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January 16, 2020
Do Higher Wages Mean Higher Standards of Living?
Editor's note: We have updated macroblog's location on our website, although archival posts will remain at their original location. Readers who use RSS should update their feed's URL to https://www.frbatlanta.org/rss/macroblog.aspx. Also, we are implementing a new commenting system for posts. In the comments section at the end of this post, you'll be able to create an account to leave comments.
A recent macroblog post used Atlanta Fed Wage Growth Tracker data to observe that the hourly wage of the lowest-paid workers has rebounded in recent years after declining for a decade. The chart below depicts this finding, showing the median hourly wage of the lowest-paid 25 percent of workers in the Tracker sample relative to the median for all workers.
Moreover, the post showed that this recovery was not just a story about states and localities increasing their minimum wages. It also appears that there has been a significant tightening in the labor market for unskilled or low-skilled jobs.
Taken at face value, this is good news for workers employed in low-wage jobs. But here's the rub: the median wage in the first quartile is still low—$11.50 in 2019, or 55 percent of the overall median wage. Moreover, these are hourly wages before taxes and transfers (we'll get back to this shortly). They don't represent what is happening to these workers' ability to make ends meet, which depends crucially on income after taxes and transfers.
For households at the bottom of the income distribution, means-tested transfers can play an especially important role. Means-tested transfers—cash payments and in-kind benefits from federal, state, and local governments designed to assist individuals and families with low incomes and few assets to meet their basic living needs—represent about 70 percent of income before taxes and transfers for households in the bottom quintile of the income distribution, according to a recent report by the Congressional Budget Office. However, the size of the transfers tends to decrease as earnings increase, and they stop altogether when a worker exceeds income- and asset-eligibility thresholds.
The interaction between changes in earnings and various means-tested public assistance programs is an important public policy issue, and it is one that staff at the Atlanta Fed are studying. In a March 2019 macroblog post, David Altig and Laurence Kotlikoff reported that this interaction results in low-income households facing a higher median effective marginal tax rate than high-income households. For low-income households with children, this effect can be especially severe because the presence of children increases the value of programs such as the Supplemental Nutrition Assistance Program (or SNAP, formerly known as the food stamp program) and the likelihood of enrollment in additional programs such as federally subsidized child care. (You can read further research on the effective or implicit marginal tax rates of low-income households at Congressional Budget Office (2016), Romich and Hill (2018), and Chien and Macartney (2019).)
To illustrate the point, the Atlanta Fed team studied the case of a hypothetical single mother with two young children who works in a near-minimum-wage, full-time job and whose basic living expenses are helped by various transfer programs. One avenue to improving her family's standard of living is if she were to return to school and pursue a higher-paying career as a nurse. Over the long term, the net gains from education and career advancement are unambiguous. However, the Atlanta Fed's analysis shows that as long as her children still require care, the reduction in payments from various benefit programs could partially or even completely offset the gains. Look for an Atlanta Fed paper discussing this very real dilemma coming soon on the Bank's Economic Mobility and Resilience webpage.
What do findings like this mean for interpreting the Wage Growth Tracker's evidence that people in the bottom part of the wage distribution are experiencing relatively larger wage gains? Perhaps there is a bit less to celebrate than meets the eye. Around 46 percent of these individuals are in households with children. To the extent that they also participate in means-tested public assistance programs, the relative increase in their family's standard of living could be much less than the size of their pay raise would suggest.
August 5, 2019
What the Wage Growth of Hourly Workers Is Telling Us
The Atlanta Fed's Wage Growth Tracker has shown an uptick during the past several months. The 12-month average reached 3.7 percent in June, up from 3.2 percent last year. But in 2016, it depicted acceleration that eventually reversed course. So is this recent increase real or illusory?
Although using a 12-month average quiets much of the noise in the monthly data, it is possible that the smoothed series still may exhibit some unwanted variation due to the way the Wage Growth Tracker is constructed. For example, how the monthly Current Population Survey reports individual earnings might be a factor introducing unwanted noise into the Tracker. Specifically, some people directly report their hourly rate of pay, and some report their earnings in terms of an amount per week, per month, or per year.
Relative to those paid an hourly rate, there are at least a couple of reasons why using the earnings of nonhourly workers might introduce additional variability into the Wage Growth Tracker's overall estimate of wage growth. First, reported nonhourly earnings include base pay as well as any overtime pay, tips, and commissions earned, and hence can vary over time even if the base rate of pay didn't change. For a worker paid at an hourly rate, reported earnings exclude overtime pay, tips, and commissions and so are not subject to this source of variation. Second, the method we use to convert nonhourly earnings to an hourly rate is likely subject to some margin of error since it involves using the person's recollection of how many hours they usually work. These two factors suggest that the earnings of workers paid at an hourly rate might be a somewhat cleaner measure of hourly earnings.
To investigate whether this distinction actually matters in practice, we created the following chart comparing the 12-month average Wage Growth Tracker since 2015 (depicted in the green line) with a version that uses only the earnings of those paid at an hourly rate (blue line).
As the chart shows, the 12-month average of median wage growth for hourly workers generally tracks the overall series—both series are about a percentage point higher than at the beginning of 2015. However, the hourly series is a bit less variable, making the recent uptick in wage growth more noticeable in the hourly series than in the overall series. This observation suggests that as we monitor shifts in wage pressure, the hourly series could complement the overall series nicely. Versions of the Wage Growth Tracker series for both hourly and nonhourly workers are now available on the Wage Growth Tracker page of the Atlanta Fed's website.
If you would like to use the Wage Growth Tracker's underlying microdata to create your own versions (or to conduct other analysis), follow this link to explore the data on the Atlanta Fed's website. See this macroblog post, "Making Analysis of the Current Population Survey Easier," from my colleague Ellyn Terry to learn more about using this dataset.
May 6, 2019
Improving Labor Force Participation
Without question, the U.S. labor market has tightened a lot over the last few years. But a shifting trend in labor force participation—and especially a rise in the propensity to seek employment by those in their prime working years—seems to be relieving some labor market pressure.
From the first quarter of 2015 to the first quarter of 2019, the labor force participation (LFP) rate among prime-age workers (those between 25 and 54 years old) increased by about 1.5 percentage points (see the chart below), adding about 2 million workers more than if the participation rate had not increased.
Changes in the distribution of the prime-age population in terms of age, education, and race/ethnicity toward groups with higher participation rates and away from groups with lower rates accounts for about a third of the rise in the overall prime-age LFP rate. The other two thirds can be pinned on an increase in LFP rates within demographic groups—what we call "behavioral" effects.
Of the increased participation behavior within demographic groups, there has been a decline in the share of the prime-age population that say they want a job but are not actively looking for work at the moment. We refer to these individuals as the "shadow labor force" because even though they are not in the labor force this month, they have a relatively high propensity to have a job next month. Second, there's been a decline in the share of the prime-age population that are not participating because they are too sick or disabled to work. The contribution of the change in behavior in these two categories (as well as several others from the first quarter of 2015 to the first quarter of 2019) are shown in the following chart, which is taken from the Atlanta Fed's Labor Force Participation Dynamics tool.
In contrast, consider the period from the first quarter of 2008 through the first quarter of 2015, a time when the rate of prime-age LFP declined by almost 2 percentage points. During that period, even though slow-moving demographic changes were putting modest upward pressure on the prime-age participation rate, that support was more than swamped by negative changes in participation rates within demographic groups. The following chart shows the relative contributions of these behavioral changes.
Within demographic groups, the increased incidence of being too sick or disabled to work stands out as the largest contributor to the decline in prime-age labor force participation between 2008 and 2014.
Since 2014, prime-age LFP has benefited from the movement of both demographics and participation behavior. But so far, less than half of the overall behavioral decline between 2008 and 2014 has been reversed.
Though demographic trends are likely to remain positive, how much more participation behavior—especially as it is related to disability and illness—can shift as the labor market tightens remains unclear. The share of the prime-age population too sick or disabled to work had been on a rising trend for the decade prior to the last recession, suggesting that there may be some deeper and structural health-related issues that could keep the disability/illness rate elevated despite an increasingly tight labor market.
March 26, 2019
Young Hispanic Women Investing More in Education: Good News for Labor Force Participation
In a recent recent macroblog post, my colleague John Robertson found that the recent rise in female prime-age (ages 25 to 54 years) labor force participation (LFP) over the last few years has been driven in large part by increased participation among Hispanic women. (Hispanic refers to people of Cuban, Mexican, Puerto Rican, South or Central American, or other Spanish culture or origin regardless of race.) Much of the LFP improvement among Hispanic women has come as they've shifted away from household duties.
To understand this development and determine whether it's a trend likely to continue, we look at trends in the activities of younger Hispanic women. In particular, we look at the so-called NEETs rate among women ages 16 to 24. The NEETs rate is the share of the youth population that is "Not Employed or pursing Education or Training." This group is sometimes referred to as "disconnected youth" or "opportunity youth" because they are generally less likely to be attached to the labor force as they move into their prime working years and are at higher risk of experiencing long-term unemployment, persistent poverty, poor health, and criminal behavior.
A look at the next chart shows substantial improvement in the NEETs rate among young Hispanic women over the last two decades. The gap has narrowed considerably and in recent years has tracked much more closely with black non-Hispanic women.
The declining NEETs rate for young Hispanic women primarily reflects shifting preferences toward more education and away from household responsibilities. As you can see in the next chart, the share of young Hispanic women who are in education or training has risen over the last two decades, up nearly 19 percentage points since 2000. Their share now more closely matches that of young black and white non-Hispanic women.
Mirroring the rise in educational activities has been a shrinking share of young Hispanic women who are not in the labor force because they are taking care of home or family, as the following chart shows.
Young Hispanic women have invested increased time in their education over the last two decades and as a result have higher average levels of educational attainment than earlier cohorts moving into their prime working years. To see this, the next chart shows the distribution of educational attainment over time for Hispanic women aged 25.
The higher levels of LFP in recent years among prime-age Hispanic women partly reflects the greater investment in education by younger Hispanic women. If this trend continues—and there is no obvious reason why it wouldn't—then it will help drive even higher labor force attachment for prime-age Hispanic women in the years to come.
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