughly one in 1,000), they support increasing it at three times the rate of the control group—akin to cutting the political gap in half (See Figure 3). This finding is mirrored in a recent study by political scientist John Sides of George Washington University, who finds that accurate information on the small number of families subject to the estate tax substantially reduces support for repealing the tax.

Similarly, after reviewing the presentation on income inequality, support for raising the minimum wage jumped by 4 percent (from an already high baseline of support of 69 percent) in the treatment group relative to the control group, sufficient to close the political gap by about 10 percent (See Figure 3). (The federal minimum wage now stands at $7.25 an hour; 28 states and the District of Columbia boast slightly higher minimum wages alongside other several cities).

Figure 3

At the same time, attitudes toward some policies were unaffected, including increasing top income tax rates and support for the Earned Income Tax Credit for low-wage workers and the Supplemental Nutrition Assistance Program—more commonly known as the food stamps program—which on average provides $150 a month toward food purchases for eligible recipients.

Importantly, our results also suggest that this aversion to government intervention is due to a deep level of distrust in government. In a sense, respondents who have learned the role of government in creating the current level of inequality seem to be telling us they do not trust that government is also the entity to address the problem.

The policy implications

This last finding is, to our knowledge, the first direct evidence of the causal effects of trust in government on redistributive policy preferences. Our findings highlight the potential role of mistrust in government in limiting enthusiasm among the general public to certain kinds of government policy programs—such as the Supplemental Nutrition Assistance Program and the Earned Income Tax Credit—designed to help close the wealth and income gap.

Research into the connection between mistrust of government and policy preferences is only just beginning. For instance, economists Paola Sapienza at Northwestern University’s Kellogg School of Management and Luigi Zingales at the University of Chicago’s Booth School of Business find that Americans support higher auto fuel standards over a carbon tax-and-rebate program because they do not trust the government to in fact rebate the tax. Given that by most measures, Americans trust in government is at record lows, future work on its consequences would be welcome.

Finally, while beyond the scope of our paper, our results do point to an intriguing possibility: that the rise in inequality may have in fact led to the rise of distrust in government. If such a connection existed, then inequality may in fact be self-reinforcing—decreasing trust in government and undercutting support for the very policies aimed to reduce inequality. We look forward to future work on the possible connections between inequality, trust in government, and support for redistribution.

View full pdf here.

—Ilyana Kuziemko is an economics professor at Princeton University, Michael Norton is a professor of business administration and marketing at Harvard Business School, Emmanuel Saez is an economics professor at the University of California-Berkeley, and Stefanie Stantcheva is an economist at Harvard University.

Taxation and fairness in an era of high inequality

Posted on March 3, 2015April 18, 2018 by Bridget Ansel

Heather Boushey, Executive Director and Chief Economist, Washington Center for Equitable Growth, testifying before the U.S. Senate Committee on Finance on “Fairness in Taxation.”

I would like to thank Chairman Hatch, Ranking Member Wyden, and the rest of the Committee for inviting me here today to testify.

My name is Heather Boushey and I am Executive Director and Chief Economist of the Washington Center for Equitable Growth. The center is a new project devoted to understanding what grows our economy, with a particular emphasis on understanding whether and how rising levels of economic inequality affect economic growth and stability.

I’m honored to be here today to discuss a very important topic: the relationship between fairness and taxation. Over the past several decades, economic inequality, on a variety of measures, has increased in the United States. The benefits of economic growth have flowed primarily to households and individuals at the top of the income and wealth ladders. We need to keep this fact in mind when we consider taxation and fairness in the years ahead.

There are three major conclusions from my testimony:

As inequality has increased, the tax code has not kept pace with this change. The tax code does less to reduce inequality than it did in the late 1970s
Efforts to reduce inequality are not in tension with economic growth. A variety of research shows that steps taken to reduce inequality do not significantly hinder economic growth
There are policy options that can make the tax code more progressive that will have broad benefits for everyone

The rest of my testimony will focus on documenting the rise in inequality, reviewing the academic research on the effects of taxation, and some thoughts about where policy should go forward.

Download the full pdf for a complete list of sources

The rise of inequality

Inequality, at least in the popular conversation about it, is talked about like it is a single phenomenon. Even the most widely used measure of inequality, the Gini coefficient, treats it as such. If the coefficient rises, we know that inequality has gone up. But what we don’t know is how exactly inequality has increased.

In short, the story of the past four decades when it comes to inequality is a rapid rise in incomes and wealth for those at the top, slower growth for the middle compared to earlier time periods, and stagnation, if not outright declines, for incomes at the bottom of the ladder.

According to data from Paris School of Economics professor Thomas Piketty and University of California-Berkeley economist Emmanuel Saez, the average pre-tax income of the top 1 percent grew by 178 percent from 1979 to 2012. Correspondingly, the top 1 percent’s share of pre-tax income has increased from 8 percent to 19 percent over the same time period.

At the same time, inequality of wealth has been rising as well. According to research by Saez and London School of Economics professor Gabriel Zucman, the share of wealth going to top 0.1 percent of households has increased to 22 percent in 2012 from roughly 7 percent in 1979. That’s a 3-times increase in the share of wealth held by the top 10 percent of the top 1 percent. The reason for this rise? The rich have a much higher savings rate than the rest of population and the increase income inequality appears to be calcifying into wealth inequality as the rich save their incomes.

According to data from the Congressional Budget Office, the pre-tax, pre-transfer income of the median U.S. household grew by an average of 0.9 percent a year from 1979 to 2007, the last year before the Great Recession. That growth rate is considerably slower than the 4.7 percent a year for the average income of the top 1 percent of households.

For those at the bottom, the reductions in poverty over the past several decades have been driven almost entirely by tax-and-transfer programs. This means that our anti-poverty programs are working to reduce material hardship. Whether they have reduced it enough is another question. But this research also raises concerns about how the labor market is working for those at the bottom of the ladder.

Another shift toward inequality has been the shift of income from labor income (salaries and wages) toward capital (business income and capital gains). This shift matters for inequality because the distribution of capital income is far more unequal than the distribution of labor income. Households at the bottom and the middle of the income ladder rely much more on labor as a source of income than capital. And capital income is concentrated much more at the top of the income ladder.

As these shifts in inequality occurred, the federal tax system was doing less to reduce inequality, though the federal tax system is still progressive. A quick look at Figure 1 below shows how much the top marginal tax rate for labor income has been declining since the early 1980s.

Figure 1

However as the top rate has decreased, the improved economic performance that we might expect given the conventional wisdom doesn’t show up in the data. Figure 2 shows no discernible relationship between employment growth and the level of the top marginal tax rate. If cutting taxes resulted in stronger employment growth then there would be a discernible pattern in the years between 1948 and 2014, represented by a green dot in Figure 2. There is no pattern.

Figure 2

The lack of any obvious relationship isn’t the case for just employment growth. Figure 3 below shows that there is no clear correlation between the growth in labor productivity, one of the key sources of long-run economic growth, and the top marginal tax rate.

Figure 3

A more in-depth treatment of the relationship between tax rates and macroeconomic growth can be found in a 2012 Congressional Research Service report by Thomas Hungerford.

Now it’s true that the federal income tax has become slightly more progressive by some measures. But more tax revenue has come from payroll taxes, which have become less progressive. And those at the top of the distribution are paying a large share of federal income taxation. According to Congressional Budget Office data, the top 1 percent of earners had 14.2 percent of federal tax liabilities in 1979. By 2011, that share increased to 24 percent.

Yet over that same time period, the top 1 percent’s share of pre-tax income increased from 8.9 percent to 14.6 percent. So if progressivity is measured by the distribution of taxes paid, then progressivity has gone up. But that measure doesn’t account for the rising inequality in the distribution of income. The result of inequality increasing as the tax system does less to reduce inequality (as a CBO report points out) is that the inequality of incomes after taxation has increased more than the inequality of income before taxation.

Why should we care about the rise in inequality? There’s an emerging consensus in economic research that high levels of inequality can threaten economic growth. My colleague Carter C. Price and I went through the research literature on the relationship between inequality and growth and found that research points toward a negative relationship. As inequality goes up, economic growth tends to go down. A recent paper by researchers at the International Monetary Fund further finds that redistribution does not necessarily hamper growth. The exact reason for this apparent relationship is unclear and my organization was founded to help better understand it. But the evidence as it stands is cause to seriously grapple with the negative effects of inequality.

Academic research on taxation

Given the rise in inequality, what can tax policy do about this trend? One potential concern about taxation is that in an effort to reduce inequality, it can reduce economic growth and cause more problems than were already there. An increase in labor taxation might cause some workers to work less or an increase in capital taxation might cause a reduction in savings, both of which are important for economic growth.

These assumptions are widely held by policymakers and economics commentators. And to a certain extent they are true. But the level of taxation at which these problems would occur is much higher than usually expected.

On the subject of income taxation, a body of new research shows that labor income taxes for those at the top of the income ladder have no adverse effect on economic growth. A paper by Nobel Laureate Peter Diamond and UC-Berkeley economist Emmanuel Saez reviewed the research literature on income taxation and finds that progressive taxation is well-supported by the research.

When it comes specifically to top rates, another paper by Saez along with Thomas Piketty and Harvard University’s Stefanie Stantcheva look at the underlying forces that determine what the optimal level of taxation could be. After accounting for a variety of factors, the three economists find that the top marginal rate could be as high as 83 percent without affecting economic growth. I wouldn’t take this paper as evidence that the United States could increase its top income rate to such a level. Rather, the result is instructive that tax rates could be significantly higher without major adverse effects.

Research also shows that reducing certain tax expenditures wouldn’t negatively affect the economy either. Research that shows tax incentives are often ineffective at incentivizing behavior. The tax code may provide a tax break for a certain behavior on the belief that this economic incentive will dramatically change behavior, but some work casts doubt on how much behavior is changed by these kind of incentives. Take, for example, Harvard economist Raj Chetty’s work on retirement savings decisions. He and his co-authors look at millions of data points on changes in retirement savings after a change in tax policy in Denmark. What they found is that 85 percent of workers were non-responsive to changes in tax incentives and savings rates didn’t decline. Of course, this result isn’t perfectly applicable to the U.S. situation. But its results are suggestive and should be considered in the U.S. policy situation.

New research also challenges the idea that capital taxation will invariably result in lower savings and consequently lower economic growth. Recent work that shows the long-held belief that capital income shouldn’t be taxed at all is flawed. A paper by Piketty and Saez shows the flaws with the famous Chamley-Judd assumptions. Chamley-Judd assumptions imply that savers have infinitely long-time horizons when thinking about saving for the future. If I care about the returns on my savings very, very far in the future, then a tax on savings would end up compounding to a point where the burden is immense. Taxing capital in this situation would drastically reduce savings. But Piketty and Saez show that this assumption doesn’t hold up under scrutiny. And a recent paper by Ludwig Straub and Ivàn Werning of the Massachusetts Institute of Technology show that the zero taxation result doesn’t even hold up within the Chamley-Judd framework.

There is also the assumption that reducing capital taxation will induce corporations into investing more. The reduction in taxation supposedly will increase the return to investment. But research by the University of California-Berkeley’s Danny Yagan finds that the 2003 dividend tax cut didn’t have any effect on investment or employee compensation. Yagan compares the investment behavior of public companies, which would were affected by the tax cut, with the behavior of privately held companies. What he found was that the public companies, which should have invested more due to the tax cut, didn’t invest more than similar privately held companies.

Another possible form of capital taxation is increased taxation of bequests and inheritances. A 2013 Econometrica article by Piketty and Saez argues that the optimal tax rate for inheritances for the United States may be as high as 60 percent. And that the rate would be even higher for those at the very top. In their paper a high inheritance tax is optimal if those bequeathing wealth are relatively unaffected by taxation, inheritances are very unequally distributed and society favors work over inheritance. And the United States fits this description, hence the high level of taxation found in their paper.

With this knowledge, what can we say about tax policy moving forward?

Possible policy steps

So we know that inequality has risen in the United States over the past several decades. At the same time we have learned from research that there is more room to make the tax code more progressive to help reduce inequality. There are quite a few policies that could move the tax code in that direction.

There are many examples of changes that would be consistent with the literature. Two that are on the table right now would be eliminating the “stepped-up basis” for taxation of bequests and expanding the Child Tax Credit and making it permanent. A rather large loophole currently exists when it comes to the taxation of capital gains. When a person inherits, say, a large amount of stock holdings from a parent, the inheritor is only taxed on the gains made after they inherit the stocks. So if a parent bought a stock at $1 and it appreciates to $99 before the child receives the stock, then the child would only be taxed on the gains over $99. So the capital gains that occurred over the lifetime on that asset since it was first purchased aren’t taxed as income.

If we are concerned about the possibility of families passing along large estates to children and the potential damages that could have on the vitality of the economy, this seems like a loophole we should close. There are a variety of other ideas for taxation in this area, including eliminating the carried interest loophole, whereby hedge fund managers do not pay the ordinary income tax. David Kamin, a professor at New York University School of Law, outlines a menu of options for taxing the wealth of the very wealthy, including transfer taxes, raising the ordinary income tax rates or limiting deductions and exclusions.

But we can also do a variety of things at the low end of the income laddee. One example is the Child Tax Credit, which provides workers with children a tax credit of up to $1,000 per child in hopes of offsetting the costs of raising a child. The tax credit is currently partially refundable for a set percentage of income (15 percent) over a set threshold (currently $3,000). The value of the tax credit has been increased and the threshold decreased, both temporarily, in recent years. I recommend making these reforms permanent. Given the rising costs of child care and the incredibly important role of children and the development of their future talents for the future growth of the economy, giving parents more funds to help raise children makes sense.

Conclusion

The past four decades have been a period of high and rising inequality in the United States. Tax policy has an important role to play in the policy response to this major shift in our economy. It cannot, and should not, be policymakers’ sole response. But changes are needed.

Our economy currently isn’t creating prosperity that is broadly shared. And it hasn’t for a while. Today’s hearing is an important contribution to the conversation about how to get our economy on a track to creating shared prosperity for all Americans.

Obamacare and long-term U.S. economic competitiveness

Posted on March 2, 2015April 18, 2018 by Bridget Ansel

The legal arguments before the U.S. Supreme Court this week in King vs. Burwell may well decide whether a key provision of the Affordable Care Act remains in effect for millions of Americans who now rely on Obamacare for affordable health insurance. But if this elite jury of nine judges is still out on the legal question before the court, the long-term economic consequences of uninsuring the many children now insured under the new health law are clear.

Several new research papers document the importance of early childhood health care for the least advantaged kids among us—on their future workforce productivity, their contributions to our national tax base, their educational attainment, and their declining use of government income supports. These robust findings mirror the results of research that I conducted with economists Sandra Decker of the National Center for Health Statistics and Wanchuan Lin of Peking University. We found that Medicaid coverage for children born between 1985 and 2005 resulted in a better health trajectory for those kids as they because adolescents and young adults, and thus improved their ability to be productive contributors to our economy.

What will happen if less-well-off kids today do not get the affordable health care they need to become successful contributors to our economy over the coming decades? Well, the best economic research shows that current government health care programs, including those recently expanded under Obamacare, already ensure many of these kids will be better and more productive citizens. So lets parse the data.

The first paper, by economists David Brown and Ithai Lurie of the U.S. Treasury Department, and economics professor Amanda Kowalski of Yale University finds that children who gained public health insurance in the 1980s and 1990s under the expanded Medicaid and the Children’s Health Insurance Program paid more in cumulative taxes by age 28, collected less in payments from the Earned Income Tax Credit, and (among the women in the group) attained higher cumulative wages. The three authors estimate that when these now young adults reach the age of 60 the federal government will have recouped at least 56 cents for each dollar spent on childhood health care.

The second paper, by economists Laura Wherry at the University of California-Los Angeles, Sarah Miller at the University of Michigan, Rober Kaestner at the University of Illinois, and Bruce Meyer at the University of Chicago, shows that providing public health insurance to low-income children results in fewer hospitalizations and emergency room visits in adolescence and adulthood. Their findings strongly suggest that substantial health care savings are in the cards for this group of Americans—a welcome boon as the U.S. economy struggles with reducing the cost of health care in our society.

The third paper, by economists Sarah Cohodes of Harvard University and Cornell University’s Samuel Kleiner, Michael Lovenheim, and Daniel Grossman, shows that better public health care among low-income children in the 1980s and 1990s resulted in higher graduation rates from high school and college for these kids, again indicating that the long-run economic benefits of public health insurance are substantial.

None of these studies examined the health of kids enrolled in public health programs due to Obamacare specifically—the program is too new for these kinds of long-term studies—but the further expansion of eligibility for low-income children through Medicaid and the Children’s Health Care Program is one of the things at stake in the Supreme Court’s oral arguments in King vs Burwell next week and the final decision sometime this summer.

What could happen should the Supreme Court decide in effect to shut down the federal health insurance exchange operating in 34 states without their own exchanges? Among the results could be more than 5 million enrollees in the joint state-federal Children’s Health Insurance Program without health insurance, estimates the Children’s Health Fund, a provider of mobile health care for homeless and low-income children. We already know the empirical long-term economic benefits of expanded health care for those least able to afford it—and can say with strong confidence that taking away health insurance for these five million now covered under Obamacare would harm our economy.

—Janet Currie is the Henry Putnam Professor of Economics and Public Affairs at Princeton University and directs the Program on Families and Children at the National Bureau of Economic Research. Her views expressed in this column are her own.

The links between institutions and shared growth

Posted on February 25, 2015April 18, 2018 by Bridget Ansel

Increasing inequality in the United States and its relationship to economic growth is getting a lot of attention lately. It is now clear that sharply rising inequality is not necessary for good economic performance and, indeed, growing evidence suggests that high and rising inequality is harmful, especially if the mechanisms generating the rise in incomes at the top of the ladder contribute to stagnant and falling incomes for the rest of us.

What matters most for the vast majority of American households is not inequality per se. Rather, individuals are mainly concerned with generating a higher standard of living via better wages and employment outcomes. Too many households confront a labor market in which it is increasingly difficult to find living-wage jobs. Despite substantial national productivity growth in the last 30 years, the average pre-tax income for the bottom 90 percent of American households was lower in 2013 than in 1980, and the wage for full-time male college graduates has not grown since the late 1990s.

This wage stagnation stands in contrast to the years following World War II until the late 1970s, when worker compensation grew in line with labor productivity. It’s true that compensation-productivity gaps have also appeared in other rich countries, but with one crucial difference—outside the United States this gap has increased despite substantial growth in worker compensation. While U.S. manufacturing workers have suffered from technology and global competition, that‘s also the case for manufacturing workers in all rich countries. For example, I’ve shown that American manufacturing workers experienced a paltry 4 percent wage increase between 1980 and 2013 (after accounting for inflation) while manufacturing compensation increased by 42 percent in Germany, 43 percent in France, and 56 percent in Sweden.

Many argue that the United States makes up for poor compensation performance with strong employment growth. Yet over the past few decades, U.S. employment rates have not only been lower than that of many European countries, but also have fallen dramatically in recent years—especially for men. In addition, while U.S. employment and GDP grew in tandem between the late 1940s and the mid-1990s, a yawning gap has developed since then, with GDP continuing upwards and employment flattening, despite a growing population.

In short, since the 1980s, U.S. economic growth failed to produce enough jobs, and equally important, enough “decent jobs, ” which I defined as those paying adequate wages with adequate hours of work. Through my research—funded in part by a 2014 grant from the Washington Center for Equitable Growth—I am seeking to uncover the conditions and mechanisms through which economic growth gets translated into a sufficient numbers of decent jobs. More specifically, I aim to identify the institutions and public policies that help explain best practices for the creation of decent jobs.

What happened to shared growth? Most economists continue to explain the explosion of earnings inequality with conventional supply-and-demand stories, in which worker compensation is believed to accurately reflect the contribution workers make to production. Thus, in this view, CEOs and financiers have received skyrocketing salaries, especially since the mid-1990s, because they are now contributing dramatically more to their firms and to the economy as a whole.

Similarly, the bottom 90 percent have seen stagnant and falling wages because they’ve fallen behind in the “race between education and technology.” The computerization of the workplace requires greater cognitive skills, but workers have not kept up, as indicated by the slowdown in college graduation rates. Assuming (nearly) perfectly competitive markets, the explosion in wage inequality in this view must reflect a similarly explosive increase in skill mismatch (too many low skill workers, too few high skill ones).

Such arguments leave little or no room for labor market institutions and public policies in determining changes in the distribution of earnings up and down the income ladder. An alternative view is that institutionally-driven bargaining power is a critical piece of the story, whether it is the noncompetitive “rents” earned by top managers and financiers, or the collapsing power of hourly wage employees. As Thomas Piketty argues in “Capital in the Twenty-First Century:”

In order to understand the dynamics of wage inequality we must introduce other factors, such as the institutions and rules that govern the operations of the labor market in each society [and explain] the diversity of wage distributions we observe in different countries at different times.

All rich countries face challenges from technology and globalization, but only the United States and the United Kingdom show inequality rising to extreme levels.

In order to understand wage inequality and unshared productivity growth in the United States, we must take a much closer look at the ways in which institutions affect labor market outcomes. In my forthcoming research, I will compare the United States with Canada, Australia, Germany, and France to answer questions such as:

How does the distribution and growth of decent jobs in these countries compare by economic sector, occupation, and demographic group?
To what extent can these outcomes be attributed to the effects of differing institutions across the five countries?

Underlying this research design is the view that institutions matter a great deal for market outcomes. The United States can do a better job of generating decent jobs, and a sensible first step is to learn from the experiences of other countries.

—David Howell is a professor of economics and public policy at The New School in New York City

The future of work in the second machine age is up to us

Posted on February 23, 2015April 18, 2018 by Nick Bunker

“Big Thinkers” about the role of technology in the U.S. economy are roughly divided into two camps when it comes to the consequences of rapid technological change on the U.S. workforce. There is the techno-optimist view that better technology complements workers and hence benefits them by raising wages. And there’s the pessimistic view that better technology substitutes for workers and therefore displaces and harms them. A debate between the two views was probably what the organizers intended for an event last week hosted by The Brookings Institution’s Hamilton Project entitled “The Future of Work in the Age of the Machine.”

The impetus for the forum was the influential 2014 book “The Second Machine Age” by professors Erik Brynnjolfsson and Andrew McAfee at the Massachusetts Institute of Technology. The authors argue that increasingly “smart” technology displaces workers by reducing the range of tasks that require human ingenuity, and by enabling economic arrangements such as off-shoring that rely on instantaneous global communication and replicability. Brynnjolfsson and McAfee are clearly in the pessimists’ camp.

Until recently, economists were largely in the optimist camp. Sure, some jobs—think buggy whip manufacturers, typists, or travel agents—might disappear, but others would arise to take their place. In the long run, increased productivity would benefit everyone in the form of higher wages.

Yet the debate last week actually highlighted a third position. If either the techno-optimists or the techno-pessimists are right, then we should see a major positive impact on worker productivity. But it just isn’t there in the data. If anything, the rate of technological change in the United States has decreased since at least 2003, specifically in the technology sectors widely thought to be most innovative.

In contrast, we definitely see worker displacement, stagnant earnings, a failing job ladder, rising inequality at the top, “over-education” (workers taking jobs for which they’re historically overqualified), and declining rates of employment-to-population and household and small business formation. What we do not see are the productivity gains, either on a micro or macro level, that are supposedly driving worker displacement. (See Figure 1.)

Figure 1

Former Treasury Secretary Larry Summers made this point forcefully at the Hamilton Project event. He said “people see there’s already a lot of disemployment but not a lot of productivity growth.” And he continued by asserting that “the core problem is that there aren’t enough jobs,” and that it’s hard to believe the future promise of labor-supplanting technology is driving current displacement. The reason, he said, is that we’d expect to see the installment of new labor-saving systems that would cause a temporary increase in labor demand during the transition.

Summers noted that back when he was an undergraduate at MIT in the 1960s, his professors said labor would not be displaced by technology. In those days, the non-employment rate for prime-age male workers was 6 percent. Now it’s 16 percent. Summers’ co-panelist David Autor added that since 2000, the education wage premium has reached a plateau and the rate of over-education has increased, both of which are hard to square with the argument that the reason for rising inequality is the advance of technology. Summers added that the idea that more education solves the problem of displaced labor is “fundamentally an evasion.” Summers’ arguments and Autor’s observation imply that if we’re wondering how things got so bad for workers, it’s not because we live in the Second Machine Age.

So if not technology, what explains labor displacement?

Broadly speaking, the explanation is this: market practices and public policies that favor managers over workers, and those who make their living by owning capital over those who make their living by earning wages. That choice lurks behind the decline in full employment as a priority in macroeconomic policymaking. It’s also behind a shift in the legal standards, mores, and incentives of corporate management in favor of the interests of owners over other stakeholders. That choice is also evident in the abandonment of long-term productive investment as a priority in public budgeting in favor of upper-income tax breaks and retirement programs for the elderly.

As Summers noted at the Hamilton Project’s event, there seems to be a lot of so-called rents—economics speak for excessive payment for something beyond its actual value—in corporate profits that can’t be understood as the fruits of productive investment. The big question is: who gets those rents? In 1988, Summers wrote an article fleshing out the idea that the division of rents between corporate stakeholders is what drives rising inequality. More than a quarter century later, he could not have been more prescient.

The good news is that if such a profound shift played out over only three or four decades, then it’s reversible. That wouldn’t be true if it were the result of the technological trends detailed in “The Second Machine Age.” So what should be the focus of public policy is to figure out ways for workers to accrue more of corporate earnings, for more unemployed and underemployed people to find full-time, productive jobs, and for the broader economy to serve the interests of the actual people who inhabit it—those who overwhelmingly derive their living from their labor.

We know what needs to be done and how to do it, because we’ve done it before. (See Figure 2.) But it’s a lot harder to actually do than doubling the number of logic gates on a computer chip every two years—the ostensible tech explanation for our current economic woes.

Figure 2

The benefits and drawbacks of using dynamic scoring in the federal budget

Posted on February 12, 2015April 18, 2018 by Ed Paisley

One of the first actions taken by the U.S. House of Representatives this year was the approval of a rule change requiring so called dynamic scoring for some proposed legislation. Under the new rule, when the non-partisan U.S. Congressional Budget Office and Joint Committee on Taxation calculate the official budgetary cost of a special category of proposed legislation they will now have to include an estimate of the effects of the legislation on economic growth and the feedback effects of that growth on the budget. The new rule goes into effect this year.

This issue brief explains what dynamic scoring is, what legislation it must be applied to under the new House rule, and what its advantages and disadvantages are in general and then more specifically under the new rule. As explained in detail below, dynamic scoring has theoretical advantages but practical problems that undercut its usefulness. The use of dynamic scoring is likely to lead to greater budgetary uncertainty and, oftentimes, less accurate budget forecasts.

Most critically, from an economic perspective, the selective application of dynamic scoring to budgetary analysis as specified in the new House rule may bias careful evaluation of tax and spending proposals and lead to public policy distortions that will slow down long-run economic growth, weaken job creation, and undermine economic well–being. Understanding the problems with dynamic scoring and the macroeconomic models it relies on to predict future economic growth will be important in particular as Congress and the Obama Administration begin to build a new budget for the fiscal year beginning in October 2015.

Download the pdf version of this brief for a complete list of sources

What is dynamic scoring?

The U.S. Congressional Budget Office, a nonpartisan federal agency that provides economic and budget information to Congress, and the Joint Committee on Taxation, a nonpartisan committee of Congress that analyzes tax legislation, evaluate the budgetary consequences of proposed legislation. Under the law that will be superseded by the new House rule, CBO and JCT would “score” legislation by estimating how much revenue would be lost or gained by a tax change proposal and how much money would be spent or saved by spending proposals such as investments in roads or reductions in federal spending on space exploration.

Sometimes proposed legislation, such as the Patient Protection and Affordable Care Act, or Obamacare, involved both tax and spending changes. In those cases, the CBO and JCT calculated the net impact of both the spending and tax changes on the budget. In the case of Obamacare, for example, CBO and JCT calculated that the various tax and spending provisions of the proposed law would raise $486 billion in federal government revenue and increase federal spending by $356 billion over the ten-year period between 2010 and 2019. In giving their final score, they concluded that the “spending and revenue effects of enacting the Patient Protection and Affordable Care Act would yield a net reduction in federal deficits of $130 billion over the 2010-2019 period.”

It is important to note that when scoring, or calculating, the budgetary consequences of proposed legislation, CBO and JCT assumed that the legislation would have no effect on economic growth, although they did take into account many individual behavioral changes or microeconomic effects. The House of Representative’s proposed “dynamic” scoring method, therefore, is different from the old scoring method because, in estimating the fiscal consequence of some proposed legislation, it will require CBO and JCT to estimate the effects of that legislation on economic growth and then factor in the estimated growth effects on the budget.

In practical terms, this means that for the special class of legislation that will be subjected to dynamic scoring under the new House rule, the budgetary impact will be estimated to be less onerous than under the conventional scoring method when that legislation is deemed to increase economic growth. By the same logic, the dynamic score will be more onerous than the conventional score when that legislation is judged to reduce growth. But under the new House rule, what legislation must be dynamically scored?

Legislation subject to dynamic scoring under the new House rule

Under the new rule, CBO and JCT are required to incorporate an estimate of the growth or macroeconomic effects of “major legislation” into their official budget cost estimates. “Major legislation” is defined as tax bills or mandatory spending bills that cause an increase or decrease in revenues, outlays, or deficits of more than 0.25 percent of GDP (approximately $45 billion in 2015) in any given year. In addition, the chair of the House Budget Committee and, for revenue legislation only, the chair or vice chair of the Joint Committee on Taxation can designate other bills as “major legislation” even when they do not meet the 0.25 percent-of-GDP threshold.

At first glance, the new rule may seem evenhanded in its treatment of proposed tax and spending legislation. But it is not. Instead, it will apply almost exclusively to tax bills and rarely, if ever, to spending bills. The rule does not apply to spending bills that are “discretionary” as opposed to “mandatory” even if discretionary spending proposals exceed the 0.25 percent-of-GDP threshold. Thus, it does not apply to all the regular appropriations bills that include almost all spending or investment in infrastructure, education, health, research, science, national defense, and hundreds of other programs.

In addition, although dynamic scoring does apply to “mandatory” spending, the largest categories of which include Social Security and Medicare spending, it does so only if the budgetary effect of a change in annual spending in those programs due to proposed legislation exceeds 0.25 percent of GDP. It is unlikely that any proposed legislation will change annual spending in mandatory programs by $45 billion or more in any given year.

The upshot: Annual appropriations or investments of hundreds of billions of dollars in highway reconstruction, early childhood education, health care, and hundreds of other programs would not be subject to dynamic scoring, but a $45 billion tax proposal would be. For all practical purposes, therefore, the new rule will apply almost exclusively to tax legislation. Indeed, the House Committee on the Budget has noted that the rule would have applied to only 3 bills in the last Congress, all of which were primarily tax bills.

What’s more, as explained in the section below describing the problems with dynamic scoring, the selective nature of the new House rule undermines theoretical arguments in favor of dynamic scoring—arguments that might lead to the adoption and application of the method to budgetary analysis should the many obvious practical hurdles to accurate dynamic scoring be overcome some day. But before describing the problems of dynamic scoring, lets first look at its theoretical advantages.

Advantages of dynamic scoring in theory

Many government tax or spending policies are likely to influence economic growth. Economic research shows that during a recession some investments in infrastructure, education, and health care spur faster growth while cutbacks in these areas can slow growth. Likewise research shows that during an economic downturn some tax cuts stimulate growth while tax increases reduce growth. Measuring these effects is very difficult to do with extreme precision, but two ways would be to

Improve the accuracy of budget scoring
Remove the bias against pro-growth policies in budget scoring

Let’s look briefly at each of these theoretical advantages.

Improving accuracy of budget scores
When policy affects economic growth, it will have a feedback effect on the budget because the policy will affect the size of the economy and influence the level of public revenues and expenditures. A larger economy generates more tax revenue and reduces expenditures on many programs such as unemployment insurance. Similarly, a smaller economy produces less tax revenue and tends to increase spending on many programs such as nutrition assistance. Under perfect dynamic scoring, then, policies that promote growth will have a smaller budgetary cost and those that slow growth will have a larger budgetary cost than conventional CBO scoring predicts.

Ignoring these growth feedback effects causes conventional CBO scores to be less accurate than they otherwise could be. In an ideal world, every tax and spending proposal would be subjected to rigorous dynamic scoring so that we could get a true picture of the revenue and expenditure impacts of all legislation. The bottom line is that dynamic scoring, at least in theory, could provide policymakers and the public with more accurate budgetary information.

Remove bias against pro-growth policies
A second theoretical advantage of accurate dynamic scoring is that it is not biased against pro-growth policies compared to the current conventional scoring method. By ignoring macroeconomic effects, the conventional method overstates the true budgetary cost of pro-growth policies, such as infrastructure investments, and understates the cost of anti-growth policies.

Consider the conventional scoring of two policies with opposite impacts on economic growth. Policymakers weighing these two alternative proposals could be misled into rejecting the policy that has a positive impact on economic growth because it would be erroneously estimated to be more costly than it truly is, while they may be pushed into selecting the anti-growth policy because it would be falsely scored as less costly than it actually is.

Disadvantages of dynamic scoring in practice

The theoretical advantages of dynamic scoring, however, run into an array of serious practical hurdles. These practical considerations overwhelm the two theoretical reasons for considering dynamic scoring, namely:

Economists do not know how to accurately measure the growth effects of most policies
Dynamic scoring relies on less-than-accurate, theory-based macro models
The macro models undergirding dynamic scoring have numerous controversial and unproven built-in assumptions
The assumptions embedded in the macro models are not always carefully empirically based
Macro models exclude theoretically and empirically supported evidence of supply-side effects of public investment
Macro models exclude evidence-based effects of economic inequality
Macro models exclude evidence-based effects of numerous policies
Macro models provide different estimates of growth impacts of policy depending on guesses of how the policy may be financed

Let’s examine each of these disadvantages in turn.

Economists do not know how to accurately measure the growth effects of most policies
The first problem is that we do not know how to accurately measure the growth effects of most policies, a problem not faced by CBO and JCT under conventional scoring, which does not require estimates of the future growth effects of policy.

Future macroeconomic outcomes, such as growth, unemployment, and inflation are a function of a vast multitude of factors that include economic policies but also many other policy-unrelated events such as technological innovation, an outbreak of war, or a catastrophic weather phenomenon, to give just a few examples. Empirically identifying, isolating, and measuring the macroeconomic consequences of one specific policy is very time consuming, often involving many years of research, and is fraught with difficulty and large errors.

Dynamic scoring relies on less than accurate, theory based macro models
In practice, instead of basing budgetary estimates on empirically verified evidence, as is often done in conventional scoring, the CBO and JCT’s dynamic scoring relies on macroeconomic forecasting models that are theory based. There are a host of such macroeconomic models that attempt to measure growth effects and the subsequent feedback effects on the budget. They all come to different conclusions, none of which may lead to more accurate budget scores than under the CBO’s and JCT’s current approach.

In May, 2003, for example, the Joint Committee on Taxation (which scores tax legislation) provided a dynamic analysis of the House version of the tax cut legislation that was enacted in 2003. JCT used three different macro models with multiple sets of assumptions to come up with 5 different predictions of the budgetary impacts.

The JCT’s dynamic analysis found that the feedback effects would be deficit reducing and would reduce the net revenue loss from the proposed tax cut legislation relative to the conventional CBO estimate by anywhere from 5.8 to 27.5 percent over the first five years (2003—2008), and 2.6 to 23.4 percent over the next five years through 2013.

Now, nearly 12 years later, we can look back and accurately assess which of the scores was most accurate. It turns out that the most accurate was the conventional JCT score because all of the macro models failed to anticipate the great recession, and their revenue estimates were thus wildly optimistic and worse than the conventional estimate. To get an idea of how off-base the dynamic scores were, consider that they all expected GDP in 2013 to be larger than the roughly $17.9 trillion that the conventional score anticipated. Actual GDP in 2013 amounted to just $16.6 trillion, a difference of $1.3 trillion.

The lesson: macro models are still in their infancy. The large differences in their predictions are a function of both the different assumptions built into the models and the varying sensitivity of each model to those assumptions. Because we do not fully understand how the economy actually works, macro models are necessarily built on theoretical assumptions or educated guesses about the way the real economy works, many of which we know are sometimes not true and many others which have little hard data to back them up. Most macro models, for example, assume that the economy is typically at full employment or will quickly return to full employment. Neither has been the case for the past six years.

The macro models undergirding dynamic scoring have numerous controversial and unproven built-in assumptions
Most macro models assume that there are significant supply-side work incentive effects due to tax cuts. The argument goes like this—when given a tax cut, people will choose to work longer and harder thereby spurring economic growth. The theoretical basis for this assumption is that a tax cut increases the returns to working as workers can keep a larger share of their earnings, causing workers to substitute more work for leisure. But there is a plausible theoretical reason to assume the opposite: Tax cuts discourage work because they raise take home pay and enable workers to afford more leisure and less work.

Similarly, most macro models assume that tax cuts on income from investments spur more investment, faster economic growth, and job creation. But here too, theory leads to contradictory conclusions. A tax cut on returns to investment, such as a dividends tax cut, may, in theory, make investment more attractive and thereby induce additional investment and faster economic growth. Yet a tax cut that raises current and future investment yields may simply cause individuals to consume more and thereby save and invest less, slowing long-run economic growth and job creation.

The assumptions embedded in the macro models are not always carefully empirically based
Whatever the merits of these theoretical arguments, there are numerous studies that have tried to quantify these incentive effects in the real world and have come to contradictory conclusions about whether there are incentive or disincentive effects. Most of these studies conclude that the effects on incentives to work and invest due to tax cuts, whether positive or negative, are very small—much smaller than typically assumed in many macro models.

It is important to understand this particular theoretical and technical problem with macro models and dynamic scoring—they have embedded within them implicit or explicit supply-side behavioral responses, in terms of work effort and investment, to tax changes that are larger than can be justified by empirical evidence. In other words, these models typically assume larger changes in work effort and investment in response to tax changes than can be supported by a careful analysis of the data. This means that they could overstate the beneficial growth effects and subsequent positive feedback effects on budgets of tax cut proposals and exaggerate the detrimental effects on growth of tax increases.

In a recent careful comparison of the empirical estimates of supply-side responses to the estimates of supply-side responses embedded in eight of the most widely used macro models, including four models used by CBO or JCT, the Congressional Research Service finds that some models “make little attempt to connect the elasticities associated with labor supply to the ones found in empirical evidence.” Elasticities in economics parlance measures how one variable responds to another variable, such as how much work and investment change in response to a tax change. The Congressional Research Service also finds that some models had assumptions about the behavioral responses to taxes on investment income that were large, “unlikely and not empirically studied.”

Macro models exclude theoretically and empirically supported evidence of supply-side effects of public investment
At the same time as they include questionable assumptions about the supply-side effects of taxes, macro models generally exclude supply-side effects of government spending programs even when they can be supported theoretically and by empirical evidence. For instance, a public investment in infrastructure could lower business transportation costs and increase productivity, thereby making private investment more attractive. If so, then the public investment will induce more private investment, stimulate growth, and create jobs. A growing body of empirical research shows that public investment does indeed have a positive supply-side impact by inducing or “crowding-in” private investment.

This supply-side effect of public investment causes faster economic growth and leads to job creation. To the extent that macro models ignore this supply-side effect of public spending, they will understate the growth effects of government investment and the positive budgetary feedback effects that dynamic scoring, if done correctly, should be able to capture. In short, macro model estimates of economic outcomes are overly determined by their built-in supply-side assumptions, which are biased in favor of tax cuts and against spending increases.

Macro models exclude evidence-based effects of economic inequality
Then there are a host of assumptions for which we have evidence but which are not included in these models, sometimes because we do not know how to incorporate them into the models. There is growing evidence, for example, that high levels of economic inequality (such as those prevailing in the United States over the past few decades) slow economic growth.

Similarly, evidence is accumulating that tax cuts benefiting the wealthiest, such as business tax cuts and reductions in the top marginal personal income tax rates, contribute to income inequality. If this new research is correct, then tax cuts for the rich may contribute to income inequality and slow economic growth—exactly the opposite growth effect of what many macro models assume and predict. Macro models generally do not take these potentially negative effects of tax cuts into account.

Macro models exclude evidence-based effects of numerous policies
Even when the empirical evidence is overwhelming, macro models may ignore the data. Fifty years of careful research demonstrates that investments in high-quality early childhood education programs have enormous long-term payoffs in the form of faster economic growth. These investments partly or largely pay for themselves by generating faster growth, more earnings, and large increases in government revenues.

Similarly, there are well-documented positive growth-and-revenue effects of policies that raise academic achievement and narrow educational achievement gaps between children from wealthy families and other children. A new study that I wrote for the Washington Center for Equitable Growth documents these positive effects on our economic growth and federal fiscal health over the next 35 and 65 years. But look for those assumptions in a macro model and you will come up empty.

Macro models provide different estimates of growth impacts of policy depending on guesses of how the policy may be financed
To make matters worse, each macro models spits out different predictions about the growth effects of legislation depending on the assumptions fed into the model about how the legislation will be financed. All tax and spending proposals are financed and the financing methods affect the economy in differing ways. Consider a $100 billion tax cut proposal. Will the tax cut be paid for by cutting $100 billion in spending, raising $100 billion in other taxes, borrowing $100 billion, or some combination of all three? The fact is, we do not know today how legislation will be financed over time, but the financing method we input into a macro model will affect the model’s prediction for future economic growth.

If JCT guesses incorrectly how the tax cut will be financed in the future, then their dynamic score will necessarily be wrong even if the macro models they use are accurately constructed. That’s why it’s important to note that under conventional scoring there is no need for CBO or JCT to guess about future and unknowable congressional actions that will impact how much a current proposal will cost or save because a conventional score does not attempt to measure growth effects.

So, if we insist on dynamic scoring, which macro model, with which assumptions, will we use? Will we rely on those models whose assumptions give the most favorable answers, the least favorable answers, or something in between? Will that make budgeting more accurate? Or will it be more susceptible to manipulation and less accurate? Right now, the answers to these questions are highly debatable compared to the consensus surrounding the current conventional method of scoring used by CBO and JCT.

Dynamic scoring causes a coordination problem with standard government economic and budget forecasts
There is also a non-trivial coordination problem that arises when dynamic scoring is used under the new House rule. At present, CBO makes a series of budget and economic forecasts using baseline economic assumptions that are updated twice every year. If dynamic scoring is used to analyze certain pieces of legislation and the new proposals are deemed to have economic impacts, even very small ones, then to maintain the consistency and accuracy of the regular CBO forecasts the baseline economic assumptions would have to be updated every time those new proposals are passed into law. If the new House rule had been in effect in 2014, then it would have required the application of dynamic scoring to three proposals which, had they passed, would have necessitated a more than doubling of the number of annual baseline updates.

The new House rule is biased against pro-growth policy

Clearly there are good reasons to be concerned about the growth-undermining biases of dynamic scoring in the new House rule. Instead of correcting the anti-growth bias of conventional scoring, dynamic scoring may exacerbate the problem because the new House dynamic scoring proposal does not apply to discretionary spending, thereby ignoring potential growth effects of investments in many areas including in research, health, education, and infrastructure.

Consider a large tax cut proposal that benefits the wealthiest taxpayers and compare it to an equal-sized investment in infrastructure. Some of the latest empirically-based economic research suggests that the true growth effect of such a tax cut proposal may be negative. But, given the assumptions built into the macro models, under dynamic scoring it would likely be judged to have a pro-growth effect and cost less than the conventional score would suggest.

The infrastructure investment, by contrast, may have a positive impact on growth and may actually cost less than the tax cut proposal. But, by the conventional scoring that the pro-growth investment would be subject to under the new House rule the investment would be assumed to have no effect on growth and would thus be incorrectly judged to cost more than the equal-sized but dynamically scored, anti-growth tax cut proposal.

To make matters much worse, macroeconomic models that find growth effects of tax cuts often do so only when they make the assumption that tax cuts will be paid for in the future by reductions in government spending and further assume that these future reductions in government investment will have no negative impact on growth. Provided this budgetary misinformation, policymakers may vote for growth-retarding, growth-neutral, or relatively slow growth-promoting tax cut proposals over relatively faster growth-promoting investments.

Conclusion

Given the uncertainty and biases inherent in the assumptions undergirding currently existing macro models, it makes little sense to use dynamic scoring. But if we are going to use dynamic scoring, at minimum it should be done in an appropriate and balanced manner and applied to expenditure programs as well as tax proposals. Unfortunately, dynamic scoring of all proposed legislation is clearly not feasible because CBO and JCT do not have the time or resources to dynamically score all proposals. While there is a cost to doing dynamic scoring there may frequently be little benefit because for most legislation the macroeconomic effects would be small and uncertain, and the feedback effects on the budget would likely be negligible.

Indeed, arguably one of the best reasons to use accurate dynamic scoring would be to check the empirically unverified claims made by some Members of Congress that their pet legislative proposals would pay for themselves by boosting growth and subsequent revenues. But given the costly nature of dynamic scoring and the insignificant budgetary impacts of most proposed legislation, it should be restricted to analyzing the macroeconomic effects of only significant proposals—all significant policies, including spending proposals as well as tax proposals.

If dynamic scoring were done across the board for all significant tax and spending proposals using highly accurate macro models then thoughtful people should be for its use. But given the reality of unsophisticated and inaccurate macro modeling, built on less than thorough, rigorous, and evidence-based assumptions, and subject to biases and manipulation, we would do better to continue using the conservative, less expensive, and transparent conventional scoring method. The use of dynamic scoring given the current state of the art, may cause greater budgetary uncertainty and less accurate budget forecasts.

Perhaps most damaging, the new House rule may preclude careful evaluation of tax and spending proposals and lead to public policy distortions that will slow down long-run economic growth, weaken job creation, and undermine economic well–being.

—Robert G. Lynch is a visiting fellow at the Washington Center for Equitable Growth and the Everett E. Nuttle Professor of Economics at Washington College. His areas of specialization include human capital, public policy, public finance, and income inequality.

ICYMI: Lynch on the economic gains from reducing education inequality

Posted on February 4, 2015April 18, 2018 by Nick Bunker

From Robert Lynch’s new report on economic growth and education inequality:

The study shows the consequences of raising the educational achievement of children from the bottom three quarters of families who are most socioeconomically disadvantaged to more closely match those of children born into the top quarter of families.

. . .

In the first and most modest scenario, we examine the consequences of simply raising the educational achievement of U.S. children so that it matches, instead of lags behind, the average of the 34 economically advanced nations who are members of the Organisation for Economic Co-operation and Development.

. . .

In the second, middle-range scenario, we explore the effects of raising the achievement of U.S. children to match that of the children of our neighbors to the immediate north in Canada

. . .

In the third and most ambitious scenario, the economic consequences of completely closing educational achievement gaps between U.S. children from lower and higher socioeconomic backgrounds are estimated.

. . .

Under scenario one, the inflation-adjusted size of the U.S. economy in 2050 would be 1.7 percent, or $678 billion, larger.

. . .

If American children matched the academic achievement of Canadian kids, then economic growth would be significantly larger. In 2050 the U.S. economy would be 6.7 percent, or $2.7 trillion, larger.

. . .

Finally, if achievement gaps between children from different socioeconomic backgrounds were completely closed, then the U.S. economy would be 10 percent, or $4 trillion, larger in 2050.

The Economic and Fiscal Consequences of Improving U.S. Educational Outcomes

Posted on February 2, 2015April 18, 2018 by David Evans

This new study addresses a key challenge confronting the United States—how to promote both widely shared and faster economic growth. It does so by analyzing and describing the effects of raising educational achievement, especially for those not at the top of the economic ladder. The results of the analysis demonstrate that improving the education of future workers accelerates economic growth and can promote more equal opportunity over the long run. This interactive below enables readers to explore the ramifications of the study swiftly and tellingly.

• Download a pdf of the overview.
• Download a pdf of the “Fast Facts”.
• Download a pdf of the methodology.
• Download a pdf of the full report.

Methodology

The results of the literature on the effects of cognitive skills on economic growth are used to estimate the increase in the U.S. gross domestic product and tax revenues that would result from narrowing or closing the educational achievement gap between children from advantaged and disadvantaged family backgrounds.

A growing body of research uses cognitive skills, as reflected in international test scores, as a measure of human capital. This research suggests that human capital accounts for a significant portion of the economic growth of economically advanced nations. The results of regression analyses conducted by Eric A. Hanushek and Ludger Woessmann found statistically significant and strong effects of cognitive skills—as measured by the internationally administered PISA test scores—on the economic growth of 24 nations in the Organization for Economic Co-Operation and Development from 1960 to 2000. Specifically, Hanushek and Woessmann (2010) found that “an increase of one standard deviation in education achievement (i.e., 100 test-score points on the PISA scale) yields an average annual growth rate over 40 years that is 1.86 percentage points higher.”

Three simulations using the Hanushek and Woessmann regression estimate, one for each of three scenarios, are done to project the economic impact of closing or narrowing the educational achievement gaps between children from socioeconomically advantaged and disadvantaged families. The projection models follow closely the model developed by Hanushek and Woessmann in 2010, though several adjustments are made to account for factors specific to this study, such as the incorporation of estimates of future impacts on federal, state, and local government revenues. For all three scenarios, the 2012 U.S. PISA test scores in math and science are used as the baseline in the analysis.

We assume that the estimated impact of the PISA test scores on economic growth is causal, meaning that any policy that increases the test scores of students will result in faster economic growth. For the interested reader, Hanushek and Woesmann (2009) provide evidence that the association between cognitive skills—as measured by the PISA test scores—and economic growth is indeed casual and reflects the effects of cognitive skills on growth. They use a variety of instrumental variables to test causality, use a difference-in-differences approach to compare country of origin-educated to U.S.-educated immigrants, and test whether countries that have improved their test scores have experienced commensurate growth rate improvements.

All three of our simulation scenarios use the PISA index of economic, social, and cultural status, or ESCS, to differentiate advantaged from disadvantaged families. The PISA index of economic, social and cultural status is based on the highest level of parental education, parental occupation, an index of home possessions related to family wealth, educational resources available in the home such as the number of books, and possessions related to culture such as works of art in the home. We follow the OECD practice of defining students as socioeconomically advantaged if they are among the 25 percent of students from families with the highest PISA index of social, economic, and cultural status in their country. The parents of socioeconomically advantaged students have higher educational attainment and work in higher skilled jobs than do the parents of other children. More advantaged students have more books and educational resources, such as desks, dictionaries, computers, and Internet connections at home. Their homes also have more material possessions such as cars or rooms with a bath or shower.

Children from the most advantaged quartile of families scored an average of 532 on the math test, while children from the most disadvantaged three quartiles of families scored (in descending order by quartile) 494, 462, and 442, respectively. On the science test, children from the most advantaged top quartile of families scored 548 while children from the most disadvantaged bottom three quartiles scored 511, 480, and 456.

The first scenario assumes that the scores of children from the most disadvantaged bottom 3 quartiles of families are increased only enough to raise the average U.S. math and science scores to match the OECD average scores. Specifically, the difference between the average OECD math and science scores and the U.S. average math and science scores is calculated. For both math and science, the OECD-U.S. average score difference is divided by three quarters and the result is then added to the average score of students in each of the bottom three quartiles of the ESCS index. The math and science scores of the top quartile are assumed to remain constant.

The national average PISA math and science test scores are then recalculated for the nation as a whole. Aside from raising the combined math and science average U.S. score from 978 to 995 so that it matches the OECD average score, this scenario also narrows the achievement gaps between children from the most advantaged and most disadvantaged quartiles by approximately 13 percent. The average test score for the nation rises by 13 points in math and 4 points in science. The 13-point improvement in math and the 4-point improvement in science represent an increase of 0.09 standard deviations on the combined average score.

The second scenario raises the math and science scores of each quartile (by socioeconomic status) of U.S. students to match the math and science scores of Canadian students. This raises the combined average U.S. math and science scores from 978 to 1,044. It also improves the scores of the bottom three quartiles of students more so than for the top quartile of U.S. students, thereby narrowing gaps. The 66-point improvement in the combined math and science average test score is roughly an increase of 0.37 standard deviations on the combined score.

The third scenario assumes that the PISA test scores for children from the most disadvantaged bottom 3 quartiles of families are raised to equal the scores of children from the most advantaged quartile of families. In other words, the achievement gap between advantaged and relatively disadvantaged children is completely eliminated. The average PISA math and science test scores are then recalculated for the nation as a whole. This raises the combined average math and science score to 1080, which represents an increase of 0.54 standard deviations on the combined average score.

To assess the “reasonableness” of PISA test score increases of the sizes assumed in the three scenarios, the history of PISA test score increases was reviewed. Unfortunately, the PISA tests have only been administered at three-year intervals for a dozen years starting in 2000, and tests results have only been standardized and made comparable for the nine-year period between 2003 and 2012. This makes it difficult to compare actual increases in PISA scores to those in the three scenarios which take place over a longer time period: 20 years.

Nonetheless, several nations have experienced PISA test score increases that exceeded those of scenario one and roughly equaled those of scenario two. Germany and Italy, for example, experienced 33 and 27 point increases, respectively, in their combined average math and science score between 2003 and 2012, far exceeding the 17-point increase assumed in scenario one and roughly matching the annual 3.3 point-increase assumed in scenario two, although short of the 66-point total increase. Poland’s 6.3-point annual increase in its combined average math and science score between 2003 and 2012 is greater than the 5.1-point annual increase assumed in scenario three, although Poland’s total increase over the nine years of 56 points is less than the 66 and 102 total point increases over twenty years of scenario’s two and three. Thus, the cognitive ability increase assumed in scenario one is clearly achievable, while those of scenarios two and three may require an unprecedented sustained national effort.

All three simulations calculate the annual GDP growth-rate increases as the educational improvements are phased in fully. The cause of the educational improvement is not specified. In general, however, improvements in cognitive skills are not necessarily a function of educational reforms but, instead, could be the function of a variety of non-education and education policies. For instance, as explained in the paper, enhancements in educational achievement could result from the adoption of high-quality, universal pre-Kindergarten, class size reductions, improvement in the education of teachers, higher wages for teachers, child health and nutrition policies, better prenatal and post-natal care, criminal justice reforms that help lessen the detrimental effects of incarceration on the children of prisoners, reductions in racial and housing segregation, changes in work place policies such as those related to family leave or schedules or vacation time, or combinations of these and many other policies.

Whatever the source of the improvement in cognitive skills, the achievement gains are not assumed to be immediate but, instead, they are phased in linearly over a 20-year period. Thus, the cognitive skills improvements are assumed to be very small after one year, but they grow steadily year after year so that after 20 years, the achievement improvements are fully phased in.

Similarly, it is assumed that the economic impacts of enhanced cognitive skills are not felt until students with better skills enter the labor force. As these new, higher-skilled workers replace older, retiring workers, the average skill of the workforce progressively improves, productivity increases, and economic growth accelerates.

It is assumed that the average laborer works for 40 years. This means that it will take 60 years to feel the full economic effects of policies to improve cognitive skills—20 years to phase in the achievement improvements and 40 years until the full workforce reaches the higher skill level.

The simulations indicate the average annual increase in economic growth that results from the narrowing (scenarios 1 and 2) or gradual closing (scenario 3) of the educational achievement gap between children from more and less advantaged families and the subsequent upgrade in the skill level of the workforce. The annual estimated growth increase is then multiplied by Congressional Budget Office’s long-term projections of real U.S. GDP to derive the annual increases in GDP over the years from 2015 to 2075 that result from closing or narrowing achievement gaps.

The Congressional Budget Office’s long-term projections of real U.S. GDP do not already assume the cognitive achievement improvements built into scenarios one, two, and three. Nor should they. The results of the National Assessment of Educational Progress (NAEP), the largest nationally representative and continuing assessment of the educational achievement of children in U.S. schools, indicate little or no progress in the educational achievement of 17-year olds over the past forty years. For example, the NAEP math score for 17-year olds was essentially unchanged over the past forty years, varying slightly from 304 in 1973 to 306 in 2010.

To estimate the federal tax revenue impacts of GDP increases that are induced by closing education achievement gaps, the Congressional Budget Office’s long-term projections of federal tax revenues as a percentage of GDP between 2015 and 2075 are used. For other revenue projections, the historical record on state and local, Social Security, and Medicare revenues as a percentage of GDP over the past 30 years is reviewed and used as a guide. Except for during the recession-affected years of 2002 and 2009, state and local revenues typically varied between 14 percent and 18 percent of GDP. It is assumed that state and local revenues derived from future increases in GDP would sum to the middle of the historical range, or 16 percent of GDP. It is further assumed that additional Social Security taxes and Medicare revenues—among the most significant subcomponents of federal revenues—would equal 4.3 percent and 1.3 percent, respectively, of annual increases in GDP, which is consistent with their current levels. These rates are applied to the calculated increases in GDP to determine increases in revenues.

To compare the worth of these future increases in GDP and tax revenues to the current value of GDP and revenues, the common practice of discounting the future increases in GDP is followed to recognize that each dollar of GDP acquired in the future is less valuable than each dollar of GDP secured today. In general, a dollar earned sometime in the future is less valuable than a dollar earned today because of the interest-earning capacity of money. For instance, if the current interest rate is 3 percent, then 97 cents earned today and put aside in an interest-bearing account would be worth approximately $1 a year from now. This is equivalent to saying that a dollar earned a year from now would be worth only 97 cents today. The discounted future value, known as the present value, allows us to state the value of future benefits in present dollars so that they can be more easily compared to current values. Thus, we calculate the present value of these future GDP and tax revenue increases by assuming a standard 3 percent discount rate. All calculations are in real (inflation-adjusted) numbers, with 2015 as the base year.

To calculate the increases in lifetime earnings for children who complete their schooling 20 years from the start of the policy reforms, we used the OECD’s estimate that 41 score points on the PISA math test is equivalent to about one year of schooling in the typical OECD country. Consistent with the literature on the relationship between schooling attainment and lifetime earnings, we then assumed that for each year of additional schooling, students would experience a 10 percent increase in lifetime earnings. Thus, for example, under scenario three a student in the bottom quartile of socioeconomic status experiences a 90 point increase in their PISA math score, which is the equivalent to 2.2 years of additional schooling or a 22 percent increase in lifetime earnings.

The Economic and Fiscal Consequences of Improving U.S. Educational Outcomes

Posted on February 2, 2015April 18, 2018 by David Evans

This study addresses a key challenge confronting the United States—how to promote both widely shared and faster economic growth. It does so by analyzing and describing the effects of raising educational achievement, especially for those not at the top of the economic ladder. The results of this analysis, which are consistent with a large body of research across a variety of academic disciplines, demonstrate that improving the education of future workers accelerates economic growth and can promote more equal opportunity over the long run. The result: stronger, more broadly shared economic growth, which in turn raises national income and increases government revenue, providing the means by which to invest in improving our economic future.

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Since the early 1970s, economic growth in the United States has been relatively slow and income inequality has risen rapidly. Over this same period, income growth has been so sluggish and unevenly distributed that families on the bottom and middle rungs of the income ladder experienced stagnating or declining incomes even as earnings among those at the top increased sharply. In contrast, the years immediately following World War II and continuing into the early 1970s were characterized by relatively rapid and broadly shared growth. Those at the top earned substantially more than those across the middle and bottom of the income spectrum, but high, middle, and low-income earners all saw their incomes grow at about the same rate.

A restoration, then, of the economic growth pattern that characterized the first three post-war decades would result in both greater and more widely shared economic growth—equitable growth. In order to address this key challenge confronting the United States, this study empirically quantifies the economic and tax benefits of raising the educational achievement of children from less advantaged socioeconomic backgrounds. In general, there are large gaps in the educational outcomes among children from families with lower and higher socioeconomic status. These gaps contribute to subsequent economic inequality, with the relatively poor performance of children from lower socioeconomic backgrounds reducing U.S. economic growth. Thus, closing income or class-based educational gaps would promote faster and more widely shared economic growth.

The study shows the consequences of raising the educational achievement of children from the bottom three quarters of families who are most socioeconomically disadvantaged to more closely match those of children born into the top quarter of families. Observing the impact of three different scenarios that all have 2015 as their starting date, the analysis quantifies various outcomes over the next 35 years—to 2050, when the pressure of supporting the retired baby boomers will have largely abated—and over the next 60 years—to 2075, when the benefits of narrowing achievement gaps under the three scenarios will have been fully phased in.

Specifically, the study quantifies how much greater U.S. economic growth (measured by gross domestic product, or GDP, the total value of goods and services produced in our economy) and tax revenues would be. The analysis also assesses the reductions in economic inequality that result from the narrowing of education gaps.

In all three scenarios we use the 2012 scores on the Programme for International Student Assessment, or PISA, math and science achievement tests as our indicator of academic achievement. For each scenario, a simulation model is used to estimate the economic effects of potential policy reforms that raise U.S. PISA scores—effects that improve the educational achievement of U.S. children and reduce disparities in educational outcomes among them. The results of this modeling suggest the extent to which appropriate policies could enhance economic growth, raise tax revenue, and reduce economic inequality. (See the Methodology section on page 45 of the full report for details on the simulation model and data used in this report.)

The three scenarios and the consequences for U.S. economic growth and fiscal stability

In the first and most modest scenario, we examine the consequences of simply raising the educational achievement of U.S. children so that it matches, instead of lags behind, the average of the 34 economically advanced nations who are members of the Organisation for Economic Co-operation and Development. Specifically, we raise the achievement scores of U.S. children from the bottom three quartiles of disadvantaged families just enough so that the national average educational achievement of all U.S. children on the PISA tests matches the average educational achievement of children from the OECD nations. This raises the combined U.S. math and science PISA score from 978 to 995 (the OECD average) and improves the nation’s relative ranking from 24th to 19th best out of the 34 OECD nations, or roughly to the middle of the pack on par with France. (See Table 1, and for a complete breakdown by OECD member country see table 6 on page 29 of the full report.)

In the second, middle-range scenario, we explore the effects of raising the achievement of U.S. children to match that of the children of our neighbors to the immediate north in Canada. This adjustment lifts the combined U.S. math and science PISA score from 978 to 1044 (the Canadian average) and improves the nation’s relative ranking from 24th to 7th, tied with Canada.

In the third and most ambitious scenario, the economic consequences of completely closing educational achievement gaps between U.S. children from lower and higher socioeconomic backgrounds are estimated. In particular, the PISA test scores of the bottom three quartiles of socioeconomically disadvantaged U.S. children are raised so that they match the PISA test scores of the most advantaged quartile of U.S. children. This increases the combined U.S. math and science score to 1,080 and raises the U.S. academic standing to third best among the OECD countries, behind only South Korea and Japan.

TABLE 1

The paper then summarizes the reductions in disparities in educational outcomes under each of the three scenarios. It reports the gap in outcomes on the PISA tests scores between children in the top and bottom quartile of family socioeconomic status as a percentage of the average PISA score. (See Table 2.)

TABLE 2

Under scenario one, the education gap is reduced from 18.6 percent to 16 percent, and the U.S. ranking on equity improves from 21st to 11th out of the 34 OECD nations. Under the second scenario, the gap falls to 13.2 percent and the U.S. ranking rises to 6th. The third scenario completely closes the educational achievement gap between students from different socioeconomic background, and the United States ranks first among the OECD countries in the equality of educational outcomes.

The paper then demonstrates how the reduction in educational achievement gaps in the United States translates into stronger economic growth over the next 35 years and 60 years. Tables 3 and 4 summarize the economic consequences of raising academic achievement and narrowing educational achievement gaps.

TABLE 3

Under scenario one, the inflation-adjusted size of the U.S. economy in 2050 would be 1.7 percent, or $678 billion, larger. The cumulative increase in real GDP (after factoring in inflation) between 2015 and 2050 would amount to $2.5 trillion in present value, or PV, the current dollar value that is equivalent to the future GDP increases calculated by the model, which allows for a comparison of future values of GDP to current values of GDP. This amounts to an average of over $72 billion per year. The economic effects of raising and narrowing achievement gaps build upon themselves so that over time the growth consequences are increasingly magnified. By 2075, when the effects of policy reforms required to reach this first scenario are fully phased in, the U.S. economy would be 5.8 percent, or $4.1 trillion, larger than it would otherwise be, and the cumulative increase in GDP over the 60-year period from 2015 to 2075 would amount to $14 trillion in present value, an average of $234 billion per year.

If American children matched the academic achievement of Canadian kids, then economic growth would be significantly larger. In 2050 the U.S. economy would be 6.7 percent, or $2.7 trillion, larger. The cumulative increase in GDP between 2015 and 2050 would amount to nearly $10 trillion in present value, $285 billion on average per year. In 2075, the real U.S. GDP would be 24.5 percent, or $17.3 trillion, larger, and the cumulative increase between 2015 and 2075 would sum to over $57 trillion in present value GPD, an average of $956 billion per year.

Finally, if achievement gaps between children from different socioeconomic backgrounds were completely closed, then the U.S. economy would be 10 percent, or $4 trillion, larger in 2050. The cumulative increase in GDP by 2050 would amount to $14.7 trillion in present value, or $420 billion per annum. In 2075, once policy reforms have fully taken effect, the real U.S. GDP would be 37.7 percent, or $26.7 trillion, larger, and the cumulative increase in present value GDP over 60 years would sum to $86.5 trillion, an average of over $1.4 trillion per year.

These results demonstrate that investments targeted at raising academic achievement and narrowing achievement gaps generate large returns in the form of economic growth. The increases in present value economic growth described above suggest the size of potential policy investments that would pay for themselves in the form of growth over the next 60 years and beyond.

Narrowing or closing achievement gaps also would also have significant positive consequences for future federal, state, and local revenues. Over the first 35 years, these would sum to $902 billion in PV federal, state, and local revenues under scenario one, $3.6 trillion under scenario two, and $5.3 trillion under scenario three. Over 60 years, the consequences would be significantly larger. Federal, state, and local revenues would sum to $5.2 trillion (scenario one), $21.5 trillion (scenario two), and $32.4 trillion (scenario three), all expressed in present value. (See Table 4.)

TABLE 4

Thus, public policy investments that raised academic achievement as described under the three scenarios and that cost less than an average of $87 billion, $358 billion, and $540 billion over each of the next 60 years would more than pay for themselves in budgetary terms. To put these revenue figures in perspective, consider that the entire budget for the federal Department of Education in 2013 was $72 billion. Keep in mind, as well, that these revenue increases are not a function of tax rate increases. Instead they are the additional revenues that would accrue to governments because U.S. GDP would be larger and Americans would be earning more income and paying taxes on their additional income.

The increased growth and subsequent revenue increases will enable us to more easily sustain public retirement benefit programs such as Medicare and Social Security. Improving educational outcomes, for example, would lift Social Security tax contributions by $256 billion, $1 trillion, and nearly $1.5 trillion under the three scenarios by 2050. Similarly, Medicare tax revenues for the Hospital Insurance Fund would increase by $77 billion, $306 billion, and $452 billion under the three scenarios from 2015 to 2050, providing a substantial boost to Medicare solvency. Revenues for Social Security and Medicare would be substantially larger by 2075.

The benefits of closing educational achievement gaps also would reduce income inequality. These effects are calculated under the three scenarios for children who complete their schooling 20 years from the start of the necessary policy reforms (in 2035) because it is assumed that it takes 20 years for the academic reforms to be fully phased in. Children who complete their schooling prior to 2035 would experience only a part of the increase in lifetime earnings. (See Table 5.)

TABLE 5

Under scenario one, the lifetime earnings of children from the bottom three quartiles of socioeconomic status would increase by an additional 4.3 percent. Under scenario two, all children would earn more, although the increases are smallest for children with the highest socioeconomic status and thus income inequality would be reduced. Finally, under the third scenario, the increase in lifetime earnings for children in the bottom three quartiles of socioeconomic status would be very large: 22 percent, 17 percent, and 9.3 percent respectively.

As explained in greater detail later in the report, these economic and tax benefit projections understate the impact of raising achievement gaps for at least four reasons. First, under scenarios one and three, they assume that educational achievement improvements are limited to children in the lower three quartiles of socioeconomic status, but in the real world policies that increase these children’s educational achievement are likely to improve all children’s achievement and further enhance growth.

Second, the model does not take into account any of the social benefits—such as better health outcomes—that are likely to occur as a result of educational improvement. Third, the model may be understating growth effects because it assumes that improvements in the educational achievement of children in the bottom three quartiles of socioeconomic status have the same impact on growth as do equal sized improvements in the educational achievement of the average child. Yet there is evidence that raising skills at the bottom improves growth more than raising skills at the top. Finally, the model does not calculate the potential positive effects on children born to future parents who, because of improved academic achievement, will have higher incomes and thus be able to provide them better educational opportunities.

If the model properly accounted for all of these factors, the benefits of improving educational achievement would be larger than those estimated in this study. Yet by a similar logic, the projections overstate the reductions in economic inequality. Helping the most disadvantaged students improve their educational outcomes will likely improve the educational outcomes of all children and thus raise the incomes of the most advantaged children as well as temper reductions in income inequality.

Closing the socioeconomic gaps in education

The potential economic gains described above illustrate in stark terms the waste of human talent and opportunity that we risk if achievement is not raised and gaps are not narrowed. They also suggest the magnitude of the public investments we should be willing to make now and in the decades to come to achieve these goals. Even from a narrow budgetary perspective, the tax revenue gains this study forecasts suggest that many investments to raise achievement and close educational achievement gaps could amply pay for themselves in the long run.

The report provides numerous examples of effective public policy strategies that promote equitable growth to illustrate that there are many ways of doing so, though their details are left to future research. Broadly, these public policy strategies fall into three categories:

Early childhood care and education
Criminal justice reform
Family friendly workplaces

Completely closing socioeconomic-based educational achievement gaps will not happen instantly, but we can begin to narrow them immediately. As the report details, we already know many of the reasons these gaps exist and policies that can help close them. Thus, we can begin to experience some of the economic gains described in this report as policies that successfully narrow achievement gaps are implemented. Raising achievement and closing socioeconomic-based educational gaps is about not only reducing the degree of inequality in our society and promoting more widely shared economic growth but also inducing faster economic growth. In short, it is about promoting equitable growth.

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Category: Research & Analysis

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The Economic and Fiscal Consequences of Improving U.S. Educational Outcomes

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TABLE 1

TABLE 2

TABLE 3

TABLE 4

TABLE 5

Closing the socioeconomic gaps in education

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