For release on delivery
10:30 a.m. EDT
September 26, 2017
Why Persistent Employment Disparities Matter for the Economy's Health
Remarks by
Lael Brainard
Member
Board of Governors of the Federal Reserve System
at
"Disparities in the Labor Market: What Are We Missing?"
a research conference sponsored by the
Board of Governors of the Federal Reserve System
Washington, D.C.
September 26, 2017
I want to compliment the organizers and others for gathering an outstanding group
of researchers and papers for this conference. Understanding why some groups
persistently fare better than others in the job market and how these disparities may affect
the economy's overall performance is vitally important to the Federal Reserve. While
opportunity and inclusion have long been central to American values, it is increasingly
clear that they are also central to the strength of our economy.1
As directed by the Congress, the Federal Reserve's dual mandate is to promote
maximum employment and stable prices. In fulfilling its dual mandate, the Federal Open
Market Committee (FOMC) has set a target of 2 percent for inflation but does not have a
similarly fixed numerical goal for maximum employment. That is because the level of
maximum employment depends on "nonmonetary factors that affect the structure and
dynamics of the labor market," which "may change over time and may not be directly
measurable."2 Understanding how close the labor market is to our full-employment goal
requires consulting a variety of evidence along with a healthy dose of judgment. The
recognition that maximum employment evolves over time to reflect changes in the
economic landscape serves us well by requiring FOMC participants to develop a nuanced
understanding of labor market developments.
This approach to maximum employment has allowed the FOMC to navigate the
current expansion in a way that has likely brought more people back into productive
employment than might have been the case with a fixed unemployment rate target based
- 2 -
on pre-crisis standards. This is especially true at a time when the traditional Phillips
curve relationship is flatter than in the past, which means that price inflation is likely to
be less informative regarding labor market tightness than it was previously.3 It therefore
seems particularly valuable to look beyond inflation and headline unemployment to
assess the strength of the labor market. Even when aggregate economic statistics look
strong, studying geographic areas and demographic groups that are not faring as well can
point to ways of further improving the economy's performance.
The Federal Reserve is also keenly interested in disparities in employment, labor
force participation, income, and wealth because they may have implications for the
growth capacity of the economy. When we consider appropriate monetary policy, we
need to have a good sense of how fast the economy can grow without fueling excessive
price inflation. At a time when the retirement of the baby-boom generation looks likely
to be something of a drag on the growth of the labor force, it is especially important to
consider whether relatively low levels of employment and labor force participation for
some prime working-age groups represent slack that, if successfully tapped, could
increase the labor force and boost economic activity.
More broadly, when a person who was previously unemployed or discouraged
secures a job, not only does it boost the economy, but that person also may gain a greater
sense of economic security, self-sufficiency, and self-worth and be better able to invest in
their family's future. With a richer understanding of economic or social barriers that
inhibit labor market success and prosperity for some groups, we may better grasp how
- 3 -
much these individuals can be helped by broad economic expansion and how much
targeted intervention is required through other policy means.
There is also an important connection between the economy's potential growth
rate and equality of opportunity. Large disparities in opportunity based on race, ethnicity,
gender, or geography mean that the enterprise, exertion, and investments of households
and businesses from different groups are not rewarded commensurately. To the extent
that disparities in income and wealth across race, ethnicity, gender, or geography reflect
such disparities in opportunity, families and small businesses from the disadvantaged
groups will then underinvest in education or business endeavors, and potential growth
will fall short of the levels it might otherwise attain.4
Aside from reducing the long-run productive potential of the economy,
persistently high levels of income and wealth inequality may also have implications for
the robustness of consumer spending, which accounts for roughly two-thirds of aggregate
spending in the United States. The gaps in household income and wealth between the
richest and poorest households are at historically high levels, as income and wealth have
increasingly accrued to the very richest households. For example, results from the
Federal Reserve's latest Survey of Consumer Finances (SCF), which is due to be released
soon, indicate that the share of income held by the top 1 percent of households reached
24 percent in 2015, up from 17 percent in 1988. The share of wealth held by the top
1 percent rose to 39 percent in 2016, up from 30 percent in 1989.5 Some research
suggests that widening income and wealth inequality may damp consumer spending in
- 4 -
the aggregate, as the wealthiest households are likely to save a much larger proportion of
any additional income they earn relative to households in lower income groups that are
likely to spend a higher proportion on goods and services.6
Disparities by Race and Ethnicity
When we disaggregate the economy-wide labor market statistics, we find
significant and persistent racial and ethnic disparities.7 In August, the national
unemployment rate of 4.4 percent, which is low by historical standards, masked
substantial differences across different demographic groups. As shown in figure 1,
unemployment rates ranged from 3.9 percent for whites to 4 percent for Asians,
5.2 percent for Hispanics, and 7.7 percent for African Americans. Labor force
participation rates, shown in figure 2, also differ substantially, although by less than
unemployment rates, with the rate for African Americans lowest at 62.2 percent. These
differences are not a recent development--similar differences across racial and ethnic
dimensions have existed for as long as these data have been collected. Even more
striking, a significant portion of the gaps in unemployment rates across racial and ethnic
groups cannot be attributed to differences in their underlying characteristics, such as age
and education levels.8
Although the differences in employment rates between racial and ethnic groups
are still quite large, they have narrowed recently, after having widened considerably
during the recession, and are near their lowest levels in decades. Differences in
unemployment rates across racial and ethnic groups tend to widen sharply during
- 5 -
recessions, as less advantaged groups shoulder an outsized share of total layoffs, and
these differences shrink during recoveries. For example, in the second quarter of 2017,
the unemployment rate for black adult men was a little more than 3 percentage points
higher than for white adult men. This differential, while sizable, is nonetheless close to
the smallest gap seen since comparable data became available in the mid-1970s.
Differences in unemployment rates are similarly near historical lows for black women
relative to white women, and for Hispanics relative to whites. Since racial disparities
tend to get smaller throughout the course of an economic expansion, it seems likely that
racial differences in unemployment rates will continue to shrink if the overall
unemployment rate falls further.9
More broadly, the persistent disparities in employment outcomes are mirrored in
significant and persistent racial and ethnic differences in families' income and wealth.
According to forthcoming findings from the latest SCF and as shown in figure 3, the
average income for white families in 2015 was about $123,000 per year, compared with
$54,000 for black families and $57,000 for Hispanic families.10 Disparities in wealth,
shown in figure 4, are even larger: Average wealth holdings for white families in 2016
were about $933,000, compared with $191,000 for Hispanic families and $138,000 for
black families.11 Moreover, these racial and ethnic gaps in average family income and
9
Data on recent estimates of unemployment rates for adult men (20 years and older) by race and ethnicity
are available from the Bureau of Labor Statistics. Historical gaps are provided by Cajner and others
(2017).
10
Staff calculations from forthcoming SCF data. Recent estimates of household income from Current
Population Survey data and reported by the Census in Semega, Fontenot, and Kollar (2017) are
qualitatively similar, in that between 2013 and 2016 for both sets of data, family income has increased for
whites, blacks, and Hispanics (with greater increases, in percentage terms, for black and Hispanic families).
11
Racial and ethnic differences in median income and wealth are somewhat smaller. For example, in 2016
median income for white families was about $61,000, compared with $35,000 for black families and
$39,000 for Hispanic families. Median wealth was about $171,000 for white families, compared with
- 6 -
wealth have generally widened rather than narrowed over the past few decades. Based on
SCF data, median family wealth has grown much more rapidly for white families than for
other families over the past few decades, while median family incomes have risen by
about the same amount for white, black, and Hispanic families.
As the economic expansion continues and brings more Americans off the
sidelines and into productive employment, it seems likely that the positive trends in
employment and participation rates for historically disadvantaged groups will continue.
That said, the benefits of a lengthy recovery can only go so far, as the research points to
some barriers to labor market outcomes for particular groups that appear to be structural.
After controlling for sectoral and educational differences, the research suggests that these
factors include discrimination as well as differences in access to quality education and
informal social networks that may be an important source of information and support
regarding employment opportunities.12 While the policy tools available to the Federal
Reserve are not well suited to addressing the barriers that contribute to persistent
disparities in labor market outcomes, understanding these barriers and efforts to address
them is vital in assessing maximum employment as well as potential growth.
Geographic Disparities
The Federal Reserve System benefits not only from our engagement with
research, statistics, and surveys, but also from our presence in communities all across
America. This local presence, by design, provides valuable perspectives on how
Americans in different communities are experiencing the economy and the varied
- 7 -
challenges that lie beneath the aggregate numbers. While traveling around the country
with our community development staff, I have been struck by the widening gulf between
the economic fortunes of our large metropolitan areas and those of our small cities,
towns, and rural areas.
The statistics bear this out. Over the past 30 years, the convergence in income
across regions of the country has slowed dramatically.13 Much of the gains in
employment, income, and wealth since the end of the recession, and more broadly over
the past few decades, have accrued to workers and families in larger cities. Since some
workers and families may find it difficult to move, this concentration of economic
opportunities in larger cities may have adverse implications for the well-being of these
households and, potentially, the growth capacity of the economy as a whole.
Although pockets of opportunity and poverty are found in large metropolitan and
rural areas alike, a greater share of the new jobs and business establishments created
during the recovery that followed the Great Recession have been in larger metro areas
than was the case in previous recoveries.14 In countless rural towns and small cities we
are seeing how a deep economic setback can leave a profound and long-lasting mark.
These experiences challenge common assumptions about the ability of local economies to
recover from a setback. This could be the legacy of the concentrated presence of an
industry that experiences decline due to trade or technology, or it could be the byproduct
of a lack of connectivity--whether by highways or broadband. Technological change,
globalization, and other shifts in demand and costs are not new to the U.S. economy, but
- 8 -
there are troubling signs that less diversified or connected localities have a diminished
ability to adapt. And the evidence suggests that concentrated economic shocks and the
associated labor market stress also have broader consequences for health and mortality.15
To provide some sense of the magnitudes, on average over the past year the
unemployment rate for adults of prime working age (25 to 54) was about 1 percentage
point higher in nonmetropolitan areas than in larger metro areas.16 But there is an even
greater gap in labor force engagement, as can be seen in figure 5. The participation rate
for prime-age adults in larger metro areas is currently nearly 3-1/2 percentage points
above the participation rate for prime-age adults in nonmetro areas. Interestingly, the
geographic participation rate gap between more and less populous areas is apparent for
all races as well as, in recent years, for both men and women.17
This gap in labor force participation between large cities and other areas has
widened substantially since just before the Great Recession: Since 2007, the
participation rate for prime-age adults in nonmetro areas has fallen nearly 3 percentage
points, as compared with less than 1 percentage point on net in larger metro areas.
Indeed, since 2007, the large decline in labor force participation in small metro and rural
areas can explain about 40 percent of the economy-wide decline in prime-age labor force
participation, even though these areas account for a smaller 25 percent of the population.
Before discussing possible contributors to this growing participation gap, it is
important to emphasize that less populous areas appear to be falling behind in ways
- 9 -
beyond these employment outcomes. Based on forthcoming SCF data, for example, the
average annual income for families in metro areas was about $54,000 higher than for
families in nonmetro areas, and the average wealth holdings for families in metro areas
exceeded average wealth for families in nonmetro areas by nearly $500,000--and these
gaps have more than doubled over the past three decades.18 The gaps in many other
measures of well-being have widened as well. In small towns and rural areas, college
attainment rates have increased by less, disability rates have increased by more, divorce
rates have risen by more, and mortality due to lung disease, cancer, or cardiovascular
disease have either improved by less or worsened by more.19 Opioid use is also most
prevalent in less populous metro and rural areas.20
I have seen many of these challenges first hand. In the small towns and hollers of
eastern Kentucky, I visited with community development financial institutions that are
trying to plug the gap in access to credit so that small businesses can continue operating
and hiring locally, and so that families can access housing that is safe and affordable. In
rural communities in the Mississippi Delta, I learned about diminished access to financial
services available to rural residents, which can be a barrier to housing and business
investment and pose vexing challenges to local governments. In Texas, I learned about
barriers to economic development in the rural colonias areas on the southern border
associated with underinvestment in physical and broadband infrastructure.21
- 10 -
As we consider the long-term health of the U.S. economy, it is important to better
understand the decade-long decline in aggregate labor force participation. It is striking
that in larger metro areas, the labor force participation rate for prime-age men has
recently retraced much of the decline experienced during the recession, while in smaller
metro and rural areas, the labor force participation rate remains well below its pre-
recession level, with only modest improvements of late. The evidence increasingly
suggests that much of the decline relates to a sustained decline in job opportunities for
prime-age men, especially less-educated prime-age men, resulting in languishing wages
relative to other groups.22 Indeed, it is notable that the striking decrease in labor force
participation rates for nonmetro areas relative to large metro areas is highly concentrated
among adults with no more than a high school education, who comprise a larger share of
the prime-age population in nonmetro areas. The labor force participation rate for adults
with no more than a high school education has fallen to 72 percent in nonmetro areas--
about 3-1/2 percent below larger metro areas.23
Although the precise causes of this decline are still not fully settled, one
contributing factor is advancing automation and computerization.24 Another contributor
is globalization. For example, a growing body of research has identified a steeper decline
in the employment and labor force attachment of prime-age men in areas of the country
22
Most analysis suggests that at least half of the decline in the aggregate labor force participation rate since
2007 is attributable to the aging of the population, with a significant portion of the decline that is not
related to aging attributable to a longer-run decline in participation for younger individuals and prime-age
men; for example, see Aaronson and others (2014) and the Council of Economic Advisors (2014). For an
overview of factors potentially attributable to the decline in labor force participation of prime-age men, see
the Council of Economic Advisors (2016).
23
These estimates are based on data from Weingarden (2017).
24
For an overview of factors that have potentially impacted job opportunities for this group, see Autor
(2010). For examples of research on the recent labor market effects of technology and automation, see
Autor, Dorn, and Hanson (2015) and Acemoglu and Restrepo (2017).
- 11 -
that specialized in the industries that were most negatively affected by increased imports
from China.25
Research suggests that some of the decline in prime-age labor force participation
relates to some individuals' reduced ability or desire to work, in some cases resulting
directly from the ongoing decline in job opportunities. There are many reasons why
some prime-age men may be less willing or able to work. One possibility is that the
unusually long spells of nonemployment associated with the Great Recession may have
eroded job skills and informal employment networks. Another possibility that is
increasingly in focus is that physical disabilities, as well as sharp increases in opioid use,
have increasingly inhibited some individuals from participating in the labor force. The
fraction of prime-age men receiving disability insurance benefits has increased from
1 percent in the late 1970s to 3 percent more recently.26 Recent research also finds that
among all prime-age men who are not in the labor force, about one-third reported having
at least one disability, and nearly one-half reported taking pain medications daily.27
These supply-side explanations may be related to the drop in labor demand: the despair
related to diminished prospects of a stable and quality job may lead to substance abuse
and related health or mortality concerns.28
- 12 -
At least some of these explanations potentially relate to the growing divide
between large metro areas and other areas of the country. As noted earlier, the opioid
epidemic appears to be particularly acute in smaller cities and rural areas. In addition,
employment in non-metro areas tends to be more concentrated in manufacturing, which is
the sector that has experienced the largest decline in employment from automation and
globalization.29 Similarly, research suggests that workers in less populous areas have
been more likely to be directly affected by increased import competition from China due
to the geographical distribution of industries.30 And for many less populous areas, job
opportunities are less diverse than in bigger cities, so that when a plant shuts down, there
are fewer local alternative job opportunities for unemployed workers, especially with
comparable levels of employment security or benefits.
These striking results naturally raise the question of whether we are seeing
heightened migration from the less populous areas to the larger metros with greater
economic opportunity. A conventional assumption in economics is that regional
differences should narrow over time as workers move toward areas where jobs are more
plentiful and wages are higher.31 In reality, Americans' propensity to move is currently
at its lowest level in many decades. In 2016, the fraction of the population that had
moved within the United States in the past year was 11 percent, down from 17 percent or
more in the early 1980s, with the steepest decline in the fraction of people moving longer
distances, across county or state lines.32 The evidence suggests that the decline in
- 13 -
geographic mobility cannot be fully explained by population aging, by the housing boom
and bust, by changes in the composition of industries, by the increasing ease of
telecommuting from longer distances, or by the rise in dual-earner households which may
make work-related relocation more difficult. Some of the decline may be related to
changes in the labor market, perhaps because workers are more likely to perceive that job
opportunities are no better elsewhere, and consequently that the labor market returns to
switching jobs or locations--in terms of better wages or higher job quality--have
declined.33 Also, zoning requirements may be boosting housing costs in cities where job
opportunities are most abundant, such as San Francisco, pricing out many potential
workers and inhibiting migration.34
Whatever the reason, the fact that families are less likely to move now than in the
past suggests that many of those in less populous areas are not able to access the
economic opportunity present in denser and more diversified large metropolitan areas at a
time when the gap in labor market outcomes for larger metros relative to other areas
continues to grow.
Federal Reserve Work on Labor Market Disparities
The Federal Reserve is deeply engaged in understanding disparities through our
data collection, research collaboration, and community development work. One way the
Federal Reserve seeks to obtain a clearer picture is by collecting data ourselves. For
instance, some of the data I have cited today come from the Federal Reserve's triennial
Survey of Consumer Finances, which provides detailed information on income and
- 14 -
wealth holdings by demographic groups. The Survey of Household Economics and
Decisionmaking provides a portrait of household finances, employment, housing, and
debt; the Survey of Young Workers provides insights into younger adults' employment
experiences soon after entering the labor force; and the Enterprising and Informal Work
Activities Survey provides information about income generating activities that are often
outside the scope of other employment and income surveys.35
Across the Federal Reserve System, a variety of initiatives are aimed at
understanding economic disparities and how to foster more-inclusive growth. The
Opportunity and Inclusive Growth Institute at the Federal Reserve Bank of Minneapolis
brings together researchers from a variety of fields to analyze barriers to economic
opportunity and advancement. The Economic Growth and Mobility Project at the
Federal Reserve Bank of Philadelphia aims to bring together researchers with community
stakeholders to focus on differences in poverty and economic mobility across
demographic characteristics. The Investing in America's Workforce Initiative is a
collaboration between the Federal Reserve System and academic research institutions to
promote investment in workforce skills that better align with employers' needs.36
All of that brings me to today's conference, which I am confident will make an
important contribution to this mission. I am heartened to see so many researchers and
- 15 -
practitioners from a variety of backgrounds focused on these important issues. This
conference is part of our efforts to hear from experts with diverse backgrounds and
perspectives to better understand the nature and implications of labor market disparities.
A deeper understanding of labor market disparities is central to the mission of the
Federal Reserve because it may help us better assess full employment, where resources
may be underutilized, and the likely evolution of the labor market and overall economic
activity. We look forward to hearing what you have to say about these important
questions and learning what other questions are in need of attention.
- 16 -
References
Aaronson, Stephanie, Tomaz Cajner, Bruce Fallick, Felix Galbis-Reig, Christopher
Smith, and William Wascher (2014). "Labor Force Participation: Recent
Developments and Future Prospects," Brookings Papers on Economic Activity,
Fall, pp. 197-255,
www.brookings.edu/bpea-articles/labor-force-participationrecent-developments-and-future-prospects.
Acemoglu, Daron, David Autor, David Dorn, Gordon H. Hanson, and Brendan Price
(2016). "Import Competition and the Great U.S. Employment Sag of the 2000s,"
Journal of Labor Economics, vol. 34 (part 2, January), S141-98.
Acemoglu, Daron, and Pascual Restrepo (2017). "Robots and Jobs: Evidence from U.S.
Labor Markets," unpublished paper, March 17,
https://economics.mit.edu/files/12763.
Adamy, Janet, and Paul Overberg (2017). "Rural America is the New 'Inner City'," Wall
Street Journal, May 26.
Alichi, Ali, Kory Kantenga, and Juan Solè (2016). "Income Polarization in the United
States," IMF Working Paper 16/121. Washington: International Monetary Fund.
https://www.imf.org/external/pubs/ft/wp/2016/wp16121.pdf.
Autor, David (2010). "The Polarization of Job Opportunities in the U.S. Labor Market:
Implications for Employment and Earnings." Washington: Center for American
Progress and Hamilton Project, https://economics.mit.edu/files/5554.
Autor, David, David Dorn, and Gordon Hanson (2013). "The China Syndrome: Local
Labor Market Effects of Import Competition in the United States," American
Economic Review, vol. 103 (October), pp. 1553-97.
Autor, David, David Dorn, and Gordon Hanson (2015). "Untangling Trade and
Technology: Evidence from Local Labor Markets," Economic Journal, vol. 125
(May), pp. 621-46.
Bernstein, Jared (2013). "The Impact of Inequality on Growth." Washington: Center for
American Progress.
Blanchard, Olivier (2016). "The U.S. Phillips Curve: Back to the 60s?" Policy Brief
PB16-1. Washington: Peterson Institute for International Economics, January,
https://piie.com/publications/pb/pb16-1.pdf.
Blanchard, Olivier and Lawrence Katz (1992). "Regional Evolutions," Brookings Papers
on Economic Activity, no. 1, pp. 1-75,
https://www.brookings.edu/wpcontent/uploads/1992/01/1992a_bpea_blanchard_katz_hall_eichengreen.pdf.
- 17 -
Brainard, Lael (2015). "Economic Outlook and Monetary Policy," speech delivered at
the 57th National Association for Business Economics Annual Meeting,
Washington, October 12,
https://www.federalreserve.gov/newsevents/speech/brainard20151012a.htm.
Brainard, Lael (2017). "Why Opportunity and Inclusion Matter for America's Economic
Strength," speech delivered at the Opportunity and Inclusive Growth Institute
Conference, sponsored by the Federal Reserve Bank of Minneapolis,
Minneapolis, Minnesota, May 22,
https://www.federalreserve.gov/newsevents/speech/brainard20170522a.htm.
Bricker, Jesse, Lisa J. Dettling, Alice Henriques, Joanne W. Hsu, Lindsay Jacobs,
Kevin B. Moore, Sarah Pack, John Sabelhaus, Jeffrey Thompson, and Richard A.
Windle (forthcoming). "Changes in U.S. Family Finances from 2013 to 2016:
Evidence from the Survey of Consumer Finances," Federal Reserve Bulletin.
Cajner, Tomaz, Tyler Radler, David Ratner, and Ivan Vidangos (2017). "Racial Gaps in
Labor Market Outcomes in the Last Four Decades and over the Business Cycle,"
Finance and Economics Discussion Series 2017-071. Washington: Board of
Governors of the Federal Reserve System,
https://www.federalreserve.gov/econres/feds/files/2017071pap.pdf.
Case, Anne and Angus Deaton (2015). "Rising Morbidity and Mortality in Midlife
among White Non-Hispanic Americans in the 21st Century," Proceedings of the
National Academy of Sciences, vol. 112 (December), pp. 15078-83.
-------- (2017). "Mortality and Morbidity in the 21st Century," Brookings Papers on
Economic Activity, Spring, pp. 397-452,
https://www.brookings.edu/wpcontent/uploads/2017/08/casetextsp17bpea.pdf.
Council of Economic Advisors (2014). "The Labor Force Participation Rate since 2007:
Causes and Policy Implications." Washington: Council of Economic Advisors,
Executive Office of the President of the United States,
https://obamawhitehouse.archives.gov/sites/default/files/docs/labor_force_particip
ation_report.pdf.
-------- (2016). "The Long-Term Decline in Prime-Age Male Labor Force Participation."
Washington: Council of Economic Advisors, Executive Office of the President of
the United States,
https://obamawhitehouse.archives.gov/sites/default/files/page/files/20160620_pri
meage_male_lfp_cea.pdf.
Economic Innovation Group (2016). The New Map of Economic Growth and Recovery.
Washington: EIG, May,
http://eig.org/wpcontent/uploads/2016/05/recoverygrowthreport.pdf.
- 18 -
Fryer, Jr., Roland G. (2011). "Racial Inequality in the 21st Century: The Declining
Significance of Discrimination," in David Card and Orley Ashenfelter, eds.,
Handbook of Labor Economics, vol. 4b. Amsterdam: North Holland,
pp. 855-971.
Ganong, Peter, and Daniel W. Shoag (2017). "Why Has Regional Income Convergence
in the U.S. Declined?" National Bureau of Economic Research Working Paper
No. 23609.
Goetz, Stephen, Mark Partridge, and Heather Stephens (2017). "The Economic Status of
Rural America in the Trump Era," MPRA Paper 77830. Munich: Munich
Personal RePEc Archive, March, https://mpra.ub.uni-muenchen.de/77830.
Guy, Jr., Gery P., Kun Zhang Kun, Michele K. Bohm, Jan Losby, Brian Lewis, Randall
Young, Louise B. Murphy, and Deborah Dowell (2017). "Vital Signs: Changes
in Opioid Prescribing in the United States, 2006-2015," Morbidity and Mortality
Weekly Report, vol. 66 (July 7), pp. 697-704,
https://www.cdc.gov/mmwr/volumes/66/wr/pdfs/mm6626.pdf.
Herkenhoff, Kyle F., Lee E. Ohanian, and Edward C. Prescott (2017). "Tarnishing the
Golden and Empire States: Land-Use Regulations and the U.S. Economic
Slowdown." NBER Working Paper No. 23790. Cambridge, Mass.: National
Bureau of Economic Research, September.
Kaplan, Greg, and Sam Schulhofer-Wohl (2017). "Understanding the Long-Run Decline
in Interstate Migration," International Economic Review, vol. 58 (February)
pp. 57-94.
Kiley, Michael T. (2015). "Low Inflation in the United States: A Summary of Recent
Research," FEDS Notes. Washington: Board of Governors of the Federal
Reserve System, November 23,
https://www.federalreserve.gov/econresdata/notes/feds-notes/2015/low-inflationin-the-united-states-a-summary-of-recent-research-20151123.html.
Krueger, Alan (forthcoming). "Where Have All the Workers Gone? An Inquiry into the
Decline of the U.S. Labor Force Participation Rate." Brookings Papers on
Economic Activity. (The conference draft is available at
https://www.brookings.edu/bpea-articles/where-have-all-the-workers-gone-aninquiry-into-the-decline-of-the-u-s-labor-force-participation-rate.)
Marrero, Gustavo, and Juan Rodrìguez (2013). "Inequality of Opportunity and Growth,"
Journal of Development Economics, vol. 104 (September), pp. 107-22.
Molloy, Raven, Christopher L. Smith and Abigail Wozniak (2011). "Internal Migration
in the United States," Journal of Economic Perspectives, vol. 25 (Summer),
pp. 173-96.
- 19 -
-------- (2017). "Job Changing and the Decline in Long-Distance Migration in the United
States," Demography, vol. 54 (April), pp. 631-53.
Pierce, Justin R