AER: Insights 2019, 1(2): 161–178
https://doi.org/10.1257/aeri.20180010

When Work Disappears: Manufacturing Decline and theFalling Marriage Market Value of Young Men†
By David Autor, David Dorn, and Gordon Hanson*
We exploit the gender-specific components of large-scale labordemand shocks stemming from rising international manufacturing
competition to test how shifts in the relative economic stature of young
men versus young women affected marriage, fertility, and children’s
living circumstances during 1990–2014. On average, trade shocks
differentially reduce employment and earnings of young adult males.
Consistent with Becker’s model of household specialization, shocks
to males’ relative earnings reduce marriage and fertility. Consistent
with prominent sociological accounts, these shocks heighten maleidleness and premature mortality, and raise the share of mothers
who are unwed and the share of children living in below-poverty,
single-headed households. (JEL F16, J12, J13, J16, J23, J31, L60)
The consequences of high neighborhood joblessness are more devastating
than those of high neighborhood poverty … Many of today’s problems inthe ­inner-city ghetto neighborhoods—crime, family dissolution, welfare,
low levels of social organization, and so on—are fundamentally a consequence of the disappearance of work.
—William Julius Wilson, When Work Disappears (1996, p. xiii)
Wilson’s book spoke to me. I wanted to write him a letter and tell him that
he had described my home perfectly. That it resonated so personally isodd, however, because he wasn’t writing about the hillbilly transplantsfrom Appalachia—he was writing about black people in the inner cities.
—J. D. Vance, Hillbilly Elegy: A Memoir of a Family and Culture in
Crisis (2016, p. 144)* Autor: Department of Economics, Massachusetts Institute of Technology, Building E52, Room 438, 77
Massachusetts Avenue, Cambridge, MA 02142, and NBER (email: dautor@mit.edu); Dorn: Department of
Economics, University of Zürich, Schoenberggasse 1, CH-8001, Zürich, Switzerland, and CEPR (email: david.
dorn@econ.uzh.ch); Hanson: University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0519,
and NBER (email: gohanson@ucsd.edu). Pete Klenow was the coeditor for this article. This paper previously circulated under the title “The Labor Market and the Marriage Market” (first circulating draft May 12, 2014). Autor,
Dorn, and Hanson acknowledge funding from the Russell Sage Foundation (RSF Project #85-12-07). Dorn acknowledges funding from the Spanish Ministry of Science and Innovation (grants CSD2006-00016 and ECO2010-16726)
and the Swiss National Science Foundation (grant BSSGI0-155804). Autor and Hanson acknowledge funding fromthe National Science Foundation (grant SES-1227334). We thank Andrew Cherlin, Janet Currie, Marianne Page,
Ann Huff Stevens, Kathleen Vohs, Jane Waldfogel, two anonymous referees, and numerous seminar and conference
participants for valuable suggestions. We are grateful to Juliette Fournier, Julius Lüttge, Ante Malenica, Timothy
Simmons, Oscar Suen, Juliette Thibaud, and Melanie Wasserman for expert research assistance.
†
Go to https://doi.org/10.1257/aeri.20180010 to visit the article page for additional materials and author
disclosure statement(s).
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An influential body of work associated with sociologist William Julius Wilson(Wilson 1987, 1996; Wilson and Neckerman 1986) hypothesizes that the declineof US blue-collar employment has diminished the pool of economically secureyoung adult men, thereby reducing women’s gains from marriage, eroding traditional parental roles, and imperiling children.1 Wilson’s narrative is a close relativeof the classic Becker (1973) framework in which the economic gains to marriage
arise in part from spousal earnings differences, which spur household specialization.2 Reflecting the difficulty of distinguishing cause from effect in the correlations between labor market opportunity and family structure, the literature has, with
important exceptions, faced a challenge in testing these hypotheses.3 We surmountthis challenge by assessing how adverse labor market shocks for young adults, emanating from rising trade pressure on US manufacturing, affect marriage, fertility,
and children’s living circumstances. Following Autor, Dorn, and Hanson (2013b)
and Acemoglu et al. (2016), we exploit ­cross-industry and cross-local labor market
variation in import competition stemming from China’s market reforms to trade to
identify labor-demand shocks that are concentrated on manufacturing.
In linking local labor demand shocks to marriage and fertility, our work is closein spirit to Black, McKinnish, and Sanders (2003) who document an increasing
prevalence of s­ ingle-headed households in four US states that suffered a decline intheir coal and steel industries, and Kearney and Wilson (2017) who observe rising
fertility but no change in marital patterns in US regions that benefited from the
2000s fracking boom. Our study complements the evidence from these episodes of­industry-specific booms and busts by assessing whether two decades of contracting
US manufacturing employment across a large set of industries and localities has
affected marital and family outcomes. Distinct from much prior work, we exploit
gender dissimilarities in industry specialization to identify demand shocks that
distinctly affect men’s and women’s employment and earnings. Our strategy thus
complements work by Shenhav (2016), who uses gender-specific Bartik shocks andgender differences in occupational choice to predict changes in relative gender earnings in US states.4 We provide three main results.
First, shocks to manufacturing labor demand, measured at the commuting-zone(CZ) level, exert large negative impacts on men’s relative employment and earnings. Although losses are visible throughout the earnings distribution, the relative
declines in male earnings are largest at the bottom of the distribution.5
Second, these shocks curtail the availability and desirability of potentially marriageable young men along multiple dimensions: reducing the share of men among
young adults in a CZ and increasing the prevalence of idleness—the state of being
neither employed nor in school—among young men who remain. Underscoring the1
See also Jahoda, Lazarsfeld, and Zeisel (1971); Murray (2012); Bailey and DiPrete (2016); and Greenwood,
Guner, and Vandenbroucke (2017).
2
Whereas Becker focuses on relative economic stature, Wilson’s argument further implies that holding genderdifferentials constant, an absolute fall in male economic stature reduces the value of marriage.
3
Exceptions include Angrist (2002) and Charles and Luoh (2010).
4
Shenhav (2016) focuses on the economic independence of women rather than the declining marriage-market
value of men, drawing in part on the empirical strategy in an earlier version of this paper (Autor, Dorn, and Hanson2014).
5
Autor, Dorn, and Hanson (2013a) find that trade shocks reduce CZ-level mean earnings and Chetverikov,
Larsen, and Palmer (2016) demonstrate that these shocks raise CZ-level earnings inequality.

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acuity of economic distress, we find, related to Case and Deaton (2015, 2017) andPierce and Schott (2016b), that these forces induce a differential and ­economicallylarge rise in male mortality from drug and alcohol poisoning, HIV/AIDS, andhomicide.
Finally, we link manufacturing decline to marriage, fertility, and children’shousehold circumstances. Much literature shows that adverse labor market shocks
reduce the fraction of young women who are currently married. The Becker (1973)
model makes a stronger prediction: a fall in the relative economic stature of men
diminishes the gains from household specialization and therefore reduces the prevalence of marriage, while a decline in women’s economic opportunities has the opposite effect. We confirm the Becker prediction using the gender-specific components
of manufacturing decline, and further show that these shocks raise the fraction of
mothers who are unwed, the fraction of children in single-headed households, andthe fraction of children living in poverty.
These results complement work by Schaller (2016) showing that improvements
in mens labor market conditions predict increases in fertility while improvements
in womens labor market conditions do the opposite.6 They also support Wilson’sobservation that manufacturing contractions shrink the pool of economicallysecure young adult men and erode traditional household arrangements. Because­trade-induced manufacturing shocks generate both an absolute fall in the employment and earnings of young adult men and a fall in these outcomes relative towomen, our empirical setting does not allow us to cleanly distinguish between the
Becker hypothesis—focusing on relative economic stature—and Wilson’s thesis,
focusing on men’s absolute economic stature.
Alongside providing support for the argument that contracting blue-collar
employment catalyzes changes in gender roles and household structures, our analysis indicates that Wilson’s conclusions apply to a far broader group of adults than theurban poor African Americans on whom he focused, and that the magnitude of theseeffects is sizable relative to observed declines in male employment rates, female
fertility, and prevalence of marriage among US young adults.
I. Empirical Approach

We examine changes in exposure to international trade for US CZs associatedwith the growth in US imports from China. Rising trade with China is responsible for nearly all of the expansion in US imports from low-income countries since
the early 1990s (Pierce and Schott 2016a). Our empirical strategy builds on Autor,
Dorn, and Hanson (2013a) and Acemoglu et al. (2016). We approximate local labormarkets using the construct of CZs developed by Tolbert and Sizer (1996), andinclude the 722 CZs that cover the US mainland.

6
Work by Ananat, Gassman-Pines, and Gibson-Davis (2013) shows that adverse local economic shocks reduceteen birthrates and sexual activity, while raising contraceptive use and abortion, while Page, Stevens, and Lindo(2009) and Lindo, Schaller, and Hansen (2013) document adverse impacts of parental job loss on children’s living
circumstances.

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Our measure of the local labor market shock is the average change in Chinese
import penetration in a CZ’s industries, weighted by each industry’s share in initial
CZ employment:​L​ij90​​∑​ ​​ ​ _ ​  ΔI​P​  cu(1)	​Δ​IP​  cuiτ​  ​ = ​jτ​  ​.​
j ​L​i90​​

M​  cu​ ​​  + ​M​j91​​  − ​X​j91​​)​is the growth of Chinese import
Here, ​
ΔI​P​  cujτ​  ​ = Δ​jτ​  ​/(​Yj91
penetration in the US for industry j over period ​τ​ , which in our data include thetime intervals 1990 to 2000 and 2000 to 2014. It is computed as the growth in
US imports from China, ​Δ​M​  cujτ​  ​​ , divided by initial absorption (US industry shipments plus net imports, ​Y​ j​91​​  + ​M​j91​​  − ​X​j91​​​) in the base year 1991, near the start ofChina’s export boom. The fraction ​​L​ij90​​​/​​Li​90​​​is the share of industry j in CZ i’s total
employment, as measured in County Business Patterns data in 1990. Differences in​ΔIP​ ​  cuiτ​  ​​across CZs stem from variation in local industry employment structure in
1990, which arises from differential concentration of employment in manufacturing
versus non-manufacturing activities and specialization in import-intensive industries
within local manufacturing. In all specifications, we control for the ­start-of-period
manufacturing share within CZs so as to focus on variation in exposure to trade
stemming from differences in industry mix within local manufacturing.
The measure ​Δ​IP​  cuiτ​  ​​captures overall trade exposure experienced by CZs but
does not distinguish between employment shocks that differentially affect male andfemale workers. To add this dimension of variation, we modify (1) to exploit thefact that manufacturing industries differ in their male and female employment intensity—so that trade shocks of a given magnitude will differentially affect male orfemale employment depending on the set of industries that are exposed. We incorporate this variation by multiplying the CZ-by-industry employment measure in(1) by the initial period female or male share of employment in each industry by
CZ (   ​​f​ij90​​​ and ​1 − ​f​ij90​​​), thus apportioning the total CZ-level measure into two addif,cutive subcomponents, ​Δ​IP​  m,cuiτ​  ​​and ​Δ​IP ​  iτ​  ​​:
​(1 − ​f​ij90)​​ ​ ​Lij90
​ ​​
(2) ​
Δ​IP​  m,cu∑​ ​​ ​ _   ​  ΔI​P​  cuiτ​  ​ = ​jτ​  ​  and​Li​90​​j​f​ij90​​ ​L​ij90​​
Δ​IP ​  f,cu∑​ ​​ ​ _ ​  ΔI​P​  cuiτ​  ​ = ​jτ​  ​.​​L​i90​​j

As shown in online Appendix Table A1, Chinese import penetration rose by 0.95
percentage points between 1990–2000, with an additional 1.15 percent rise per
decade over 2000–2014. Sixty percent of this rise accrued to male employment.7
We identify the supply-driven component of Chinese imports by instrumenting
for growth in Chinese imports to the United States using the contemporaneous composition and growth of Chinese imports in eight other developed countries.8 Our
7
We construct (1) using trade data from UN Comtrade that we harmonize to 4-digit SIC industries, and data
on CZ employment by industry from the County Business Patterns. In (2), we further use census IPUMS data tocompute gender shares within industries and CZs, and assign to each SIC industry in a CZ the gender share of thecensus industry in the CZ encompassing it. Most outcome variables are based on census IPUMS samples for 1990
and 2000 (Ruggles et al. 2004) and pooled American Community Survey samples for 2013 through 2015. We allocate PUMAs to CZs using the algorithm in Dorn (2009) and Autor and Dorn (2013).
8
The eight comparison countries—determined by the availability of comparable trade data for the full sample
period—are Australia, Denmark, Finland, Germany, Japan, New Zealand, Spain, and Switzerland.

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i­nstrument for the measured import-exposure variable Δ​ ​IP​  cuit​  ​​is a non-US exposure
covariable Δ​ I​P​  it​  ​​that is constructed using data on industry-level growth of Chinese
exports to other high-income markets:​L​ij80​​∑​ ​​ ​ _ ​  ΔI​P​  co(3)	​ΔIP​ ​  coiτ​  ​ = ​jτ​  ​.​
L
​
​
i80​​
j
This expression differs from (1) by using realized imports from China by other​ ​  cu
high-income markets (​Δ​M​  cojτ​  ​​) in place of China-US import penetration (​ΔMjτ​  ​​); andby replacing other variables with lagged values to mitigate simultaneity bias.9 The
exclusion restriction underlying our instrumentation strategy is that the common
component of import growth in the United States and in other high income countries
derives from factors specific to China, associated with its rapidly evolving productivity and trade costs. Autor, Dorn, and Hanson (2013a) provide tests against correlated demand shocks and develop an alternative estimation strategy based on thegravity model of trade.
II. Main Results

A. Employment and Earnings
We assess the causal effect of trade shocks on employment by fitting models of
the form​  ​2​​  + ​e​siτ​​  , ​(4)	​Δ​Y​siτ​​  = ​α​t​​  + ​β​1​​  ΔIP​ ​  cuiτ​  ​ + ​X​  ′i ​t​βwhere ​ΔY
​ ​sit​​​is the decadal change in the manufacturing employment share of theyoung adult population ages 18–39 in CZ ​i​among gender group ​s​ (males, females,or both) during time interval ​τ​. Our focus is on employment of young adults because
this population is disproportionately engaged in marriage and child-rearing.10 We
estimate (4) by stacking ten-year equivalent first differences for 1990 to 2000 and2000 to 2014, while including dummies for each decade (​​αt​ ​​​). The explanatory variable of interest is the change in CZ-level import exposure Δ​ ​IP​  cuiτ​  ​​ , instrumented byco11​​ ​  it′ ​​​  contains start-of-period CZ-level covariates,​ΔI​P​  iτ​  ​​as in (3). The control vector X
including: time trends for US census divisions; the lagged share of CZ employment
in manufacturing, absorbing general shocks to the sector; controls for employment
9
The start-of-period employment shares ​L​ ​ij80​​​/​​Li80​ ​​​and the gender shares ​f​ ​ij80​​​are replaced by their ten-year lags,
while initial absorption in the expression for industry-level import penetration is replaced by its three-year lag.
10
Our sample is individuals who are not residents of institutionalized group quarters such as prisons. Here,we pool individuals of all races and ethnicities; we show in online Appendix Table A5 that our results also holdseparately for non-Hispanic whites, for whom we have sufficient statistical power to conduct a separate CZ-level
analysis.
11
m,cu
f,cu
When performing gender-specific estimates, we replace ​Δ​IP​  cuiτ​  ​​with ​Δ​IP​  iτ​  ​​and ​Δ​IP ​  iτ​  ​​ , and use the corresponding gender-specific instruments. In online Appendix Table A2, we report OLS and 2SLS estimates separately
by time period, with corresponding first stages for 2SLS models. A falsification test in this table shows that thenegative effect of the China trade shock on employment is not present in the 1980s (i.e., prior to its occurrence) andhas the opposite sign in the 1970s, when labor-intensive US manufacturing was still expanding in some US regions.

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Table 1—Estimated Impact of Manufacturing Trade Shock on Manufacturing Employment
by Gender and Gender Differential in Employment Status, Earnings, and Idleness,
1990–2014: 2SLS Estimates
Panel A. Manufacturing
employment as a share of
population, age 18–39

Panel B. Male-female differential by
employment status, age 18–39

M+ F
(1)

Males
(2)

Females
(3)

Emp
(1)

Unemp
(2)

NILF
(3)

−1.06
(0.17)−0.99(0.17)−1.09(0.20)−0.65(0.26)

0.19(0.09)

0.46(0.24)

II. Male industry versus female industry shock
Δ Import penetration−1.21
× (male ind emp share)(0.44)Δ Import penetration−0.88
× (female ind emp share)(0.35)
Mean outcome variable−2.61
Level in 1990
12.98

−2.59
(0.51)
0.82(0.46)
−3.19
17.37

0.20
(0.43)−2.56(0.38)
−2.06
8.68

−3.13
(0.78)
2.17(0.65)
−2.74
14.64

0.38
(0.26)−0.02(0.26)
0.03
1.22

2.75
(0.62)−2.15(0.64)
2.71−15.87

I. Overall trade shock
Δ Import penetration

Panel C. Male-female differential
in annual earnings ($), age 18–39

Panel D. Male-female differential in
idleness, age 18–25

P25
(1)

Median
(2)

P75
(3)

Emp
(1)

No emp inschool(2)

No empno school
(3)

−672
(193)

−445
(191)

−847
(334)

−0.64
(0.34)−0.02(0.26)

0.66(0.20)

II. Male industry versus female industry shock
Δ Import penetration−2,216× (male ind emp share)
(516)
1,086
Δ Import penetration× (female ind emp share)(529)
Mean outcome variable level−1,894
Level in 1990
6,926

−2,945
(593)
2,400
(630)
−2,126
13,376−3,685(1,081)
2,384(814)−2,491
17,489−3.16(1.03)
2.24(0.92)
−2.83
7.70

0.56
(0.73)−0.68(0.74)
−0.25
0.87

2.60
(0.60)−1.55(0.56)
3.08−8.56

I. Overall trade shock
Δ Import penetration

Notes: Observations = 1,444 (722 CZ × 2 time periods). Dependent variables: change in percentage of populationage 18–39 that is employed in manufacturing, change in gender differentials in employment status (in percent pts);change in gender differential in annual earnings (in dollars); change in gender differential in percentage of young
adults age 18–25 that are employed, not employed but in school, or neither employed nor in school. Panel C analyzes the change over time in the difference between a percentile of the unconditional male earnings distribution in
a commuting zone and the corresponding percentile in the unconditional female earnings distribution. All models
include a dummy for the 2000–2014 period, occupational composition controls (start-of-period indices of employment in routine occupations and of employment in offshorable occupations as defined in Autor and Dorn 2013),
start-of-period shares of commuting zone population that is Hispanic, black, Asian, other race, foreign born, andcollege educated, as well as the fraction of women who are employed. Models are weighted by the product of period
length and commuting zone share of start-of-period US mainland population. Robust standard errors in parentheses are clustered on state.

in occupations susceptible to automation and offshoring (see Autor and Dorn 2013
and Goos, Manning, and Salomons 2014); and CZ demographics (race, education,and the fraction of working-age adult women who are employed).
Panel A of Table 1 estimates the impact of rising trade exposure on the locus
of proximate impact: manufacturing employment. In 1990, ​17.4​percent of young
men and ​8.7​percent of young women ages 18–39 were employed in manufacturingVOL. 1 NO. 2

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167(­bottom row of panel A).12 Estimates in panel A, columns 1–3, find that rising importcompetition reduces manufacturing employment among both sexes. A one unit trade
shock—roughly equal to the average decade-level CZ-level rise in trade exposure
over the 1990–2014 period—depresses the share of young adults employed in manufacturing by ​1.06​percentage points (​t = − 6.3)​with similar effects on young men(​​βˆ ​ = −0.99​, ​t = −5.8​) and young women (​​βˆ ​ = −1.09​  , ​t = − 5.5​). These estimates imply large declines in manufacturing employment among young adults over
our ​24​-year window: a drop of 2​ .5​points (​20​ percent) among men and of 2​ .7​points(​31​ percent) among women.
We introduce the gender-specific trade shocks in panel A-II. Despite the high correlation between these by-gender measures (​ρ = 0.80​), there is abundant power for
distinguishing their independent effects. Panel A-II, column 1, finds that a unit risein import penetration of either male or female-dominated industries reduces manufacturing employment by ​1​point. Panel A-II, columns 2 and 3, demonstrate thatthe employment effects of sex-specific shocks fall mostly on their corresponding
genders. A unit trade shock to male-specific industries reduces male manufacturing
employment by ​2.6​points (​t = − 5.1​) and has a small and statistically insignificant impact on female employment; a unit trade shock to female-specific industries
reduces female manufacturing employment by ​2.6​points (​t = − 6.7​), while having
a modest positive effect on male employment. Our ability to cleanly differentiate
male- and female-specific shocks is crucial to the analysis that follows.
We consider the broader impacts on overall employment in Table 1, panel B. Hereand below, we report the causal effects of trade shocks on the male-female gap inoutcomes rather than on their levels so as to measure impacts on relative economic
stature. Although trade shocks have similar impacts on male and female manufacturing employment, the estimate in panel B-I, column 1, shows that these shocks
significantly depress the male relative to female employment-to-population rate—aunit trade shock reduces male relative employment by 0​ .65​points (​t = − 2.5)​.13
While one might have predicted this differential effect based upon men’s overrepresentation in manufacturing, the results in panel A—showing that trade shocks
reduce male and female manufacturing employment in lockstep—underscore thatdifferential manufacturing exposure is not the explanation. Rather, these estimatesindicate that trade shocks differentially reduce male employment in non-manufacturing. Panel A of online Appendix Table A3 indicates that the overall employment
loss of males due to a unit trade shock is larger than the decline in manufacturing
employment seen in panel A of Table 1 (​−1.5​versus ​−1.0​ points) while the overall
employment decline for women is slightly smaller than in manufacturing (​−0.9​
versus ​−1.1​ points).14
We next quantify the impact of gender-specific trade shocks on the distributionof annual wage and salary income. For this analysis, we implement the Chetverikov,
Larsen, and Palmer (2016) technique for performing instrumental-variable estimatesof the distributional effects of group-level treatments. Panel C of Table 1 shows12
The denominator for this calculation is the noninstitutionalized adult population ages 18–39. Among
employed adults in this demographic group, these fractions were ​21.1​and ​12.9​percent, respectively.
13Here, “relative” means the difference in levels rather than the ratio of levels.
14
Acemoglu et al. (2016) find substantial employment losses in industries that sell their outputs to­import-competing manufacturing, including mining whose workforce is strongly male-dominated.

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male earnings substantially exceed female earnings at all quantiles, with the size of the
gap rising steeply with the quantile index. In 1990, this gap was $​ 6,926​, ​$13,376​, and​
$17,489​at the twenty-fifth, fiftieth, and seventy-fifth quantiles, respectively (bottom
rows of panel C). Between 1990 and 2014, these gaps compressed by ​$1,894​, ​$2,126​,and ​$2,491​ per decade at the twenty-fifth, fiftieth, and seventy-fifth quantiles.
Reinforcing the panel B findings for the gender gap in employment, the first row
of estimates in panel C demonstrates that trade shocks differentially curtail maleearnings. A one-unit trade shock reduces male relative to female earnings by ​$672​
at the twenty-fifth percentile (panel C, column 1, t​ = −3.5​), by $​ 445​at the median(panel C, column 2, ​t = − 2.3​), and by ​$847​at the seventy-fifth percentile (panel
C, column 3, t​ = − 2.5​).15
Since the male-female earnings gap is smaller at lower wage quantiles, the relative impact of trade shocks on the male-female wage gap is largest among l­owearners, as seen in Figure 1. Panel A details that trade-induced earnings losses arelarger for males than females at every quantile from the fifteenth to ninety-fifth percentile.16 Panel B reports the impact of a unit trade shock on the male-female annual
earnings gap expressed as a percentage of baseline male earnings in 1990 at thecorresponding percentile. Trade shocks modestly compress the male-female annual
earnings gap in the upper half of the annual earnings distribution. The effect is more
dramatic below: the male-female wage compression is 2 points at the median, 4points at the thirty-fifth percentile , and 6 points at the twentieth percentile.17
The gender-specific estimates in online Appendix Table A3 confirm that trade shocks
reduce employment and earnings of both genders; that employment and absolute earnings losses are larger for males than for females; and that proportional earnings losses
for both sexes are larger at low than high percentiles. These findings support Wilson’sobservation that manufacturing contractions shrink the pool of economically secureyoung adult men, generating both an absolute fall in the employment and earnings ofyoung adult men and a fall in these outcomes relative to women.
B. Gender Gaps in Idleness, Absence, and Mortality
The heart of the Wilson hypothesis is that adverse shocks to blue-collar employment catalyze a broader deterioration in adult social function. We test for suchconsequences with three nonmarket measures: idleness, absence, and mortality.
Idleness is the state of being neither employed nor in school; we focus on the ages
18–25, which cover the transition between school and work.18 In panel D of Table 1,
we estimate a variant of (4) where the dependent variable is the male-female gap
15
Panel C-II of Table 1 shows that shocks centered on male employment have a larger effect on the genderearnings gap than do shocks centered on female employment.
16
Annual earnings for both genders are zero below the tenth percentile. Above the ninety-fifth percentile, earnings are largely censored and then imputed by the Census Bureau.
17
These reductions are relative to the baseline male earnings level not the baseline gap, indicating large changes.
We truncate the estimates at the twentieth percentile because the low values of the denominator below this point
make estimates uninformative.
18
Aguiar et al. (2017) document that young men devote more time to video games and recreational computeruse, while working fewer hours.

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Panel A. Impact on male and female annual earnings by percentile, 1990–2014
0−1,000Dollars (2015)−2,000−3,000−4,000−5,000Male earnings
Female earnings−6,000
0

10

20

30

40

50

60

70

80

90

100

Percentile of income distribution
Panel B. Impact on male-female annual earnings gap 1990–2014 as a percentage of1990 male earnings

Percentage of male earnings

0
−2
−4
−6
−8
−10
−12
−14
0

10

20

30

40

50

60

70

80

90

100

Percentile of income distribution
Figure 1. Impact of Manufacturing Trade Shock on Earnings of Males and Females Age 18–39, 1990–2014
Notes: Panel A measures the impact of a unit trade shock on the unconditional distribution of annual earnings (in$2015) separately for males and females. Each dot indicates a coefficient estimate from a separate IV quantile
regression with group-level treatment (Chetverikov, Larsen, and Palmer 2016) that controls for the covariates indicated in Table 1, and shaded areas indicate a 95 percent confidence interval. Panel B reports the effect of a unit trade
shock on the difference in the male-female annual earnings gap expressed as a percentage of male earnings in 1990
at the indicated percentile.

in three main (mutually exclusive) activity statutes: currently employed (panel D,column 1), not employed but enrolled in school (panel D, column 2), and neither
employed nor enrolled in school (panel D, column 3), which we refer to as idleness. Panel D, column 1, shows that a unit trade shock lowers the fraction of young
men employed by 0​ .64​percentage points relative to women of the same age range(​t = −2.5​). This is nearly identical to the effect found for the broader set of adults

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Table 2—Estimated Impact of Manufacturing Trade Shock on Male Share of Adult Residents andGender Differentials in Death Rates 1990–2015: 2SLS Estimates
Panel A.
Percentage ofmale residents

I. Overall trade shock
Δ Import penetration

18–39
(1)

18–25
(2)

−0.25 −0.28(0.11) (0.16)

II. Male versus female industry shock
Δ Import penetration−0.62 −0.76
× (male ind emp share)(0.27) (0.43)
0.18
0.26
Δ Import penetration× (female ind emp share) (0.15) (0.29)

Panel B. Male-female death rate differential ages 20–39
D&A
All
Total Poison HIV Homicide Suicide Accident other
(3)
(4)
(5)
(6)
(7)
(8)
(9)
14.0
(8.5)

−2.4
(4.3)

4.0
(8.4)

7.7
(5.5)

189.7
60.3
66.5 103.0(60.0) (23.6) (20.3) (27.5)−77.1 −26.6 −29.1 −86.4(49.0) (18.9) (15.5) (29.2)−9.8(11.1)
6.0(10.9)−40.2(27.2)
53.9(27.1)

9.9(16.3)
5.1(16.7)

168.9
218.6
49.7

262.5
378.6
116.1

146.10548.92402.8264.4(22.3)

19.5
(6.7)

21.6
(8.6)

III. Summary stats: cumulative mortality 1990–2015 (decadal averages)
Male-female gap
936.0
93.8 110.3
Males
1,644.6 153.3 146.6
Females
708.7
59.5
36.3

154.4
198.6
44.3

Notes: Observations = 1,444 (722 CZ × 2 time periods). Dependent variables: Change in percentage of male shareof residents; cumulative male-female difference in death rates per 100,000 population by cause of death. The percentage of male residents is measured for the period 1990–2014 among all individuals who do not reside in institutionalized group quarters. Male share of CZ residents in 1990 was 49.6 percent among ages 18–39 and 50.2 percent
among ages 18–25. Weighted mean changes in these variables were 0.11 and 0.19, respectively. Cumulative decadalmortality rates cover the period 1990–2015. All regressions include the full set of control variables from Table 1,
and regressions in panel B control for a ten-year lag of the male-female differential in total mortality. Regressions
are weighted by the product of period length and CZ population share, and standard errors are clustered on state.

ages 18–39 considered in panel B, column 1. Panel D, column 3, finds that the entire
differential rise in nonparticipation among young males is due to increased idleness(​0.66​ points, ​t = −2.5​), with little effect on school enrollment (panel D, column
2). This pattern is reinforced when focusing on the gender-specific components oftrade exposure (panel D-II): shocks to male-intensive manufacturing generate alarger differential increase in male idleness (​2.6​ points, ​t = 4.3​) than do shocks
to female-intensive manufacturing (​−1.6​points, ​t = −2.8​).19 Panel C of online
Appendix Table A3 reports these impacts separately by gender. The differential
effect of manufacturing shocks on the male-female idleness gap stem entirely fromincreases in male idleness. By contrast, reductions in female employment accrue
almost entirely to increases in female school enrollment.
Table 2 presents complementary evidence on absence and mortality. Columns 1
and 2 indicate that trade shocks significantly reduce the supply of ­noninstitutionalized
young men in a local labor market. A one unit trade shock reduces the fraction ofmales among adults ages 18–39 and 18–25 by about 0​ .25​points, with the effecton the broader age group more precisely estimated (​t = −2.2)​than the effecton the narrower age group (​t = −1.8​). The lower row of estimates (panel A-II)
demonstrates that these (modest) shifts in the relative availability of young men
19
In panel D, unemployed adults are categorized as either students or as idle. If we define idleness as the stateof being neither employed, unemployed, nor in school, we continue to find a significant differential impact of trade
shocks on male idleness.

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stem from shocks to male-intensive employment: a unit shock to male-intensive manufacturing reduces the male fraction of young adults by two-thirds tothree-quarters of a percentage point. A unit shock to female-intensive manufacturing
generates a countervailing effect, but it is only one-third as large and is statisticallyinsignificant.
The reduced supply of young adult males in trade-impacted CZs may reflect
gender differences in migration, incarceration, homelessness, or mortality.20 We
focus on mortality, which is well-measured, has an unambiguous interpretation, andhas attracted attention following Case and Deaton (2015, 2017). Using US Vital
Statistics files enumerating person-level death certificates for all US residents, Table
2 reports the impact of trade shocks on the gender gap in cumulative mortality per
decade—overall and by cause—per 100,000 adults ages 20–39.21 Our analysis isrelated to Pierce and Schott (2016b), who link county-level trade exposure to risingmortality due to accidental poisoning and suicide in the working-age population.
Guided by our focus on the interaction between labor markets and marriage markets, our analysis examines mortality among young adults ages 20–39 and differential effects on males versus females.
Shocks to import penetration significantly increase the male-female mortality gap
among young adults. The point estimate in Table 2, panel B, column 3, indicatesthat a unit trade shock induces an additional ​64.4​male relative to female deaths per
100,000 adults (of each gender) per decade. Given an average differential mortality
rate of ​936​per 100,000 adults per decade over 1990–2015, this increment is large.
Subsequent columns decompose the overall mortality effect into by-cause categories. Case and Deaton (2015, 2017) show that drug and alcohol (D&A) related mortality rose by epidemic proportions among working-age adults in this time period.
The bottom of panel B, column 4, indicates that D&A deaths accounted for ​10​percent of all young adult male deaths between 1990–2015, while the upper row of thecolumn demonstrates that the male-female gap in D&A deaths surged in trade-impacted CZs. The point estimate of ​19.5​(​t = 2.9​) accounts for 30 percent of thetotal contribution of trade shocks to differential male mortality.
Panel B, columns 5–9 test for corresponding trade shock-related increases in differential male mortality from HIV/AIDS (often related to IV drug use), homicide,suicide, accidents, and all other causes.22 A one unit trade shock causes a differential
increase in male mortality due to HIV that is strongly significant (​t = 2.5)​and to
homicides that is marginally significant (​t = 1.7)​, where the former impact (​21.6)​
20
Autor, Dorn, and Hanson (2013b) and Autor et al. (2014) do not find robust evidence for increased outmigration from import-competing CZ, but do not test for gender-specific migration patterns. Deiana (2015); Feler andSenses (2017); and Pierce and Schott (2016b) document statistically significant increases in crime incidents andarrests in such CZs during the 1990s and 2000s. Because incarceration and homelessness are disproportionately
prevalent among males (West and Sabol 2008, Table 1 and online Appendix Table 7; US Conference of Mayors
2007, Exhibits 2.3 and 2.4), a rise in either may reduce the number of males enumerated in the noninstitutional
population.
21
These data, used under agreement with the US Center for Disease Control, cover deaths occurring in 1990
through 2015. The corresponding birth data (used below) extend through 2016. The denominator for death rates is
the CZ-level population reported by the Census Bureau, which is available for the age bracket 20–39. The dependent variable is normalized to correspond to a ​10​-year cumulative value. Our regressions include the start-of-period
control variables used in previous tables and the ten-year lag of the male-female differential in total mortality, suchthat we capture how trade shocks induce deviations in male-female mortality from long-run CZ-specific trends.
22
The first row of section III in Table 2 shows that combined with D&A poisoning, the first four of these causes
account for 84 percent of the male-female mortality gap over 1990–2015.

172

AER: INSIGHTSSEPTEMBER 2019is slightly larger than the D&A effect and the latter impact (​14.0)​is moderately
smaller. In panel B, columns 7–9, trade shocks have small and insignificant effects
on differential male mortality related to suicide, accidents, and all other causes.
In net, the differential increase in male mortality can account for 1​ 4​percent of
the fall in the fraction of males among young adults in trade-impacted CZs (panelA, column 1, of Table 2).23 While only a small minority of adults who engage inrisky behaviors experience fatal consequences, the remainder may be less attractive
marital partners due to substance abuse and exposure to HIV and violent crime,
suggesting that ­marriage-market values may fall for a broader set of young males.24
C. Fertility, Marriage, and Children’s Living Circumstances
We test finally for impacts of trade shocks on fertility, marriage, and children’s
circumstances. Panel A of Table 3 presents the impact of trade exposure on marital
status among women ages 18–39, whom we classify as currently married, currently
widowed, divorced or separated, or never-married.25 Trade shocks deter marriage
formation: a one-unit trade shock predicts a ​0.95​percentage-point decline in thefraction of young women who are currently married (panel A, column 1, t​ = −3.1)​,
a further 0​ .21​point decline (panel A, column 2, t​ = − 2.0​) in the fraction of women
who are previously married, and a corresponding rise of ​1.2​points in the fraction of
women never married (panel A, column 3, t​ = 3.5)​. Shocks to male and female-intensive employment have opposing and precisely estimated effects on marriage formation (panel A-II, columns 1–3): a one unit shock to male-intensive employment
reduces the fraction of young adult women ever married by 4​ .2​points (​t = 6.6​, a​12​percent rise on a 1990 base of 3​ 4.8​percent) and the fraction currently married by​
3.6​ points (​t = −5.8​); a unit shock to female-intensive employment has a countervailing impact on marital status that is about two-thirds as large as the impact of ashock to male-intensive employment.
We find corresponding results for fertility, measured as births per 1​ ,000​womenages 20–39. Trade shocks significantly deter fertility, with a one-unit shock reducing
births by ​1.5​per ​1,000​women (panel B, column 4, ​t = −4.2​). While it is tempting
to interpret this pattern as indicative of the procyclicality of fertility, the lower rowsof estimates (panel B-II) show otherwise. Shocks to male-intensive employment
diminish fertility (​​βˆ ​ = −4.7, t = −5.4)​while shocks to female-intensive employment raise it (​​βˆ ​  = 2.0, t = 2.3)​.
These results support Becker (1973), in which the gains to household formation are increasing in gender-based specialization. Here, shocks that diminish
earnings capacity for the high-earning spouse (typically male) reduce these gains,deterring marriage and fertility—and vice versa for shocks that diminish earnings
23
A unit trade shock reduces the male fraction of population by 0.25 per ​100​adults among those ages 18–39
over the course of a decade (Table 2), implying an effect of ​500​per 100,000 men. A unit trade shock raises excess
male versus female mortality by 6​ 4​for every 100,000 adults of each sex among those ages 20–39 over the courseof a decade (Table 2). Adjusting for the wider age range of the population versus mortality bracket (​22​ versus ​20​
years), this number rises to ​71​per 100,000 adults. Thus, excess mortality can account for a share of ​71/500 = 0.14​
of the decline in the male share of the young adult population in trade-impacted CZs.
24
See also Charles and Luoh (2010) and Caucutt, Guner, and Rauh (2016).
25
If a woman is currently married, we cannot determine if she was previously widowed, divorced, or separated.

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Table 3—Estimated Impact of Manufacturing Trade Shock on Marriage, Fertility, Maternal
Status, Childhood Poverty, and Household Structures of Adult Women and Dependent Children,
1990–2014: 2SLS Estimates
Panel A. Women’s marital status

Panel B. Fertility and maternity

Married(1)

Nevermarried(3)−0.95(0.30)−0.21(0.11)

1.16(0.33)−1.54(0.37)−0.66(0.23)

0.52(0.31)

0.61(0.26)

II. Male versus female industry shock
Δ Import penetration−3.57
× (male share)(0.62)−0.66(0.22)

4.23(0.64)−4.65(0.84)−1.79(0.63)

3.28(0.73)

2.13(0.70)

−6.92
53.05

−1.62
12.11

8.55
34.84

−1.45
86.87

−3.53
53.24

I. Overall trade shock
Δ Import penetration

Δ Import penetration× (female share)

Mean outcome variable
Level in 1990

2.03
(0.55)

0.29(0.19)−2.32(0.58)

Percent ofBirths per women w/
1,000 women children(4)(5)

Panel C.
Percent
Percent ofmothers children in HH
unmarried < poverty line
(6)
(7)

Widoweddivorced
separated(2)2.01(0.87)

Panel D. Women’s household type0.62
(0.52)−2.62(0.85)
6.56
23.98

−1.12
(0.82)
1.65
17.99

Panel E. Children’s household type

Living w/ Living w/ Other HH
spousepartnerstructure
(1)
(2)
(3)

Marriedcouple(4)

Parent +unmarried Single parent, Grandparent
partnerno partner
or other
(5)
(6)
(7)

−0.81
(0.27)−0.22(0.12)

1.03(0.30)−0.35(0.19)−0.11(0.07)

0.30(0.11)

0.15(0.16)

II. Male versus female industry shock
Δ Import penetration−3.21
× (male share)(0.55)

0.04(0.28)

3.17(0.60)−1.85(0.50)

0.28(0.23)

1.43(0.32)

0.14(0.42)

5.93
44.45
n/a

−4.69
71.39
8.7%

1.79
16.82
47.4%

1.28
8.96
28.8%

I. Overall trade shock
Δ Import penetration

Δ Import penetration× (female share)

Mean outcome variable
Level in 1990
Poverty rate 1990

1.93
(0.54)

−7.57
50.30
n/a

−0.52
(0.20)
1.65
5.25
n/a

−1.41
(0.52)

1.36(0.55)−0.55(0.25)
1.62
2.82
42.3%

−0.98
(0.42)

0.17(0.29)

Notes: Observations = 1,444 (722 CZ × 2 time periods). Dependent variables: changes in women’s marital status, births per 1,000 women, fraction of women with children, and fraction of mothers unmarried; fraction of children living in poverty; and household type of women and children. Outcomes in panels A, B, and D consider adultwomen ages 18–39 while those in panels C and E consider children ages 0–17. Fertility in panel B, column 4 ismeasured through 2016 while all other outcomes are measured through 2014. Dependent variables are: fractionof women with any biological, adopted, or stepchildren in the household (panel B, column 5); fraction currently
married among women with children in the household (panel B, column 6); the fraction of children in households
below the official census poverty line (panel C, column 7). Panel D, columns 1 and 2, refer to households where
either (1) the woman is the spouse or partner of the household head or (2) she is the household head and has a
spouse or partner who is living in the household. Panel D, column 3, comprises all other household structures.
Dependent variables in panel E, columns 4–7 are the fraction of children in each household type: household head isa married parent of the child (panel E, column 4); household head is a parent with cohabiting partner (panel E, column 5); household head is a single parent (panel E, column 6); or household head is a grandparent, other relative,
or non-related caregiver (panel E, column 7). All regressions include the full set of control variables from Table 1,
are weighted by the product of period length and CZ population share, and standard errors are clustered on state.

and ­employment for the low-earnings spouse. This reasoning, and the corroborating evidence in Table 3, helps explains why shocks to manufacturing employment
are so damaging to adult social function: by differentially impairing male earnings capacity, such shocks reduce the attractiveness of marriage, fertility, and joint
child-rearing.
Despite its strong predictions for marriage and fertility, the Becker (1973) framework is silent on the implications of shifts in relative economic status on children’s174

AER: INSIGHTSSEPTEMBER 2019living circumstances since this framework does not consider non-marital fertility. As
an empirical matter, if a fall in males’ relative economic stature deters fertility by atleast as much as it deters marriage, more children will live in two-parent, ­married,and ­non-poor households.26 Conversely, if motherhood is less elastic than marriage
to shocks to relative economic stature, then children’s household ­circumstances
will move in the opposite direction. This latter possibility is implicit in the Wilson
hypothesis.
Panel B, columns 5 and 6, and panel C, column 7 of Table 3 affirm Wilson’s
prediction. Panel B, column 5, shows that a unit trade shock reduces by 0​ .66​points
the fraction of adult women ages 18–39 with children in the household (​t = −2.9​).
Because this effect is only half as large as the increase in the fraction of women ages
18–39 who are never-married (panel A, column 3), the shock raises the share of mothers who are unmarried (panel B, column 6, ​β​ˆ ​  = 0.52, t = 1.70​), while the share ofchildren living in poverty also increases (panel C, column 7, ​​βˆ ​  = 0.61, t = 2.3)​.
Disaggregating the trade shock into its gender-specific components (panel B-II, columns 5 and 6, and panel C-II, column 7), trade shocks to male employment reduce
the fraction of women with children (by ​1.8​ points) while raising the share of mothers who are unmarried by 3​ .3​points (​t = 4.5​) and the share of children living inpoverty by ​2.1​points (​t = 3.0​); shocks to female employment raise the prevalenceof motherhood, reduce the fraction of mothers who are unmarried, and reduce thefraction of children living in poverty.
Panels D and E consider women’s and children’s living circumstances. Consistent
with the panel A findings for marriage, a unit trade shock reduces the fraction of
women living with a married partner by 0​ .81​points and the fraction cohabiting withan unmarried partner by additional ​0.22​points. The declining marriage rate is thus notcompensated by a rising propensity of young unmarried women to live with a partner.
Panel E documents how these countervailing effects on fertility, marriage, andsingle motherhood net out for children’s circumstances. In panel E, column 4, thefraction of children living in married two-parent households falls by ​0.35​points perunit trade shock (​t = − 1.7​), while the fraction living in single-parent, non-cohabiting households rises by 0​ .30​points (panel E, column 6, t​ = 2.8​). Echoing our
findings for marriage and fertility, in panel E-II these adverse effects on children
run entirely through shocks to male employment, which raise the share of children living in single-headed, non-cohabiting couples. Adverse shocks to female
employment have protective effects for children, significantly raising the share ofchildren in married households, reducing the share in non-married cohabiting andsingle-headed households, and weakly reducing the fraction of children living inpoverty.
III. Conclusions

Our analysis confirms Wilson’s (1996) hypothesis that contracting blue-collar
employment catalyzes changes in marriage, fertility, household structures, and26
As the bottom row of Table 3 panel E shows, the fraction of children living in poverty in 1990 was 8​ .7​
percent in married households, ​42.3​percent in unmarried cohabiting households, and ​47.4​in single-headed
non-cohabiting households.

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children’s living circumstances. Contractions in the supply of economically secureyoung adult men stemming from rising trade pressure spur a surge in male idlenessand premature mortality, a decline in marriage and fertility, an increase in the fraction of mothers who are unmarried and who are heads of single, non-cohabiting
households, and a growth in the fraction of children raised in poverty. Whereas
Wilson was writing about the African-American urban poor, our analysis encompasses all US young adults. Online Appendix Table A5 shows that our findings for
employment, earnings, women’s marital status, and childhood poverty are also confirmed when we focus on non-Hispanic whites.27
The implied magnitudes of our estimated impacts are quantitatively important.
Scaling the observed rise in China import penetration between 1990 and 2014 by theestimates above, we would infer that rising trade pressure reduced the employment
to population rate of young adult males by ​3.9​percentage points as compared to an
observed decline of ​7.2​points; reduced the prevalence of marriage among young
adult women by ​2.4​percentage points as compared to an observed decline of ​16.7​
points; and increased the fraction of children living in poor households by ​1.6​percentage points as compared to an observed increase of ​4.0​points.
A key question unanswered by our work is whether reversing these adverse currents in blue-collar employment would undo their effects on marriage, fertility, andchildhood poverty, or whether—as in Kearney and Wilson (2017)—some of these
consequences would persist even where opportunities for blue-collar men improve.
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