Page 198 – Equitable Growth

Should-Read: Jörg Mayer: Industrial robots and inclusive growth

Posted on October 12, 2017October 12, 2017 by Brad DeLong

Should-Read: Jörg Mayer: Industrial robots and inclusive growth: “Robots are not yet suitable for a range of labour-intensive industries…

…leaving the door open for developing countries to enter industrialisation processes along traditional lines. At the same time, it suggests ways that developing countries should embrace the digital revolution….

Assessments of the employment impact of robots have generally been based on a task-based approach, which hypothesises that a job is composed of different tasks and that new technology does not always favour better-skilled workers but often complements workers in certain tasks of their job, while substituting for them in others (Autor et al. 2003). This approach distinguishes between manual, routine, and abstract tasks…. Routine-task intensity indices, which link routine tasks to occupations that workers perform on their jobs…. Studies indicating robots’ dramatic job displacement potential… emphasise this technical feasibility of workplace automation….

The technical feasibility of job displacement in manufacturing is highest in food, beverages and tobacco, followed by the textiles, apparel and footwear sector…. Job displacement by robots is more profitable in relatively skill-intensive and well-paying manufacturing, such as the automotive and electronics sectors, than in relatively labour-intensive and low-paying sectors, such as apparel. The sizes of the bubbles reflect the sectoral distribution of actual global robot stocks in 2015…. Taken together… economic factors are more important for robot deployment than the technical possibilities of automating workers’ tasks…. Robot deployment has remained very limited in those manufacturing sectors where labour compensation is low, even if these sectors have high values on the routine task intensity index….

[This] suggests that robot-based automation per se does not invalidate the traditional role of industrialisation as a development strategy for lower income countries. Yet the dominance of robot use in sectors higher up on the skill ladder implies greater difficulty for latecomers in attaining sectoral upgrading and may limit their scope for industrialisation to low-wage and less dynamic (in terms of productivity growth) manufacturing sectors. This could seriously stifle these countries’ economic catch-up and leave them with stagnant productivity and per capita income growth…

Just how tight is the U.S. labor market?

Posted on October 12, 2017April 9, 2018 by Emilie Openchowski

People head to a job fair at Dolphin Mall in Florida.

Overview

Is the U.S. unemployment rate as low as it can go? After years of a very weak labor market, during which many jobless workers gave up trying to find employment due to the lack of employer demand, many economists and analysts now believe the labor market is now as tight as it can sustainably be. The unemployment rate is close to 4 percent, and most of the participants of the policy committee of the Federal Reserve believe the unemployment rate is at or below its long-term rate.¹

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Just how tight is the U.S. labor market?

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What’s more, recent research by economist Alan Krueger of Princeton University argues that fewer workers will actively search for jobs these days due in part to opioid addiction.² This kind of view, also articulated by New York Times columnist Eduardo Porter, holds that the problem in the labor market now is one of supply—a lack of available workers eager to work—rather than demand for labor among employers.³

But if the current unemployment rate is indicative of a very tight labor market, then why does wage growth continue to be so tepid? If the supply of potentially employable workers is tapped out, then the price of labor—wages—should grow at an increasingly faster pace. Yet as the unemployment rate has declined and hit levels many associate with “full employment,” wage growth has yet to break out of the range of 2 percent to 2.5 percent per year. One simple explanation of this anomaly of a tight labor market with weak wage growth is that the labor market is not actually that tight.

Indeed, the unemployment rate currently does not do a good job of predicting wage growth. What the data show is that a given unemployment rate can be associated with a wide range of wage-growth levels. This issue brief examines, through regression analysis, the strength of the relationships between various measures of labor market slack and wage and compensation growth. The strongest association for wage and compensation growth is with the prime employment rate, the share of workers ages 25 to 54 with a job. This statistic still stands below its pre-recession peak, suggesting the U.S. labor market is not yet at full employment.

The unemployment rate is still a useful measure of the health of the labor market. But it should be taken in the context of other measures. Even if two labor markets have the same unemployment rate, one will be tighter than the other if their employment rates vary significantly. When assessing the health of the labor market, policymakers have to look at both unemployment and employment. If the U.S. labor market still has room to run, then policymakers should look favorably at monetary and fiscal policies that would increase aggregate demand. This information is particularly important for policymakers at the Federal Reserve as they consider the pace at which they raise interest rates.

Measures of labor market tightness

The unemployment rate is by far the most commonly cited labor market statistic. Using responses from the Bureau of Labor Statistics’ monthly Current Population Survey, workers are reported as unemployed if they do not have a job and have been actively searching for a job in the recent past. The unemployment rate is the number of officially unemployed workers as a percentage of the total labor force. The labor force is the combined ranks of these unemployed workers and workers with a job.

This is why the unemployment rate is a very appealing measure of the health of the labor market. It’s trying to capture the share of workers who want a job (the labor force) but don’t have one (the unemployed). A declining share of the labor force who are unemployed means a tighter labor market because those who want a job are increasingly finding them. It’s possible, however, that there are many workers who are not actively searching but are willing and able to take a job if they think they can find one. If there is a significant number of these workers, then the unemployment rate could be overestimating the health of the labor market.

A measure of the labor market that doesn’t have to make a judgement call about who is or is not likely to get a job is the prime employment rate. This statistic—better known as the prime-age employment-to-population ratio—is simply the share of the population ages 25 to 54 with a job. It, similar to the unemployment rate, comes from the Current Population Survey. By simply counting a worker as employed or not employed, it avoids the ambiguity of the unemployment rate. Whereas a declining unemployment rate may be due to more workers leaving the labor force, a rising prime employment rate will always mean more workers with a job.

The restriction of only looking at prime-age workers helps eliminate potential biases by looking at all workers. Lower employment rates for younger individuals and older workers are mostly for reasons society looks upon favorably: education and retirement, respectively. Looking at workers in their prime working years means the prime employment rate doesn’t see these positive phenomena as a sign of a weak labor market. Indeed, as the U.S. population ages with the baby boomer generation, more workers will retire and push down the employment rate for all workers. Again, that’s not necessarily an indication of a weakening labor market, while including these workers would bias the employment rate toward underestimating the health of the labor market.

Both the unemployment rate and the prime employment rate are “stock” measures of the labor market. They compare the total pool of employed or unemployed workers to the total pool of potential workers. Compare that to “flow” measures that look at the movement of workers between unemployed and employed, or from job to job. These flow statistics not only help us understand what’s driving changes in the two stock measures. Looking at flows can also tell us, for example, if the unemployment rate is increasing because more workers are moving from employment to unemployment or if it’s because of more flows out of the labor force into unofficial unemployment.

A particularly useful group of flow measures is the statistics that capture the movement of workers between jobs. An employment rate can tell us how many people have a job, but data on job switching tell us how frequently already-employed workers are moving to new jobs. Job switching moves with the health of the labor market. As the labor market strengths, employers will poach employees from other firms, adding to the amount of job switching. As more already-employed workers switch jobs, unemployed workers are likely to be hired. But because data on job switching only directly looks at the already employed, it’s useful to think about this measure as showing the tightness of the labor market for already-employed workers. The Job-to-Job Flows (J2J) data from the Longitudinal Employer-Household Dynamics complied by the U.S. Census Bureau is a good source for job switching data.

Measures of wage growth

Before we can determine which labor market indicator can best predict wage growth, we need to decide on what measure of wage growth to use. The most commonly cited measure of wage growth is the average hourly earnings series from the monthly Current Employment Statistics survey. This series measures the average hourly wage rate for workers using data from the payroll of their employers. This means the data are only for cash wages and do not include other forms of compensation such as employer-provided health insurance or employer contributions to retirement savings vehicles such as 401(k) plans.

The lack of coverage of other forms of compensation might be a concern for using the average hourly earnings series, as nonwage compensation has become a larger share of total compensation in recent decades. A data series that had comparable data for both wage growth and compensation growth would be quite useful. Alas, the Current Employment Statistics series only looks at wages.

Another potential issue with average hourly earnings is that it does not adjust for the composition of workers. Why would this matter? Consider the changes in demographics of the workforce. Let’s say a lot of jobs that are created during an economic recovery go to younger workers, who tend to get jobs in occupations with lower wages. The growth of the average wage would be reduced, as the average wage gets pushed down by the addition of these new jobs. Yet there could be stronger wage growth within occupations that gets washed out by the changes in worker composition. Again, the average hourly earnings data from the Current Employment Statistics series does not adjust for composition.

Luckily, though, data from the Employment Cost Index overcomes these limitations.⁴ The dataset has series for both wages and total compensation, and adjusts for the compensation of the workforce. But one downside to the series is that these data are released on a quarterly basis, so they are updated less frequently than the monthly Current Employment Survey. In this analysis, we will use the series that cover only private-sector workers.

Another question is whether wage and compensation growth should be adjusted for inflation or left in nominal terms. The argument for keeping the wage and compensation in nominal terms notes that changes in inflation that seemingly change wage growth may be transitory in the short-term. But workers care about how much their wages will be able to purchase in the future, so adjusting for inflation also makes sense. This analysis will present results for both nominal series and series adjusted for consumers’ expectations of future inflation, using data from the Survey of Consumers by the University of Michigan.

What measures best predict wage and compensation growth?

The analysis in this issue brief uses two ways of seeing how well various labor market statistics can predict wage and compensation growth. Both involve running what’s known as an Ordinary Least Squares regression analysis on the data to calculate a linear relationship between the labor market statistics and wage or compensation growth. First, the analysis looks at how much of the variation in wage and compensation growth each labor market statistic can explain on its own. Second, the exercise shows how much explanatory power each statistic has when tested in conjunction with other statistics.

A fuller description of the analysis is below, but the results are quite clear. The prime employment rate is the single strongest predictor of wage and compensation growth, both in nominal and inflation-adjusted terms. This is true both when looking at a period starting in the early 1990s, covering the past three expansions, or in early 2000s, covering the past two. When all three variables are included in a regression, a percentage point increase in the prime employment rate is associated with a larger or at least as large an increase in wage or compensation growth as the other two variables.

First, let’s run through the results for the explanatory power of each individual labor market statistic. From the end of the 1991 recession until the second quarter of 2017, the prime employment rate explains about 80 percent of the variation in nominal wage growth, about 52 percent of variation in nominal compensation growth, roughly 60 percent of variation in wages adjusted for inflation expectations, and about 43 percent for adjusted compensation growth.

Compare that to the results for the unemployment rate during that same time period. It explains 50 percent of the variation in nominal wage growth, about 25 percent of variation in nominal compensation growth, roughly 37 percent of variation in wages adjusted for inflationary expectations, and about 22 percent for adjusted compensation growth. (For full results of the regressions covering this time period, see Table 1 in the Online Appendix.)

Figures 1 and 2 below show the relative predictive power of the two different statistics. Figure 1 looks at the relationship between the unemployment rate and nominal wage growth. Not only are the dots spread farther apart, indicating a weak relationship, but the most recent data (the second quarter of 2017) also show wage growth is much lower than would be expected from the historical relationship (the red trend line). Using the trend line, we would have expected 3.2 percent nominal wage growth in the second quarter of 2017 with a 4.4 percent unemployment rate. In reality, wage growth was 2.4 percent. (See Figure 1.)

Figure 1

Figure 2 shows much tighter plots for the prime employment rate and a much smaller difference between the actual most recent data and the prediction from the trend line. We would have expected 2.7 percent nominal wage growth using the prime employment rate as a predictor, which was close to the 2.4 percent of reality. (See Figure 2.)

Figure 2

When we restrict the time period for which we have data from the J2J data (the second quarter of 2000 until the fourth quarter of 2015), the prime employment rate still explains the most variation. For nominal wages, the prime employment rate explains 85 percent, the unemployment rate explains 65 percent, and the job-switching rate explains 67 percent. For inflation-adjusted compensation, the prime employment rate explains 50 percent, the unemployment rate explains 31 percent, and job switching explains 37 percent.

As Tables 3 and 4 in the appendix show, when all three labor market statistics are included in the analysis, the prime employment rate is associated with the strongest increase in wage or compensation growth. Models that include both the unemployment rate and the job switching rate explain less of the variation in wage or compensation growth than the prime employment rate by itself. Interestingly, the job-switching rate seems to have about the same explanatory power as the unemployment rate, with job switching explaining more of the variation in several regressions. (See the Online Appendix for more details on the results from the regressions used in this analysis.)

The results for the unemployment rate are particularly interesting in these regressions. When unemployment is included with the prime employment rate, an increase in the unemployment rate is associated with an increase in wage and compensation growth—the opposite of what we might expect. By including the prime employment rate, these regressions are calculating the relationship between wage and compensation growth and the unemployment rate for a given prime employment rate. In other words, it’s asking how wage growth would change if the unemployment rate went up or down while the prime employment rate stayed the same.

The only way for the unemployment rate to change while the overall employment rate is constant is for either a shift of workers into the labor force from unemployment or from unemployment to out of the labor force. An increase in the unemployment rate in this case would be due to more workers joining the ranks of the officially unemployed, most likely because they are feeling optimistic about the chances of getting a job. In the flip case, a decline in the unemployment rate would be due to flows from unemployment out of the overall labor force. The association with wage and compensation growth makes more sense in this interpretation. (Though, of course, this analysis uses the prime employment rate, so there could be something else going on here. But regressions using the unemployment rate for workers ages 25 to 54 also show a positive relationship). A rising unemployment rate with a constant employment rate would likely be a sign of more labor market optimism—and conversely, a declining unemployment rate in that situation probably indicates pessimism.

Implications

The analysis in this issue brief demonstrates that the U.S. labor market is not as tight as the unemployment rate would have us believe. While this analysis does not directly look at the possibility of workers moving into the labor force, the strong relationship between the prime employment rate and several measures of wage and compensation growth suggest a number of nonemployed workers who can and may find a job are not being counted in the unemployment rate.

As of the third quarter of 2017, the prime employment rate was 78.7 percent. The level of the employment rate associated with a nominal wage growth of 3 percent—the lowest level that could reasonably be called healthy—is 79.2 percent. It would take roughly six more months to get to that level if the prime employment rate grows at the same rate as the previous year. And that would only get the labor market to the lower edge of acceptable nominal wage growth. The labor market does not yet appear to be at full employment.

Many workers who seem locked out of the labor force may, in fact, be able to get a job if the labor market continues to tighten. Research by University of California, Berkeley economist Danny Yagan finds that about three-fourths of the decline in the age-adjusted employment rate was caused by the still-reverberating shocks from the Great Recession of 2007–2009.⁵ It’s possible that these shocks can be reversed by increasing labor demand via monetary or fiscal policy, bringing workers back into the labor force. Overestimating the strength of the labor market and leaving these workers unemployed would be a tragedy not only for those workers, but for the U.S. economy as a whole.

JOLTS Day Graphs: August 2017 Report Edition

Posted on October 11, 2017April 19, 2018 by Nick Bunker

Every month the U.S. Bureau of Labor Statistics releases data on hiring, firing, and other labor market flows from the Job Openings and Labor Turnover Survey, better known as JOLTS. Today, the BLS released the latest data for August 2017. This report doesn’t get as much attention as the monthly Employment Situation Report, but it contains useful information about the state of the U.S. labor market. Below are a few key graphs using data from the report.

The quits rate continued its trend of not moving much in August, registering at 2.1 percent. With the exception of a few ticks up, it’s been stuck at that level since May 2016.

While the ratio of unemployed workers to job openings did not hit a record low in August, it’s still near the lowest levels seen during the period JOLTS data cover.

Employers are still getting fewer than one hire per job opening, with the vacancy yield coming in at 0.89 in August.

The Beveridge Curve is quite close to its pre-Great Recession relationship, but is not quite there yet.

Should-Read: Justin Fox: Nobel Winner Richard Thaler’s Savvy, Calculating Insurrection

Posted on October 11, 2017October 11, 2017 by Brad DeLong

Should-Readz: Justin Fox: Nobel Winner Richard Thaler’s Savvy, Calculating Insurrection: “‘Dumb stuff people do’ was an expansion, not a rejection, of mainstream economics…

…In the late 1970s, Richard Thaler thought most of his fellow economists deeply misunderstood how actual people make actual economic decisions, and his renegade ideas risked derailing his career. But they didn’t. Thaler’s was a lonely struggle for a while, but it evolved into a savvy, calculating operation. And it was successful…. This relatively cautious approach has occasioned some sneering… John Cochrane… in 2015 after the publication of Thaler’s memoir, “Misbehaving”:

Really, now, complaining about being ignored and mistreated is a bit unseemly for a Distinguished Service professor with a multiple-group low-teaching appointment at the very University of Chicago he derides, partner in an asset management company running $3 billion dollars, recipient of numerous awards including AEA vice president, and so on.

The AEA is the American Economic Association, of which Thaler soon afterward became president. Now, of course, Cochrane could add Nobel Prize to that list. Unlike a true-blue revolutionary, then, Dick Thaler is not spending his latter years muttering away in an unheated garret. So disappointing!

But also so intriguing…. Thaler’s career offers useful pointers on how to bring meaningful change to a large, dispersed organization while not getting thrown out of it…. Key themes…. Use humor…. Find allies outside the organization…. Build an infrastructure…. Stay respectable….

It’s always the students who matter most. Thaler told me in 2015 that “I don’t think I’ve changed a single person’s mind in 40 years.” But generations of graduate students have now come of age in an economics profession where behavioral research is, if still not central, perfectly respectable. That’s the change that Thaler has brought. It’s not a revolution, but it is something.

Should-Read: Michael Strain: Republicans, It’s Way Past Time for a Real Tax Plan – Bloomberg

Posted on October 10, 2017October 11, 2017 by Brad DeLong

Should-Read: Does Michael Strain really believe that the Trump Administration and the Congressional Republican caucus have mistakenly fuzzed the “details” of their tax plan? They have deliberately fuzzed the “details” because they have calculated that their plan is more popular with the details fuzzed. Saying exactly what you want to do is not a way to keep critics from assuming “the worst” if your intentions are in fact “the worst”—and if the goal is to keep the professional centrists from saying: “yep: the critics are right”. Michael Strain wants his party to play policy ball. But his party wants to play Calvinball.

And, of course, his blithe assumption that they really want to play policy ball is a form of meta-Calvinball itself:

Michael Strain: Republicans, It’s Way Past Time for a Real Tax Plan: “The way to keep critics from assuming the worst about your intentions is to say exactly what you want to do…”

Should-Read: Paul Krugman: Rationality and Rabbit Holes

Posted on October 10, 2017October 11, 2017 by Brad DeLong

Should-Read: I think Paul Krugman gets this one wrong because he fails to distinguish between two versions of the Efficient Financial Markets Hypothesis. The first version, which is right, is that “asset price movements are unpredictable, that patterns are subtle, unstable, and hard to make money off of”. The second version, which is wrong, is that financial markets are optional aggregators of information that get prices right. The first has done good. The second has done a lot of harm. Distinguish!

Paul Krugman: Rationality and Rabbit Holes: “Like the vast majority of economists, I was delighted to see Richard Thaler get the Nobel…

…The assumption of hyperrationality still plays far too large a role in the field. And Thaler didn’t just document deviations from rationality, he showed that there are consistent, usable patterns in those deviations…. One [camp] says that imperfect rationality changes everything; the other that the assumption of rationality is still the best game out there, or at least sets a baseline from which departures must be justified at length. Which camp is right?… Let me talk about two fields I know reasonably well: macroeconomics, which I think I know pretty well, and finance, where I am much less well-informed in general but am pretty familiar with at least some international areas. What strikes me is that vaguely Thalerish reasoning is hugely important in one, in the other not so much.

Let me state two propositions derived from the proposition that people are perfectly rational:

Rational investors will build all available information into asset prices, so movements in these prices will be driven only by unanticipated events – that is, they’ll follow a random walk, with no patterns you can exploit to make money.

Rational wage- and price-setters will take all available information into account when setting labor and goods prices, implying that demand shocks will have real effects only if they’re unanticipated – in particular, that monetary policy “works” only if it’s a surprise, and can’t play a stabilizing role.

Now, (1) is basically efficient markets theory, which we know is wrong in detail – there are lots of anomalies. In international finance, for example, there is the well-known uncovered interest parity puzzle: differences in national interest rates should be unbiased predictors of future changes in exchange rates, but in fact turn out to have no predictive power at all. And anyone who believed that rationality of investors precluded the possibility of massive, obvious mispricing – say, of subprime-backed securities – has not had a happy decade. Yet the broader proposition that asset price movements are unpredictable, that patterns are subtle, unstable, and hard to make money off of, seems to be right. On the whole, it seems to me that considering the implications of rational behavior has done more good than harm to the field of finance.

What about (2)?… Robert Lucas… took the whole field down a rabbit hole…. Everything we know suggests that there is a lot of nominal downward rigidity and a lot of money illusion in general. And assertions that this might be true in practice, but can’t be true in theory, and must therefore be assumed away both in research and in policy have been hugely destructive…

Why, despite post-racial rhetoric, do racial health disparities increase at higher income levels?

Posted on October 10, 2017April 9, 2018 by Emilie Openchowski

A research technician at the University of Pittsburgh Medical Center collects blood pressure data from a patient.

Persistent disparate health outcomes between black and white Americans are a major contributor to the United States’ poor performance on international measures of health. These disparities cannot be explained by socioeconomic status alone. While health outcomes generally improve with socioeconomic status, the disparity in health outcomes between black and white Americans not only persists but often worsens with higher socioeconomic status.

In my new working paper, “Post-racial rhetoric, racial health disparities, and health disparity consequences of stigma, stress, and racism,” I explore this paradox, placing it within the context of neoliberal rhetoric and the political narrative that the United States has entered a “post-racial” era, and I propose a new framework for empirical research to explore and explain this trend.

One example is that the racial differences in infant mortality actually worsen with higher levels of both education and income. The infant mortality rate for all white women, regardless of education, was 5.07 per 1,000 from 2007 to 2013; for black women, the corresponding figure was 10.81 per 1,000, a ratio of 2.13. When you break down infant mortality rates by education, you find that disparity actually worsens at higher levels of educational attainment. While the infant mortality rate for babies born to white women with at least a bachelor’s degree is 3.36 per 1,000, for babies born to black women with the same level of education the rate is 7.5 per 1,000, which is still more than the rate for babies born to white women with less than a high school degree. The ratio of black to white infant mortality rates for babies born to a mother with at least a bachelor’s degree is 2.23, the highest ratio for any level of educational attainment. (See Table 1). In fact, the ratio rises fairly steadily for each level of educational attainment.

Table 1

This pattern is not limited to infant mortality. An analysis of health data by Ahmedin Jemal and his co-authors found this pattern of mortality disparities with rising levels of educational attainment across many major disease types, including cancer, heart disease, stroke, and HIV-related causes.

That disparities in health outcomes increase with educational attainment flies in the face of American political rhetoric that emphasizes personal responsibility and hard work. This neoliberal rhetoric, combined with a narrative that the United States is now post-racial, places responsibility for continued disparities in outcomes squarely on individual choices and actions and ignores structural factors and an environment of continuing racism. Personal responsibility, hard work, perseverance and—especially—education are supposedly all that one needs to achieve better life outcomes, regardless of where you come from, how much your parents earned, or the color of your skin.

But the evidence contradicts that rhetoric. Across health, wealth, employment, and education, racial disparities persist, regardless of socioeconomic status, in all four outcomes with the exception of one: educational attainment. Ironically, education is an indicator in which blacks perform relatively better than whites once family socioeconomic background is controlled.

So, what explains the increasingly disparate health outcomes for more highly educated black Americans? Research on racial health disparities has largely focused on socioeconomic status as an explanatory factor, as William W. Dressler and his co-authors point out in their literature review of models of racial health disparity. According to this theory, it is because black Americans are overrepresented in lower socioeconomic strata that they have worse health outcomes. However, as we have just seen, even when controlling for socioeconomic status, not only is there still a disparity in health outcomes, but the racial disparities often worsen with more education.

A new framework is needed to analyze the paradoxical health outcomes for high socioeconomic black Americans. Research by Sherman James offers a starting point for this new theoretical framework: “John Henryism,” a reference to the fable of the black railroad worker who beats a machine in a race to dig a tunnel only to collapse to death from his overexertion.

In this framework, the disparate health outcomes of black Americans—especially related to hypertension—are analyzed within the context of the disproportionate race-related stress they face. Blacks from lower socioeconomic strata are presumed to be chronically exposed to psychosocial stress such as threat of job loss, having to make ends meet, and social insults related to race and class, among other factors. They have to exert considerable energy on a daily basis in order to cope with these conditions of uncertainty and corresponding high anxiety. James developed a scale, which he labeled John Henryism, to quantify this “effortful coping,” which measured efficacious mental and physical vigor, a strong commitment to hard work, and single-minded determination to succeed.

In a series of experiments, James found that the combination of high John Henryism ranking and low socioeconomic status was associated with high blood pressure. When examining within races, James found that there was very little difference in blood pressure within socioeconomic strata among whites, regardless of their John Henryism scores. However, among blacks, those with low socioeconomic status and high John Henryism scores had the highest blood pressure. The unfortunate irony is that these findings suggest that blacks who work the hardest to cope with a stressful situation experience worse health outcomes.

While James only analyzed the intersection of socioeconomic status, health, and “effortful coping” within race, his work could provide a framework for further analysis and empirical studies into the paradox of worsening health disparities for black Americans of higher socioeconomic status across race.

The forces behind the highly unequal U.S. wealth distribution

Posted on October 10, 2017April 19, 2018 by Emilie Openchowski

People line up at a food pantry at Sacred Heart Community Service in San Jose, CA.

The Federal Reserve’s Survey of Consumer Finances release last week reported that 38.6 percent of wealth in the United States was owned by the top 1 percent of families in 2016. The wealth distribution in the United States has always been incredibly skewed toward the wealthiest, with the share going to the top 1 percent moving up from 36.3 percent in 2013. Mathematical models of wealth distributions, however, have had a hard time accounting precisely for this “fat right tail” at the top of the U.S. wealth distribution. What key factors explain this continuing concentration of wealth?

Two papers released today as part of the Equitable Growth Working Paper series give us some guidance on the forces that have led to such an unequal wealth distribution in the United States. The first of the two papers, by Jess Benhabib and Alberto Bisin of New York University, gives an overview of previous research looking at the causes of wealth inequality. These mathematical and macroeconomic models have, in the past, fallen short of recreating the distributions of wealth we actually see in the world. The problem is that they fail to identify the high concentration of wealth among the very wealthy.

Benhabib and Bisin highlight three broad mechanisms or explanations that have been the focus of previous research and consider how much they could help explain this “fat tail” of wealth concentrated at the right side of distributional graph. The first mechanism deals with income inequality and how that arises from shocks to individuals’ earnings. The second is related to capital income risk, or differences in the rate of return on investments at different levels of wealth. The third factor is “explosive” wealth accumulation, asking whether or not savings rates differ across the wealth or income distributions. Regarding the first mechanism, the authors steer us away from high levels of income inequality as a major driving force behind wealth inequality, as the distribution of wealth is far more unequal than the distribution of income across the U.S. population. The other two factors, as the second paper shows, are more likely to explain the level of wealth inequality.

The second paper, by Benhabib, Bisin, and Mi Luo (also of NYU), is an attempt to parse out the influences of these three factors on U.S. wealth distribution. Using data from the Survey of Consumer Finances, as well as mobility data from previous research, the three economists use a model of consumption to recreate the wealth distribution. By varying the parameters in the model that account for income shocks and differences in returns and savings rates, the authors tease out the importance of each factor.

They find, in short, that differences in the rate of return on capital and in savings rates are the main factors explaining the distribution of wealth in the United States. As suggested by the first paper, the differences in income caused by shocks don’t explain much—though shocks do contribute significantly to mobility up and down the rungs of the wealth ladder.

The differences in savings rates—documented elsewhere in research by University of California, Berkeley economists Emmanuel Saez and Gabriel Zucman—are motivated by a desire to pass wealth down to children. If this is true, then an inheritance tax may do quite a bit to reduce wealth inequality. Citing other research on differences in returns on capital, the authors note that the variances in returns are high for housing, business ownership, and private equity investments. The role of housing may be key in explaining the differences in wealth, not only among the entire population but between racial groups as well.

While the rise of income inequality has inspired a great deal of research on its causes, the economic literature on wealth inequality is relatively sparse. The new findings in these papers are an important guide for future research in this area. Understanding why wealth inequality is so high may very well help us understand its effects on our economy.

Should-Read: Paul Krugman: Why Do You Care How Much Other People Work? Revisited

Posted on October 10, 2017October 10, 2017 by Brad DeLong

Should-Read: Paul Krugman: Why Do You Care How Much Other People Work? Revisited: “Greg Leiserson has an interesting post on assessing tax reform, in which he argues that distribution tables… https://krugman.blogs.nytimes.com/2017/09/25/why-do-you-care-how-much-other-people-work-revisited/

…showing the direct gains and losses from a tax change — properly measure welfare gains, and don’t need to be revised to consider the induced effects on labor supply, effort etc. This caught my eye because I made a similar point three years ago with regard to projections of labor supply reduction from Obamacare. The point in each case is that while changes in taxes or transfers may induce changes in how much people work, when you assess these changes you have to bear in mind that, to a first approximation, workers are paid their marginal product. This means that if increased transfers induce some people to work less, it also causes them to earn less, so that the rest of society isn’t any worse off; if lower taxes induce high earners to work more, it also means that they’re paid more, so that the rest of society doesn’t reap any of the gains. This is also, by the way, the logic behind the Diamond-Saez proposition that the optimal top tax rate is the one that maximizes revenue: aside from the taxes they pay, increased effort by the very rich to a first approximation makes no difference to everyone else, because the increase in output is fully captured by higher top incomes.

All of this gets obscured by talk about economic growth. Reminder: workers care about their welfare, not what happens to GDP. Making the rich richer without trickle down does the rest of us no good…

Must-Read: Noah Smith: Defending Thaler from the guerrilla resistance