The Surprisingly Swift Decline of U.S.
Manufacturing Employment∗†

Justin R. Pierce

Board of Governors of the Federal Reserve System
Peter K. Schott‡

Yale School of Management & NBER
This Draft: October 2012PRELIMINARY

Abstract
This paper nds a link between the sharp drop in U.S. manufacturing employment
after 2001 and the conferral of permanent normal trade relations on China in late
2000. We nd that employment losses are larger in industries where the threatof tari hikes declined the most, and that the shift in policy is associated with
suppressed job creation, exaggerated job destruction and substitution away fromlow-skill workers. Consistent with greater oshoring, employment losses coincidewith an acceleration of U.S. imports from China and the number of U.S. importers,
Chinese exporters and importer-exporter pairs.

∗

Schott thanks the National Science Foundation (SES-0241474 and SES-0550190) for research support.

We thank Teresa Fort for helpful comments. Any opinions and conclusions expressed herein are thoseof the authors and do not necessarily represent the views of the U.S. Census Bureau or the Board ofGovernors or its research sta. All results have been reviewed to ensure that no condential information
is disclosed.
†
20th & C ST NW, Washington, DC 20551, tel: (202) 452-2980, email: justin.r.pierce@frb.gov, fax:(202) 736-1937.
‡
135 Prospect Street, New Haven, CT 06520, tel: (203) 436-4260, fax: (203) 432-6974, email: peter.schott@yale.edu.

11 Introduction
According to the Bureau of Labor Statistics, U.S. manufacturing employment grew from
13.2 million in 1950 to 19.5 million in 1979 before falling back to 13.8 million in 2007.
Surprisingly, two-thirds of this decline occurred in the years following the relatively mild
2001 recession.

In fact, the 1.5 million jobs lost in the rst year of that downturn isalmost twice the 900 thousand jobs lost during the rst year of the Great Recession.
This paper uses establishment-level production and trade data from the U.S. Census
Bureau to examine the relationship between the sharp drop in U.S. manufacturing employment after 2001 and a major change in U.S. trade policy in 2000. In October of thatyear  just a few months before the March 2001 peak  the U.S. Congress granted China

permanent Normal Trade Relations (P NTR). With this change in status, Chinese imports became permanently eligible for the relatively low taris the United States applies
to WTO members, versus the generally higher rates applied to imports from countries
outside the WTO, like North Korea.
While trade liberalization is a prime suspect in the overall decline of U.S. manufacturing employment in recent decades, the October 2000 liberalization is notable for having
had little eect on the actual taris applied to Chinese imports. Indeed, these rates do
not change in the years before and after China's change in status. Rather, it appears as

P NTR was to alter expectations about whether U.S. taris on
Chinese imports would remain low. Prior to its receipt of P NTR, China's exports were
if the principal impact ofsubject to potentially large increases in U.S. import taris if the politically contentious
annual renewals of its

temporary NTR status were rejected by Congress.1 The shift in

U.S. policy in October 2000 and the related entry of China into the WTO in December
2001 eliminated the possibility of these jumps.
Models of investment under uncertainty demonstrate that rms are more likely toundertake irreversible investments as the ambiguity surrounding their expected prot
decreases. These models provide insight into why the U.S. conferral of

P NTR on Chinamight cause a decline in U.S. manufacturing employment via the responses of rms inboth China and the United States.

In China,

P NTR provides producers with greater

incentives to enter or expand into the U.S. market, raising the level of competition in
the United States and putting further price pressure on U.S. producers. In the UnitedStates, greater assurance of continued low Chinese import taris raises U.S. rms' expected prot from nding or establishing low-cost Chinese suppliers of inputs and nalgoods. These incentives can encourage the local producers of these goods to shrink orexit, discourage new domestic producers from entering, and promote the growth of intermediaries that allow U.S. producers and consumers to import indirectly.

The shift

in U.S. trade policy can also lower the demand for manufacturing workers by inducing
producers to invest in more skill-intensive production technologies and mixes of productsthat are more consistent with U.S. comparative advantage.
We measure the uncertainty associated with China's temporary NTR status before

1

China was rst granted temporary NTR in February 1980, and the annual votes by Congress needed

to maintain this status proceeded smoothly until the Tiananmen Square incident in June 1989, after
which they became controversial and less certain of success.

2

2001 as the NTR gap, dened as the dierence between the NTR applied tari rates andthe non-NTR rates to which they would be raised without annual Congressional approval.
Handley (2012) demonstrates theoretically and empirically that the maximum level towhich a tari might be raised is an important dimension of trade policy uncertainty, and
it is one that is easily captured by the NTR gap in our setting. We nd that while NTR
gaps are more-or-less stable in the years leading up to China's change in status, they
exhibit substantial variation across industries. Our identication strategy exploits thiscross-sectional variation by using a dierence-in-dierences specication to test whether
employment loss in industries with high NTR gaps (rst dierence) is larger after the
2001 peak than after previous peaks (second dierence). One attractive feature of thisapproach is its ability to isolate the role of the change in policy. While high- and low-gap
industries are not identical, comparing outcomes within industries across peaks isolates
the dierential impact of China's change in status.

At the same time, comparison of

employment changes across similar intervals of the business cycle allows us to control forthe manufacturing sector's cyclicality.
Given the potential importance of input-output linkages, we consider three dimensions of exposure to risk reduction: the direct eect of the eliminating the NTR gap
in one's own major output industry as well as two indirect eects corresponding to theremoval of uncertainty in one's upstream supplier and downstream customer industries. Beginning with an analysis of employment growth at the industry level, we ndnegative and statistically signicant relationships between import-tari uncertainty andrelative industry employment growth up to six years after the 2001 peak. Our estimates
imply that for an industry with average NTR gaps, the shift in U.S. policy reduces employment growth from 2001 to 2002 by an additional -8.7 percentage points comparedwith the same interval after the 1981 and 1990 peaks. Six years after the 2001 peak, theimplied cumulative dierence grows to -29.6 percentage points. All three NTR gaps areinuential in these declines.
A major benet of using establishment-level data to examine employment growth at
the industry level is the ability to evaluate changes along gross margins of adjustmentthat are intensive and extensive to establishments. We nd thatP NTR is associatedwith both exaggerated job destruction and suppressed job creation, with the relative
contribution of the latter rising in the years after 2001. These results help explain the welldocumented joblessness of the 2001 recovery in the manufacturing sector. Faberman(2012), for example, has demonstrated that this joblessness is the result of a shift down in
job creation beginning in the late 1990s coupled with a sharp increase in job destruction
starting in 2001. Here, we nd thatP NTR coincides with both of these trends.

Analysis of employment growth at the plant level both conrms our industry ndings
and provides evidence that U.S. manufacturing plants respond to greater competition
from China by substituting human or physical capital for low-skilled workers. In particular, our results show that while the relationship between employment growth and theown-industry NTR gap is negative and statistically signicant for production workers,
it is positive and statistically signicant for non-production workers. To the extent thatthe latter embody higher levels of skill, this substitution is consistent with trade-induced
technical change and product-mix upgrading.

3

Finally, we use rm-level U.S. import data to examine the relationship between

P NTR

and U.S. trade with China. As these data are unavailable until the mid-1990s, we amend
our dierence-in-dierences specication to evaluate outcomes across trading partners
in the years before and after 2001 rather than across business cycles.

These compar-

isons reveal a positive and statistically signicant relationship between the shift in U.S.
trade policy and the relative growth of U.S. imports from China in terms of importvalue, the number of U.S. importers, the number of foreign exporters, and the number ofimporter-exporter pairs. Consistent with the existence of a relationship between

P NTR

and oshoring, these relationships demonstrate that U.S. imports surge in precisely theset of goods where domestic employment loss is concentrated, and with the exact trading
partner that is the subject of the shift in U.S. trade policy.
The paper proceeds as follows: Section 2 outlines our contribution to existing research,
Section 3 describes our data, Section 4 provides a brief discussion of the models used to
guide our empirical analysis, Sections 5 through 7 present our results, and Section 8
concludes with suggestions for further research.

2 Related LiteratureThis paper contributes to a large body of research spanning international trade, labor andmacroeconomics that explores a link between globalization and domestic employment. In
the international trade literature, a number of studies have found a negative relationshipbetween import competition and manufacturing employment, especially when that com-

2 The macroe-

petition is from low-wage countries in general, and China in particular.

conomics literature, by contrast, has generally not found a major role for international
trade in patterns of job creation and destruction over the business cycle, or in the joblessrecoveries to recessions observed in the past two decades.
We make contributions to several strands of these literatures. First, we show that asubstantial portion of the loss of U.S. manufacturing employment during and after the
2001 recession is related to a discrete and easily identiable change in policy  the U.S.
conferral of

P NTR on China. While others, including most recently Autor, Dorn and

Hansen (2012), have highlighted a negative relationship between U.S. employment andChinese imports, our research points to a specic cause for the acceleration of Chinese
imports starting in 2001, and relates it to a wide range of outcomes across U.S. and Chi-

3 In particular, we show that the largest relative declines in employment

nese producers.

2

Early work in this area, by Freeman and Katz (1991) and Revenga (1992), documents a negative

relationship between growth in U.S. manufacturing employment and either imports or changes in importprices at the industry level. Subsequent research focuses on the impact of imports from low-wage countries
across industries (e.g., Sachs and Shatz 1994) and establishments within industries (Bernard, Jensen andSchott 2006).

More recent papers investigate the eect of China on manufacturing employment in arange of countries, including Denmark (Mion and Zhu 2012), the EU (Bloom, Draca and Van Reenen2012), Mexico (Utar and Torres Ruiz 2011) and the United States (Autor, Dorn and Hansen 2012).

3

In focusing on the impact of a particular policy, this paper is closest to Bloom, Draca and Van Reenen(2012) and Utar and Torres Ruiz (2011), who show that employment losses across EU and Mexican
apparel and textile manufacturers coincide with the removal of import quotas on Chinese exports ofthese goods.

4

in the years after 2001 are concentrated in industries characterized by high NTR gaps,
and that these industries experience the largest surges in Chinese import value as wellas the number of U.S. importers and Chinese exporters.
Second, our examination of rms' reactions to import-tari uncertainty contributes
to the broad literature analyzing investment under uncertainty as well as its recent application to international trade.

4 Our eort in this regard is most closely related to thework of Handley (2012) and Handley and Limao (2012), who show that if uncertainty
regarding either the timing or the magnitude of tari changes in a destination market
falls, the productivity threshold for entry into exporting to that market falls, as relatively
low-productivity rms lose their incentive to wait and see how taris will change before
absorbing the sunk costs associated with entry. Our contribution to this literature is todemonstrate the strong and wide-ranging eects of perhaps the most signicant change
in import-tari uncertainty since the turn of the century  the granting ofP NTR toChina.
Third, we provide evidence of a link between international trade and the joblessness ofthe 2001 recovery in manufacturing. Several papers, including Baily and Lawrence (2004)
and Mankiw and Swagal (2006) argue that international trade plays a small role in thisphenomenon. Here, our use of highly disaggregated data and our focus on a specic policy
yields strong evidence that trade is directly and indirectly associated with the large andlong-lasting decline in U.S. manufacturing employment in the years after 2001. Moreover,
our nding thatP NTR has a more profound eect on production workers than non-production workers relates to recent research by Jaimovich and Siu (2012), which showsthat the increasing joblessness of both manufacturing and non-manufacturing recoveries
in recent decades is driven by the disproportionate loss of jobs that perform routine tasks.
Finally, our consideration of own-, upstream- and downstream-industry NTR gaps
contributes to a growing literature related to supply-chain co-location.

Baldwin and

Venables (2012), for example, consider dierent forms of supply chains that emerge in
response to the forces that encourage (e.g., transport costs) or discourage (e.g., factors
costs) co-location. A key implication of the model they develop is that oshoring may
jump discretely if a change in trade costs triggers a relatively large portion of a supply
chain to move abroad. Relatedly, Ellison, Glaeser and Kerr (2010) show that proximity
to input suppliers and nal customers is the most important factor in the agglomeration
patterns of U.S. manufacturing industries. Here, we use the own NTR gap to identify
employment loss associated with potential increased competition from China in one's own
industry. The upstream and downstream NTR gaps, by contrast, help identify employment loss due to greater potential Chinese competition in the industries of establishments'
major suppliers and customers.

4

Dixit and Pindyck (1994) provide an overview of this real options literature.

53 Data
3.1

Measuring Establishments' Employment

Our principal dataset for tracking manufacturing employment is the the U.S. Census
Bureau's Longitudinal Business Database (LBD), assembled and updated annually byJarmin and Miranda (2002).

The LBD tracks the employment and major industry ofvirtually every establishment with employment in the non-farm private U.S. economy
annually as of March 12, from 1976 to 2009.

5 In these data, establishments correspondto facilities in a given geographic location, such as a manufacturing plant or retail outlet,
and their major industry is dened as the four-digit SIC or six-digit NAICS category
representing their largest share of shipments. Information from Census' Company Organization Survey is used to map establishments to rms, and longitudinal identiers
in the LBD allow establishments and rms to be followed over time. With these identiers, we can determine the births and deaths of establishments and rms and thereby
decompose changes in industry employment along intensive and extensive margins ofadjustment.
For some of our results, we augment the data in the LBD with detailed establishmentlevel characteristics from Census's quinquennial Census of Manufactures (CM), conductedin years ending in 2 and 7. For every manufacturing establishment, the CM tracks thevalue of shipments, value added, capital (book value), production hours and a breakdown

6of employment between production and non-production workers.

Nominal data aredeated using industry-level price indexes in the NBER-CES Manufacturing Industry

7

Database from Becker and Gray (2009).

The long time horizon considered in this paper encompasses the use of two dierent
industry classication schemes: the Standard Industrial Classication (SIC), used until
1997, and the North American Industry Classication System (NAICS) used thereafter.
The switch between these schemes as well as changes within each scheme complicate creation of an accurate and consistent set of manufacturing establishments across which

8 In addition, the set of activities that are classied

outcomes can be compared over time.

as manufacturing changes between schemes. Some industries within printing and publishing, for example, are characterized as manufacturing under the SIC, but not the NAICS,
while others, e.g., retail bakeries, are considered manufacturing under the NAICS, but
not the SIC.
We develop a new SIC-NAICS concordance to ensure that our results are not driven

5

Excluded industries include Agriculture, Forestry and Fishing, U.S. Postal Service, Certicated Pas-

senger Air Carriers, Elementary and Secondary School, Colleges and Universities, Labor Organizations,
Political Organizations, Religious Organizations and Public Administration. Businesses without employment are also excluded.

6

Information in the CM is reported directly by establishments, whereas data in the LBD are drawn

primarily from administrative IRS data. As a result, we use data from CM when those data are availableforboth the beginning and end years of any sample we examine.

7

These data are available at http://www.nber.org/data/nbprod2005.html. As they end in 2005, we

assume that prices rise in 2006 and 2007 at the same rate as observed for 2005.

8

The SIC and NAICS classications are revised every ve years as part of the CM. Some of theserevisions are more extensive than others, e.g., the 1987 revision of the SIC.

6

by such changes, and use it to construct a conservative set of constant manufacturingindustries and establishments that span intervals of interest. Starting with the standard
SIC to SIC, SIC to NAICS and NAICS to NAICS concordances used by the U.S. Census
Bureau, graciously provided by Randy Becker and Wayne Gray, we use the algorithm
developed in Pierce and Schott (2012) to create families of four-digit SIC and six-digit

9 Unless

NAICS codes that group related SIC and NAICS categories together over time.

noted otherwise, our use of the word industry below refers to these families. While themajority of these families contain either manufacturing or non-manufacturing categories
exclusively, a subset contains a mixture of the two.
To create a constant manufacturing sample for a given time interval, we drop twosets of establishments from the universe of manufacturing establishments available forthat interval. First, we drop all establishments from families containing a mix of constantmanufacturing and non-constant-manufacturing SIC or NAICS industries.

This drop

occurs over all intervals, as the composition of a family is time invariant.

Second, we

drop all establishments whose major industry switches between manufacturing and nonmanufacturing over the interval of interest. This drop does depend on the interval, as thesame establishment might remain in manufacturing for some intervals but not others.
Figure 1 displays annual employment in our constant manufacturing sample against
the manufacturing employment series available publicly from the U.S. Bureau of Labor
Statistics.

10

As expected, given the procedure outlined above, the constant manu-

facturing sample accounts for less employment than the BLS series. Despite this leveldierence, however, the two series track each others' movements over time quite closely.
Figure 2 compares the cumulative percent change in employment for constant manufacturing versus non-manufacturing as a share of their respective 1981, 1990 and 2001
levels.

As indicated in the left panel of the gure, the decrease in manufacturing em-

ployment after 2001 stands out in several respects. First, it exhibits no recovery; while
employment declines after 1981 and 1990 begin to reverse in two and four years, respectively, employment falls more or less steadily from 2001 to 2007.

Second, except forthe second year of the 1981 recession, the drop in manufacturing employment after 20019

For example, if an SIC code splits into several NAICS codes between 1997 and 2002, the SIC code andits NAICS children would be grouped into the same family. If one of those NAICS codes later concords
with an updated NAICS code, whose family history includes a broader set of SIC, those subsequent
NAICS and SIC codes also join the original family. Given this process, it is easy to see that some families
can grow to be quite large. For this reason, we have created several concordances that limit the inclusionof children that do not account for some threshold level of the parent's activity. (Industry-to-industry
concordances generally specify both the identity of a parent's children as well as the share of activity 
usually output or employment  which they inherit.) These limits create a tradeo. Higher thresholds
generate a larger number of families with more closely related underlying SIC and NAICS codes. Lower
thresholds lead to a smaller number of families, most of which are likely to include both manufacturing
and non-manufacturing codes. As a result, lower thresholds create spawn constant manufacturing sample
results in a much smaller share of raw manufacturing employment than higher thresholds.

10

Series CEU3000000001, available at www.bls.gov. As the BLS series is NAICS-based, manufacturing

employment prior to 1997 excludes SIC industries that do not map into NAICS manufacturing industries.
As noted above, our sample is SIC-NAICS-based, meaning that we also drop NAICS industries notclassied as manufacturing under the SIC. For further detail on construction of the BLS series, see
Morisi (2003).

7

dwarfs the previous declines by a wide margin. Third, losses within manufacturing aresubstantially more severe and long-lasting than those outside manufacturing.

3.2

11

Calculating the NTR Gap

The United States has two principal tari classications. NTR or column 1 taris are
the rates applied to countries with which the United States has normal trade relations,
including members of the WTO. Non-NTR or column 2 taris, originally set in the
Smoot-Hawley Tari Act of 1930, are applied to countries with which the United States
does not have normal trade relations, e.g., North Korea. Both types of taris are set atthe product level, where products are dened according to the aggregation scheme used
to track international trade. In 1989 the United States joined most other countries inusing the Harmonized System (HS) to classify imports and exports. The rst six digits ofthese categories are common across countries and are managed by an international nongovernmental organization. Use of additional digits (i.e., beyond 6) is at the discretionof individual countries. While U.S. import and export transactions are tracked according
to ten-digit HS categories, U.S. import taris are assigned according to eight-digit HS
categories, often referred to as tari lines.
As discussed in greater detail in Pregelj (2005), the U.S. Trade Act of 1974 gives the
President the power to temporarily grant NTR status to non-market economies otherwiseclassied as non-NTR, subject to certain conditions. While this act does not require Congressional approval, it can be overturned by a congressional vote of disapproval. Chinarst received NTR status on an annually renewable basis in 1980.

Initially uncontro-

versial, these renewals began facing strong opposition in Congress after the Tiananmen
Square incident in 1989, with the result that their success became less certain during
the 1990s (see, for example, Wang 1993). On October 10, 2000, Congress granted China

P NTR) in another controversial vote that cleared the way for China's

permanent NTR (12 As discussed in Pregelj (2005) and numerous

accession to the WTO in December 2001.

popular press articles written at the time, Congress' willingness to confer

13
was uncertain right up until the vote.

Feenstra, Romalis and Schott (2002), henceforth FRS, report

P NTR status

ad valorem equivalent

14 We measure the potential tari

U.S. NTR and non-NTR tari rates for 1989 to 2001.

increase faced by U.S. importers of a Chinese good prior to

P NTR as the gap between

these rates, where a higher gap indicates a greater potential increase. Figure 3 plots the11

Dey, Houseman and Polivka (2012) note that manufacturers' use of temporary employment services

as a substitute for permanent employees increased from 1989 to 2004, but that this substitution does notaccount for the steep employment decline observed after 2000.

12

While the United States also signed a bilateral trade agreement with Vietnam in December 2001,

trade with Vietnam in the early 2000s was small.

13

Press coverage in 1999 and 2000 gives no indication that conferral of

P NTR was imminent. Support
The New York Times,

Shrinks for China's Trade Status by David Sanger in the June 4, 1999 edition offor example, discusses the high level of controversy associated with the mere annual renewal of NTR

P NTR was implemented. See also China and the WTO: Dire Straits in the May
The Economist.
14
U.S. import taris can include both ad valorem and specic (i.e., per unit) components. The tari
rates tracked by FRS are the ad valorem equivalent of all taris applied to a tari line.

status the year before
23, 2000 edition of8

distribution of the NTR gap across tari lines at four-year intervals from 1989 to 2001.
As indicated in the gure, these distributions are relatively stable across time, with the
major change being a shift toward higher NTR gaps as Uruguay-Round reductions in
U.S. NTR rates are implemented in the mid 1990s. This shift does not aect the rankingof tari lines over time substantially: correlation coecients for the 1989 versus 1993,
1993 versus 1997 and 1997 versus 2001 NTR gaps, for example, are 0.97, 0.77 and 0.94,
respectively. This stability, the fact that non-NTR tari rates were initially set decades
before, and uncertainty surrounding China's eventual receipt ofP NTR, argue in favorof their plausible exogeneity with respect to lobbying or other activity associated withP NTR and China's subsequent accession to the WTO.

For each NAICS industry n we compute the average NTR gap across the eight-digit
HS tari lines with which it is associated using concordances provided by the U.S. Bureau
of Economic Analysis (BEA).

15 We then compute an upstream and downstream NTR

gap for each NAICS industry n using information provided in the use table of the BEA's
1997 input-output matrix, which reports the value of all industries g that are used to

16 The upstream NTR gap for industry n is the weighted average

produce industry n.

NTR gap across the industries g that supply n, using the use values (v ) as weights,p
N T R GapU
n =

X

Up
wgn
N T R Gapg ,(1)gwhere,

Up
vgn
Up
wgn
= P Up .
g vgn

(2)

To compute the downstream NTR gap, we follow the same procedure after reversing theg and n indexes in the use table.17 As the input-output matrix includes many services
that are not traded, and therefore are assumed to have an NTR gap of zero, the weighted
averages taken in equation 1 are lower than the own-industry NTR gap.
We use eight-digit HS NTR and non-NTR rates from 1999, the year before the policychange, to create the own-, upstream- and downstream-industry NTR gaps used throughout the paper. This choice has little substantive impact, as we obtain ndings very similar
to those reported below using NTR gaps from any of the years available to us, 1989 to
2001.
Table 1 reports the average own-, upstream- and downstream-industry NTR gaps by
three-digit NAICS categories. We nd that own-industry NTR gaps tend to be higher for

15

We

cord.txt

map

tari

available

lines
at

to

BEA

input-output

categories

using

the

http://www.bea.gov/industry/zip/NDN0317.zip.

1997

concordance

We

then

output categories to NAICS categories using the 1997 concordance NAICS-IO.xls

HSCon-map

input-

available at

http://www.bea.gov/industry/zip/ndn0306.zip.

16

This

matrixiscontained

in

the

le

NAICSUseDetail.txt

availableat

http://www.bea.gov/industry/zip/ndn0306.zip.

17

The use values in the input-output matrix are assigned according to BEA input-output categories.

As a result, we split the use value evenly among all NAICS industries to which a BEA input-output
category maps.

9

labor-intensive industries such as apparel, textiles and furniture, and lower for capitalintensive industries such as food and petroleum.

As noted in the introduction, onebenet of our dierence-in-dierences specication is its ability help control for suchvariation: even though high-gap industries are not identical to low-gap industries, ourapproach controls for any time-invariant dierences between industries.

At the sametime, comparison of industries across similar stages of the business cycle allows us toaccount for the cyclicality of U.S. manufacturing employment.
Table 1 also highlights variation in the extent to which industries are exposed torisk reduction via their upstream suppliers and downstream customers.

Industries in

Computers and Electrical Equipment, for example, tend to face both high upstream andhigh downstream exposure. Machinery and Transportation Equipment industries, on theother hand, exhibit high upstream gaps but relatively low downstream gaps, while thereverse is true for industries in Chemicals and Primary Metals. Across all six-digit NAICS
industries, we nd that the correlations between own and upstream, own and downstream,and upstream and downstream NTR gaps are 0.79, 0.18 and 0.22, respectively.
While we nd substantial variation in NTR gaps across industries, we nd no change
in the revealed taris imposed on Chinese goods before and after the change in U.S. policyin late 2000.

We compute the revealed tari on Chinese imports as the average ratio

between duties collected and dutiable value across all manufacturing products imported
from China using the trade data in FRS, and extended by Schott (2008). As demonstrated
in Table A.1 of the electronic appendix, there is no statistically signicant dierence inthe revealed taris applied to Chinese imports during the period of interest. This result
suggests that the surge in U.S. imports from China documented below is not due to a18

reduction in applied import tari rates.

3.3

U.S. Business Cycle Peaks

The NBER Business Cycle Dating Committee identies four business cycle peaks during
the period for which our employment data are available: January 1980; July 1981; July

19 Given the proximity of the 1980 and 1981 recessions, as well as

1990; and March 2001.

the greater severity of the latter, we ignore the 1980 peak and focus on employment loss
following the 1981, 1990 and 2001 peaks.
As illustrated in Figure 4, the 1981 recession stands out in terms of declining economic
activity, measured either in terms of aggregate GDP or the Industrial Production Index(IPI). Peak to trough, the IPI falls 8.6 percent during the 1981 recession versus less18

As part of its entry into the WTO in late 2001, China beneted from a relaxation of quotas on

apparel and textile products associated with a phasing out of the global Agreement on Textiles andClothing (Brambilla et al. 2009). The implicit change in taris associated with removal of these quantitative restrictions is not captured by this regression. The entire set of apparel and textile industries(irrespective of whether quotas on their products were relaxed) accounted for approximately 7 percent of
U.S. manufacturing employment in 2000. Dropping the apparel and textiles industries from our analysis
yields results similar to those reported below for all industries.

19

These data are available at www.nber.org/cycles.html. We note that these peaks do not necessarily

correspond to the manufacturing employment peaks of these business cycles. We ignore the 2007 recession
as the the LBD is available only until 2009, and the last CM available is 2007.

10

than half that amount during the 1990 and 2001 recessions.

Loss of manufacturing

employment, by contrast, is far more severe following the 2001 recession than the 1981
and 1990 recessions.
While this loss is dramatic, it is important to note that it is not accompanied bya decline in value added.

Indeed, as illustrated in Figure 5, real value added in U.S.

manufacturing as measured by the BEA continues to increase after 2001, though at a
slower rate (2.8 percent) compared with the average from 1948 to 2000 (3.7 percent). This
growth, combined with the sharp decline in employment, implies a substantial increasein labor productivity, which we examine in greater detail below.

4 Theoretical Motivation
Our empirical analysis is guided by recent models of exporting under uncertainty andapplication of their insights to deterministic models of importing.
Handley and Limao (2012) consider the eect of import-tari uncertainty in a standard, Melitz- (2003) style model of exporting, where rms that are heterogeneous in unit
costs consider the irreversible investment needed to enter an export market. As is wellknown in the deterministic versions of these models, rms enter the export market if
their unit cost is below a cuto that equates the xed cost of exporting with the protearned in the export market. The authors show that if uncertainty regarding either thetiming or the magnitude of tari changes in the destination market falls, the cost cuto
for exporting rises, as relatively low-cost rms lose their incentive to wait and see howtaris change before entering. In an extension, Handley (2012) demonstrates that entry
also rises with reductions in the maximum level to which taris might increase, even if
the actual applied taris remain the same. Here, we interpret China's receipt of

P NTR

as just such a decline, and therefore expect it to be associated with an increase in thenumber of Chinese rms that export to the United States as well as the value of goods
they export.
For insight into the potential reaction of rms in the United States, we rely on models
of importing similar in spirit to Melitz (2003).

20 Groizard, Ranjan and Rodriguez-Lopez(2012), for example, build a framework in which heterogeneous domestic manufacturers
choose the fraction of inputs to source from a low-wage country based on their productivity draw and xed and variable costs of importing. A decline in import taris raises thedemand for foreign inputs and thereby reduces domestic employment in the manufacturing sector for two reasons. First, rms that are not productive enough to import shrink
relative to rms that use imports to lower costs and gain market share. Second, rmsproductive enough to engage in oshoring see their employment fall as foreign inputs aresubstituted for those previously produced in-house. In theory, these rms may experience a net increase in employment if price declines associated with oshoring induce a21

substantial enough increase in demand for the their output.

20

Though trade costs in these models are deterministic, we assume that reductions in import-tari

uncertainty yield the same implications as declining import taris.

21

Empirically, Groizard, Ranjan and Rodriguez-Lopez (2012) show that shows that reductions in U.S.

trade costs between 1992 and 2004 inuenced both gross job creation and gross job destruction among

11

P NTR might aect U.S. employment by promoting indirect as well as direct oshoring. Though many models of importing, like the one just described, focus on producers who import inputs directly from foreign suppliers, one can imagine the emergence ofintermediaries (e.g., wholesalers) that match foreign goods to purely domestic producers.
Bernard et al. (2010), for example, report that wholesalers and retailers account for morethan half of U.S. imports from China in 2002. In the model ofexporting developed byAhn, Khandelwal and Wei (2010), rms that are not suciently productive to overcome
the costs associated with nding their own foreign customers export via an intermediary.
In our setting, the shift in U.S. trade policy might promote the expansion and entry ofagents that facilitate indirect importing. By further driving down the relative demand fordomestically produced but higher-priced substitutes, these agents may encourage local
producers to shrink, exit or decide not to enter.
Finally, a reduction in import-tari uncertainty may encourage rms to invest in thedevelopment of either labor-saving technologies (other than direct or indirect oshoring),
or product mixes that compete less directly with imports from China. Acemoglu (2002),
for example, develops a model in which trade liberalization can aect the skill bias oftechnical change.

5 P NTR and Industry Employment
In this section we show that employment losses are larger in industries where the threatof tari hikes declined the most. Examining employment loss by intensive and extensive
margins of adjustment, we also nd that the shift in U.S. policy is associated with bothsuppressed job creation and exaggerated job destruction.

5.1

Industry Employment Growth

We investigate the relationship between

d Years After Each PeakP NTR and industry-level cumulative employ-ment growth using an OLS dierence-in-dierences specication that identies dierential growth in high NTR gap industries (rst dierence) d years after 2001 compared toanalogous periods after the 1981 and 1990 peaks (second dierence). We estimate thefollowing equation using data from the LBD and CM:∆Empit:t+d /Empti = αdo 1{t = 2001} ×

X

N T R Gapio + γd Xit + δid + δtd + εitd .

(3)oThe dependent variable is the cumulative percent change in industry i's employment
relative to its peak t level. To examine how the eect of
estimate this equation separately for intervals from d

P NTR evolves over time, we= 1 to d = 6 years after eachpeak. The rst three variables on the right-hand-side of the specication are the threedierence-in-dierence (DID) terms: interactions of an indicator variable for the 2001
peak and the own, upstream and downstream NTR gaps for industry i, which vary by
manufacturing establishments in California.

12industry but not by t or d. These three NTR gaps are indexed by o.

Xit is a vector ofindustry characteristics in peak year t. For regressions using the LBD, Xit contains onlyt

the log level of peak-year t employment, ln(Empi ). Regressions based on the CM include

δid andδtd are industry and peak-year xed eects, which control for time-invariant dierencesa larger set of controls and are estimated across decades that span each peak.

between industries and common macro-level shocks. These xed eects are indexed byd, i.e., that they are specic to a particular interval after each peak.
The rst six columns of Table 2 report results using the LBD. We nd that almost
all estimates of

αdo are negative and statistically signicant at the ten percent level(noted with bold-faced type), and that their absolute magnitudes generally rise with d,indicating that the association between employment growth and

P NTR is persistent. We

assess the economic signicance of the coecients in the last block of rows of the table bymultiplying the estimate for each DID term by the average NTR gaps for manufacturing
as a whole reported in the nal row of Table 1.

The implied eects are substantial:

the cumulative employment growth of an industry with the average NTR gaps is -8.7
percentage points (-0.052*0.34-0.450*0.11-0.189*.11) lower one year after the 2001 peakcompared to previous downturns. This implied eect rises in absolute magnitude to -29.6
percentage points (-0.334*0.34-1.362*0.11-0.334*.11) six years after the peak.
The nal four rows of of Table 2 reveal that all three NTR gaps contribute signicantly to the implied impact ofP NTR, implying that an industry's employment may fall

substantially even if it is only indirectly exposed to trade liberalization via its upstream
suppliers or downstream customers.

22 Of the -29.6 percentage-point dierential reduc-tion in cumulative industry employment just discussed, half (-14.7 percentage points) is
accounted for by the upstream NTR gap, 38 percent (-11.2 percentage points) by the
own-industry NTR gap and 12 percent (-3.7 percentage points) by the downstream NTR
gap.
The negative and statistically signicant coecient with respect to the upstream NTR
gap is particularly interesting as, in theory, the elimination of import-tari uncertainty on
upstream industries can have two countervailing eects on own-industry employment. As
noted in Section 4, if reduction in upstream uncertainty results in greater availability oflower-cost inputs, own-industry costs and therefore prices might decline, boosting demand
and the need for workers.

23

On the other hand, if eliminating upstream uncertainty

pushes upstream production oshore, customers of that production may nd it optimal
to move to the same oshore location in order minimize transportation or other costs that
depend on geographic proximity.

The strong association between employment growth

and the upstream NTR gap exhibited in our results suggests that the second channel
dominates.
The nal column of Table 2 reports results based on the CM, which are estimated22

To the extent that an establishment contains several sections of a supply chain, the upstream anddownstream NTR gaps may identify exposure to risk reduction that is more direct than indirect.

23

A number of papers, beginning with Amiti and Konings (2007), show tari reductions on the inputs

of a rm's major output industry make a substantial contribution to productivity growth following tradeliberalization.

13

across the 1977-87, 1987-97 and 1997-07 CM decades.

24 Use of the CM allows us toinclude capital and skill intensity as two additional industry-level control variables, andthe longer time interval captures employment changes that might precede ocial NBER
peaks. We measure capital intensity as the log of the ratio of real book value of capitalt

to total employment, ln(K/Empi ), and skill intensity as the log log of the ratio of non-t

production workers to total employment, ln(N P/Empi ). As indicated in the table, allthree DID estimates remain negative and statistically signicant at conventional levels.
Economic signicance is also similar, with the implied impact of the shift in U.S. policy
being -31.8 percentage points for growth between 1997 and 2007 versus previous decades,
compared with the -29.6 percentage point decline implied for the longest of the LBD
intervals, 2001 to 2007.

5.2

Industry Employment Growth by Margins of Adjustment

We use the longitudinal identiers in the LBD to decompose industry job loss into six
mutually exclusive and comprehensive gross margins of adjustment that are extensiveand intensive to establishments. We examine one intensive and two extensive margins.
The intensive margin traces plant expansion (PE) and plant contraction (PC) in

25 The rst extensive margin

employment within continuing plants at continuing rms.

tracks changes in employment due to plant birth (PB) and plant death (PD) within
continuing rms. For this margin, a plant is coded as being born within an incumbent
rm if it appears in the data for the rst time during the noted interval and is associatedwith an already present rm identier. Similarly, a plant is classied as dying within acontinuing rm if it is part of the rm at the beginning of the interval but is no longer
present afterward. The second extensive margin accounts for employment growth due torm birth (FB) and rm death (FD). A rm is classied as being born during in aninterval if none of its plants are present in the LBD before that interval. Likewise, a rm
is classied as dying if all of its plants no longer appear in the data after the interval.
Figure 6 reports a decomposition of cumulative manufacturing employment growthin the years after 1981, 1990 and 2001 according to the three gross job creation (PE, PB
and FB) and three gross job destruction (PC, PD and FD) margins.

26 As indicated inthe gure, the contribution of gross job creation in the years after 2001 is small relative
to the years after previous peaks, especially four to six years out. As a result, the job
destruction that occurs immediately after 2001 is not oset by subsequent job creation,
as in the two previous business cycles. Figure 7 performs a similar decomposition withrespect to the net intensive and net extensive margins of adjustment, referred to as PEC,

24
25

In this case, d = 10 and the rst indicator variable for each DID term is 1{t = 1997}.
Surviving plants whose ownership is transferred between surviving rms are included in the intensive

margin. Excluding these plants from the intensive margin has no material impact on our results. We notethat while this margin is intensive with respect to the establishment, it may be extensive with respect toproduction units with a plant, such as production lines. Unfortunately, the change from SIC to NAICS
discussed in Section 3 prevents us from investigating product-mix changes across business cycles within
continuing plants.

26

Growth by gross and net margin of adjustment is presented in tabular form in Table A.3 of theelectronic appendix.

14

PBD and FBD, respectively.

Here, the behavior of the intensive and rm birth-death

extensive margins stand out. In particular, initial declines in the net intensive margin
disappear four or ve years after the 1981 and 1990 peaks. That is not the case after the
2001 peak, where the declines are both more substantial and more persistent.
We assess the impact of China's receipt ofP NTR status on gross margins of employ-ment adjustment by estimating equation 3 separately for each margin m and intervald. The dependent variable in each of these regressions is the cumulative change in employment in industry i and margin m from t to d as a percent of initial industry (notindustry-margin) employment,

t:t+d∆Empim
=
Empti



t
Empt+dim − Empim

Empti


.

(4)

As a result, changes across margins sum to the dependent variable in equation 3, i.e.,

P

t:t+d
t:t+d
t
/Empti .
m ∆Empim /Empi = ∆Empi
Table 3 reports results for d = 6. Given the normalization in equation 4, the sumof the DID coecients for the six gross margins, in the rst six columns of the table,
sum to the DID coecient for overall employment, estimated in the previous section andrepeated in the nal column of Table 3 for comparison. As above, estimates are in bold
if they are statistically signicant at the ten percent level, and the nal block of rows
summarizes economic signicance.
Coecient estimates for the DID terms in Table 3 are negative for all of the gross margins and, while their statistical signicance varies, the p-values reported in the eleventh
row of the table indicate their joint signicance for all margins. In terms of economic
signicance, we nd a strong link between

P NTR and both gross job creation and gross

job destruction. As indicated in the nal rows of the table, -13.0 percentage points (.063-.020-.047) of the predicted -29.6 percentage point dierential decline in cumulative
employment associated withP NTR from 2001 to 2007 is due to suppression of plant

expansion (PE), plant birth (PB) within continuing rms and rm birth (FB), versus-16.6 percentage points (-.053-.067-.045) for elevated plant contraction (PC), plant death(PD) within continuing rms and rm death (FD).
DID estimates for all time periods and margins are reported in Table A.4 of the electronic appendix. We summarize their economic signicance in Table 4 by decomposing
the overall implied impact of the shift in U.S. policy over each interval by NTR gap(panel A), gross job creation versus destruction (panel B) and both of these dimensions
simultaneously (panels C and D). As illustrated in panel B, anemic job creation accounts
for an increasingly large share of the overall implied cumulative eect ofP NTR, risingfrom 22 percent (-1.9/-8.7) between 2001 and 2002 to 44 percent (-13.0/-29.6) between
2001 and 2007.
This relationship between gross margins and

P NTR suggests a strong role for trade

policy in the well-documented joblessness of the 2001 manufacturing recovery. Faberman's (2012) analysis of post-war U.S. manufacturing employment, for example, highlights a precipitous and persistent decline in job creation rates beginning in the late 1990s.
That decline, coupled with elevated job destruction in the years after 2001, are the reason that manufacturing employment does not recover after the 2001 recession. Here, we

15

provide an explanation for this fundamental shift in manufacturing trends by showingthat anemic job creation and exaggerated job destruction coincide with reductions inimport-tari uncertainty.
Our results also provide intuition for why previous studies have failed to nd a connection between import competition and manufacturing job loss during the 2000s. Mankiw
and Swagal (2006), for example, argue against a role for trade because few of the rms thatreport mass layos during this period attribute them to oshoring.

27 But this approach

misses two potentially important links between employment and trade policy. First, it
focuses on job destruction, whereas here we demonstrate that a large portion of the job
loss associated withP NTR is due to a lack of job creation  particularly plant expansion

and rm birth. Second, it concentrates on direct oshoring, i.e., within-rm movementof jobs abroad. But trade liberalization may induce several forms of indirect oshoring(beyond suppressing job creation). Table 2 reports a strong relationship between job loss
and a reduction in import-tari uncertainty in rms' downstream industries, potentiallydriven by the disappearance of one's domestic customers. In addition, the strong contribution of rm death in Table A.4 suggests that rms competing head-to-head with China
may simply exit rather than move production oshore.
Panels C and D of Table 4 reveal that the contributions of the three NTR gaps
to gross job creation and destruction vary.

We nd that the own-industry NTR gap

contributes relatively more to the implied impact ofP NTR via suppressed job creation

than exaggerated job destruction. For d = 6, for example, it accounts for 45 percent (.059/-.130) of the implied decline associated with gross job creation, but just 32 percent(-.053/-.166) of the implied decline associated with gross job destruction. The up- anddownstream NTR gaps, by contrast, are relatively more inuential with respect to gross
job destruction.
Estimations using the CM are similar to those from the LBD in terms of statistical andeconomic signicance, with one intriguing dierence: lackluster job creation accounts fora larger share of the implied impact ofP NTR over the longer 1997-07 CM time intervals(54 percent) compared to the shorter 1997-07, d = 6 LBD time intervals (44 percent).
Examination of DID estimates by gross margin in Table A.4 indicates that this dierenceis driven by more anemic rm birth and plant birth within continuing rms across theCM time intervals.
One interpretation of this dierence between the LBD and CM estimations is that
anticipation of an impending change in China's status in the years leading up

P NTR

discouraged entry before encouraging exit. Such an outcome is consistent with the onset ofa decline in job creation a few years ahead of

P NTR (as documented in Faberman 2012),

and can be rationalized by models of investment under uncertainty: as the likelihood ofa change in China's status rises, the expected prots of entering decline, suppressing rmbirth. Firms that have already paid the sunk costs of entry to be in the market, however,

27

The

Mass

Layo

Statistics

database

from

the

U.S.

BureauofLabor

Statistics(http://www.bls.gov/mls/) identies rms who lay o more than 50 workers for more than 30
days. If the layo is associated with the movement of production to either another location within thecompany or to another company under a formal contract, respondents are also asked to identify whether
the new location is inside the United States.

16

have no reason to exit until the greater competitive pressure from China manifests, which
presumably does not happen until China's change in status actually occurs.

5.3

P NTR and the Jobless 2001 Recovery in Manufacturing: A Coarse
Counterfactual

We provide an alternate estimate of

P NTR's contribution to the joblessness of the 2001

manufacturing recovery by comparing observed employment to a coarse counterfactual.
We multiply the estimated DID coecients for each gross margin and time interval inelectronic appendix Table A.4 by the corresponding NTR gaps for each industry to compute the margin-industry-interval dierential cumulative growth rate associated with theshift in U.S. policy. We then add the employment implied by these growth rates back to
the actual employment for each margin, industry and interval. Summing over industries
yields counterfactual employment by margin for the manufacturing sector as a whole.
Figure 8 displays the results. The solid black line in the gure traces out the deviationof actual employment from the 2001 peak from 2001 to 2007.

The two dashed lines

reveal the implied eect of adding back employment due to exaggerated job destruction
and anemic job creation.

As indicated in the gure, our estimates imply that, absent

the shift in U.S. policy, U.S. manufacturing employment would have risen nearly 10
percent between 2001 and 2007, versus an actual decline of more than 15 percent. WhileP NTR cannot explain the overall joblessness of the 2001 recovery, its implied impact
in manufacturing is substantial.

Indeed, the data displayed in Figure 8 indicate that

manufacturing employment would have been higher by over 4 million employees in 2007
without the eect ofP NTR.

6 P NTR and Plant Outcomes
In this section we examine the eect ofP NTR on manufacturing plants using data fromthe CM. These data allow us to examine a broad range of outcomes and to use a rich setof establishment characteristics as controls. We begin by estimating a plant-level variantof equation 3:∆ln(Empt:t+10) = αdo 1{t = 1997} ×ei

X

N T R Gapio + γ ln(Xet ) + δt + δe + εeit ,(5)oThe dependent variable is the log dierence in establishment e's employment across CM
decades, i.e., t ∈ {1977, 1987, 1997}. The three DID terms capture the own, upstream
or downstream NTR gaps of the establishment's major industry i.

Xet is a matrix ofplant characteristics in year t that includes employment, capital and skill intensity, plant

28 We include plant xed eects, δ , toage and plant total factor productivity (TFP).

28e

We measure TFP with an index number approach in which output is measured as the logarithmof deated revenue and inputscost of materials, production employment, non-production employment
and book value of capitalare weighted by the average cost share for each input, at the industry level.
While we deate revenue with industry-level price deators from Bartelsman, Becker and Gray (2005)

17

control for unobserved plant characteristics that are time invariant and, as above, include
peak-year t xed eects to mitigate the impact of cyclicality. Our use of a log dierence
as the dependent variable restricts our examination to the intensive margin.
Table 5 reports results for total employment growth as well as growth in non-production
employment, production employment and production hours. As above, the nal block ofrows in the table computes economic signicance, in this case by multiplying the DID
estimates by the respective averages for manufacturing as a whole (from the nal row ofTable 1), exponentiating, and subtracting 1.
Three trends stand out.

First, we nd that the implied impact ofP NTR on 1997

to 2007 employment growth is almost four times stronger for production workers (-17.0
percentage points) than non-production workers (-4.6 percentage points). Second, resultsfor production hours are very similar to those for production workers, suggesting no
substantial change in hours worked per employee.

Finally, the relationship between

the own-industry NTR gap and the dierential growth of non-production workers is
positive and statistically signicant.

This implies that non-production worker growth

for a continuing plant with the average own-industry NTR gap is 8.3 percentage points
higher from 1997 to 2007 than in the previous two decades.
To the extent that non-production workers embody higher levels of human capitalthan production workers, this outcome suggests a relative increase in continuing plants'
skill intensity that is consistent with evidence of skill-biased technical change and productmix upgrading found elsewhere in the literature. Bloom, Draca and Van Reenen (2012),
for example, demonstrate innovation and greater use of computers by European rmsin response to heightened competition from China, while results in Bernard, Jensen andSchott (2006), Khandelwal (2010) and Schott (2008) suggest that high-wage countries
alter their product mix to escape competition from low-wage countries like China. Moreover, to the extent that production workers are more likely to engage in routine tasks, ourresults are in accord with Jaimovich and Siu's (2012) demonstration that jobs focusing
on such tasks are more likely to disappear during economic downturns. Here, we showthat in manufacturing, this disappearance can be associated with a major change in U.S.
trade policy.
Table 6 examines the eect ofP NTR on plant attributes other than employment.

We analyze changes in continuing plants' real capital stock, real value added and real
shipments, as well as the ratios of each of these outcomes to total employment. The rstcolumn of the table repeats results for overall employment from Table 5. The second andthird columns reveal that the

P NTR-implied decline in continuing plants' real capitalstock is less severe than the decline their employment, yielding a relative increase in
implied capital intensity over 1997 to 2007.

This link between

P NTR and increased

capital intensity dovetails the just-discussed rise in skill intensity.
The nal four columns of Table 6 examine the relationship between the shift in U.S.
trade policy and the relative growth of continuing plants' labor productivity. Coecient
to obtain our measure of output, we note that this approach does not control for within-industry pricedierences and the level of observed productivity can be aected by changes in plant-level prices. Fora detailed discussion of the properties of this TFP measure, as well as the limitations of revenue-based
forms of productivity, see Foster, Haltiwanger and Syverson (2008).

18

estimates imply thatP NTR is associated with declines in real shipments and real value

added that are about two times stronger than the decline in employment, i.e., -30.6 and-32.1 percentage points versus the -13.2 percentage points reported in the rst column ofTable 6. As a result, continuing plants possessing the average NTR gaps are predicted
to experience labor productivity growth that is -18.1 and -19.6 percentage points lower
between 1997 and 2007 than was experienced by these plants in the prior two decades.
Given the trend in aggregate labor productivity growth implicit in Figure 5, these results imply that most of the growth in labor productivity experienced in the years after
2001 is due to continuing plants with relatively low-exposure to reductions in importtari uncertainty as well as reallocation towards higher labor-productivity entrants along
the extensive margin. Price declines associated with indirect importing is one potential
channel for these gains.

7 P NTR and Trade Flows
In this section we use the U.S. Census Bureau's Longitudinal Firm-Trade Transaction
Database (LFTTD) assembled by Bernard, Jensen and Schott (2009) to assess the behavior of U.S. importers and Chinese exporters. The LFTTD tracks all U.S. international
trade transactions from 1992 to 2008 and links them to the
LBD and CM.

rm identiers used in the

29 For each import transaction we observe the identity of the U.S. importer,

the ten-digit Harmonized System (HS) product traded, the U.S. dollar value and quantity
shipped, the shipment date, the origin country and the foreign supplier of the imported
product.
As the rm-level U.S. import data are unavailable prior to the 1990s, we amend ourdierence-in-dierences specication to compare outcomes across trading partners rather
than business cycles,

1997:2007∆Zch= α{c = China} ∗ N T R Gaph + γln(Zh ) + δc + δh + εch .

(6)

We analyze the 1997 to 2007 interval to mimic the CM decades examined above, but

30

note that we nd similar relationships across narrower windows that begin in 2001.

1997:2007 represents the change in one of several dimensions of U.S. imports of ten∆Zch
digit HS product h imported from country c. The rst term on the right-hand side is aninteraction of an indicator variable picking out China and the NTR gap for the product. δcand δh represent country and product xed eects that control for unobserved countryand product-specic variation.

The DID coecient α captures the dierential changeof high NTR gap products (rst dierence) imported from China versus all other U.S.
trading partners (second dierence).

29
trol

The U.S. Census' Shippers Export Declaration Form and the U.S. Customs and Border PaForms

7501

and

7533

used

to

collect

information

on

U.S.

exportandimporttransac-tions are available at http://www.census.gov/foreign-trade/regulations/forms/new-7525v.pdf.
http://www.cbp.gov/xp/cgov/toolbox/forms/, respectively.

and

See U.S. Census (2002) for a discussionof the quality of the U.S. trade data.

30

Figure A.1 in the electronic appendix demonstrates a similar relationship with respect to U.S. importpenetration from China using publicly available data.

19

As product-country trade data exhibit an abundance of zeros, we use the normalized growth rate introduced by Davis, Haltiwanger and Schuh (1996) for the dependent
variable,1997
Z 2007 − Zch
1997:2007
,∆Zch= 1 ch2007
1997
+ Zch
2 Zch



(7)

which is bounded by 2 and -2 and equals those values for observations that start or end
at zero, respectively. For the same reason, we dene ln(Zh ) to be the denominator of theright-hand side term in equation 7 rather than the initial level.
Results for import value, the number of U.S. rms that import from China, thenumber of Chinese rms that export to the United States, and the number of importerexporter pairs are reported in Table 7.

We nd a positive and statistically signicantrelationship between the NTR gap and all of these dimensions of U.S. importing. Ourestimates imply that a product with the average NTR gap (0.32) exhibits growth inimport value from China between 1997 and 2007 that is 19.6 normalized percentage
points higher than the growth in import value across all other U.S. trading partners
over this period.

The dierential growth for the numbers of U.S. importers, Chinese

exporters and importer-exporter pairs is 32.2, 39.5 and 34.7 normalized percentage
points, respectively.
These large relative increases in U.S. imports from China as well as export and import
participation by Chinese and U.S. rms are impressive given thaton the tari rates actually applied to Chinese imports.

P NTR had no eect

They are also in remarkableaccord with the predictions of Handley's (2012) model of trade policy uncertainty, and an
application of the insights of that framework to models of oshoring. Indeed, U.S. imports
surge in precisely the set of goods where domestic employment loss is concentrated, andwith the exact trading partner that is the subject of the shift in U.S. trade policy.

8 Conclusion
This paper examines the sharp decline in U.S. manufacturing employment that occurs
after 2001. We show that this decline coincides with a major shift in U.S. trade policy
 the conferring of permanent normal trade relations status on China  that aected
expectations about how import taris on Chinese goods might change rather than theactual levels of these taris.
Our results provide evidence in favor of the two dominant explanations for the decline
in U.S. manufacturing employment that have appeared in the literature. Consistent withmodels of oshoring, we show that the largest declines in employment and increases inimports were found in the industries that experienced the greatest reductions in tari
uncertainty.

At the same time, in accord with models of skill-biased technical change

and product-mix upgrading, we show that reductions in import tari uncertainty areassociated with increases in continuing plants' skill and capital deepening.
Our results also raise a number of questions worthy of further study, including the
precise mechanism by which labor productivity grew in the years after 2001, the eectof

P NTR on skilled- and unskilled workers' wages, and the aect of the shift in U.S.
20

policy on the nature of U.S. manufacturing.
these questions in the near future.

21

We hope to contribute answers to all ofReferences[1] Acemoglu, Daron. 2002. Directed Technical Change. Review of Economic
Studies, 69(4): 781-809.
[2] Baily, Martin and Robert Z. Lawrence. 2004. What Happened to the Great
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[3] Baldwin, Richard and Anthony J. Venables. 2012. Relocating the Value
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Microeconomic Evidence and Macroeconomic Implications.

22

in Oliver Jean Blanchard and Stanley Fischer, eds., NBER Macroeconomics
Annual 1990. Cambridge, MA: MIT Press, pp. 123-168.
[14] Davis, Steven J. and Haltiwanger, John C. 1999. On the Driving Forces behind Cyclical Movements in Employment and Job Reallocation. American
Economic Review, 89(5), pp. 1234-1258.
[15] Davis, Steven J., John C. Haltiwanger and Schott Schuh. 1996. Job Creation
and Destruction. Cambridge: MIT Press.
[16] Dixit, A. K. and Robert S. Pindyck. 1994. Investment Under Uncertainty.
Princeton: Princeton University Press.
[17] Ellison, Glenn, Edward Glaeser, and William R. Kerr. What Causes Industry Agglomeration? Evidence from Coagglomeration Patterns. American
Economic Review 100 (June 2010): 11951213.
[18] Faberman, R. Jason. 2012. Job Flows, Jobless Recoveries and the Great
Moderation. Mimeo, Federal Reserve Bank of Philadelphia.
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Exports and Tari Data, 1989-2001. NBER Working Paper 9387.
[20] Groshen, Erica L. and Simon Potter. 2003. Has Structural Change Contributed to a Jobless Recovery? Federal Reserve Bank of New York 9(8):17.
[21] Jarmin,

Ron

and

Miranda,Javier.

2002.

The

LongitudinalBusiness

Database. Center for Economic Studies Discussion Paper 101647.
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[27] Levinsohn, James. 1999. Employment Responses to International Liberalization in Chile. Journal of International Economics 47:321-344.

23

[28] Mankiw, N.G. and P. L. Swagal. 2006. The Politics and Economics of Oshore Outsourcing. Journal of Monetary Economics. 53(5):1027-1056.
[29] Morisi, Teresa L, 2003. Recent Changes in the National Current Employment Statistics Survey. Monthly Labor Review (June).
[30] Pierce, Justin and Peter K. Schott. 2012. Concording U.S. Harmonized
System Categories Over Time Journal of Ocial Statistics (Forthcoming).
[31] Pregelj, Vladimir N. 2005. Normal-Trade-Relations (Most-Favored-Nation)
Policy of the United States. CRS Report For Congress RL31558.
[32] Revenga, A.L., 1992. Exporting Jobs? The Impact of Import Competition
on Employment and Wages in U.S. Manufacturing, Quarterly Journal of
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Brookings Papers on Economic Activity 1994(1), 1-69.
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[35] Sullivan, Daniel G. and Till von Wachter. 2009. Job Displacement andMortality: An Analysis using Administrative Data, Quarterly Journal of
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[36] Wang, Yangmin. 1993. The Politics of U.S.-China Economic Relations:
MFN, Constructive Engagement, and the Trade Issue Proper Asian Survey
33(5): 441-462.

24

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Table 1: Average NTR Gap Across NAICS Manufacturing Industries25ϭ΂ƚсϮϬϬϭ΃ǆEdZ'ĂƉKǁŶ

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Table 2: Industry Employment Growth and China's Change in NTR Status26∆ŵƉƚ͗ƚнϲ ;>Ϳ'ƌŽƐƐ:ŽďƌĞĂƚŝŽŶ

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>>

ͲϬ͘ϬϱϱϬ͘ϬϯϴͲϬ͘ϬϮϴϬ͘ϬϮϲͲϬ͘ϬϵϯϬ͘ϬϰϯͲϬ͘ϬϴϳϬ͘ϬϯϵͲϬ͘ϬϯϳϬ͘ϬϰϰͲϬ͘ϬϯϰϬ͘ϬϮϵͲϬ͘ϯϯϰϬ͘Ϭϵϰϭ΂ƚсϮϬϬϭ΃ǆEdZ'ĂƉhƉͲϬ͘ϯϲϴϬ͘ϭϭϯͲϬ͘ϬϱϰϬ͘ϬϵϱͲϬ͘ϭϭϳϬ͘ϭϯϭͲϬ͘ϮϬϴϬ͘ϭϮϳͲϬ͘ϯϰϴϬ͘ϭϴϴͲϬ͘ϮϲϴϬ͘ϭϰϰͲϭ͘ϯϲϮϬ͘ϯϰϭϭ΂ƚсϮϬϬϭ΃ǆEdZ'ĂƉŽǁŶͲϬ͘ϬϰϮϬ͘ϬϰϮͲϬ͘ϬϰϲϬ͘ϬϮϳͲϬ͘ϬϯϭϬ͘ϬϰϴͲϬ͘ϬϭϳϬ͘ϬϰϱͲϬ͘ϭϱϮϬ͘ϬϲϯͲϬ͘ϬϰϲϬ͘ϬϯϴͲϬ͘ϯϯϰ
Ϭ͘ϭϭϴůŶ;ŵƉƚͿͲϬ͘ϬϰϰϬ͘ϬϭϳͲϬ͘ϬϭϱϬ͘ϬϭϬͲϬ͘ϭϭϭϬ͘ϬϮϳͲϬ͘ϬϮϲϬ͘ϬϬϵͲϬ͘ϬϮϵ
Ϭ͘ϬϬϲ

Ϭ͘ϬϬϬ
Ϭ͘ϬϬϴ

ͲϬ͘ϮϮϱ
Ϭ͘ϬϯϵKďƐĞƌǀĂƚŝŽŶƐ
ZϮ

&ŝǆĞĚĨĨĞĐƚƐ
ůƵƐƚĞƌŝŶŐ

WͲǀĂůƵĞŽĨ:ŽŝŶƚ^ŝŐŶŝĨŝĐĂŶĐĞ
/ŵƉůŝĞĚ/ŵƉĂĐƚŽĨWEdZ
KǁŶhƉƐƚƌĞĂŵ

ŽǁŶƐƚƌĞĂŵ

dŽƚĂů

ϭ͕Ϭϴϵ

ϭ͕Ϭϴϵ

ϭ͕Ϭϴϵ

ϭ͕Ϭϴϵ

ϭ͕Ϭϴϵ

ϭ͕Ϭϴϵ

ϭ͕Ϭϴϵ

Ϭ͘ϲϭϴ

Ϭ͘ϰϴϵ

Ϭ͘ϲϯϭ

Ϭ͘ϰϮϵ

Ϭ͘ϱϬϰ

Ϭ͘ϳϴϱ

Ϭ͘ϲϯϵ

ŝ

ŝ

ŝ

ŝ

ŝ

ŝ

ŝ

ŝ͕ƚ

Ϭ͘ϬϬϬ
ͲϬ͘Ϭϭϴ

ͲϬ͘ϬϰϬ

ͲϬ͘ϬϬϱ

ͲϬ͘Ϭϲϯ

ŝ͕ƚϬ͘ϭϬϯͲϬ͘ϬϬϵͲϬ͘ϬϬϲ

ͲϬ͘ϬϬϱ

ͲϬ͘ϬϮϬ

ŝ͕ƚϬ͘ϬϱϱͲϬ͘Ϭϯϭ

ͲϬ͘Ϭϭϯ

ͲϬ͘ϬϬϯ

ͲϬ͘Ϭϰϳ

ŝ͕ƚϬ͘ϬϬϲͲϬ͘ϬϮϵͲϬ͘ϬϮϮ

ͲϬ͘ϬϬϮ

ͲϬ͘Ϭϱϯ

ŝ͕ƚϬ͘ϬϬϰͲϬ͘ϬϭϮ

ͲϬ͘Ϭϯϴ

ͲϬ͘Ϭϭϳ

ͲϬ͘Ϭϲϳ

ŝ͕ƚϬ͘ϬϬϳͲϬ͘Ϭϭϭ

ͲϬ͘ϬϮϵͲϬ͘ϬϬϱ

ͲϬ͘Ϭϰϱ

ŝ͕ƚϬ͘ϬϬϬͲϬ͘ϭϭϮ

ͲϬ͘ϭϰϳ

ͲϬ͘Ϭϯϳ
ͲϬ͘Ϯϵϲ

EŽƚĞƐ͗ĂĐŚĐŽůƵŵŶĚŝƐƉůĂǇƐƚŚĞƌĞƐƵůƚƐŽĨĂŶK>^ƌĞŐƌĞƐƐŝŽŶŽĨƚŚĞĐƵŵƵůĂƚŝǀĞƉĞƌĐĞŶƚĐŚĂŶŐĞŝŶŝŶĚƵƐƚƌǇ;ŝͿĞŵƉůŽǇŵĞŶƚĂůŽŶŐƚŚĞŶŽƚĞĚŵĂƌŐŝŶƐŝǆǇĞĂƌƐĂĨƚĞƌƚŚĞϭϵϴϭ͕ϭϵϵϬĂŶĚϮϬϬϭƉĞĂŬƐ;ƚͿ͘&ŝƌƐƚƐŝǆĐŽůƵŵŶƐƌĞƉŽƌƚƌĞƐƵůƚƐĨŽƌƚŚĞŐƌŽƐƐŵĂƌŐŝŶƐŽĨĂĚũƵƐƚŵĞŶƚ͖ƚŚĞĨŝŶĂůĐŽůƵŵŶƌĞƉŽƌƚƐƌĞƐƵůƚƐĨŽƌƚŚĞŽǀĞƌĂůůĐŚĂŶŐĞŝŶĞŵƉůŽǇŵĞŶƚ͘^ƚĂŶĚĂƌĚĞƌƌŽƌƐĂĚũƵƐƚĞĚĨŽƌĐůƵƐƚĞƌŝŶŐĂƚƚŚĞŝŶĚƵƐƚƌǇůĞǀĞůĂƌĞĚŝƐƉůĂǇĞĚďĞůŽǁĞĂĐŚĐŽĞĨĨŝĐŝĞŶƚ͘ŽĞĨĨŝĐŝĞŶƚƐŝŶďŽůĚĂƌĞƐƚĂƚŝƐƚŝĐĂůůǇƐŝŐŶŝĨŝĐĂŶƚĂƚƚŚĞϭϬƉĞƌĐĞŶƚůĞǀĞů͘ƐƚŝŵĂƚĞƐĨŽƌƚŚĞĐŽŶƐƚĂŶƚĂŶĚĨŝǆĞĚĞĨĨĞĐƚƐĂƌĞƐƵƉƉƌĞƐƐĞĚ͘ůĞǀĞŶƚŚƌŽǁƌĞƉŽƌƚƐƉͲǀĂůƵĞŽĨĂŶ&ͲƚĞƐƚŽĨƚŚĞũŽŝŶƚƐŝŐŶŝĨŝĐĂŶĐĞŽĨƚŚĞĨŝƌƐƚƚŚƌĞĞĐŽǀĂƌŝĂƚĞƐ͘&ŝŶĂůďůŽĐŬŽĨƌŽǁƐƌĞƉŽƌƚƐƚŚĞĚŝĨĨĞƌĞŶƚŝĂůĞŵƉůŽǇŵĞŶƚŐƌŽǁƚŚŝŵƉůŝĞĚďǇƚŚĞƐŚŝĨƚŝŶh͘^͘ƉŽůŝĐǇ;ƐĞĞƚĞǆƚͿ͘

Table 3: Industry Employment Growth and China's Change in NTR Status Six Years
After Each Peak

27

EdZ'ĂƉŽǁŶ

d=3

d=4

d=5

d=6

28

ͲϬ͘ϬϱϬ ͲϬ͘Ϭϲϰ ͲϬ͘Ϭϳϱ ͲϬ͘ϬϳϮ ͲϬ͘Ϭϴϭ ͲϬ͘ϬϴϵͲϬ͘ϬϬϭ ͲϬ͘ϬϬϯ ͲϬ͘Ϭϭϵ ͲϬ͘Ϭϯϴ ͲϬ͘Ϭϰϭ ͲϬ͘ϬϱϯͲϬ͘ϬϬϯ Ϭ͘ϬϬϭ ͲϬ͘ϬϬϱ ͲϬ͘ϬϬϴ ͲϬ͘Ϭϭϯ ͲϬ͘ϬϭϯͲϬ͘Ϭϭϵ ͲϬ͘ϬϮϭ ͲϬ͘Ϭϳϱ ͲϬ͘ϭϬϬ ͲϬ͘ϭϬϭ ͲϬ͘ϭϯϬϬ͘ϬϬϭ ͲϬ͘ϬϮϲ ͲϬ͘Ϭϯϲ ͲϬ͘Ϭϰϱ ͲϬ͘Ϭϯϵ ͲϬ͘ϬϱϴͲϬ͘Ϭϭϲ Ϭ͘ϬϬϰ ͲϬ͘Ϭϯϰ ͲϬ͘Ϭϰϴ ͲϬ͘Ϭϰϵ ͲϬ͘ϬϱϵͲϬ͘Ϭϭϵ ͲϬ͘ϬϮϭ ͲϬ͘Ϭϳϱ ͲϬ͘ϭϬϬ ͲϬ͘ϭϬϭ ͲϬ͘ϭϯϬͲϬ͘Ϭϲϴ ͲϬ͘ϬϴϮ ͲϬ͘ϭϭϰ ͲϬ͘ϭϯϯ ͲϬ͘ϭϰϰ ͲϬ͘ϭϲϲͲϬ͘Ϭϴϳ ͲϬ͘ϭϬϯ ͲϬ͘ϭϴϵ ͲϬ͘Ϯϯϯ ͲϬ͘Ϯϰϱ ͲϬ͘ϮϵϲͲϬ͘Ϭϰϵ ͲϬ͘ϬϵϬ ͲϬ͘ϭϭϭ ͲϬ͘ϭϭϳ ͲϬ͘ϭϮϬ ͲϬ͘ϭϰϳͲϬ͘ϬϲϵͲϬ͘Ϭϰϱ

ͲϬ͘Ϭϭϲ
ͲϬ͘ϭϳϮ

ͲϬ͘ϬϳϵͲϬ͘Ϭϳϳ

ͲϬ͘ϭϳϮ
ͲϬ͘ϭϰϱ
ͲϬ͘ϯϭϳ

ͲϬ͘Ϭϰϳ
ͲϬ͘ϯϭϴ

ͲϬ͘ϭϰϴ

d=10ͲϬ͘ϭϮϯ

D

ϳϯ

ϭ

ϭϴ
ϭϬϬ

Ͳϲ

ϴϳ

Ϭ
ϮϮ
ϳϴ
ϭϬϬ

Ϯϰ
ϭϬϬ

ϱϲ

ϮϬ

d=1

ϳϳ

ϰ

Ͳϲ
ϭϬϬ

ϭϮϴ

ͲϮϭ

Ϭ
ϮϬ
ϴϬ
ϭϬϬ

ϭϰ
ϭϬϬ

ϴϳ

Ͳϭ

d=2
Ϯϴ

ϲϲ

ϭϳ

ϳ
ϭϬϬ

ϰϴ

ϰϱ

Ϭ
ϰϬ
ϲϬ
ϭϬϬ

ϭϯ
ϭϬϬ

ϱϵ

d=3

ϱϰ

Ϯϴ

ϴ
ϭϬϬ

ϰϱ

ϰϳ

Ϭ
ϰϯ
ϱϳ
ϭϬϬ

ϭϯ
ϭϬϬ

ϱϬ

ϯϲ

d=4

WĞƌĐĞŶƚ
>

ϱϲ

Ϯϴ

ϭϯ
ϭϬϬ

ϯϴ

ϰϵ

Ϭ
ϰϭ
ϱϵ
ϭϬϬ

ϭϰ
ϭϬϬ

ϰϵ

ϯϳ

d=5

ϱϰ

ϯϮ

ϭϬ
ϭϬϬ

ϰϱ

ϰϱ

Ϭ
ϰϰ
ϱϲ
ϭϬϬ

ϭϯ
ϭϬϬ

ϱϬ

ϯϴ

d=6

ϰϴ

ϯϭ

ϵ
ϭϬϬ

ϰϲ

ϰϱ

Ϭ
ϱϰ
ϰϲ
ϭϬϬ

ϭϱ
ϭϬϬ

ϰϳ

ϯϵ

d=10

D

ͲϬ͘Ϭϭϴ ͲϬ͘Ϭϭϲ ͲϬ͘ϬϮϬ ͲϬ͘ϬϮϰ ͲϬ͘ϬϮϮ ͲϬ͘ϬϮϰ ͲϬ͘Ϭϯϭ
Ϯϲ
ϭϵ
ϭϳ
ϭϴ
ϭϱ
ϭϰ
Ϯϭ
ͲϬ͘Ϭϲϴ ͲϬ͘ϬϴϮ ͲϬ͘ϭϭϰ ͲϬ͘ϭϯϯ ͲϬ͘ϭϰϰ ͲϬ͘ϭϲϲ ͲϬ͘ϭϰϱ
ϭϬϬ
ϭϬϬ
ϭϬϬ
ϭϬϬ
ϭϬϬ
ϭϬϬ
ϭϬϬ
dĂďůĞĚĞĐŽŵƉŽƐĞƐƚŚĞŝŵƉůŝĞĚĞĐŽŶŽŵŝĐŝŵƉĂĐƚŽĨWEdZŽŶĐƵŵƵůĂƚŝǀĞŐƌŽǁƚŚĚ ǇĞĂƌƐĂĨƚĞƌƚŚĞϮϬϬϭƉĞĂŬĂĐĐŽƌĚŝŶŐƚŽƚŚĞƚŚĞ ƚŚƌĞĞEdZŐĂƉƐ;ĨƌŽŵdĂďůĞϮͿŽƌũŽďĐƌĞĂƚŝŽŶǀĞƌƐƵƐĚĞƐƚƌƵĐƚŝŽŶ;ĨƌŽŵdĂďůĞƐϯĂŶĚϰŝŶƚŚĞŵĂŝŶĂŶĚdĂďůĞ͘ϰŝŶƚŚĞĞůĞĐƚƌŽŶŝĐĂƉƉĞŶĚŝǆͿ͘>ĞǀĞůƐƌĞƉŽƌƚĞĚŝŶƉĂŶĞůƐĂŶĚƐƵŵƚŽƚŚĞůĞǀĞůƐƌĞƉŽƌƚĞĚŝŶƉĂŶĞůƐĂŶĚ͘EdZ'ĂƉhƉ

WĂŶĞů͗:ŽďĞƐĐƚƌƵĐƚŝŽŶ

EdZ'ĂƉŽǁŶEdZ'ĂƉhƉ

EdZ'ĂƉKǁŶ

WĂŶĞů͗:ŽďƌĞĂƚŝŽŶ

WĂŶĞů
'ƌŽƐƐ:ŽďƌĞĂƚŝŽŶ
'ƌŽƐƐ:ŽďĞƐĐƚƌƵĐƚŝŽŶ

EdZ'ĂƉhƉ
ͲϬ͘ϬϮϭ ͲϬ͘Ϭϭϱ ͲϬ͘ϬϮϱ ͲϬ͘Ϭϯϭ ͲϬ͘Ϭϯϱ ͲϬ͘ϬϯϳͲϬ͘Ϭϴϳ ͲϬ͘ϭϬϯ ͲϬ͘ϭϴϵ ͲϬ͘Ϯϯϯ ͲϬ͘Ϯϰϱ ͲϬ͘Ϯϵϲd=2

EdZ'ĂƉŽǁŶ
dŽƚĂů

d=1

>ĞǀĞůƐ
>ͲϬ͘Ϭϭϳ Ϭ͘ϬϬϭ ͲϬ͘Ϭϱϯ ͲϬ͘Ϭϴϱ ͲϬ͘ϬϵϬ ͲϬ͘ϭϭϮEdZ'ĂƉKǁŶ

WĂŶĞůP NTR

EdZ'ĂƉKǁŶ

Table 4: Decomposing the Overall Implied Impact of∆ŵƉůŽǇͲ
ŵĞŶƚ

∆EŽŶͲ
WƌŽĚƵĐƚŝŽŶ ∆WƌŽĚƵĐƚŝŽŶ ∆WƌŽĚƵĐƚŝŽŶ,ŽƵƌƐtŽƌŬĞƌƐ
tŽƌŬĞƌƐ

EdZ'ĂƉKǁŶ Ύϭ΂ƚсϭϵϵϳ΃

ͲϬ͘ϭϯϲ
Ϭ͘Ϭϰϯ

Ϭ͘Ϯϯϴ
Ϭ͘ϬϱϮ

ͲϬ͘ϯϰϯϬ͘ϬϱϭͲϬ͘ϯϳϭ
Ϭ͘Ϭϱϱ

EdZ'ĂƉhƉ Ύϭ΂ƚсϭϵϵϳ΃

ͲϬ͘ϱϳϵϬ͘ϭϰϵͲϬ͘ϴϬϴϬ͘ϭϳϲͲϬ͘ϰϬϮϬ͘ϭϳϲͲϬ͘ϭϴϬ
Ϭ͘ϭϴϲ

EdZ'ĂƉŽǁŶ Ύϭ΂ƚсϭϵϵϳ΃

ͲϬ͘ϮϰϴϬ͘ϬϱϳͲϬ͘ϰϭϴϬ͘ϬϲϲͲϬ͘ϭϳϱϬ͘ϬϲϳͲϬ͘ϯϮϳ
Ϭ͘ϬϳϮůŶ;ŵƉƚͿͲϬ͘ϴϴϮϬ͘ϬϬϳͲϬ͘ϴϵϳϬ͘ϬϬϴͲϬ͘ϴϱϲϬ͘ϬϬϴͲϬ͘ϴϯϴ
Ϭ͘ϬϬϴůŶ;<ͬ>ƚͿ

Ϭ͘Ϭϯϰ
Ϭ͘ϬϬϰ

Ϭ͘ϬϰϬ
Ϭ͘ϬϬϱ

Ϭ͘Ϭϭϭ
Ϭ͘ϬϬϱ

ͲϬ͘Ϭϭϴ
Ϭ͘ϬϬϱůŶ;^ͬ>ƚͿ

Ϭ͘ϬϭϬ
Ϭ͘ϬϬϳ

Ͳϭ͘ϯϭϮ
Ϭ͘ϬϬϵ

Ϭ͘ϱϮϰ
Ϭ͘ϬϬϵ

Ϭ͘ϰϰϳ
Ϭ͘ϬϭϬ

ůŶ;ŐĞͿͲϬ͘Ϭϭϱ
Ϭ͘Ϭϭϭ

Ϭ͘ϬϬϳ
Ϭ͘ϬϭϮ

ͲϬ͘ϬϮϰ
Ϭ͘Ϭϭϯ

Ϭ͘ϬϭϬ
Ϭ͘Ϭϭϰ

ůŶ;d&WͿ

Ϭ͘ϬϮϱ
Ϭ͘ϬϬϯ

Ϭ͘ϬϮϲ
Ϭ͘ϬϬϯ

Ϭ͘Ϭϯϰ
Ϭ͘ϬϬϯ

Ϭ͘ϬϮϬ
Ϭ͘ϬϬϯ

KďƐĞƌǀĂƚŝŽŶƐ

Ϯϴϯ͕ϳϮϬ

Ϯϴϯ͕ϳϮϬ

Ϯϴϯ͕ϳϮϬ

Ϯϴϯ͕ϳϮϬ

ZϮ

Ϭ͘ϴϯϳ

Ϭ͘ϴϯϱ

Ϭ͘ϴϭϱ

Ϭ͘ϳϴϱ

&ŝǆĞĚĨĨĞĐƚƐ

Ğ͕ƚ

Ğ͕ƚ

Ğ͕ƚ

Ğ͕ƚ

ůƵƐƚĞƌŝŶŐ

Ğ

Ğ

Ğ

Ğ

KǁŶ

ͲϬ͘ϬϰϱϬ͘ϬϴϯͲϬ͘ϭϬϵͲϬ͘ϭϭϳ

hƉƐƚƌĞĂŵ

ͲϬ͘Ϭϲϭ

ͲϬ͘Ϭϴϰ

ͲϬ͘ϬϰϮ

ͲϬ͘ϬϭϵŽǁŶƐƚƌĞĂŵ

ͲϬ͘ϬϮϳ

ͲϬ͘Ϭϰϱ

ͲϬ͘ϬϭϵͲϬ͘Ϭϯϲ

ͲϬ͘ϭϯϮ

ͲϬ͘Ϭϰϲ

ͲϬ͘ϭϳϬ

ͲϬ͘ϭϳϮ

/ŵƉůŝĞĚ/ŵƉĂĐƚŽĨWEdZ

dŽƚĂůEŽƚĞƐ͗dĂďůĞƌĞƉŽƌƚƐĞƐƚĂďůŝƐŚŵĞŶƚͲůĞǀĞůK>^ƌĞŐƌĞƐƐŝŽŶƐŽĨƚŚĞůŽŐĚŝĨĨĞƌĞŶĐĞŝŶŶŽƚĞĚĞŵƉůŽǇŵĞŶƚŽƵƚĐŽŵĞĂĐƌŽƐƐƚŚƌĞĞDĚĞĐĂĚĞƐ;ƐĞĞƚĞǆƚͿ͘^ĂŵƉůĞŝƐƌĞƐƚƌŝĐƚĞĚƚŽƚŚĞŝŶƚĞŶƐŝǀĞŵĂƌŐŝŶ͘ůůƌĞŐƌĞƐƐŝŽŶƐŝŶĐůƵĚĞƉĞĂŬǇĞĂƌ;ƚͿĂŶĚĞƐƚĂďůŝƐŚŵĞŶƚ;ĞͿĨŝǆĞĚĞĨĨĞĐƚƐ͘^ƚĂŶĚĂƌĚĞƌƌŽƌƐĂĚũƵƐƚĞĚĨŽƌĐůƵƐƚĞƌŝŶŐĂƚƚŚĞƉůĂŶƚůĞǀĞůĂƌĞĚŝƐƉůĂǇĞĚďĞůŽǁĞĂĐŚĐŽĞĨĨŝĐŝĞŶƚ͘ŽĞĨĨŝĐŝĞŶƚƐŝŶďŽůĚĂƌĞƐƚĂƚŝƐƚŝĐĂůůǇƐŝŐŶŝĨŝĐĂŶƚĂƚƚŚĞϭϬƉĞƌĐĞŶƚůĞǀĞů͘ƐƚŝŵĂƚĞƐĨŽƌƚŚĞĐŽŶƐƚĂŶƚĂŶĚĨŝǆĞĚĞĨĨĞĐƚƐĂƌĞƐƵƉƉƌĞƐƐĞĚ͘&ŝŶĂůďůŽĐŬŽĨƌŽǁƐƌĞƉŽƌƚƐƚŚĞĚŝĨĨĞƌĞŶƚŝĂůĞŵƉůŽǇŵĞŶƚŐƌŽǁƚŚŝŵƉůŝĞĚďǇƚŚĞƐŚŝĨƚŝŶh͘^͘ƉŽůŝĐǇ͕ŝ͘Ğ͕͘ƚŚĞĚŽƚƉƌŽĚƵĐƚŽĨƚŚĞĨŝƌƐƚƚŚƌĞĞĐŽǀĂƌŝĂƚĞƐĂŶĚƚŚĞƌĞƐƉĞĐƚŝǀĞĂǀĞƌĂŐĞƐŽĨƚŚĞƚŚƌĞĞEdZŐĂƉƐ;Ϭ͘ϯϮ͕Ϭ͘ϭϭĂŶĚϬ͘ϭϭ͕ƌĞƉƐĞĐƚŝǀĞůǇͿ͘

Table 5: Plant Employment Growth Across CM Decades
29∆sĂůƵĞĚĚĞĚ∆sͬ
ŵƉůŽǇŵĞŶƚ

ͲϬ͘ϭϱϬϬ͘ϬϱϮͲϬ͘ϮϰϰϬ͘ϬϳϯͲϬ͘ϭϬϴϬ͘ϬϲϳͲϭ͘ϬϵϯϬ͘ϮϭϯͲϬ͘ϱϭϰϬ͘ϭϴϵͲϭ͘ϭϬϭϬ͘ϮϱϰͲϬ͘ϱϮϮ
Ϭ͘Ϯϯϭ

Ϭ͘Ϭϱϲ
Ϭ͘Ϭϳϱ

ͲϬ͘ϵϳϵϬ͘ϬϴϬͲϬ͘ϳϯϭϬ͘ϬϲϵͲϭ͘ϮϱϴϬ͘ϬϵϲͲϭ͘ϬϬϵϬ͘ϬϴϳͲϬ͘ϴϵϲϬ͘ϬϭϬͲϬ͘ϬϭϰϬ͘ϬϬϴͲϬ͘ϳϬϵ
Ϭ͘ϬϬϵ

Ϭ͘ϭϳϯ
Ϭ͘ϬϬϴ

ͲϬ͘ϳϬϯ
Ϭ͘Ϭϭϭ

Ϭ͘ϭϳϵ
Ϭ͘ϬϭϬ

Ϭ͘Ϭϯϰ
Ϭ͘ϬϬϰ

Ͳϭ͘ϯϯϮϬ͘ϬϬϴͲϭ͘ϯϲϱϬ͘ϬϬϴͲϬ͘ϭϴϰϬ͘ϬϬϲͲϬ͘ϮϭϳϬ͘ϬϬϲͲϬ͘ϭϳϵϬ͘ϬϬϳͲϬ͘Ϯϭϯ
Ϭ͘ϬϬϳůŶ;^ͬ>ƚͿ

Ϭ͘ϬϭϬ
Ϭ͘ϬϬϳ

Ϭ͘ϬϯϬ
Ϭ͘ϬϭϬ

Ϭ͘ϬϮϭ
Ϭ͘ϬϬϵ

Ϭ͘ϬϯϬ
Ϭ͘ϬϭϬ

Ϭ͘ϬϮϬ
Ϭ͘ϬϬϵ

Ϭ͘ϬϮϯ
Ϭ͘ϬϭϮ

Ϭ͘Ϭϭϯ
Ϭ͘Ϭϭϭ

ůŶ;ŐĞͿͲϬ͘Ϭϭϱ
Ϭ͘Ϭϭϭ

Ϭ͘Ϭϯϴ
Ϭ͘Ϭϭϲ

Ϭ͘Ϭϱϯ
Ϭ͘Ϭϭϰ

ͲϬ͘ϬϱϰϬ͘ϬϭϱͲϬ͘ϬϰϬϬ͘ϬϭϰͲϬ͘ϬϯϳϬ͘ϬϭϴͲϬ͘ϬϮϮϬ͘Ϭϭϲ

ůŶ;d&WͿ

Ϭ͘ϬϮϱ
Ϭ͘ϬϬϯ

Ϭ͘ϬϮϰ
Ϭ͘ϬϬϰ

ͲϬ͘ϬϬϭϬ͘ϬϬϯͲϬ͘ϬϱϭϬ͘ϬϬϰͲϬ͘ϬϳϲϬ͘ϬϬϰͲϬ͘ϬϴϵϬ͘ϬϬϱͲϬ͘ϭϭϯ
Ϭ͘ϬϬϱKďƐĞƌǀĂƚŝŽŶƐ

Ϯϴϯ͕ϳϮϬ

Ϯϴϯ͕ϳϮϬ

Ϯϴϯ͕ϳϮϬ

Ϯϴϯ͕ϳϮϬ

Ϯϴϯ͕ϳϮϬ

Ϯϴϯ͕ϳϮϬ

Ϯϴϯ͕ϳϮϬ

ZϮ

Ϭ͘ϴϯϳ

Ϭ͘ϴϰϮ

Ϭ͘ϴϰϴ

Ϭ͘ϳϱϲ

Ϭ͘ϲϲ

Ϭ͘ϳϬϭ

Ϭ͘ϱϵϲ

&ŝǆĞĚĨĨĞĐƚƐ

Ğ͕ƚ

Ğ͕ƚ

Ğ͕ƚ

Ğ͕ƚ

Ğ͕ƚ

Ğ͕ƚ

Ğ͕ƚ

ůƵƐƚĞƌŝŶŐ

Ğ

Ğ

Ğ

Ğ

Ğ

Ğ

Ğ

KǁŶ

ͲϬ͘Ϭϰϱ

ͲϬ͘Ϭϵϲ

ͲϬ͘Ϭϱϯ

ͲϬ͘ϬϵϮ

ͲϬ͘ϬϰϵͲϬ͘Ϭϳϴ

ͲϬ͘Ϭϯϲ

hƉƐƚƌĞĂŵ

ͲϬ͘Ϭϲϭ

Ϭ͘ϬϮϮ

Ϭ͘Ϭϴϴ

ͲϬ͘ϭϭϭ

ͲϬ͘Ϭϱϰ

ͲϬ͘ϭϭϮ

ͲϬ͘Ϭϱϱ

ŽǁŶƐƚƌĞĂŵ

ͲϬ͘ϬϮϳ

ͲϬ͘ϬϮϭϬ͘ϬϬϲͲϬ͘ϭϬϯ

ͲϬ͘Ϭϳϴ

ͲϬ͘ϭϯϬ

ͲϬ͘ϭϬϲ

ͲϬ͘ϭϯϮ

ͲϬ͘ϬϵϰϬ͘ϬϰϭͲϬ͘ϯϬϲ

ͲϬ͘ϭϴϭ

ͲϬ͘ϯϮϭ

ͲϬ͘ϭϵϲ

∆ĂƉŝƚĂůͬ∆^ŚŝƉŵĞŶƚƐͬŵƉůŽǇŵĞŶƚ ∆^ŚŝƉŵĞŶƚƐ ŵƉůŽǇͲŵĞŶƚ∆ŵƉůŽǇͲŵĞŶƚ∆ĂƉŝƚĂů

EdZ'ĂƉKǁŶ Ύϭ΂ƚсϭϵϵϳ΃

ͲϬ͘ϭϯϲϬ͘ϬϰϯͲϬ͘ϯϬϬϬ͘ϬϲϯͲϬ͘ϭϲϯϬ͘ϬϱϲͲϬ͘Ϯϴϳ
Ϭ͘Ϭϱϵ

EdZ'ĂƉhƉ Ύϭ΂ƚсϭϵϵϳ΃

ͲϬ͘ϱϳϵ
Ϭ͘ϭϰϵ

Ϭ͘ϮϬϱ
Ϭ͘Ϯϭϰ

Ϭ͘ϳϴϰ
Ϭ͘ϭϵϮ

EdZ'ĂƉŽǁŶ Ύϭ΂ƚсϭϵϵϳ΃

ͲϬ͘ϮϰϴϬ͘ϬϱϳͲϬ͘ϭϵϮ
Ϭ͘ϬϴϰůŶ;ŵƉƚͿͲϬ͘ϴϴϮ
Ϭ͘ϬϬϳůŶ;<ͬ>ƚͿ/ŵƉůŝĞĚ/ŵƉĂĐƚŽĨWEdZ

dŽƚĂůEŽƚĞƐ͗dĂďůĞƌĞƉŽƌƚƐĞƐƚĂďůŝƐŚŵĞŶƚͲůĞǀĞůK>^ƌĞŐƌĞƐƐŝŽŶƐŽĨƚŚĞůŽŐĚŝĨĨĞƌĞŶĐĞŝŶŶŽƚĞĚŽƵƚĐŽŵĞĂĐƌŽƐƐƚŚƌĞĞDĚĞĐĂĚĞƐ;ƐĞĞƚĞǆƚͿ͘^ĂŵƉůĞŝƐƌĞƐƚƌŝĐƚĞĚƚŽƚŚĞŝŶƚĞŶƐŝǀĞŵĂƌŐŝŶ͘ůůǀĂƌŝĂďůĞƐĂƌĞŝŶƌĞĂůϮϬϬϱĚŽůůĂƌƐ;ƐĞĞƚĞǆƚͿ͘ůůƌĞŐƌĞƐƐŝŽŶƐŝŶĐůƵĚĞƉĞĂŬ ǇĞĂƌ;ƚͿĂŶĚĞƐƚĂďůŝƐŚŵĞŶƚ;ĞͿĨŝǆĞĚĞĨĨĞĐƚƐ͘^ƚĂŶĚĂƌĚĞƌƌŽƌƐĂĚũƵƐƚĞĚĨŽƌĐůƵƐƚĞƌŝŶŐĂƚƚŚĞƉůĂŶƚůĞǀĞůĂƌĞĚŝƐƉůĂǇĞĚďĞůŽǁĞĂĐŚĐŽĞĨĨŝĐŝĞŶƚ͘ŽĞĨĨŝĐŝĞŶƚƐŝŶďŽůĚĂƌĞƐƚĂƚŝƐƚŝĐĂůůǇƐŝŐŶŝĨŝĐĂŶƚĂƚƚŚĞϭϬƉĞƌĐĞŶƚůĞǀĞů͘ƐƚŝŵĂƚĞƐĨŽƌƚŚĞĐŽŶƐƚĂŶƚĂŶĚĨŝǆĞĚĞĨĨĞĐƚƐĂƌĞƐƵƉƉƌĞƐƐĞĚ͘&ŝŶĂůďůŽĐŬŽĨƌŽǁƐƌĞƉŽƌƚƐƚŚĞĚŝĨĨĞƌĞŶƚŝĂůĞŵƉůŽǇŵĞŶƚŐƌŽǁƚŚŝŵƉůŝĞĚďǇƚŚĞƐŚŝĨƚŝŶh͘^͘ƉŽůŝĐǇ;ƐĞĞƚĞǆƚͿ͘

Table 6: Other Plant Outcomes Across CM Decades30

EdZ'ĂƉŚΎϭ΂ĐсŚŝŶĂ΃
ůŶ;sĂůƵĞĐŚͿ∆sĂůƵĞ

∆h^/ŵƉŽƌƚĞƌƐ

∆ŚŝŶĞƐĞǆƉŽƌƚĞƌƐ

∆/ŵƉŽƌƚĞƌͲǆƉŽƌƚĞƌ
WĂŝƌƐ

Ϭ͘ϭϳϵ
Ϭ͘ϬϱϮ

Ϭ͘Ϯϳϵ
Ϭ͘Ϭϰϲ

Ϭ͘ϯϯϯ
Ϭ͘Ϭϰϱ

Ϭ͘Ϯϵϴ
Ϭ͘Ϭϰϲ

ͲϬ͘ϬϬϵϬ͘ϬϬϮůŶ;h^/ŵƉŽƌƚĞƌƐĐŚͿͲϬ͘Ϭϲϴ
Ϭ͘ϬϬϯůŶ;ŚŝŶĞƐĞǆƉŽƌƚĞƌƐĐŚͿͲϬ͘Ϭϵϱ
Ϭ͘ϬϬϯůŶ;WĂŝƌƐĐŚͿͲϬ͘Ϭϲϲ
Ϭ͘ϬϬϯKďƐĞƌǀĂƚŝŽŶƐ

Ϯϳϳ͕ϬϳϬ

Ϯϳϳ͕ϬϳϬ

Ϯϳϳ͕ϬϳϬ

Ϯϳϳ͕ϬϳϬ

ZϮ

Ϭ͘ϯϯ

Ϭ͘ϯϲ

Ϭ͘ϯϲ

Ϭ͘ϯϳ

&ŝǆĞĚĨĨĞĐƚƐ

Ś͕Đ

Ś͕Đ

Ś͕Đ

Ś͕Đ

ůƵƐƚĞƌŝŶŐ

Ś

Ś

Ś

Ś

Ϭ͘ϭϵϲ

Ϭ͘ϯϮϮ

Ϭ͘ϯϵϱ

Ϭ͘ϯϰϳ

/ŵƉůŝĞĚ/ŵƉĂĐƚŽĨWEdZEŽƚĞƐ͗dĂďůĞƌĞƉŽƌƚƐƚŚĞƌĞƐƵůƚƐŽĨƚĞŶͲĚŝŐŝƚ,^ďǇĐŽƵŶƚƌǇK>^ƌĞŐƌĞƐƐŝŽŶƐŽĨƚŚĞϭϵϵϳƚŽϮϬϬϳŶŽƌŵĂůŝǌĞĚĐŚĂŶŐĞ;ƐĞĞƚĞǆƚͿŽŶƚŚĞŶŽƚĞĚĚŝĨĨĞƌĞŶĐĞͲŝŶͲĚŝĨĨĞƌĞŶĐĞƐƚĞƌŵ͘ZĞŵĂŝŶŝŶŐƌŝŐŚƚͲŚĂŶĚͲƐŝĚĞǀĂƌŝĂďůĞƐĂƌĞƚŚĞĂǀĞƌĂŐĞůĞǀĞůŽĨƚŚĞĚĞƉĞŶĚĞŶƚǀĂƌŝĂďůĞŽďƐĞƌǀĞĚŝŶƚŚĞďĞŐŝŶĂŶĚĞŶĚǇĞĂƌ͘ZĞŐƌĞƐƐŝŽŶƐŝŶĐůƵĚĞƚĞŶͲĚŝŐŝƚ,^ƉƌŽĚƵĐƚ;ŚͿĂŶĚĐŽƵŶƚƌǇ;ĐͿĨŝǆĞĚĞĨĨĞĐƚƐ͘^ĂŵƉůĞŝƐƌĞƐƚƌŝĐƚĞĚƚŽƉƌŽĚƵĐƚͲĐŽƵŶƚƌǇŝŵƉŽƌƚƚƌĂŶƐĂĐƚŝŽŶƐĂƐƐŽĐŝĂƚĞĚǁŝƚŚĨŝƌŵƐŝŶŽƵƌĐŽŶƐƚĂŶƚŵĂŶƵĨĂĐƚƵƌŝŶŐƐĂŵƉůĞ͘ŽĞĨĨŝĐŝĞŶƚƐŝŶďŽůĚĂƌĞƐƚĂƚŝƐƚŝĐĂůůǇƐŝŐŶŝĨŝĐĂŶƚĂƚƚŚĞϭϬƉĞƌĐĞŶƚůĞǀĞů͘ƐƚŝŵĂƚĞƐĨŽƌƚŚĞĐŽŶƐƚĂŶƚĂŶĚĨŝǆĞĚĞĨĨĞĐƚƐĂƌĞƐƵƉƉƌĞƐƐĞĚ͘&ŝŶĂůƌŽǁƌĞƉŽƌƚƐƚŚĞĚŝĨĨĞƌĞŶƚŝĂůĞŵƉůŽǇŵĞŶƚŐƌŽǁƚŚŝŵƉůŝĞĚďǇƚŚĞƐŚŝĨƚŝŶh͘^͘ƉŽůŝĐǇ;ƐĞĞƚĞǆƚͿ͘

Table 7: U.S. Import Growth, 1997 to 200731

U.S. Manufacturing Employment

12

Employment (Million)
14
16
18

20

LBD Constant Manufacturing Sample vs www.bls.gov

1948

1958

1968

1978
BLS

1988

1998

2008

LBD

EŽƚĞƐ͗ &ŝŐƵƌĞ ĐŽŵƉĂƌĞƐ ĂŶŶƵĂů ŵĂŶƵĨĂĐƚƵƌŝŶŐ ĞŵƉůŽǇŵĞŶƚ ĂƐ ŽĨ DĂƌĐŚ
ĂĐĐŽƌĚŝŶŐ ƚŽ ƚŚĞ h͘^͘ ƵƌĞĂƵ ŽĨ >ĂďŽƌ ^ƚĂƚŝƐƚŝĐƐ ;ƐĞƌŝĞƐ hϯϬϬϬϬϬϬϬϬϭͿ ĂŶĚƚŚĞ ĂƵƚŚŽƌƐ͛ ĐŽŶƐƚĂŶƚ ŵĂŶƵĨĂĐƚƵƌŝŶŐ ƐĂŵƉůĞ͕ ĐŽŶƐƚƌƵĐƚĞĚ ĨƌŽŵ ƚŚĞ ĞŶƐƵƐ
ƵƌĞĂƵΖƐ >ŽŶŐŝƚƵĚŝŶĂů ƵƐŝŶĞƐƐ ĂƚĂďĂƐĞ ;ƐĞĞ ƚĞǆƚͿ͘ ^ŚĂĚĞĚ ĂƌĞĂƐ ĐŽƌƌĞƐƉŽŶĚƚŽ ƉĞƌŝŽĚƐ ϭϵϴϭ ƚŽ ϭϵϳϴ͕ ϭϵϵϬ ƚŽ ϭϵϵϲ ĂŶĚ ϮϬϬϭ ƚŽ ϮϬϬϳ͘
Figure 1: Post-War U.S. Manufacturing Employment

32

Cumulative Employment Loss Across Recessions
Non-Manufacturing

.15
.1

-.1
-.2

0

.05

-.15

Share of Peak

-.05

.2

0

.25

Constant Manufacturing Sample

1 2 3 4 5 6

1 2 3 4 5 6

1 2 3 4 5 6

1 2 3 4 5 6

1 2 3 4 5 6

1 2 3 4 5 6

1981

1990

2001

1981

1990

2001

Figure 2: Cumulative Employment Loss, by Recession

Distribution of NTR Gap Across Tariff-Lines

0

.5

1

Density
1.5

2

2.5

By Year

0

1
1989

2
NTR Gap
1993

3

4

1997

2001

Figure 3: Distribution of Risk Across Tari Lines

33

Comparing Recessions
1. Real GDP

2. Industrial Production Index

1.15
1.1

Peak=1

1.1

1.05
1

1

.95

.9
-4

0

4

8

12

16

-4

0

4

8

12

16

Quarters from Peak
1981

1990

2001

Figure 4: Real GDP and IPI During the 1981, 1990 and 2001 Recessions

U.S. Manufacturing
Employment vs Log Real Value Added
1600

18
800

16
14

400
12
10

Real Value Added (Billion)

Employment (Million)

20

200
1948

1958

1968

1978
year

BLS Employment

1988

1998

2008

BEA Real Value AddedEŽƚĞƐ͗ &ŝŐƵƌĞ ĐŽŵƉĂƌĞƐ ĂŶŶƵĂů ŵĂŶƵĨĂĐƚƵƌŝŶŐ ĞŵƉůŽǇŵĞŶƚ ĂƐ ŽĨ DĂƌĐŚ
ĂĐĐŽƌĚŝŶŐ ƚŽ ƚŚĞ h͘^͘ ƵƌĞĂƵ ŽĨ >ĂďŽƌ ^ƚĂƚŝƐƚŝĐƐ ;ƐĞƌŝĞƐ hϯϬϬϬϬϬϬϬϬϭͿ ƚŽ ƌĞĂůǀĂůƵĞ ĂĚĚĞĚ ĂƐ ŵĞĂƐƵƌĞĚ ďǇ ƚŚĞ ƵƌĞĂƵ ŽĨ ĐŽŶŽŵŝĐ ŶĂůǇƐŝƐ͘
Figure 5: Post-War U.S. Manufacturing Employment

34

Manufacturing Employment Loss Across Recessions
By Gross Margin
1990

2001

0
-.2
-.4

Share of Peak

.2

.4

1981

1 2 3 4 5 6CM

1 2 3 4 5 6CM

1 2 3 4 5 6

PE

PB

FB

PC

PD

FD

CM

Source: LBD constant manufacturing sample.

EŽƚĞƐ͗ &ŝƌƐƚ Ɛŝǆ ďĂƌƐ ŝŶ ĞĂĐŚ ƉĂŶĞů ƌĞƉŽƌƚ ƚŚĞ ĚĞĐŽŵƉŽƐŝƚŝŽŶ ŽĨ ŽǀĞƌĂůů ŐƌŽǁƚŚ ďǇŐƌŽƐƐ ŵĂƌŐŝŶ ĨŽƌ ĨŝƌƐƚ Ɛŝǆ ǇĞĂƌƐ ĂĨƚĞƌ ŶŽƚĞĚ ƉĞĂŬ͘ &ŝŶĂů ďĂƌ ŝŶ ĞĂĐŚ ƉĂŶĞů͕ ůĂďĞůĞĚ
͞D͕͟ ĚŝƐƉůĂǇƐ ƚŚĞ ĐƵŵƵůĂƚŝǀĞ ĐŚĂŶŐĞ ĂĐƌŽƐƐ ƚŚĞ ĂƐƐŽĐŝĂƚĞĚ D ĚĞĐĂĚĞ͗ ϭϵϳϳ ƚŽϭϵϴϳ͕ ϭϵϴϳ ƚŽ ϭϵϵϳ ĂŶĚ ϭϵϵϳ ƚŽ ϮϬϬϳ͘
Figure 6: Cumulative Manufacturing Employment, by Recession and Gross Margin

35

Manufacturing Employment Loss Across Recessions
By Net Margin
1990

2001

-.05
-.1
-.2

-.15

Share of Peak

0

.05

1981

1 2 3 4 5 6CM

1 2 3 4 5 6Net PECNet PBDCM

1 2 3 4 5 6

CMNet FBD

Source: LBD constant manufacturing sample.

EŽƚĞƐ͗ &ŝƌƐƚ Ɛŝǆ ďĂƌƐ ŝŶ ĞĂĐŚ ƉĂŶĞů ƌĞƉŽƌƚ ƚŚĞ ĚĞĐŽŵƉŽƐŝƚŝŽŶ ŽĨ ŽǀĞƌĂůů ŐƌŽǁƚŚ ďǇŶĞƚ ŵĂƌŐŝŶ ĨŽƌ ƚŚĞ ĨŝƌƐƚ Ɛŝǆ ǇĞĂƌƐ ĂĨƚĞƌ ŶŽƚĞĚ ƉĞĂŬ͘ &ŝŶĂů ďĂƌ ŝŶ ĞĂĐŚ ƉĂŶĞů͕ůĂďĞůĞĚ ͞D͕͟ ĚŝƐƉůĂǇƐ ƚŚĞ ĐƵŵƵůĂƚŝǀĞ ĐŚĂŶŐĞ ĂĐƌŽƐƐ ƚŚĞ ĂƐƐŽĐŝĂƚĞĚ D ĚĞĐĂĚĞ͗ϭϵϳϳ ƚŽ ϭϵϴϳ͕ ϭϵϴϳ ƚŽ ϭϵϵϳ Žƌ ϭϵϵϳ ƚŽ ϮϬϬϳ͘
Figure 7: Cumulative Manufacturing Employment, by Recession and Net Margin36

Counterfactual U.S. Manufacturing Employment
Based on Difference-in-Difference Estimates
Actual.1
Cumulative Change from 2001

+ Counterfactual JD+ Counterfactual JD and JC

0

-.1

-.2
2001

2002

2003

2004

2005

2006

2007

EŽƚĞƐ͗ ŽƚƚŽŵ ůŝŶĞ ƌĞƉƌĞƐĞŶƚƐ ƚŚĞ ĂĐƚƵĂů ĚĞĐůŝŶĞ ŝŶ ĞŵƉůŽǇŵĞŶƚ ĨŽůůŽǁŝŶŐ ƚŚĞϮϬϬϭ ƉĞĂŬ͘ ^ƵĐĐĞƐƐŝǀĞůǇ ŚŝŐŚĞƌ ůŝŶĞƐ ƌĞƉƌĞƐĞŶƚ ƚŚĞ ĂĚĚŝƚŝŽŶ ŽĨ ŝŵƉůŝĞĚ ũŽďƐ ůŽƐƚĚƵĞ ƚŽ ĞǆĂŐŐĞƌĂƚĞĚ ũŽď ĚĞƐƚƌƵĐƚŝŽŶ ĂŶĚ ĂŶĞŵŝĐ ũŽď ĐƌĞĂƚŝŽŶ͕ ƌĞƐƉĞĐƚŝǀĞůǇ͘
Figure 8: Cumulative Manufacturing Employment, by Recession and Net Margin37

Electronic Appendix
This appendix contains the additional empirical results referenced in the main text.

A Revealed Taris on Chinese Imports
We compute the revealed tari on Chinese imports as the ratio between duties collected
and dutiable value across all ten-digit HS manufacturing products imported from China
using the trade data in Feenstra, Romalis and Schott (2002) and extended by Schott(2008). We regress the revealed taris for all countries over the years 1998 to 2002 and1997 to 2003 on an interaction of two indicator variables: whether the observation is for
China and whether the observation is for a year after 2000. As indicated in Table A.1,
there is no statistically signicant relationship between this dierence-in-dierences term
and revealed taris over these periods.

B Chinese Import Penetration and P NTR
A number of papers in the international trade literature have reported a correlation
between increases in U.S. import penetration (or share of import value) by low-wagecountries and employment losses across U.S. establishments, industries or regions. China
is by far the most inuential low-wage country in this trend, and we nd that the acceleration of its penetration of the U.S. market after 2001 is concentrated in industries
most aected by China's change in NTR status.
China's U.S. import penetration is its import share multiplied by the standard expression for import penetration,

P ENitChina =where Mit ,MitChina
Mit,
Mit Shipmentsit + Mit − Xit(A.1)

Xit and Shipmentsit represent total imports, exports and domestic shipChina using

ments for manufacturing industry i in year t, respectively. We compute P ENit

industry-level domestic shipment data from the publicly available NBER-CES Manufacturing Industry Database assembled by Becker and Gray (2009) and HS-level import andexport data from Feenstra, Romalis and Schott (2002) and Schott (2008). We map theHS-level trade data to either SIC or NAICS industries using the concordances of Pierce
and Schott (2012).

China separately for industries that are above and below the

Figure A.1 plots P ENit

median own-industry NTR gap. As indicated in the gure, the sharp acceleration of theoverall trend in Chinese manufacturing import penetration starting in 2001 is due almost
entirely to industries with above-median NTR gaps.
Table A.2 uses our main dierence-in-dierences specication to examine whether thechange in the U.S. import penetration from China six years after each peak is relatively
greater after 2001.

The rst column of the table demonstrates that this penetration

rises with the NTR gap across the 2001 recession. The second column reveals that this38

CHN and the NTR gap also appears relative to the 1981 andrelationship between ∆P ENit

1990 recessions. We nd similar results across longer and short time windows, e.g., the
LBD intervals analyzed in the main text.

C Employment Growth by Margin of Adjustment
The rst six columns of Table A.3 report the cumulative change in U.S. manufacturing
employment up to six years after each business cycle peak, by gross and net margin ofadjustment. By comparison, the nal column of the table reports the same breakdownof employment across three CM decades: 1977 to 1987, 1987 to 1997 and 1997 to 2007.
Table A.4 reports estimates of equation 4 for d = 1 to d = 5. As indicated in thetable, the estimated magnitudes of the dierence-in-dierence terms generally increasein absolute value over time. This trend suggests that the relative lack of a recovery in
manufacturing employment following the 2001 recession displayed in Figure 2 is driven
by both exaggerated job destruction and anemic job creation.

Appendix References
1. Becker, Randy and Wayne B. Grey.

2009.

NBER-CES Manufacturing Industry

Database. www.nber.org/data/nbprod2005.html.
2. Feenstra, Robert C., John Romalis and Peter K. Schott.

2002.

U.S. Imports,

Exports and Tari Data, 1989-2001. NBER Working Paper 9387
3. Pierce, Justin and Peter K. Schott. 2012. Concording U.S. Harmonized System
Categories Over Time Journal of Ocial Statistics (Forthcoming).
4. Schott, Peter K. 2008. The Relative Sophistication of Chinese Exports, Economic
Policy 53:5-49.

39

Appendix Tables and Figures
Table A.1: Change in Revealed TarisZĞǀĞĂůĞĚdĂƌŝĨĨŽŶŚŝŶĞƐĞ/ŵƉŽƌƚƐ

ZĞǀĞĂůĞĚdĂƌŝĨĨŽŶŚŝŶĞƐĞ/ŵƉŽƌƚƐ

KďƐĞƌǀĂƚŝŽŶƐ

ϳϴϳ͕ϲϭϰ

ϭ͕ϭϭϵ͕ϲϮϳzĞĂƌƐϭϵϵϴͲϮϬϬϮ

ϭϵϵϳͲϮϬϬϯ

ϭ΂ĐсŚŝŶĂ΃Ύϭ΂ƚхϮϬϬϬ΃

ZͲƐƋƵĂƌĞĚ

ͲϬ͘ϬϬϬϮ
Ϭ͘ϬϬϰϱϬ͘ϬϭϱͲϬ͘ϬϬϭϬ
Ϭ͘Ϭϭϱϲ
Ϭ͘Ϭϭϭ&ŝǆĞĚĨĨĞĐƚƐ
dĞŶͲŝŐŝƚ,^͕ŽƵŶƚƌǇ
dĞŶͲŝŐŝƚ,^͕ŽƵŶƚƌǇ
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Table A.2: Change in China Import Penetration Across Recessions

EdZ'ĂƉ

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ͲϬ͘Ϭϱϯ
WůĂŶƚŝƌƚŚ;WͿ
Ϭ͘Ϭϭϭ
Ϭ͘ϬϮϭ
Ϭ͘ϬϯϬ
Ϭ͘Ϭϯϱ
Ϭ͘Ϭϰϲ
Ϭ͘Ϭϴϱ
WůĂŶƚĞĂƚŚ;WͿͲϬ͘ϬϮϲ
ͲϬ͘Ϭϰϯ
ͲϬ͘Ϭϲϰ
ͲϬ͘ϬϳϵͲϬ͘Ϭϵϯ
ͲϬ͘ϭϲϭ
EĞƚtŝƚŚŝŶͲ&ŝƌŵW
ͲϬ͘Ϭϭϱ
ͲϬ͘ϬϮϮ
ͲϬ͘Ϭϯϰ
ͲϬ͘Ϭϰϰ
ͲϬ͘Ϭϰϳ
ͲϬ͘Ϭϳϲ
&ŝƌŵŝƌƚŚ;&Ϳ
Ϭ͘Ϭϭϴ
Ϭ͘Ϭϯϭ
Ϭ͘Ϭϯϵ
Ϭ͘Ϭϰϲ
Ϭ͘Ϭϱϴ
Ϭ͘Ϭϵϯ
&ŝƌŵĞĂƚŚ;&ͿͲϬ͘ϬϮϮ
ͲϬ͘Ϭϰϯ
ͲϬ͘ϬϲϬ
ͲϬ͘Ϭϳϰ
ͲϬ͘ϬϵϬ
ͲϬ͘ϭϲϮ
EĞƚ&
ͲϬ͘ϬϬϰ
ͲϬ͘ϬϭϮ
ͲϬ͘ϬϮϭ
ͲϬ͘ϬϮϴ
ͲϬ͘ϬϯϮ
ͲϬ͘ϬϲϵdŽƚĂůͲϬ͘ϬϵϮ
ͲϬ͘ϭϭϱ
ͲϬ͘ϭϯϴ
ͲϬ͘ϭϰϰ
ͲϬ͘ϭϰϰ
ͲϬ͘ϭϵϴ
EŽƚĞ͗dĂďůĞƌĞƉŽƌƚƐĚĞĐŽŵƉŽƐŝƚŝŽŶŽĨƚŽƚĂůƉĞƌĐĞŶƚĐŚĂŶŐĞŝŶh͘^͘ŵĂŶƵĨĂĐƚƵƌŝŶŐĞŵƉůŽǇŵĞŶƚŽǀĞƌƚŚĞŶŽƚĞĚŝŶƚĞƌǀĂůƐĂĐĐŽƌĚŝŶŐƚŽŐƌŽƐƐĂŶĚŶĞƚŵĂƌŐŝŶƐŽĨĂĚũƵƐƚŵĞŶƚ;ƐĞĞƚĞǆƚͿ͘ůůĐŚĂŶŐĞƐĂƌĞĞǆƉƌĞƐƐĞĚĂƐĂƉĞƌĐĞŶƚŽĨďĞŐŝŶŶŝŶŐǇĞĂƌĞŵƉůŽǇŵĞŶƚ͘

Table A.3: Cumulative Change in U.S. Manufacturing Employment by Recession andMargin of Adjustment

41

>/ŶƚĞƌǀĂůƐ
∆ƚ͗ƚнϭW

KǁŶhƉƐƚƌĞĂŵ
ŽǁŶƐƚƌĞĂŵ

W

KǁŶhƉƐƚƌĞĂŵ
ŽǁŶƐƚƌĞĂŵ

W

KǁŶhƉƐƚƌĞĂŵ
ŽǁŶƐƚƌĞĂŵ

∆ƚ͗ƚнϮ∆ƚ͗ƚнϯ∆ƚ͗ƚнϰD∆ƚ͗ƚнϱ∆ƚ͗ƚнϲ∆ƚ͗ƚнϭϬͲϬ͘ϬϮϱ ͲϬ͘ϬϬϵ ͲϬ͘ϬϯϮ ͲϬ͘Ϭϱϯ ͲϬ͘Ϭϲϰ ͲϬ͘ϬϱϱϬ͘ϬϮϭ Ϭ͘ϬϮϯ Ϭ͘Ϭϯϱ Ϭ͘ϬϮϵ Ϭ͘Ϭϯϱ Ϭ͘ϬϯϴͲϬ͘ϬϳϬϬ͘ϬϰϮͲϬ͘Ϭϴϵ ͲϬ͘Ϯϱϴ ͲϬ͘ϯϵϭ ͲϬ͘ϯϱϯ ͲϬ͘ϯϬϱ ͲϬ͘ϯϲϴϬ͘Ϭϳϭ Ϭ͘Ϭϳϲ Ϭ͘Ϭϵϵ Ϭ͘ϭϬϱ Ϭ͘ϭϭϯ Ϭ͘ϭϭϯ>/ŶƚĞƌǀĂůƐ

D∆ƚ͗ƚнϭ∆ƚ͗ƚнϮ∆ƚ͗ƚнϯ∆ƚ͗ƚнϰ∆ƚ͗ƚнϲ∆ƚ͗ƚнϭϬ

Ϭ͘ϬϮϱ
Ϭ͘ϬϮϰ

Ϭ͘ϬϯϮ
Ϭ͘ϬϯϬ

Ϭ͘Ϭϭϴ
Ϭ͘Ϭϯϯ

ͲϬ͘ϬϬϰ ͲϬ͘ϬϮϮ ͲϬ͘ϬϯϳϬ͘Ϭϯϰ Ϭ͘ϬϰϬ Ϭ͘Ϭϰϰ

Ϭ͘ϬϬϮ
Ϭ͘Ϭϱϴ

ͲϬ͘ϮϵϯϬ͘ϭϮϴͲϬ͘Ϯϯϳ ͲϬ͘ϮϯϮ ͲϬ͘Ϯϭϰ ͲϬ͘ϮϬϱ ͲϬ͘Ϯϰϳ ͲϬ͘ϯϰϴϬ͘ϭϭϵ Ϭ͘ϭϱϭ Ϭ͘ϭϱϰ Ϭ͘ϭϱϰ Ϭ͘ϭϴϲ Ϭ͘ϭϴϴͲϬ͘ϯϮϳϬ͘ϮϮϭͲϬ͘ϬϬϭ ͲϬ͘Ϭϭϳ ͲϬ͘ϬϮϴ ͲϬ͘Ϭϯϰ ͲϬ͘ϬϰϮ ͲϬ͘ϬϰϮϬ͘ϬϯϬ Ϭ͘ϬϮϴ Ϭ͘ϬϯϮ Ϭ͘Ϭϰϭ Ϭ͘Ϭϰϰ Ϭ͘ϬϰϮͲϬ͘ϬϱϬϬ͘ϬϰϭͲϬ͘Ϭϱϯ ͲϬ͘Ϭϱϳ ͲϬ͘ϬϴϬ ͲϬ͘ϭϮϰ ͲϬ͘ϭϯϴ ͲϬ͘ϭϱϮϬ͘Ϭϯϵ Ϭ͘Ϭϰϲ Ϭ͘Ϭϰϴ Ϭ͘ϬϱϬ Ϭ͘Ϭϱϳ Ϭ͘ϬϲϯͲϬ͘ϭϴϴϬ͘ϬϳϯͲϬ͘Ϭϯϲ ͲϬ͘Ϭϱϱ ͲϬ͘Ϭϳϰ ͲϬ͘Ϭϵϱ ͲϬ͘ϬϳϮ ͲϬ͘ϬϴϳϬ͘ϬϮϴ Ϭ͘ϬϯϮ Ϭ͘ϬϯϮ Ϭ͘Ϭϯϳ Ϭ͘ϬϰϬ Ϭ͘ϬϯϵͲϬ͘ϬϱϬϬ͘ϬϯϴͲϬ͘Ϭϭϳ
Ϭ͘ϬϭϮ

Ϭ͘Ϭϭϴ
Ϭ͘ϬϮϯ

ͲϬ͘Ϭϰϵ ͲϬ͘Ϭϳϭ ͲϬ͘Ϭϲϲ ͲϬ͘ϬϵϯϬ͘ϬϮϭ Ϭ͘ϬϮϲ Ϭ͘Ϭϰϳ Ϭ͘ϬϰϯͲϬ͘ϭϮϳϬ͘ϬϰϵͲϬ͘ϭϭϭ ͲϬ͘ϭϰϱ ͲϬ͘ϮϭϮ ͲϬ͘ϭϳϵ ͲϬ͘ϮϰϬ ͲϬ͘ϮϬϴϬ͘ϭϬϳ Ϭ͘ϭϬϳ Ϭ͘ϭϭϯ Ϭ͘ϭϬϱ Ϭ͘ϭϯϮ Ϭ͘ϭϮϳͲϬ͘Ϭϰϴ
Ϭ͘ϭϮϳ

Ϭ͘ϬϮϴ
Ϭ͘Ϭϯϱ

Ϭ͘Ϭϯϯ
Ϭ͘Ϭϲϰ

Ϭ͘Ϭϵϱ
Ϭ͘Ϭϳϵ

ͲϬ͘ϭϭϳϬ͘ϭϯϭͲϬ͘ϭϳϮϬ͘ϭϲϮͲϬ͘Ϭϵϯ ͲϬ͘Ϭϲϱ ͲϬ͘Ϭϳϯ ͲϬ͘Ϭϰϴ ͲϬ͘Ϭϭϲ ͲϬ͘ϬϭϳϬ͘Ϭϯϵ Ϭ͘ϬϰϬ Ϭ͘Ϭϰϰ Ϭ͘Ϭϰϰ Ϭ͘Ϭϰϴ Ϭ͘ϬϰϱͲϬ͘ϬϰϯϬ͘ϬϰϭͲϬ͘Ϭϭϱ
Ϭ͘ϬϭϮ

Ϭ͘Ϭϭϳ
Ϭ͘ϬϮϴ

ͲϬ͘ϬϬϲ ͲϬ͘ϬϯϬ ͲϬ͘ϬϰϮ ͲϬ͘ϬϯϭϬ͘ϬϮϰ Ϭ͘ϬϯϮ Ϭ͘ϬϰϮ Ϭ͘Ϭϰϴ

Ϭ͘Ϭϭϭ
Ϭ͘Ϭϱϯ

ͲϬ͘ϬϬϳ Ϭ͘ϬϬϰ ͲϬ͘ϬϮϬ ͲϬ͘Ϭϭϴ ͲϬ͘Ϭϭϳ ͲϬ͘ϬϮϴϬ͘ϬϬϳ Ϭ͘ϬϮϰ Ϭ͘ϬϮϰ Ϭ͘Ϭϭϵ Ϭ͘ϬϮϮ Ϭ͘ϬϮϲͲϬ͘Ϭϯϰ
Ϭ͘Ϭϱϲ

Ϭ͘ϬϬϵ
Ϭ͘Ϭϭϯ

Ϭ͘Ϭϭϰ
Ϭ͘ϬϮϭ

ͲϬ͘ϬϬϮ ͲϬ͘Ϭϭϯ ͲϬ͘ϬϮϴ ͲϬ͘ϬϯϰϬ͘ϬϮϯ Ϭ͘ϬϮϰ Ϭ͘ϬϮϲ Ϭ͘ϬϮϵͲϬ͘Ϭϴϳ
Ϭ͘ϬϯϱϬ͘Ϭϳϭ ͲϬ͘Ϭϭϴ ͲϬ͘Ϭϯϵ ͲϬ͘ϭϬϳ ͲϬ͘ϭϭϯ ͲϬ͘ϬϱϰϬ͘ϬϰϬ Ϭ͘ϬϴϬ Ϭ͘Ϭϴϭ Ϭ͘Ϭϲϳ Ϭ͘Ϭϳϵ Ϭ͘ϬϵϱͲϬ͘ϮϲϳϬ͘ϭϱϬͲϬ͘ϭϭϭ ͲϬ͘ϮϭϮ ͲϬ͘Ϯϲϱ ͲϬ͘ϮϴϬ ͲϬ͘ϮϲϮ ͲϬ͘ϮϲϴϬ͘Ϭϰϵ Ϭ͘Ϭϳϲ Ϭ͘Ϭϵϲ Ϭ͘ϭϬϵ Ϭ͘ϭϮϳ Ϭ͘ϭϰϰͲϬ͘ϮϲϰϬ͘ϭϯϵͲϬ͘Ϭϭϱ Ϭ͘ϬϭϮ ͲϬ͘Ϭϭϱ ͲϬ͘ϬϬϰ ͲϬ͘Ϭϯϳ ͲϬ͘ϬϰϲϬ͘ϬϭϬ Ϭ͘ϬϮϲ Ϭ͘ϬϮϰ Ϭ͘ϬϮϮ Ϭ͘ϬϮϲ Ϭ͘ϬϮϳͲϬ͘ϭϬϱϬ͘ϬϰϯͲϬ͘ϬϭϮ ͲϬ͘ϬϮϬ ͲϬ͘ϬϮϯ ͲϬ͘Ϭϰϭ ͲϬ͘Ϭϰϯ ͲϬ͘ϬϰϲϬ͘Ϭϭϱ Ϭ͘ϬϮϯ Ϭ͘ϬϮϵ Ϭ͘ϬϯϮ Ϭ͘Ϭϯϱ Ϭ͘ϬϯϴͲϬ͘Ϭϰϰ
Ϭ͘Ϭϰϱ

W

&

&

Ϭ͘ϬϰϬ
Ϭ͘ϭϬϬ

∆ƚ͗ƚнϱ

Ϭ͘ϬϲϬ
Ϭ͘ϭϱϵ

EŽƚĞƐ͗ĂĐŚďůŽĐŬŽĨƚŚĞƚĂďůĞƌĞƉŽƌƚƐƚŚĞĚŝĨĨĞƌĞŶĐĞͲŝŶͲĚŝĨĨĞƌĞŶĐĞĞƐƚŝŵĂƚĞĨƌŽŵĂƐĞƉĂƌĂƚĞK>^ƌĞŐƌĞƐƐŝŽŶŽĨƚŚĞĐƵŵƵůĂƚŝǀĞƉĞƌĐĞŶƚĐŚĂŶŐĞŝŶŝŶĚƵƐƚƌǇĞŵƉůŽǇŵĞŶƚĂůŽŶŐĂŶŽƚĞĚŵĂƌŐŝŶŽŶŽǁŶͲ͕ƵƉƐƚƌĞĂŵͲ ĂŶĚĚŽǁŶƐƚƌĞĂŵͲŝŶĚƵƐƚƌǇEdZŐĂƉ;ƐĞĞƚĞǆƚͿ͘&ŝƌƐƚƐŝǆĐŽůƵŵŶƐƌĞƉŽƌƚ ƌĞƐƵůƚƐƵƐŝŶŐ
ƚŚĞ>͕ĚŝƐƉůĂǇŝŶŐƌĞŐƌĞƐƐŝŽŶƌĞƐƵůƚƐĨƌŽŵĐƵŵƵůĂƚŝǀĞĐŚĂŶŐĞƐĨƌŽŵŽŶĞƚŽĨŝǀĞǇĞĂƌƐĂĨƚĞƌĞĂĐŚŽĨƚŚĞϭϵϴϭ͕ϭϵϵϬĂŶĚϮϬϬϭƉĞĂŬƐ͘&ŝŶĂůĐŽůƵŵŶƵƐĞƐDĚĞĐĂĚĞƐ;ƐĞĞƚĞǆƚͿ͘WĂŶĚWĂƌĞƉůĂŶƚĞǆƉĂŶƐŝŽŶĂŶĚĐŽŶƚƌĂĐƚŝŽŶǁŝƚŚŝŶĐŽŶƚŝŶƵŝŶŐĨŝƌŵƐ͘WĂŶĚWĂƌĞƉůĂŶƚďŝƌƚŚĂŶĚ ƉůĂŶƚĚĞĂƚŚǁŝƚŚŝŶĐŽŶƚŝŶƵŝŶŐĨŝƌŵƐ͘&ĂŶĚ&ĂƌĞĨŝƌŵďŝƌƚŚĂŶĚĚĞĂƚŚ͘ŽĞĨĨŝĐŝĞŶƚƐŝŶďŽůĚĂƌĞƐƚĂƚŝƐƚŝĐĂůůǇƐŝŐŶŝĨŝĐĂŶƚĂƚƚŚĞϭϬƉĞƌĐĞŶƚůĞǀĞů͘&ŝŶĂůƌŽǁŽĨĞĂĐŚƉĂŶĞůƌĞƉŽƌƚƐƉͲǀĂůƵĞŽĨĂŶ&ͲƚĞƐƚŽĨƚŚĞũŽŝŶƚƐŝŐŶŝĨŝĐĂŶĐĞŽĨƚŚĞĨŝƌƐƚƚŚƌĞĞĐŽǀĂƌŝĂƚĞƐ͘

Table A.4: Industry Employment Growth and China's Change in NTR Status, by Yearand Gross Margin of Adjustment

42China's U.S. Import Penetration0

Import Penetration
.02
.04
.06

.08

Manufacturing Products

1970

1980

1990

Below Median NTR Gap

2000
Above Median NTR Gap

Figure A.1: U.S. Manufacturing Import Penetration

43

2010