Category: Economic Stability

Building a strong foundation for the U.S. economy

Posted on by Ed Paisley

The U.S. economy experienced several structural shifts over the past several decades, including a large increase in inequality across a variety of dimensions. Despite headlines about inequality as a single issue, there are several aspects to the phenomenon. To be sure, incomes are skyrocketing among the top earners, income growth for the middle class is slower than in the past, and income growth is all but stagnant for those at the bottom.

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Yet income isn’t the only dimension of inequality. We have seen increases in inequality in wages and salaries, access to quality jobs, educational attainment, family and household workplace policies, and, of course, wealth. Considered together, the top members of our society are quickly pulling away from the rest of us across a variety of dimensions, with those in the middle and the bottom of our society experiencing little to no gains.

We are not the only ones to notice these trends. Nor are we the only ones to be asking what this means for our society and for our economy. Last year, just after we launched the Washington Center for Equitable Growth, President Obama argued that inequality was “the defining challenge of our times.” Soon after, Sen. Marco Rubio (R-FL) and Rep. Raul Ryan (R-WI) called on policymakers to grapple with specific aspects of inequality and what it means for our nation.

A robust set of academic research seeks to understand how the changes in income inequality affect our economy. Looking at the overall picture, this research suggests that in cases of extreme inequality, such as prior to the Great Depression in the 1920s as well as today in the United States, inequality is negatively associated with economic growth and stability. But this research on the overall relationship between inequality and growth does not necessarily help us understand why or how inequality affects the economy or provide policymakers with solutions to address these challenges.

Then, last spring, Paris School of Economics professor Thomas Piketty spurred an international debate with his book, “Capital in the 21st Century.”4 He sought to understand the interrelations between rising inequality and economic growth. The publication of his book led to many an econo-geek sporting t-shirts with the now-famous, but still cryptic “r>g” equation. One of Piketty’s fundamental conclusions is that so long as the rate of return on capital continues to be greater than the rate of economic growth—or, wage growth—then capital will become ever more concentrated.

There are for a variety of reasons to think that this calcification of wealth is not in the interest of long-term economic growth, which brings us to a set of empirical questions that Equitable Growth seeks to understand:

  • What are the mechanisms through which inequality affects the economy?
  • Which ones play out in the short term and which play out in the long term?
  • Are they mostly on the supply side or on the demand side, or both?

We have prepared this report for our second annual conference on September 19, 2014, and have asked a diverse array of scholars and policymakers with expertise in issues such as human capital development, productivity growth, entrepreneurship, and wage growth to examine these developments across our economy. Because the trends—and their implications—play out differently across the income spectrum, we have organized our discussion around trends and policies focused on the bottom, the middle, and the very top of the income ladder.

We seek to begin a conversation that not only accelerates analysis on whether and how these factors affect economic growth and stability but also inspires policy solutions that reduce inequality and expand economic growth, mobility and opportunity for all.

—Heather Boushey, executive director and chief economist, Washington Center for Equitable Growth

A White Paper on Piketty’s Theory of Inequality and its Critics

Posted on by Ed Paisley

In “Capital in the 21st Century,” Thomas Piketty of the Paris School of Economics proposes an economic theory of rising inequality over time thanks to the growing prevalence of capital over labor. That theory’s analysis of recent trends and its prediction about future inequality—and the capital-centered channel that he specifies for it to play out—have been subjected to criticism from economists, most pointedly from some who conduct research in macroeconomic theory. There are substantial differences between the theory Piketty uses and some of the economics profession’s received wisdom. This short paper examines how his theory relates to key ideas in macroeconomics, and, where they are not consistent with Piketty’s empirically-based analysis and conclusions, why Piketty’s assumptions, reasoning, and predictions are more likely to be correct than those of his critics.

Piketty argues that there are two mechanisms by which capital is and will continue to be the reason for rising wealth and income inequality. Both mechanisms are premised on the long-run empirical relation r > g, meaning that the rate of return to owning capital is higher than the economy-wide growth rate (which determines the growth rate of wages). Both mechanisms are also based on the empirical fact that the distribution of capital is highly skewed: the top 10 percent of the wealth distribution has always owned more than 50 percent of total wealth, and has historically owned 90 percent or more of total wealth.

The two mechanisms that determine rising wealth and income inequality are:

• The wealthy are likely to accumulate more and more wealth (as a percentage of the economy’s annual output) because the return they get from existing wealth net of consumption and of wealth taxes is higher than the growth rate of output. As they do, the share of annual output that accrues to the owners of capital will increase. That growing capital share increases the incomes of the already-wealthy owners of capital relative to the much larger portion of the population who earn income mostly or solely from their labor.

• Even stipulating that capital’s share of income remains constant, the wealth and income distributions can still become more and more skewed thanks to capital accumulation if the rate of return earned by the wealthy is an increasing function of initial wealth, or if the saving rate is an increasing function of initial wealth, or both.

Each of the three challenges considered in this white paper casts doubt on one or both elements of Piketty’s capital channel. (Please click here to read the full white paper and citations)

Miscalculating the wealth of the rich reveals unintended biases

Posted on by Ed Paisley

In an ambitious effort, economists Philip Armour and Richard Burkhauser of Cornell University, and Jeff Larrimore of the Joint Committee on Taxation attempt to produce an estimate of trends in inequality based on a definition of income more relevant to understanding economic wellbeing. Specifically, they try to estimate the so-called Haig-Simons metric that defines annual income as consumption plus change in net wealth. According to this definition of income, the authors claim inequality has not been rising over time, leading some strident conservative voices to latch on to it—contrary to numerous other studies and measures

It is important to understand the Haig-Simons metric (named after early 20th century economists Robert Haig and Henry Simons), the methods used by Armour, Burkhauser, and Larrimore for estimating income using this metric, and their presentation of the data regarding this income metric. By my assessment, their working paper does not constitute an informative addition to the inequality discussion. The Haig-Simons measure is interesting but convolutes wealth and income. What’s more, their methodological choices bias the results to downplay relative income growth at the top, and the statistics they report are not sufficiently detailed to assess the implications of their findings.

Is the Haig-Simons income measure useful?

The Haig-Simons income measure has the advantage of avoiding volatility based on the timing of the realization of capital gains on the sale of assets by individuals. Other annual income measures may be too volatile because some people time asset sales based on taxes or other factors, which makes annual income a noisy proxy for wellbeing. Thus, this measure could provide a more comprehensive assessment of the variation in household balance sheets by cutting out timing decisions.

Yet the Haig-Simons measure introduces substantial volatility as well based on changes in the market valuation of assets. Someone with a large stock portfolio, for example, whose portfolio fell substantially could have a negative Haig-Simons income despite being high in the income distribution. Using this measure, the billionaire founder of Facebook, Mark Zuckerberg, would have been considered one of the poorest people in the world in 2012 because his net worth fell by $4.2 billion.

The key point is that assets held in company stock or in stock market indices vary substantially in valuation over any time scale. The Haig-Simons measure attempts to factor out volatility in realized capital income but at the same time introduces potentially higher volatility in the valuation of capital holdings.

To understand the relative merits of the Haig-Simons metric in assessing comprehensive income it is important to discern the relative magnitudes of these volatilities across the entire distribution of income. One way to think about it is that people can choose to sell a stock or other asset whenever they want, which will introduce volatility in their income because of the asset sale. But the value of assets someone holds will vary substantially, too, because stock prices change every day whether or not shares are sold. Thus, measures of income that include capital gains can be variable because of the sale of stock while measures that look at net wealth will also be variable because the stock prices are also volatile. Because it exacerbates the volatility in the valuation of wealth, the Haig-Simmons measure’s primary advantage—that it reduces volatility from the sale of assets—may be swamped.

Additionally, by ignoring the liquidity of assets, the Haig-Simons measure obfuscates changes in wealth and economic wellbeing. Inflation in housing prices during the 2000s led the net wealth of many to increase, which would show up as a rising Haig-Simons income. With hindsight we know that much of this valuation was a bubble and that there was a minimal real improvement in economic wellbeing.

It is unclear why a single reductionist measure is needed for both wealth and income when one measure for wealth and a separate measure for income could be used. Looking at several measures can provide a textured understanding beyond a single number. If asset volatility from timing decisions were determined to be a primary source of noise in short-term inequality trends, then a multi-year average could be applied to other measures of income. This would have the advantage of reducing the volatility from timing decisions while avoiding obviously absurd assessments of income.

Are their methods for determining the Haig-Simons measure sufficient?

The authors attempt to determine a comprehensive measure of both consumption and the change in net worth using the Haig-Simons metric. To compute this measure of income, they make several adjustments to the data to include near cash benefits such as estimating the value of health insurance provided to individuals either from the government of from employers. For changes in net worth, a single national housing index was used to account for changes housing wealth across the country and only the Dow Jones Industrial Average was used for all types of stock income. There are also limitations on details of high-income households in the survey data that they use. Each of these methodological choices will artificially bias their estimates toward a lower valuation of income growth at the top of the distribution.

So let’s examine each of the components they use in turn.

Their method for assessing the value of health insurance is not useful in comparing time trends because it ignores changes in the structure of insurance products over time, variation in the quality of the insurance products provided, and the substantial growth of health care costs beyond inflation over most of they period of their study. Before the enactment and initial implementation of the Affordable Care Act of 2010, there was a trend in health insurance for higher deductible products (these have a lower actuarial value), which would mean that comparisons across time in the value of health insurance would overstate their value in later years relative to early years.

Thus, the relative value of the health insurance product used in their analysis is inflated and will lead to artificially inflated income growth for people whose health insurance constitute a larger share of their “comprehensive” income than others. Because health insurance will probably constitute a smaller share of the consumption of high-income people, the authors’ methodological decisions on accounting for health insurance will artificially imply a reduction in income growth at the top of the income spectrum relative to other segments.

Similarly, the authors’ methodological choices for valuation of housing and stocks are likely to artificially reduce their estimates of the net worth of those at the top. For each of these factors, the authors used a single growth rate for all incomes. By using a single product and not testing the impact of heterogeneous rates—meaning variations in the rates of return—the authors artificially reduce the variability. This reduction in variation will particularly attenuate growth in asset values on the high side and therefore result in artificially low incomes among the top of the distribution.

To their credit, the authors’ acknowledge this shortcoming:

“…when imputing yearly accrued capital gains we assume that all investments receive the ordinary rate of return. Hence we will not capture extranormal returns received by some individuals on their investments.”

Yet after acknowledging this flaw they did not attempt to vary rates of return on the high end of the income distribution or otherwise account for this bias. In fact, a recent paper by Fabian T. Pfeffer, Sheldon Danziger, and Robert F. Schoeni from the University of Michigan find the wealth of the richest households grew faster than the median household, and much faster than households below the median.

Another issue with this approach has to do with their choices of survey data sets. This work relies heavily on surveys such as the Current Population Survey, the Medical Expenditure Panel Survey, and the Survey of Consumer Finances. Economist Philip Vermeulen of the European Central Bank finds that survey data on wealth tend to have a hard time assessing wealth at the top because of systematically biased underreporting among the wealthy skews the estimates lower. Thus, a fundamental limitation of this work is its ability to measure trends at the top, which are central to the claims made about the study’s findings.

Are the data reported sufficient to assess trends in the Haig-Simons measure?

The three authors report average growth estimates by quintile for several different income measures. This is good because it allows for a partial assessment about which components of the income construction are driving the results. But more information is needed. The authors’ choices about which rates of return to apply strongly influence the results; providing results from sensitivity tests (checks to see how much the results rely on various assumptions) with alternate measures would be useful. Without information on these sensitivity tests, there is no way to assess the effect of the biases discussed above.

Furthermore, they do not provide error estimates or statistics on their matching approaches across data sets. Statistics regarding the matching approach (and sensitivity tests of the matching) are particularly important to assess the implications on quality of the methodological choices. These omissions are disconcerting because it makes it impossible to assess the quality of the results and therefore their utility to the current discussion.

Conclusion

The authors attempted an ambitious analysis of incomes, which should be commended, but their execution is insufficient to support the broad proclamations made by many pundits about declines in inequality. Given the study’s clear methodological biases and weaknesses, the claims of the paper’s first author in a report from the Manhattan Institute dramatically overstate the implications. The Haig-Simons income measure may be useful, but there are better ways of getting the same, if not more, information using other measures of wealth and income without conflating the two.

The authors’ methods should work well enough for estimates of the Haig-Simons income for measuring the assets of low- and middle-income people for whom capital gains are a relatively unimportant source of income, but there are fundamental biases that will result in artificially low growth rates among their high-income counterparts. Finally, their reported results are insufficient to assess whether the trends are entirely driven from their methodological choices and also if the results are discernible from noise. I look forward to further research by the three authors and others that more effectively measures the assets of the wealthy—a key to understanding the links between economic inequality and growth.

Factoring inequality into economic growth

Posted on by Ed Paisley

The National Bureau of Economic Research released a working paper by Harvard University economist Nathaniel Hendren on August 4 that provides a new way of looking at the relationship between inequality and growth. His paper develops a new statistic, the inequality deflator, which allows researchers to adjust the value of an economic variable, such as average household income, for different levels of inequality. Because averages don’t tell us anything about distribution, the deflator lets us compare those averages by adjusting for the different distributions.

080814-Inequal-deflator

The accompanying graphic shows how much household income increased in the United States after adjusting for rising income inequality between 1979 and 2012. With more attention being paid to the relationship between inequality and growth, Hendren’s inequality deflator can become a powerful tool for understanding the linkages between the two.

Nothing new under the labor market sun

Posted on by Ed Paisley

The Bureau of Labor Statistics released new labor market data today showing that the U.S. economy added 209,000 jobs and that the unemployed rate ticked up slightly to 6.2 percent. Overall, the data show an economy continuing on its path of the past several years—a moderate recovery that is inadequate in light of the severity of job losses during the Great Recession.

The slight increase in the unemployment rate was due to an increase in the labor force and not a decline in the number of employed workers. According to the BLS household survey, the number of employed workers increased by 131,000 while the overall labor force increased by 329,000. This resulted in an increase in the labor-force participation rate to 62.9 percent in July from 62.8 percent in June.

The share of the population with a job, the employment-to-population ratio, was unchanged from 59 percent, still 4 percentage points below the most recent peak in December 2006. The ratio for the working age population (workers ages 25 to 54) slightly decreased to 76.6 percent from 76.7 percent.

The number of long-term unemployed workers (those without a job for 27 weeks or more) was essentially unchanged, according to BLS. This group continues to be a large share of the unemployed at 32.9 percent of all unemployed workers. The debate about the future of the long-term unemployed will continue. Some analysts, including economists at the Board of Governors of the Federal Reserve, claim that the long-term unemployed are getting jobs while others remain quite skeptical of the evidence.

Businesses added 209,000 total jobs during July, 198,000 coming from the private sector. The employment gains were less broadly based than in recent months. The diffusion index for private industries, a measure of how many industries added jobs, was only 61.9 percent in July compared to 65.3 percent in June and 64.4 percent in May.

Manufacturing added 28,000 jobs, and all of the gains (30,000) came from industries that manufacture durable goods. Specifically, 14,600 jobs came from the auto industry. Nondurable manufacturing industries shed 2,000 jobs in July led by food manufacturing (a loss of 3,600 jobs).

The data on wage growth, relevant to the current debate about slack in the labor market and the future of Federal Reserve policy, also showed little change. The year-on-year change in the average wage for all workers was 2 percent. Wage growth has hovered around this rate for the last year and shows no sign of acceleration. And the rate is well below its pre-recession level in 2007, which was closer to 3.5 percent.

080114-wage-growth-01

The data released today show a labor market that continues to heal from the Great Recession. But the recovery continues to come up short given the damage done in the past. With wage growth still subdued and no sign that the long-term unemployed are locked out from jobs gains, policy makers should be skeptical of calls to pull back on growth-boosting measures. Overly cautious policy would not only leave our economy weaker in the short run but undermine our long-term economic growth potential as well.

A post-war history of U.S. economic growth

Posted on by Ed Paisley

Five years removed from the end of the Great Recession, economists, policymakers, investors, business leaders, and everyday Americans from all walks of life remain concerned about the future of economic growth in the United States. The severity of that two-year recession and the lackluster recovery ever since sparks fear among economists and policymakers that the U.S. economy is in for a perhaps new and long period of slow growth. Economist Tyler Cowen of George Mason University raised this concern in his book “The Great Stagnation.” And Harvard University economist and former Treasury Secretary Larry Summers recently warned about secular stagnation where the economy suffers from a prolonged period of inadequate demand.

Read a PDF of the full document with all citations

While these fears are surfacing today, the anemic economic conditions that prevail at present and from which these concerns spring may be the result of structural changes in the U.S. economy over the past 40 years. Since the mid-1970s, the U.S. economy has undergone a variety of changes that may help or hinder economic growth over the long-term, among them:

  • An employment shift from manufacturing to services
  • The advent of the Internet
  • The entrance of women into the paid labor force
  • The greater participation of people of color in all sectors of the economy
  • The greater openness of the economy to international trade
  • The ever-evolving role of government
  • A rapid increase in income inequality

The mission of the Washington Center for Equitable Growth is to understand whether and how these structural changes, particularly the rise in inequality, affect economic growth and stability. But before we can understand how these forces may affect economic growth, we need a baseline understanding of how the U.S. economy grew in the past.

This report helps in that endeavor by looking at the past 65 years of economic growth in the United States—measured by examining our country’s Gross Domestic Product, both its rate of growth and sources of growth, from 1948 to 2014. The starting point, of course, is what this oft-cited statistic GDP actually measures. GDP is comprised of aggregate statistics based upon four major components: consumption, investment, government expenditures, and net exports.

The report then looks at the overall growth of real (inflation adjusted) per capita GDP as well as the contributions of each component to growth over time, specifically over business cycles, or patterns of economic recessions and expansions. (See graph.)

web-econgrowth01

Based on the overall trends, we divide the post-World War II into three eras of growth—the booming post-war period to the early 1970s (the fourth quarter of 1948 to the fourth quarter of 1973), the transition period to the early-1980s characterized by a series of economic shocks and high inflation (the fourth quarter of 1973 to the third quarter of 1981), and the ensuing period of low economic volatility and heightened growth known as the Great Moderation up until the start of the Great Recession in 2007 (the third quarter of 1981 to the fourth quarter of 2007).(See graph.)

web-econgrowth02

Specifically, economic growth in the third period, leading up to the Great Recession, was:

  • Not as brisk as it once was
  • More dependent upon consumption
  • Held back by net exports
  • Less driven by government expenditures and investment

The current business cycle, starting with the beginning of the Great Recession, appears to be the beginning of a new era—one tentatively defined by tepid consumer demand, stagnant real-wage gains, and growing economic inequality.

This report will have achieved its purpose if it spurs new thinking about how exactly we can and should promote economic growth in the United States.

Designing a research agenda to move the minimum wage forward

Posted on by Ed Paisley

During the most recent push to raise the federal minimum wage in the United States, more than 600 economists signed a letter encouraging Congress to do so, including seven Nobel laureates. This letter highlighted research that the minimum wage has little to no impact on the employment of minimum-wage workers and that a raise would provide a small stimulus effect on the economy. A few weeks later a letter opposing a rise in the minimum wage was released with the signatures of more than 500 economists, including three Nobel laureates. The opposing letter focused on the increase in labor costs and pointed to a Congressional Budget Office analysis that finds an increase would reduce overall employment, although the 90 percent confidence interval included a zero effect. These economists fundamentally disagree about the response of employment to minimum wage increases, contributing to the paralysis at the national level on the minimum wage, but both claim to point to “the research.”

Read a pdf of the full document.

We propose a series of research projects targeted at advancing the policy debate. In their February 2014 report the Congressional Budget Office highlighted several areas where they argued that there was not enough information or consensus to make strong assessments. We are reaching out to advocates and policymakers to better understand the questions about the minimum wage they want and need answered, with the intention of shaping a research agenda on the minimum wage that directly answers their questions.

Below we identify research questions that may be of interest to policymakers and advocates inspired by the existing academic research as well as the recent CBO paper. This discussion paper should be treated as the name implies—a jumping-off point for a conversation about a research agenda designed to move the policy process forward.

The 2014 Congressional Budget Office report, “The Effects of a Minimum-Wage Increase on Employment and Family Income,” addressed the questions posed to them by Congress on the impact of an increase in the minimum wage, and relied on the most up-to-date academic research in doing so.

Consequently, the CBO report had to adjudicate between a wide variety of studies on the minimum wage, not all of which pointed to the same conclusions. In many cases, the report splits the difference, such as when it cites “uncertainty about the responsiveness of employment to an increase in wages.” Given these inconsistencies, a minimum wage research agenda that addressed the following questions could help clarify and focus the empirical evidence:

  • How does the minimum wage affect production?
  • How do outputs, profits, and prices change?
  • Does a rise in the minimum change worker efficiency?
  • Do increases affect low- and high-productivity firms differently?
  • Are there changes to workforce composition or hours worked?
  • How does the minimum wage affect the overall wage distribution?
  • How large are“ripple effects”for workers who already earn more than the minimum wage?
  • How much does the minimum wage change income inequality?
  • Does the minimum wage affect the macroeconomy?
  • How much less is spent on government benefits for low-income people?
  • How do consumption patterns change from increased wages?
  • How does the structure of the minimum wage policy impact outcomes?
  • How do effects vary by the size of the minimum wage increase?
  • Do minimum wage changes have different short- and long-run effects?

Many of these questions have been addressed directly or indirectly in the economics literature, but work will be needed to synthesize and effectively communicate the results in a way that allow for a more direct, effective response to CBO’s analysis. Yet many
of these topics are under-researched or rely on older data, suggesting a need for new research. This discussion paper explores several of these questions as a starting point for encouraging new research.

How do employment effects vary by the size of the minimum wage increase?

While recent research suggests that modest increases in the minimum have strong effects on earnings and small effects on employment, little work exists on whether this pattern holds for larger raises. Economic theory suggests that the effects will vary by the “bite” of the minimum wage into the underlying wage or productivity distribution. In a study of the 1996 and 1997 federal minimum wage changes, Economist Jeffrey P. Thompson— now at the Federal Reserve Board and previously a professor at the University of Massachusetts, Amherst, found that in 2009, counties with low average earnings (where the minimum’s “bite” was greater) had larger falls in employment after the wage change. Offering an international perspective on the debate, economists Yi Huang, Prakash Loungani, and Gewei Wang estimated that after China strengthened minimum wage enforcement, firms with low profit margins reduced employment, but firms with high profit margins expanded.

Seattle has just passed legislation to increase the city minimum wage from $9.32 per hour today to $15 by 2017-2021, depending on the type of employer. San Francisco is now con- sidering following suit. Opponents of the minimum wage frequently respond by highlight- ing the arbitrariness of the levels proposed by legislators. Additional research could ground the levels in analysis and help policymakers identify the best targets.

Do minimum wage changes have different short-run and long-run effects?

In his review of the research fifteen years ago, University of Michigan economist Charles Brown emphasized that understanding the long-run effects of the minimum wage remains “the largest and most important gap in the literature.”  Perhaps the research overall found no short-term employment effects because firms are unable to modify production in response to a minimum wage increase in the short-run, but in the medium- to long-run, they are less constrained in terms of hiring patterns and substituting capital for labor.

More recently, Texas A&M University economists Jonathan Meer and Jeremy West argued that the minimum wage primarily influences employment growth, rather than the employment level. Therefore, an increase in the minimum wage has a small effect on employment levels in the short-run , but a large effect in the long-run. In contrast, economists Arindrajit Dube at the University of Massachusetts, Amherst, T. William Lester at the University of North Carolina, Chapel Hill, and Michael Reich at the University of California, Berkeley, failed to find effects on employment levels up to four years after minimum wage increases. Additional work must reconcile conflicting evidence on long-term effects of an increase in the minimum wage.

How does the minimum wage affect production?

To respond to a minimum wage increase, employers and workers may choose a variety of “channels of adjustment,” such as raising prices or improving efficiency. The most comprehensive evidence suggests that restaurants raise prices in response to a minimum wage increase, passing a portion of increased labor costs onto consumers. Unfortunately, the city-level data used in this analysis is almost two decades old, and has not been subjected to alternative specifications. With more recent but less comprehensive data, economists Emek Basker and Muhammad Khan at the University of Missouri, Columbia, find similar price increases for two out of three restaurant items. New research with better quality price data has a high probability of informing how much affected businesses raise prices after a minimum wage increase.

By improving worker and managerial efficiency, minimum wage increases may boost labor productivity. Productivity effects would be consistent with current research confirming that worker turnover falls sharply after a minimum wage increase, both in the United States and Canada.  In addition, restaurant managers’ survey responses suggest that minimum wage increases provide an opportunity to portray the “cost shock as ‘a challenge to the store’” in order “energize employees and to improve productivity,” according to a study by economists Barry Hirsch and Bruce Kaufman at Georgia State University. Similarly, using plant-level data in the United Kingdom, economists at the National Bureau of Economic Research find that revenue-per-worker increases in response to a minimum wage rise, but the effect is statistically insignificant.

Firms may also adjust production practices in the face of a minimum wage increase by hiring more highly skilled workers, or by reducing hours of the lower-skilled work- force. Existing high-quality studies do not generally find large effects on workforce composition and hours, but the estimates remain too statistically imprecise to rule out substantive effects. One recent study, for example, estimates that teen hours either fall somewhat or not much at all, depending on the specification.

More recent but preliminary work suggests that relatively small employment-level impacts of the minimum wage may conceal large changes in the mix of firms. The study finds that restaurants in three states that raised minimum wages during the 2000s experienced increases in employees’ hiring and departures from firms. New research must provide more comprehensive and precise evidence on how firm composition and output change in response to the minimum wage.

How does the minimum wage affect the overall wage distribution?

By raising the wage floor, the minimum wage reduces inequality, but current research has not settled on the size of these effects. One study in 1999 estimated that the falling real value of the minimum wage accounted for the entire increase in wage inequality between the median wage and the 10th percentile wage during 1979-1989. In contrast, a new study this year by economists David Autor and Christopher L. Smith at the Massachusetts Institute of Technology and Alan Manning at the London School of Economics finds that the falling real minimum wage accounted for about one-third of the inequality increase. Better data quality and more recent empirical techniques can improve estimates of the minimum wage’s impact on inequality.

In raising the minimum wage, workers just above the minimum wage will often see a wage increase. While many studies observe these “ripple effects” or wage spillovers, existing empirical work does not evaluate any underlying mechanisms. Do the spillovers occur within firms, as workers paid just above the minimum also demand raises? Or do they occur in the market, as firms are forced to raise wages to attract new workers? Or do they occur as employers attempt to maintain established wage structures (internal pay scales) within firms?

What are the macroeconomic effects of the minimum wage?

By lifting workers out of poverty, the minimum wage may reduce fiscal spending on income support and welfare programs. Two economists at the Institute for Research on Labor and Employment, Rachel West and Michael Reich, find that the minimum reduces the use of food stamps as well as state-level expenditures on that program. Additional empirical work could examine other needs-based programs and quantify state-level budget impacts.

Minimum wage raises likely translate into increased consumption, but little work exists
on directly measuring these effects. One recent study finds a minimum wage change leads to large increases in consumption; these expenditures seem concentrated in automobile purchases partially financed by debt. New research with high quality individual-level data will help to improve estimates of the consumption response to minimum wages.

A final related issue is whether minimum wage increases affect the economy differently during times of economic slack or expansion. One recent study finds that the minimum has large negative effects on employment when unemployment is high, but another one finds no such evidence. More work is needed to identify credible estimates of how the minimum wage interacts with the broader economy.

 

Update on Research for Equitable Growth’s Issue Brief “A Regional Look at Single Moms and Upward Mobility”

Posted on by Ed Paisley

About a month ago, I put out a short piece assessing the attention given to the relationship between the share of single mothers in an area and economic mobility. As part of the piece, I noted that many places had high economic mobility despite having a high share of single mothers and that these places tended to have had parental leave laws prior to the Family Medical Leave Act of 1993. Richard Reeves and Joanna Venator of Brookings objected to my analysis noting that some places, such as New Jersey, have low mobility despite having long standing family leave laws. In response, I have added a statistical appendix to the original piece showing that these pre-FMLA parental leave laws are statistically significant factors. You can also read my blog post over on the Brookings Social Mobility Memos blog responding to Reeves and Venator.

The Importance of Private Equity Supermanagers among Top Income Earners

Posted on by Ed Paisley

A new data interactive published today by the Washington Center for Equitable Growth should help elevate discussion about the growth of incomes at the very high end of the U.S. income ladder and how that growth affects economic inequality and growth. To help inform that debate about who the people in the 0.1 percent are, we here at the Washington Center for Equitable Growth have produced a data interactive about who is in the top 0.1 percent of the income distribution, based on a 2012 white paper by economists Jon Bakija of Williams College, Adam Cole of the Office of Tax Analysis at the U.S. Department of the Treasury, and Bradley Heim of Indiana University. Looking at tax data, their report looked at the types of jobs held by the top earners in the United States between 1979 and 2005..

Their work provides a great window into how our economy has been changing at the top end of the income spectrum. The data in their study indicate that “supermangers” (as economist Thomas Piketty of the Paris School of Economics refers to business executives and managers in his book “Capital in the 21st Century”) constituted about 60 percent of the top 0.1 percent of the income distribution over that period. While this level did not changed substantially over time, the nature of the supermanager definitely did. People working in finance in 2005 claimed 18 percent of that 0.1 percent, up from 11 percent of the 0.1 percent in 1979. And the types of executives in this mix went from being 20 percent private equity or closely held firms in 1979 to more than half in 2005.

This mirrors the trend in corporate structure in the United States toward more private ownership, most likely because of the elevated role of private equity investing over this period. Big Wall Street private equity firms and financial institutions are huge players in in private equity investing.

James Manzi, a senior fellow at the Manhattan Institute for Policy Research, wrote a piece attacking Piketty’s discussion of supermangers by arguing about the decline in the share of the 0.1 percent that work in publically held companies. But Manzi fails to discuss the rise of those in private equity and closely held firms—which is particularly odd because he cites the Bakija, Cole, and Heim white paper and even found the correct table in the document to find these observations. By missing this point, both his number crunching and critique of Piketty fall way off the mark.

That said, understanding the changing nature of the top 0.1 percent is important for understanding the changes that have been driving our economy. Piketty’s work on the supermanagers is interesting, but only serves to highlight how little we know about this extremely high-income group.

Who are today’s supermanagers and why are they so wealthy?

Posted on by Ed Paisley

What explains the changes in top-earning occupations over the past four decades? Perhaps the most intriguing argument about the current state of income inequality in the English speaking economies that Thomas Piketty makes in his bestseller “Capital in the 21st Century” is this—“the vast majority (60 to 70 percent, depending on what definitions one chooses) of the top 0.1 percent of the income hierarchy in 2000-2010 consists of top managers.” He goes on to argue on page 302 of his book that the rise in labor income “primarily reflects the advent of ‘supermanagers,’ that is, top executives of large firms who have managed to obtain extremely high, historically unprecedented compensation packages for their labor.”

top-earners-infographic

This really begs the question as to how and why these supermanagers came into existence. Nobel Laureate Robert M. Solow points out in The New Republic that this is primarily an American outcome. And Henry Engler at Thomson Reuters Accelelus’ Compliance Complete recently published an excellent piece on Piketty’s supermanagers in the United States and the United Kingdom. Both writers agreed with Piketty that these supermanagers were being vastly overly compensated given their questionable contributions to productivity.

I hope to shed a little more light on this issue by examining the change in professions comprising the top 0.1 percent of tax filers between 1979 and 2005. The purpose: to examine whether the changing composition of this super elite reflects changes in our economy that may explain the link between rising economic inequality and anemic economic growth over this period.

To do so, I used data from the April 2012 white paper “Jobs and Income Growth of Top Earners and the Causes of Changing Income Inequality: Evidence from U.S. Tax Return Data,” by economists Jon Bakija of Williams College, Adam Cole of the Office of Tax Analysis at the U.S. Department of the Treasury, and Bradley Heim of Indiana University. They used tax data on the top 0.1% of filers to identify the top earning professions. The infographic below tells the tale, charting the change in occupations at the tippy top of the income ladder in 1979 and 2005.

The biggest change in the distribution of top earners is in the types of executives, managers, and supervisors at non-financial firms. In 1979, most of these people worked for large, publicly traded firms but by 2005 more were working in closely held firms. There is not enough information to provide a clearer picture as to who exactly these people are, but chances are they are employed by firms that are owned by private equity firms—the growth in the private equity industry over this period of time was substantial—and because financial professionals saw large gains, too. The share of people in the top 0.1 percent working in finance also increased substantially, to 18 percent in 2005 from 11 percent in 1979.

These findings are consistent with Piketty’s analysis in his new book. But there are alternative explanations. One is presented in George Mason economist Tyler Cowen’s latest book, “Average is Over.” He claims a skill biased-technological change is responsible for the shift in top occupations over roughly the same period. He argues that technology allows top performers to capture more of the market and thus earn substantially more than average performers. He and many other people hypothesize that this is a driver of increased economic inequality.

But if technology were a primary driver of inequality, then one would expect that skilled trades would have larger incomes and would have become a larger share in the top 0.1 percent. While there are slightly more technical types and entertainers among top earners (as can be seen in the data presented in our interactive) the biggest gains in both percentage terms and magnitude were among privately held business professionals.

Thus, the so called “average is over” argument—that that the top performers in each field will capture a bigger share of the pie—may be a driver of inequality, but it does not appear to explain the bulk of the changes in occupations at the top of the income ladder. Instead, the supermanagers appear to be capturing greater share of the wealth as is argued by Piketty and others. More detailed data would be required to assess who these people are and how workplace dynamics changed from 1979 to 2005 that would explain the change in income. The Washington Center for Equitable Growth will be examining this data in more detail in forthcoming publications.