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August 05, 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.
July 15, 2019
Making Analysis of the Current Population Survey Easier
Speaking from experience, research projects often require many grueling hours of deciphering obtuse data dictionaries, recoding variable definitions to be consistent, and checking for data errors. Inevitably, you miss something, and you can only hope that it does not change your results when it's time to publish the results. It would be far less difficult if data sets came prebuilt with time-consistent variable definitions and a guidebook that makes the data relatively easy to use. Not only would research projects be more efficient, but also the research would be easier to replicate and extend.
To this end, we have worked closely with our friends at the Kansas City Fed's Center for the Advancement of Data and Research in Economics (CADRE) to produce what we call a harmonized variable and longitudinally matched (HVLM) data set. This particular data set uses the basic monthly Current Population Survey (CPS) data published by the U.S. Census Bureau and the Bureau of Labor Statistics. The HVLM data set underlies products such as the Atlanta Fed's Wage Growth Tracker and the various tools on the Atlanta Fed's Labor Force Participation Dynamics web page.
You may be wondering how this data set is different from the basic monthly CPS data available at IPUMS. Like the IPUMS-CPS data, the HVLM-CPS data set uses consistent variable names and includes identifiers for longitudinally linking individuals and households over time. Unlike the IPUMS-CPS data, the HVLM-CPS also has time-consistent variable definitions. For example, the top-coded values for the age variable in the IPUMS-CPS is not the same in all years, whereas the HVLM-CPS age variable is consistently coded by using the most restrictive age top-code. As another example, the number of race categories is not the same in every year in the IPUMS-CPS (having increased from 3 to 26), while the race variable in the HLVM-CPS data set is consistently coded by using the original three race categories. Applying these types of restrictions means that the resulting data set can be more readily used to make comparisons over time.
The screenshot below shows how accessible the HVLM-CPS data are. For a visual of each variable over time, click on Charts at the top to see a PDF file of time-series charts. Code Book is an Excel file containing the details of how each variable has been coded. You can see in the screenshot how each variable ends with two numbers. These two numbers correspond to the first year that variable is available. For example, mlr76 is coded with consistent values (1 = employed, 2 = unemployed and 3 = not in labor force) from 1976 until today. The Data File is a Stata (.dta) format file with variable labels already attached. For users wishing to use the panel structure of the CPS survey, lags of many variables are provided on the data set already—for example, mlr76_tm12 is an individual's labor force status from 12 months ago).
Clicking on the c icon under Code Book opens a screen with the values of the corresponding variable. The screenshot shows lfdetail94 and nlfdetail94 as examples. The first variable, lfdetail94, contains a large amount of detail on those engaged in the labor market, while nlfdetail94 contains detailed categories for those not engaged in the labor market.
The HVLM-CPS data set is freely available to download and is updated within hours of when the CPS microdata are published, thanks to sophistical coding techniques and the fast processors at the Kansas City Fed. To access the data, go to the CADRE page (using Chrome or Firefox). At the top right, select Sign in, then Google Login. Then, under schema, select Harmonized Variable and Longitudinally Matched [Atlanta Federal Reserve] (1976–Present).
June 24, 2019
Mapping the Financial Frontier at the Financial Markets Conference
The Atlanta Fed recently hosted its 24th annual Financial Markets Conference, whose theme was Mapping the Financial Frontier: What Does the Next Decade Hold? The conference addressed a variety of issues pertinent to the future of the financial system. Among the sessions touching on macroeconomics was a keynote speech on corporate debt by Federal Reserve Board chair Jerome Powell and another on revitalizing America by Massachusetts Institute of Technology (MIT) professor Simon Johnson. The conference also included a panel discussion of the Fed's plans for implementing monetary policy in the future. This macroblog post reviews these macroeconomic discussions. A companion Notes from the Vault post reviews conference sessions on blockchain technology, data privacy, and postcrisis developments in the markets for mortgage backed securities.
Chair Powell's thoughts on corporate debt levels
Chair Powell's keynote speech focused on the risks posed by increases in corporate debt levels. In his speech, titled "Business Debt and Our Dynamic Financial System" (which you can watch or read), Powell began by observing that business debt levels have increased by a variety of measures including the ratios of debt to gross domestic product as well as the debt to the book value of corporate assets. These higher debt ratios alone don't currently pose a problem because corporate profits are high and interest rates are low. Powell noted some reasons for concern, however, including the reduced average quality of investment-grade bonds, with more corporate debt concentrated in the "lowest rating—a phenomenon known as the 'triple-B cliff'".
Powell noted several differences between the recent increase in corporate debt and the increase in household debt prior to the 2007–09 crisis that offset these risks. These differences include a more moderate rate of increase in corporate debt, the lack of a feedback loop from debt levels to asset prices, reduced leverage in the banking system, and less liquidity risk.
Powell concluded his remarks by saying that although business debt does pose a risk of amplifying a future downturn, it does not appear to pose "notable risks to financial stability." Finally, he noted that the Fed is working toward a more thorough understand of the risks.
Simon Johnson on jumpstarting America
Simon Johnson started his keynote speech by discussing Amazon's search for a second headquarters city. The company received proposals from 238 cities across the country (and Canada). However, in the end, it selected two large metropolitan areas—New York and Washington, DC—that were already among the leaders in creating new tech jobs. Although many places around the country want growth in good jobs, he said the innovation economy is "drawn disproportionately to these few places."
Johnson's remedy for this disproportionate clustering is for the federal government to make a deliberate effort to encourage research and development in various technical areas at a number of research universities around the country. This proposal is based on his book with fellow MIT economist Jonathan Gruber. They argue that the proposal encourages "exactly what the U.S. did in the '40s, '50s, and '60s," which was to help the United States develop new technology to be used in World War II and the Cold War.
Johnson proposed that the funding for new technical projects be allocated through a nationwide competition that intentionally seeks to create new tech hubs. In making his case, Johnson observed that the view that "all the talent is just in six places is fundamentally wrong." Johnson said that he and his coauthor found 102 cities in 36 states that have a substantial proportion of college graduates and relatively low housing prices. Moreover, Johnson observed that existing tech centers' cost of living has become very high, and those cities have substantial political limits on their ability to sustain new population growth. If some of these 102 potential hubs received the funding to start research and provide capital to business, Johnson argued, overall growth in the United States could increase and be more evenly distributed.
Discussing the implementation of monetary policy
The backdrop for the session on monetary policy implementation was postcrisis developments in the Fed's approach to implementing monetary policy. As the Fed's emergency lending programs started to recede after the crisis, it started making large-scale investments in agency mortgage backed securities and U.S. Treasuries. This program, widely (though somewhat misleadingly) called "quantitative easing," or QE, pumped additional liquidity into securities markets and played a role in lowering longer-term interest rates. As economic conditions improved, the Fed first started raising short-term rates and then adopted a plan to shrink its balance sheet starting in 2018. However, earlier this year, the Fed announced plans to stop shrinking the balance sheet in September if the economy performs as it expected.
Julia Coronado, president of MacroPolicy Perspectives, led the discussion of the Fed's plans, and a large fraction of that discussion addressed its plans for the size of the balance sheet. Kevin Warsh, former Federal Reserve governor and currently a visiting fellow at Stanford University's Hoover Institution, provided some background information on the original rationale for QE, when many financial markets were still rather illiquid. However, he argued that those times were extraordinary and that "extraordinary tools are meant for extraordinary circumstances." He further expressed the concern that using QE at other times and for other reasons, such as in response to regulatory policy, would increase the risk of political involvement in monetary policy.
During the discussion, Chicago Fed president Charles Evans argued that QE is likely to remain a necessary part of the Fed's toolkit. He observed that slowing labor force growth, moderate productivity growth, and low inflation are likely to keep equilibrium short-term interest rates low. As a result, the Fed's ability to lower interest rates in a future recession is likely to remain constrained, meaning that balance sheet expansion will remain a necessary tool for economic stimulus.
Ethan Harris, head of global economics research at Bank of America Merrill Lynch, highlighted the potential stress the next downturn would place on the Fed. Harris observed that "other central banks have virtually no ammunition" to fight the next downturn, a reference to the negative policy rates and relatively larger balance sheets of some other major central banks. This dynamic prompted his question, "How is the Fed, on its own, going to fight the next crisis?"
The conference made clear the importance of the links between financial markets and the macroeconomy, and this blog post focused on just three of them. I encourage you to delve into the rest of the conference materials to see these and other important discussions.
June 07, 2019
The Tax Cut and Jobs Act, SALT, and the Blue State Blues: It's All Relative
Nearly two months have passed since tax day, but the full impact of the 2017 Tax Cut and Jobs Act (TCJA) has yet to be fully assessed. Both the data, and in fact the rules themselves, are still incomplete. Nonetheless, conventional wisdom seems to hold that the legislation created winners and losers, and that the losers primarily reside in so-called "blue" states—those where the majority of voters have consistently gone for the Democratic presidential candidate in recent elections.
The source of this belief springs from the newly imposed limitations on federal deductions of state and local taxes, or SALT, and the disproportional impact of these limitations on taxpayers in high-tax, high-income states—the majority of which are blue. A CNBC report from last week on pushback from blue-state politicians summarizes a typical reaction: "Lawmakers from high-tax districts say their constituents have suffered from the provision in the tax plan."
Is this view justified? In our own research, we focus on the long-term effects of the TCJA with the assumption that the legislation's provisions eventually become permanent. (The individual tax cuts are currently scheduled to expire in 2025.) Examining individual households from the 2016 Federal Reserve Board of Governors' Survey of Consumer Finances and incorporating state-specific tax provisions, we reached a few major conclusions regarding TCJA's impact.
First, the overwhelming majority of taxpayers across the country stand to enjoy lifetime gains in after-tax income as a result of the TCJA. The following chart documents our estimates of lifetime gains in every state and the District of Columba, by state-specific wealth quintile. (Wealth here is defined inclusive of human wealth—that is, it includes the present-value of expected wage and salary income.) The chart has a lot of information, but the key point here is the preponderance of blue-shaded areas, which represent the proportion of gainers in each wealth quintile, in each state. Outright losers—represented in the chart by the red shaded areas in each row—are confined to a very small proportion of the wealthy.
What is true is that the tax cuts were relatively more favorable, in percentage terms, to red-state residents. Our estimates show that the percentage reduction in the present value of lifetime taxes for red states is nearly twice that of blue states—but not in absolute terms. We calculate the average change in lifetime after-tax income for individuals in blue states to be $25,781, compared to a $23,094 average for red states. (In absolute terms, "purple" states—those averaging less than a 5 percent margin for either party over the past five election cycles—had the largest average gain of all, at $27,042.)
Another point worth emphasizing: the relatively smaller blue-state gains don't result from the fact that they are high-income states but instead result from the fact that they are high-tax states. When we control for the demographic make-up of states—and hence keep the income distribution across states constant—we get essentially the same implications for the distribution of TCJA tax gains.
It is likely true that blue-state taxpayers didn't gain as much as their red-state counterparts as a result of the TCJA. But for the most part, our estimates suggest they did indeed gain.
May 06, 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.
March 22, 2019
A Different Type of Tax Reform
Two interesting, and important, documents crossed our desk last week. The first was the 2019 edition of the Economic Report of the President. What particularly grabbed our attention was the following statement from Chapter 3:
Fundamentally, when people opt to neither work nor look for work it is an indication that the after-tax income they expect to receive in the workforce is below their "reservation wage"—that is, the minimum value they give to time spent on activities outside the formal labor market.
That does not strike us as a controversial proposition, which makes the second of last week's documents—actually a set of documents from the U.S. Department of Health and Human Services (HHS)—especially interesting.
In that series of documents, HHS's Nina Chien and Suzanne Macartney point out a couple of things that are particularly important when thinking about the effect of tax rates on after-tax income and the incentive to work. The first, which is generally appreciated, is that the tax rates that matter with respect to incentives to work are marginal tax rates—the amount that is ceded to the government on the next $1 of income received. The second, and less often explicitly recognized, is that the amount ceded to the government includes not only payments to the government (in the form of, for example, income taxes) but also losses in benefits received from the government (in the form of, for example, Medicaid or child care assistance payments).
The fact that effective marginal tax rates are all about the sum of explicit tax payments to the government and lost transfer payments from the government applies to us all. But it is especially true for those at the lower end of the income distribution. These are the folks (of working age, anyway) who disproportionately receive means-tested benefit payments. For low-wage workers, or individuals contemplating entering the workforce into low-wage jobs, the reduction of public support payments is by far the most significant factor in effective marginal tax rates and the consequent incentive to work and acquire skills.
The implication of losing benefits for an individual's effective marginal tax rate can be eye-popping. From Chien and Macartney (Brief #2 in the series):
Among households with children just above poverty, the median marginal tax rate is high (51 percent); rates remain high (never dipping below 45 percent) as incomes approach 200 percent of poverty.
Our own work confirms the essence of this message. Consider a representative set of households, with household heads aged 30–39, living in Florida. (Because both state and local taxes and certain transfer programs vary by state, geography matters.) Now think of calculating the wealth for each household—wealth being the sum of their lifetime earnings from working and the value of their assets net of liabilities—and grouping the households into wealth quintiles. (In other words, the first quintile would the 20 percent of households with the lowest wealth, the fifth quintile would be the 20 percent of households with the highest wealth.)
What follows are the median effective marginal tax rates that we calculate from this experiment:
Median Effective Marginal Tax Rate
Consistent with Chien and Macartney, the median effective marginal tax rates for the least wealthy are quite high. Perhaps more troubling, underlying this pattern of effective tax rates is one especially daunting challenge. The source of the relatively high effective rates for low-wealth individuals is the phase-out of transfer payments, some of which are so abrupt that they are referred to as benefits, or fiscal, cliffs. Because these payments differ widely across family structure, income levels within a quintile, and state law, the marginal tax rates faced by individuals in the lower quintiles are very disparate.
The upshot of all of this is that "tax reform" aimed at reducing the disincentives to work at the lower end of the income scale is not straightforward. Without such reform, however, it is difficult to imagine a fully successful approach to (in the words of the Economic Report) "[increasing] the after-tax return to formal work, thereby increasing work incentives for potential entrants into the labor market."
March 06, 2019
X Factor: Hispanic Women Drive the Labor-Force Comeback
The share of the prime-age population engaged in the U.S. labor market is on the rise, led by a sharp rebound in the labor force participation (LFP) rate by prime-age female workers (those ages 25–54). This point was highlighted in a recent Wall Street Journal article.
Since 2015 the LFP rate for prime-age women has increased by about 1.8 percentage points, reversing an almost 16-year slide. Using the data underlying the Atlanta Fed’s new Labor Force Participation Dynamics tool, some of the factors behind this increase become apparent. Of particular note is that Hispanics (people of Cuban, Mexican, Puerto Rican, South or Central American, or other Spanish culture or origin regardless of race) account for a bit less than one-fifth of the prime-age female population, but they accounted for almost two-thirds of the increase in female LFP during the last three years. This increase was the result of both a rising share of the population that is Hispanic and the rising LFP rate among Hispanic women. The share of the prime-age, Latina population increased by 1 percentage point between 2015 and 2018, and the LFP rate for this group increased 3 percentage points. (The reason why rising Hispanic LFP didn’t result in the overall female LFP rate increasing by more than 1.8 percentage points is because Hispanic women are still 8 percentage points less likely to be in the labor force than non-Hispanic women. But this participation gap is closing rapidly.)
The Atlanta Fed’s web tool also allows us to further explore what is behind the 3 percentage point LFP rate increase for prime-age Latinas in the last three years. (My Atlanta Fed colleague Ellyn Terry provides a longer-term view on Hispanic female labor force dynamics in this related macroblog post.) It’s particularly noteworthy that almost two-thirds of the recent increase is the result of a decline in family or household responsibilities keeping people out of the labor force (see the chart).
This shift away from household duties is attributable to a combination of the shifting demographics of the Hispanic population (such as being more likely to have a college degree and thus obtaining a higher-wage job and being better able to afford child care) and a lower propensity to not participate for family reasons within Hispanic age and education groups.
The rebound in female LFP in the last three years is good news, with rising wages, particularly at the low end, and higher demand in traditionally female-dominated occupations contributing to the increase. But making the labor market a truly viable option for women still poses a number of challenges. The LFP rate of U.S. women has fallen behind that of many other countries, many of which have enacted family-friendly policies to help support women in the workplace.
February 25, 2019
Tariff Worries and U.S. Business Investment, Take Two
Last summer, we reported that one fifth of firms in the July Survey of Business Uncertainty (SBU) were reassessing capital expenditure plans in light of then-recent tariff hikes and retaliation concerns. Roughly 6 percent had already cut or deferred capital spending as a result of tariff worries.
Since then, tariff hikes and trade policy tensions have continued to mount, as recounted in the Peterson Institute's Trade War Timeline. U.S. stock market volatility also rose sharply in the last four months of 2018, partly in reaction to trade policy concerns. These developments led us to pose another round of questions about trade policy and investment in the January 2019 SBU.
We first asked each firm if tariff hikes and trade policy tensions caused it to alter its capital expenditures in 2018 and, if so, in which direction and by how much. We use the responses to estimate the net impact of tariff hikes and trade policy tensions on U.S. business investment in 2018.
We estimate that tariff hikes and trade policy tensions lowered gross investment in 2018 by 1.2 percent in the U.S. private sector and by 4.2 percent in the manufacturing sector. The larger response for manufacturing makes sense, given its relatively high exposure to international trade. In constructing these estimates, we consider firms that raised and lowered investment due to trade policy, and we weight each firm by its size.
To estimate the dollar impact of trade policy developments, we multiply the percentage amounts by aggregate investment values. The resulting amounts for U.S. business investment in 2018—minus $32.5 billion for the private sector and minus $22 billion for manufacturing—are modest in magnitude, in line with our forward-looking assessment last summer.
In January, we also asked forward-looking questions about the potential impact of trade policy worries on business investment. As reported in Exhibit 2 below, 20 percent of firms said they are reassessing their capital expenditure plans in 2019 because of tariff hikes and trade policy tensions, a share very similar to what we obtained in our forward-looking question last July. As before, manufacturing firms were more likely to reassess their capital spending plans due to trade policy concerns.
Exhibit 3 below speaks to the question of how firms have reassessed their capital expenditure plans. Here, too, results are similar to what we reported last summer, with one important exception. Among firms reassessing, more than half have either postponed or dropped some portion of their capital spending for 2019, compared to just 31 percent in July 2018. Thus, it appears that firms anticipate somewhat larger negative effects of trade policy developments on capital expenditures in 2019 than they did in 2018.
All told, our results continue to suggest that tariff hikes and trade policy tensions have had a rather modest impact on U.S. business investment. Of course, tariffs and other trade barriers affect U.S. and foreign economies through multiple channels. Even if the near-term business investment effects of trade policy developments are modest in magnitude, trade barriers can disrupt supply chains, raise input prices, and lead to higher prices for consumer goods. That's important to keep in mind as the trade policy outlook remains murky.
February 14, 2019
Trends in Hispanic Labor Force Participation
Although the labor force participation (LFP) rate has fallen significantly for the overall population during the past two decades, the trends can differ a great deal depending on which demographic group you examine. One way to view these varied, ever-changing patterns is to use the Atlanta Fed's Labor Force Participation Dynamics tool. We recently redesigned the tool, adding a new interface and more options for understanding specific demographic groups' LFP. The tool also allows users to see what factors (such as disability/illness, being in school, retirement, or family responsibilities) influence changes in the LFP rate for different groups.
While the tool shows us many stories, a particularly interesting one is the experience of people of Cuban, Mexican, Puerto Rican, South or Central American, or other Spanish culture or origin regardless of race (henceforth referred to as Hispanic). Hispanics are a growing share of the U.S. population (the U.S. Bureau of Labor Statistics [BLS] projects that nearly a fifth of the people in the labor force will be Hispanic by 2024, up from a tenth in 1994), and therefore Hispanics' differences in attitudes and preferences for work will exert an increasingly great effect on headline LFP numbers.
Let's parse the LFP rate among Hispanics. First, the Hispanic population is more likely to engage in the labor force than non-Hispanics. (In 2017, the LFP rate of Hispanics was 66.1 percent, compared to 62.2 percent for non-Hispanics). Second, their LFP has fallen by less during the past two decades.
The pictures below come from the redesigned tool. The first two charts compare the decline in the LFP rate for all ethnicities (Chart 1) versus Hispanic (Chart 2) as the combination of six nonparticipation categories. Each colored bar represents how much a particular category of nonpartipation has changed since the fourth quarter of 1998. The red line shows the summation of the change in each nonparticipation category, or the net change in the LFP rate. For example, the LFP rate overall has declined 4.2 percentage points (ppts) during the past two decades. However, among Hispanics, it has fallen significantly less—just 1.0 ppt. A comparison of the size and direction of each of the nonparticipation categories between the two charts shows many differences in the factors affecting the decline in the LFP rate of each group.
Because differences across ethnicity could reflect differences in their age distributions—Hispanics are younger on average than the population as a whole—it is important to control for this difference. Using the tool, it's easy to narrow this comparison to look specifically at 26–55 year olds.
In particular, the LFP rate for women of all ethnicities from 26 to 55 years old has declined by 1.0 ppt since 1998. In sharp contrast, the LFP rate for Hispanic women 26 to 55 years old has actually increased by 3.8 ppts. Compared to 20 years ago, this group is less likely to say they don't want a job because of disability/illness (1.1 ppt) and family responsbilities (1.4 ppt). This group is also less likely to be part of the shadow labor force (1.4 ppt) compared to two decades ago. (The shadow labor force, as we define it, is made up of individuals who say they want a job but are not considered unemployed by the BLS.) This article from the BLS delves into more detail about Hispanics in the labor force.
The LFP tool allows you to explore many other labor force stories. Users can cut the LFP data by three education categories (less than a high school degree, high school or some college, or associate's degree or higher), two age groups (26–55 or all ages), three race/ethnicity categories (white non-Hispanic, black non-Hispanic, and Hispanic) and for men and women. One thing that the tool makes clear is that the factors that influence individual decisions to work, look for work, or to pursue other activities vary across demographic groups, and each group's experience contributes to our understanding of movements in the overall LFP rate.
- Is Job Switching on the Decline?
- Private and Central Bank Digital Currencies
- New Evidence Points to Mounting Trade Policy Effects on U.S. Business Activity
- Digging into Older Americans’ Flat Participation Rate
- What the Wage Growth of Hourly Workers Is Telling Us
- Making Analysis of the Current Population Survey Easier
- Mapping the Financial Frontier at the Financial Markets Conference
- The Tax Cut and Jobs Act, SALT, and the Blue State Blues: It's All Relative
- Improving Labor Force Participation
- Young Hispanic Women Investing More in Education: Good News for Labor Force Participation
- November 2019
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- August 2019
- July 2019
- June 2019
- May 2019
- March 2019
- February 2019
- January 2019
- December 2018
- Business Cycles
- Business Inflation Expectations
- Capital and Investment
- Capital Markets
- Data Releases
- Economic conditions
- Economic Growth and Development
- Exchange Rates and the Dollar
- Fed Funds Futures
- Federal Debt and Deficits
- Federal Reserve and Monetary Policy
- Financial System
- Fiscal Policy
- Health Care
- Inflation Expectations
- Interest Rates
- Labor Markets
- Latin America/South America
- Monetary Policy
- Money Markets
- Real Estate
- Saving, Capital, and Investment
- Small Business
- Social Security
- This, That, and the Other
- Trade Deficit
- Wage Growth