Using Equity Market Reactions to Infer Exposure to
Trade Liberalization
Andrew Greenland∗

Mihai Ion†

John Lopresti‡

Peter K. SchottFirst Draft: September 2018
This Draft: November 2018PRELIMINARY DRAFT!

Abstract
We propose using average abnormal equity returns (AAR) to identify rm sensitivity
to changes in trade policy. This approach captures the net impact of all avenues of
exposure and yields estimates for both goods-producing and service rms, provided
they are publicly traded. Applying our method to an important US trade liberalization
towards China, we nd that higher AARs predict relative increases in employment,
sales and capital stocks, that AARs provide explanatory power beyond the standard
measure of exposure used to study this liberalization, and that they highlight variationin outcomes across rms even within narrowly dened industries.
∗

Martha and Spencer Love School of Business, Elon University; agreenland@elon.edu†

Eller College of Management, University of Arizona; mihaiion@email.arizona.edu‡

The College of William & Mary; jwlopresti@wm.edu

 Yale School of Management & NBER; peter.schott@yale.edu

1Introduction

A large and expanding literature documents the distributional implications of trade liberalization (Topalova (2010); Autor et al. (2014); Dix-Carneiro (2014); Hakobyan and McLaren(2016)).

In most of this research, exposure to changes in trade policy is dened in termsof import competition, measured via changes in taris or import volumes among the set ofgoods a worker, rm or region produces.

This standard approach has two disadvantages.

First, by concentrating on import competition, it may ignore other, potentially osetting avenues of exposure. For example, trade liberalization may allow some rms within an industry
subject to the same level of import competition to expand by taking greater advantage of
low-cost foreign inputs, either via arm's-length transactions or by oshoring (Antràs et al.
(2017); Bernard et al. (2018)). Second, because changes in trade barriers and import volumes
are not easily observed for service rms, the standard approach often ignores rms outside
goods-producing industries, which account for the vast majority of employment in developed
countries. These rms may be exposed to the change in policy through various channels,
including via their downstream customer base, upstream suppliers or local labor markets.
In this paper we propose an alternative method for measuring rms' sensitivity to tradeliberalization. Our approach is based on nancial markets' reactions to key legislative events
associated with the change in policy, and assumes that all new information about the liberalization that is relevant for rm value is fully reected in its stock price. Hence, by measuringrms' average abnormal returns (AAR) relative to the market during these events, we canobtain traders' overall assessment of the policy change's eect on rm value. This approachaddresses both of the limitations noted above: it captures the expected

net impact of all

forms of exposure, and it yields estimates for rms in all sectors of the economy, provided
they are publicly traded.
We apply our method to a much-studied event in the international trade literature, the US
granting of permanent normal trade relations (PNTR) to China in October, 2000. PNTR wasa non-traditional trade liberalization in that it substantially reduced expected rather thanactually applied US import taris on many Chinese goods.

1

We focus on rms' equity returns

across the two days before and after ve legislative milestones: the introduction of the bill for
PNTR in the House, the House vote for PNTR, the Senate vote to invoke cloture to proceed
to a vote on PNTR, the Senate vote for PNTR, and Clinton's signing of PNTR into law. For

1 We describe this change in policy in more detail in Section 2. Pierce and Schott (2016a) show that
US industries with greater exposure to PNTR exhibit relative reductions in manufacturing employment andmanufacturing establishments, and relative increases in US rms importing from China and Chinese rmsexporting to the United States.

Handley and Limão (2017) estimate that the reduction in trade policy

uncertainty associated with PNTR is equivalent to a reduction in tari rates of approximately 13 percent.
Related research by Autor et al. (2013) and Autor et al. (2014) nds that regions more exposed to Chinese
import competition during this period experience relative declines in employment and increase in the uptakeof social welfare programs, and that workers more exposed to Chinese imports exhibit relative declines inearnings.

2

goods-producing rms, we nd that average abnormal returns across these ve events arecorrelated with the standard measure of exposure to PNTR used in the literature, known
as the NTR gap. Further support for our approach comes from comparing PNTR AARs toabnormal returns estimated across two other prominent events in US-China relations: theaccidental NATO bombing of the Chinese embassy in Belgrade in 1999, and Donald Trump's
election to President in 2016. We nd negative relationships in both cases, consistent with2

these events' potential negative implications for US-China trade.

Importantly, we ndthat both of these relationships also hold among service rms, for which direct measures of
exposure to the change in policy are unavailable.
Having established that PNTR AARs are related to the expected tari reductions mandated by the change in policy among goods producers, we employ a generalized dierencein-dierences specication to investigate their relative explanatory power with respect tovarious rm outcomes studied in the literature. Our estimates imply that a one standard
deviation increase in AARs is associated with relative increases of 12, 17 and 14 percent
in rms' employment, sales and capital stocks, and commensurate relative decreases in theprobability of rm exit. Lending further support for our approach, we nd that these relationships persist even after controlling for the NTR gap, indicating that average abnormal
returns capture information beyond that associated with the standard measure of importcompetition in this setting. Finally, highlighting a key advantage of our approach, we ndthat rms in service industries exhibit relative changes in outcomes that are of similar inmagnitude to those observed among goods producers.
Beginning with Fama et al. (1969), event studies have been used extensively in corporate
3

nance to estimate the eect of new information on rm value.

While this approach is

not widely used in international economics, we are not the rst to examine the relationship between stock returns and trade liberalization. For example: Mosder and Rose (2014)use equity market reactions to document that regional trade agreements are received morepositively the greater the extent of pre-existing trade with the proposed partners; Breinlich(2011) shows that as passage of the Canada-US Free Trade Agreement became more likely,
Canadian exporters experienced equity-market gains relative to non-exporters; and, mostrecently, Huang et al. (2018) nd a negative relationship between rms' abnormal returns
following President Trump's March 22, 2018 memorandum signifying a potential trade war
between the US and China and rms' sales to China.
In contrast to this research, we use average abnormal stock returns to measure exposure to
trade liberalization, and show that this measure can be used to predict subsequent changes

2 Wagner et al. (2018) nd that foreign-oriented rms' equity fare worse than domestically-oriented rms'
following Trump's election. As discussed further in Section 3, we do not explore this relationship at the rmlevel given rm attrition between 2000 and 2016.

3 Khotari and Warner (2006) document that this approach has been used in over 565 articles appearing

in the top nance journals through 2006. For a recent critique of this literature, see Wolfers and Zitzewitz(2018).

3in rm outcomes.

In this respect, our aim is similar to that of prior research seeking to

identify the multiple channels by which rms might be exposed to globalization. A numberof papers, for example, examine the impact of trade liberalization on downstream rms'
intermediate inputs costs' and productivity (Amiti and Konings (2007); Fernandes (2007);
Golberg et al. (2010); Topalova and Khandelwal (2011)), while others emphasize its eect
on investment, product scope and innovation (Bernard et al. (2006); Bustos (2011); Bloom
et al. (2016); Pierce and Schott (2017); Autor et al. (2017); Gutierrez and Phillipon (2017))
or the transmission of labor demand shocks through supply chains and exports (Acemoglu
et al. (2016); Feenstra et al. (2017); Feenstra and Sasahara (2017); Wang et al. (2018)).
A virtue of our approach is that it identies thenet impact of all of these forces without

requiring any information about rms' actual supply chains, innovative activity or labormarket relationships.

4

The paper proceeds as follows.

In Section 2 we discuss the details of the event study

and describe our estimates. Section 3 relates our estimates to existing measures of Chinese
import competition as well as the equity market response to the 2016 Presidential Election.
Sections 4 and 5 examine the relationship between rms' average annual returns and rmoutcomes, and compare the relative explanatory power of the standard measure of importcompetition used to evaluate PNTR. Section 6 concludes.

2

Estimating the Expected Impact of PNTR

Assuming markets are ecient, a rm's stock price reects all available information about its
future protability. Thus, news that shifts expectations about future prot streams causes
5

a re-valuation of the rm, with positive news raising value and negative news lowering it.

In this section we outline how we use an event study framework to measure rms' exposure
to the US granting of permanent normal trade relations (PNTR) to China.

2.1 A Brief Overview of PNTR
Broadly speaking, the United States has two sets of tari rates.

The rst set, known asnormal trade relations or NTR taris, are generally low and are applied to goods imported
from other members of the World Trade Organization (WTO). The second set, known as
non-NTR taris, were set by the Smoot-Hawley Tari Act of 1930 and are often substantiallyhigher than NTR rates. While imports from non-market economies such as China are by

4 Beyond the international trade literature, our approach is most similar to to Mobarak and Purbasari(2006) and Kogan et al. (2017), who use equity event studies to identify politically connected rms inIndonesia and the value of new patents among innovating rms, respectively.

5 As the stock price is the net present value of the cash ows of the rm, changes in expected prots may

reect either changes in the expected cash ows or the rate at which they are discounted.

4

default subject to the higher non-NTR rates, US law allows the President to grant suchcountries access to NTR rates on a year-by-year basis, subject to potential overrule byCongress.
US Presidents began requesting that China be granted such a waiver in 1980.

Con-

gressional approval of these requests were uncontroversial until the Chinese government's
crackdown on Tiananmen Square protests in 1989, after which it became politically contentious and less certain. This uncertainty reduced US rms' incentives to invest in closer
economic relations with China, andvice versa.6 It ended with Congress' passage of a bill

HR 4444 granting China permanent normal trade relations (PNTR) status in October, 2000,
which formally took eect upon China's entry into the WTO in December, 2001.

2.2 Using Event Studies to Estimate Firm Exposure to PNTR
Our event study focuses on ve legislative hurdles required for PNTR's passage: (1) the May
15, 2000 introduction of the bill in the US House of Representatives; (2) the May 24, 2000 voteto approve China's PNTR status by the US House of Representatives; (3) the successful July27, 2000 cloture motion to proceed with a vote on PNTR; (4) the September 19, 2000 voteto approve China's PNTR status by the US Senate; and (5) the October 10, 2000 signature
7of PNTR into law by President Clinton.

We assume that these are the key events during

which the substantial reduction in tari rate uncertainty associated with PNTR discussed
in Pierce and Schott (2016a) and Handley and Limão (2017) was incorporated into rms'
stock prices.
Estimation of abnormal returns requires taking a stand about the period of time during
which information is absorbed into stock prices. Windows that are too wide risk incorporating price changes associated with confounding events, while intervals that are too narrow
may miss changes in value that leak out slowly over time.

Here, given that PNTR's at-tributes and the schedule of its associated votes were well known, we use relatively shortwindows  the two trading days preceding and following the day of the vote  designed tocapture the information revealed by their success or failure.
Partial support for this window is provided by Figure 1, which plots the number of new
articles appearing in major news outlets that contain the three phrases Permanent Normal
Trade Relations, China and United States during calendar year 2000, highlighting the8

two trading days before and two trading days after each of the ve events noted above.

As

6 As noted in Pierce and Schott (2016a), for example, a representative from Mattel testifying before theHouse Ways and Means Committee asserted that  [w]hile the risk that the United States would withdraw
NTR status from China may be small, if it did occur the consequences would be catastrophic for US toy
companies given the 70 percent non-MFN US rate of duty applicable to toys. (St. Maxens 2000, p. 185).

7 For the full list of actions related to PNTR passage, see https://www.congress.gov/bill/106th-congress/

house-bill/4444/actions.

8 The news outlets are: Associated Press, BBC Monitoring International Reports, The Boston Globe, The

5

indicated in the gure, the number of articles mentioning PNTR jumps substantially during
the event windows surrounding the introduction of the bill into the House as well as thevotes in both the House and Senate. The number rises modestly during the event windows
9

surrounding the cloture vote in the Senate and the Clinton signing.

We isolate the impact of PNTR on rm value by computing a rm's average abnormal
return over each event window, i.e., the dierence between the average actual (or realized)
return and an estimate of the average normal return that would have prevailed during thewindow absent any news of PNTR. We derive estimates of rms normal returns from the
Capital Asset Pricing Model (CAPM) developed by Sharpe (1964), which relates rm j 's
10realized return between trading days t − 1 and t (Rjt )to the risk-free return (Rf t ) andthe rm's exposure to systematic risk. The model predicts that that the market portfolio
captures all sources of systematic risk. Hence, a regression of rm-level excess daily returns(Rjt −Rf t ) on market excess returns (Rmt −Rf t ) should yield the rm's exposure to systematic
risk (βj ),Rjt − Rf t = βj (Rmt − Rf t ) + jt .

(1)

ˆj by running a separate regression for each rm overWe recover each rm's estimated β
all trading days in 1999.

11

ˆj do notWe choose this period to assure that our estimations of β

occur during the period when relevant legislative information about PNTR became known.

12

We run these regressions for all publicly-traded rms incorporated in the United States that
trade on one of the three main stock exchanges (NYSE, AMEX and NASDAQ) and arepresent for at least 120 days of the 250-day estimation period. Data on rms' daily returns
are provided by the Center for Research in Security Prices (CRSP), while the daily market
13

return and risk-free rate are taken from Kenneth French's website.

e
The average abnormal return for rm j over event window e, AARj , is then calculated as
Chicago Tribune, CNN Transcripts, The Financial Times, The Los Angeles Times, The New York Times,
The Washington Post, PR Newswire, and The Wall Street Journal.

9 We caution that the magnitudes of the peaks in Figure 1 do not necessarily reect the relative importanceof each event.

For example, while PNTR received considerably less discussion surrounding the Clinton

signing, this was the event in which any remaining uncertainty regarding the policy's implementation was
resolved.

Note that the substantial gap between the cloture vote in the Senate and the vote on PNTR

corresponds to an August recess.

10 The rm's daily return is simply the percent change in its market value from time t-1 to t.
11 We note that equation 1 also imposes an intercept of zero for all rms, i.e., α

j = 0. Were this not thecase, rms' expected returns would include a persistent component unrelated to market risk, in violationof our maintained assumption of market eciency.

That is, if markets are ecient, any such persistentrm-specic return is arbitraged away.

12 Results are qualitatively and quantitatively similar utilizing β s that are estimated separately using the

250 days that end 30 days before each event.

13 See http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/f-f_factors.html.

Notethat Rf t is the one-month Treasury bill rate and Rmt is the daily value-weighted return on the portfolio of all rms on the NYSE, AMEX and NASDAQ.

6

14

the average of abnormal return during the ve-trading-day window

centered on the event

date, t0 ,

tX
0 +2
e
Rjt − βˆj Rmt )/5.
AARj = (t=t0 −2(2)

For most of our analysis, we use the average abnormal return across all ve events,

AARjP N T R = (AARjHouseIntro + AARjHouseV ote + AARjSenateCloture(3)+ AARjSenateV ote + AARjClinton )/5.
P NT R
Multiplying AARj
by 25, therefore, yields the cumulative average abnormal return across
P NT Rfor the 5,353
the 25 days captured by the 5 events. This procedure yields an AARjrms that are present during at least one of the ve events (as well as the pre-period used toˆj ).15 We refer to AARP N T R as rm j 's PNTR average abnormal return, and note
estimate βjthat insofar as market returns incorporate some element of exposure to international trade
P NT R
policy, a given rm's AARj
captures that rm's deviation from that market component.
P NT Rto rm outcomes provides an estimate of the relative eects ofThus, relating AARj
exposure to the change in policy, compared to the market. The three panels of Figure 2ecompare the distributions of AARj for each of the ve legislative events, as well as the
P NT R
distribution of the overall average abnormal returns, AARj
. As indicated in the gure,16

there is substantial heterogeneity in rm responses in each case.

Using data from COMPUSTAT, we classify rms into three mutually exclusive categories
 goods-producing, non-goods producing and diversied  depending on the mix of 6-digit
17

NAICS codes spanned by their major business segments.
As illustrated in Figure 3, we nd
P NT Rof goods-producing rms is more left-skewed than non-goods-producingthat the AARi
(hereafter service) rms, and that the distribution of diversied rms lies between the

14 We choose a relatively narrow, 5 day window around each event to minimize the inuence of alternate
factors on rms' returns.

Such windows are particularly appropriate if the market can be presumed tounderstand the implications of the information revealed, but the timing and outcome of the event itself is
unknown. In this case, for example,while the details HR4444 were well known, whether the bill would bescheduled for a vote, and what the outcome of the vote would be, were unknown.

15 If a rm is missing from an event, its abnormal returns represent the average over events for which it ispresent.

16 The AARe associated with the ve events have means and standard deviations as follows (in chronologicaljorder, and all in percentage terms). Means: 0.1, -0.6, -0.2, -0.4, -0.6, and -0.3. Standard deviations: 1.8, 2.0,
2.0, 1.8, and 2.2.

17 COMPUSTAT reports rms' sales in up to ten business segments. We dene goods-producing sectors as

Manufacturing (NAICS 31-33), Mining (NAICS 21), and Agriculture, Forestry, Fishing and Hunting (NAICS
11). Our sample consists of 2078 goods rms, 2544 service rms and 623 diversied rms. Information on
business segments is missing for 48 rms, which are treated as diversied here and for the remainder of theanalysis.

7

distributions of these two groups of rms. The means, standard deviations and inter-quartile
ranges for the three groups of rms are -0.48, 1.05 and 1.24 percent, -0.28, 1.00 and 0.85
percent, and -0.31, 1.02 and 1.12 percent, respectively.
Finally, we highlight variation of rm abnormal returns within industries in Figure 4,
P NT RP NT R
which compares AARj
to AARi, the market capitalization-weighted average abnormal return of all rms whose largest segment is 6-digit NAICS industry i. Results forgoods-producing rms are in the left panel, while results for non-goods-producing rms,
hereafter service rms, are in the right panel. To the extent that import competition inrms' major business segment is the sole determinant of their exposure to PNTR, the points
in this gure would be clustered along the 45 degree line. Instead, we nd a broad cloud of
points, potentially reecting underlying heterogeneity in other forms of exposure to PNTR.
For example, some rms within an industry subject to the same degree of import competition might be better able to take advantage of freer trade with China, or may be located inlabor markets with lower average exposure to Chinese imports.

3

Validity

P NT Ris
In this section we establish the validity of our approach by demonstrating that AARi
correlated with the standard measure of exposure to PNTR, known as the NTR gap, as wellas with rms' abnormal returns during two other important events in US-China relations:the accidental US bombing of China's embassy in Belgrade in 1999 and Donald Trump's
surprise election to President in 2016.

3.1

AARjP N T R vs the NTR Gap

Existing research on PNTR measures industries' exposure to the change in trade policy interms of the NTR gap, dened as the dierence between the higher non-NTR rate to whichtaris would have risen if annual renewal had failed, and the often much lower NTR rate
permitted under temporary NTR status,

N T R GapOwn= N on − N T R Ratei − N T R Ratei .
i(4)

In this equation, i indexes six-digit NAICS industries and the superscript refers to the factthat this measure represents industry i's own exposure.

18

These gaps are computed for

1999, the year before the change in policy, using data on US tari rates reported in Feenstra
et al. (2002).

19

Their mean and standard deviation are 0.27 and 0.17, and we summarize

their distribution visually in Figure 5.

18 Below, we consider rms' exposure to PNTR via up- and downstream linkages.

19 Tari rates are assigned according to 8-digit Harmonized System (HS) commodity codes. Following
Pierce and Schott (2016b), we take the average NTR gap across HS codes within each 6-digit NAICS code,8Consistent with the idea that annually renewable NTR status prior to PNTR inhibited
US-China integration, Pierce and Schott (2016a) show that US manufacturing industries
with higher NTR gaps exhibit the largest relative declines in employment after 2000. We
present a simple demonstration of this result in the left panel of Figure 6, which divides US
manufacturing industries into two groups according to whether their NTR gaps lie above or
20

below the median across all industries.

As indicated in the gure, employment for the twogroups of industries behaves similarly prior to 2000.

After 2000, however, the decline forindustries with above-median NTR gaps is more precipitous.
The right panel of Figure 6 is similar, but instead uses the industry-level PNTR average
P NT R) estimated in the previous section to split industries into twoabnormal returns (AARi
P NT R
groups. As noted in the gure, AARi
is similarly predictive of dierential industry
P NT Routcomes after, but not before, the change in policy. This outcome suggests AARj
captures at least part of the information contained in the NTR gap.
As noted above, we observe rms' worldwide sales in up to ten business segments. We
P NT Rand the sales-weighted average NTR
investigate the relationship between rm's AARj
gap of their major segments (N T R Gapj ) using an OLS specication of the form

AARje = αN T R Gapj + Xj β + ji .

(5)

N T R Gapj is not dened for service rms; for diversied rms, we substitute a gap of zerofor any service segments when computing the sales weighted average. Xj represents a seriesof rm attributes, including rm size (employment), physical capital per worker, return on
assets and a dummy variable for leading rms proposed by Gutierrez and Philippon (2017)
which indicates whether a rm's market value is in the upper tertile of rms whose largest
21

segment is in the same 4-digit NAICS industry.

All variables used in the estimation are

de-meaned and divided by their standard deviation so that estimated coecients indicate
the implied standard-deviation impact on the dependent variable of a one standard deviation change in the independent variable.

Standard errors are clustered at the four-digit

NAICS level and reported in parenthesis below coecient estimates. The regression sample
is restricted to rms with sales in at least one goods-producing industry.
As indicated in the rst ve columns of the Table 1, we nd negative and statisticallysignicant relationships between N T RGapj and average abnormal returns for each legislativeeevent (AARj ) except the introduction of the bill in the House of Representatives. Column
using the concordance reported in Pierce and Schott (2012).

20 The manufacturing employment data used to construct this gure are from the NBER-CES manufac-

turing industry database (Bartelsman et al. (2000)), available on the NBER website.

21 Firm attributes are for 2000 and are drawn from COMPUSTAT. As is common in studies utilizing

COMPUSTAT data, the accounting data are winsorized at the 1 percent level to reduce the inuence ofoutliers; i.e., observations below the rst percentile and above the ninety-ninth percentile are replaced withthe observations at those percentiles.

96 reveals that this relationship also holds for the average abnormal return across all ve
P NT R). The coecient estimate in that column implies that the relationship is
events (AARj
economically as well as statistically signicant: a one standard deviation increase in the NTR
P NT R, which represents a
gap is associated with a -0.223 standard deviation decline in AARj
22

drop in market value of 6 percent, or about 177 million dollars.
P NT Rand the NTR gap in the
In Table 1 we examine the relationship between AARj
presence of the rm attributes noted above.

As indicated in the table, the relationshipdrops in magnitude but remains negative and statistically signicant.

Among the added

rm attributes, we nd positive and statistically signicant relationships for three out of
P NT R
rises with rm size, physical capital per worker and return on
the four. That is, AARj
assets. To the extent that these attributes are correlated with rms' innate eciency, these
relationships are consistent with models predicting that rms with greater productivity might
be better able to take advantage of the oshoring opportunities related to PNTR (Antràs
et al. (2017); Bernard et al. (2018)).

P NT Rand US import growth from
Finally, Table 3 examines the link between rms' AARj
China before and after PNTR in the rm's largest segment.

As indicated the rst twocolumns, we nd a negative and statistically signicant relationship with respect to postPNTR imports and no relationship with respect to pre-PNTR imports. The latter suggests
equity markets correctly anticipated an increase in import competition as a result of PNTR,
while the former indicates a lack of prior trend. Column 3 reveals that these relationships
are robust to inclusion of the rm attributes noted above. Interestingly, in contrast to theresults in Table 3, physical capital per worker retains a positive sign but loses statisticalsignicance. The coecient estimate on post-2000 import growth from China in the nalcolumn, -0.082, indicates that a 1 standard deviation increase in subsequent imports from
China is associated with a drop in market value of about 2 percent.

3.2 PNTR and the Belgrade Bombing
As discussed in more detail in Pierce and Schott (2016a), several events in US-China relations
during the 1990s likely increased uncertainty regarding annual renewal of China's NTR status
in the United States. One of the more prominent of these events was the accidental NATObombing of the Chinese embassy in Belgrade, Yugoslavia on May, 7 1999.

The bombing

occurred during an 11-week NATO campaign intending to end Serbian aggression against
ethnic Albanians in Kosovo, and was recognized at the time as a potential threat to China's
entry into the WTO. For example, three days after the bombing, the Wall Street Journal
noted that prospects for a speedy end to negotiations on China's accession to the World

22 The average market value of a rm in 2000 in our sample is 3 billion dollars. Multiplying the coecientof -0.223 by the standard deviation of AAR 1.06 percent) yields a reduction in market value of about 6
percent over 25 days.

10

Trade Organization just got a lot worse.

23

Given the proximity of the bombing to thepassage of PNTR, we examine how rms' average abnormal returns in the seven trading
Bombing
P N T R 24
after it occured, AARj
, compare to AARj.
A virtue of this external validity
check, relative to the results reported in Table 1, is that we are able to investigate responses
for both goods-producing and service rms.

Bombing
Our rst step is to investigate via OLS the link between AARi
and the NTR gap,

AARjBelgrade = αN T R Gapj + Xj β + i ,(6)

where Xj continues to represent rm attributes in 2000 and, as above, all variables havebeen de-meaned and divided by their standard deviations.Results, reported in Table 4, indiBelgrade
cate that rms' NTR gaps exhibit arelationship with AARj
, both in a simple

positivebivariate regression and when the additional controls are included, though statistical signicance is only found for the former. These positive relationships are consistent with the ideathat rms that receive greater protection from pre-PNTR US trade policy towards Chinamight benet from a breakdown in US-China relations due to the bombing, e.g., if protests
in China prompt the US Congress to reject China's temporary NTR status. The negative
Belgrade
relationship between AARj
and rm attributes in column 2 suggests such a breakdown
may be relatively more costly for more capital-intensive and higher-ROA rms.

P NT RIn Table 5, we analyze the association between returns after the bombing and AARj,
also via OLS,AARjP N T R = αAARjBelgrade + Xj β + i .

(7)

The sample for this regression includes both goods-producing and service rms, and we reportresults both with and without the inclusion of rm attributes. We nd that α isnegativeand generally statistically signicant in all cases, indicating that rms that might benet in
relative terms from a potential breakdown of US-China relations due to the bombing might
be harmed in relative terms from the trade liberalization. Interestingly, the magnitude andstatistical signicance of α is greater for service rms.

3.3 PNTR and the 2016 Presidential Election
During his campaign for President, Donald Trump emphasized his intent to overturn what
he perceived to be bad deals in international trade, particularly those with respect to25

China and the North American Free Trade Agreement.

As a consequence, his surprise

23 https://www.wsj.com/articles/SB926284661489396187.

24 We employ a longer event window for studying the bombing given that information about it unfolded
slowly.

25 For example, in a 2016 campaign rally in Staten Island, Trump stated, China's upset because of theway Donald Trump is talking about trade with China. They're ripping us o, folks, it's time. I'm so happy

11

P NT R
victory oers another opportunity to examine the external validity of AARj
.

Here,

however, we conduct the analysis at the industry level, given the degree of rm attrition
and industry-switching that occurs between 2000 and 2016. We compare the market capitalP NT R
P NT R, to similarly
across rms' major industries, AARi
ization weighted average AARj
T rump26
constructed returns in the seven days
following the election, AARi
, using an OLS
specication of the form

AARiT rump = αAARiP N T R + Xi β + i .

(8)

As above, i indexes 6-digit NAICS industries, all variables are de-meaned and divided bytheir standard deviations, and standard errors are clustered at the four-digit NAICS level.
27

All specications include the full set of controls appearing in Table 1.

Results, reported in Table 6, are consistent with the idea that industries whose expectedprots might rise with PNTR are those whose prots might fall with Trump's election.
P NT R
and
That is, we nd a negative and statistically signicant relationship between AARi
T rumpAARi, where the coecient estimate in the rst column implies that a one standard
P NT Ris associated with a 0.16 standard deviation decrease indeviation increase in AARi
T rump
AARi. Results in the second column reveal that this relationship is robust to theinclusion of industry-level controls, while those in columns 3 and 4 show that it holds among
both goods and services industries.

4

AARjP N T R and Continuing-Firm Outcomes

The NTR gap measures exposure to import competition.

AARjP N T R , by contrast, capturestraders' assessment of the net impact of all avenues of exposure, of which there might bemany. For example, the decline in expected taris on Chinese goods may prompt some USrms to locate part of their production process in China. Depending on the eect of thisrelocation on rms' costs, and the extent to which it is conducted within the boundaries ofthe rm, it might raise or lower rms' domestic and worldwide employment, as discussed
they're upset. Similarly, when discussing NAFTA, Trump stated, "NAFTA is the worst trade deal maybe
ever signed anywhere, but certainly ever signed in this country.

Wagner et al. (2018) shows that rms'

abnormal returns in the days surrounding Donald Trump's election are negatively correlated with their
exposure to international markets, and that more internationally exposed sectors exhibit declines relative tomore domestically oriented sectors.

26 We choose this window to reect the unexpected nature of his election and uncertainty over how hemight react in the rst few days after election. At the beginning of the Trump campaign in 2015, betting
markets were oering 25:1 odds against his success. These odds never became shorter than 5:1, even on the
day before the election (http://fortune.com/2016/11/09/donald-trump-president-gamble/).

27 These attributes are for 2000 and are drawn from COMPUSTAT. They represent market capitaliza-tion weighted averages of each attribute across rms within each six-digit NAICS industry. As before, allaccounting ratios derived from COMPUSTAT are winsorized at the 1 percent level.

12

in the theoretical models of Antràs et al. (2017) and Bernard et al. (2018).

A similar

mechanism might operate among purely domestic rms, which, as a result of the change intrade policy, might have greater access to lower-cost suppliers of inputs previously produced
or sourced domestically.

Greater import competition from China may also prompt somerms to increase investment in process or product innovations, boosting physical, human orintangible capital. To the extent that these innovations lower production costs or increasedemand, they may also result in higher sales and employment.
In this section we investigate the relative explanatory power of rms' PNTR averageabnormal returns

vis a vis the NTR gap in explaining various rm outcomes that haveappeared in the literature.

4.1 Baseline Specication
Our baseline specication is a rm-level OLS dierence-in-dierences regression,ln(Outcomejt ) = δP ost × AARjP N T R + P ost × Xj β

(9)+αj + αt + jt .
The sample period is 1990 to 2006, where the endpoint is chosen to avoid the onset of theGreat Recession. The left-hand side variable represents one of several rm outcomes available in COMPUSTAT: employment, sales, markup (sales divided by the cost of goods sold),

do not observe rms' US
employment: rather, in COMPUSTAT, rm employment represents a rm's total employ-physical capital, intangible capital and total assets. We note that wement across all divisions and locations around the world. Likewise, sales encompasses total
revenue globally. Physical capital is dened as property, plant, and equipment. Intangible
capital is a quantity available in COMPUSTAT that is meant to capture rms' investments
in both knowledge capital and organizational capital. As discussed in greater detail inPeters and Taylor (2017), it is the sum of rms' externally purchased and internally createdintangible assets, which includes items such as goodwill from rms' balance sheets and capitalized spending on knowledge, patents, software and brand from their income statements.
Finally, total assets is the sum of the rms' assets from their balance sheets.
The rst term on the right-hand side of equation 9 is the dierence-in-dierences termof interest  an interaction of rms' average abnormal return and an indicator variable(P ost) for years after 2000  which captures the relative change in outcomes among rmswith dierential exposure to the change in policy after versus before it occurs. The second
term on the right-hand side represents a series of initial (1990) rm attributes that proxy forinvestment opportunities and rms' ability to nance future investments, also interacted with

P ost. These attributes are the market-to-book value of assets (i.e., Tobin's q), operating
13

cash ows divided by total assets (i.e., return on assets), log of total assets (i.e., size), bookleverage (the book value of debt divided by total assets), and cash holdings divided by total
assets.

28

The nal terms on the right-hand side of equation 9 are the rm and year xed eects
required to identify the dierence-in-dierences coecient.

Firm xed eects capture theimpact of any time-invariant rm characteristics, while year xed eects account for aggregate shocks that aect all rms equally. Regressions are weighted by the initial value of thedependent variable and, as in previous regressions, initial rm attributes from COMPUSTAT are winsorized at the 1 percent level and standardized. Standard errors are clusteredby 4-digit NAICS industry.NBER_Tables/PrePost_EMP_dvarwgtd_cln4_fmtd

Employment: Results for rms' total world employment are reported in Table 7. The
rst column presents estimates for all rms. As indicated in the table, we nd a positiveand statistically signicant relationship between abnormal returns and log employment, inP NT R
dicating that rms with higher AARj
exhibit a relative increase in employment after thechange in policy versus before. The coecient estimate, 0.119, implies that a one standard
P NT R
deviation increase inAARj
is associated with a relative increase in employment of 0.119
log points in the post period.
As noted above, an attractive feature of using average abnormal returns to assess exposure
to PNTR is the ability to analyze rms outside goods-producing industries, i.e., those in
Own
service industries for which an N T R Gapi
is not dened because their output does notexist in the US tari schedule. Toward that end, we estimate equation 9 separately for both
goods-producing and service rms. As indicated in columns 2 and 3, we nd positive andstatistically signicant relationships in both cases, with the coecient for services higher inmagnitude, 0.134 versus 0.118 for goods-producing rms. These estimates indicate that a one
P NT Ris associated with relative increases in employment
standard deviation increase in AARj
of 0.118 and 0.134 log points, respectively, after the change in policy.

29

P NT Rand
The next three columns of Table 7 examine the relative predictive power of AARj
30
Own
the NTR gap.
Column 4 introduces N T R Gapi
, and includes only rms for which thismeasure is dened. Column 5 also focuses on goods-producing rms, adding the diagonalU p3
Down3
excluding up- and downstream NTR gaps noted above, N T R Gapi
and N T R Gapi.
Own
Column 6 concentrates on service rms, for which N T R Gapi
is not dened. For these
U p3
Down3
rms we include only N T R Gapi
and N T R Gapi.
The estimates in these columns have several noteworthy trends. First, we nd that
AARjP N T R remains positive and statistically signicant in all three specications, with mag-

28 For rms that enter the sample after 1990, we use their attributes upon entry in constructing X .
j29

30 In Appendix Table A.1, we report similar regressions omitting AARP N T R . In those results, we nd thatj

N T R GapOwn, has the expected sign and is statistically signicant at conventional levels in the majority ofispecications

14

nitudes that are largely unchanged relative to columns 2 and 3. Second, we nd that while
Own
have the expected negative sign in each specication, they
the estimates for N T R Gapi
are not statistically signicant at conventional levels.

Together, these two trends suggestthat our proposed measure incorporates all of the information contained in the standard
measure of exposure to PNTR used in the literature, and, indeed, has explanatory power
beyond it. As such, they provide further support for the assumptions underlying our eventU p3
Down3
study approach. Similarly, we nd that N T R Gapi
and N T R Gapi
are generally notstatistically signicant at conventional levels. The lone exception  the downstream eect
among service rms  exhibits the expected sign, as greater exposure downstream may reducedemand, thereby dampening employment.
In the nal three columns of the table, we re-estimate columns 4 through 6 and introduce
two-digit NAICS sector-by-year xed eects. Inclusion of these xed eects mitigates theconcern that employment growth in the years following PNTR was driven by sector-level
shifts  for instance, due to changes in the real estate sector prior to the housing crisis  andthat growing sectors coincidentally responded positively to PNTR legislation. By includingthese xed eects, we are exploiting variation across rms within a given sector in a given
year. Perhaps unsurprisingly, the magnitudes of the point estimates in these specications
P NT Rare reduced slightly, but AARj
remains statistically signicant in all three specications.

Comparison with the literature: We provide context for the results in Table 7 by compar-

ing them to others in the literature, and to estimates from our data based solely on the NTR
gap. Pierce and Schott (2016a), whose analysis is closest to that performed here, nd thata one-standard deviation increase in an establishment's NTR gap (computed as the value
weighted average gap across its products) is associated with a relative decline in employment of approximately 0.053 log points. While this estimate is smaller in magnitude than
P NT R
reported in Table 7 for goods-producing rms, this could be due
our estimate for AARj
to the dierence in the sample of rms used in this analysis. Specically, the data used in
Pierce and Schott (2016a) focus solely on establishments' US manufacturing employment.
The employment data in COMPUSTAT, by contrast, are at therm level and include work-ers both outside the United States and in divisions outside manufacturing. To address thisdierence directly, we re-estimate this table solely using the NTR gapin our sample of rmsP NT Rand omit rm-level AARjin Appendix Table A.1. Here we nd that a one standard
Own
deviation decrease in N T R Gapi
is associated with a relative increase in employment of
0.066 log points, about half of the magnitude reported in Table 7. Notably, however, thispoint estimate is strikingly similar to the eect document in Pierce and Schott (2016a). ThisP NT Rsuggests that the dierence in the estimated eect of PNTR via AARj
and the NTR
gap is not driven by our sample of publicly traded rms.

Other Outcomes : To conserve space, we report results for the other outcomes we examine
visually, in Figure 9. Each panel of the gure reports results for a dierent outcome, withthe rst panel duplicating the results for employment that appear in Table 7. Within each15

panel, each line reports the 95 percent condence interval for the dierence-in-dierences
terms of interest from the specications reported in columns 1 to 3, 5, 6, 8, and 9 from Table
7. Results for other specications and variables are suppressed but available upon request.
As indicated in the second panel of the gure, results for sales are similar to those for
P NT R
employment. That is, we nd that the interactions of AARj
and the P ost indicatorvariable are positive and statistically signicant across all rms as well as across goodsproducing and service rms, with coecients of similar magnitude as in the employment
regressions. The point estimate for all rms, 0.167, implies that implies that a rm whose
AARjP N T R is one standard deviation above the mean exhibits a relative increase in sales of0.167 log points in the post period. As with employment, relatively greater sales growth
P NT R
among rms with higher AARj
may indicate a relative expansion of domestic or foreign
demand driven by PNTR's impact on rm costs. Results for the remaining specications
indicate that these estimates are robust to inclusion of the NTR gap terms as well as 2-digit
NAICS by year xed eects.
The third panel of Figure 9 investigates the relationship between PNTR and a coarse
measure of rm protability, the ratio of sales to cost of goods sold.

As indicated in thegure, estimates are positive and statistically signicant at conventional levels among goods
producing rms, but negative and statistically signicant among service rms. These results
suggest, implicitly, that rm sales relative to costs rise among goods-producing rms, while
the opposite is true for service rms.
The bottom three panels of Figure 9 report estimates for physical capital, intangible
capital, and total assets. We nd positive coecients among both goods and service rms inboth cases, though results for intangible capital are only marginally statistically signicant.
These results suggest rms may engage in product or process upgrading in response to importcompetition, consistent with existing research on US and European rms by Bernard et al.
31(2006), Khandelwal (2010), Bernard et al. (2011) and Bloom et al. (2016).

4.2 Annual Specications
If changes in rm outcomes are actually attributable to PNTR, abnormal returns should
be correlated with rm outcomes after passage of PNTR, but not before. To determine
P NT R
whether there is a relationship between AARj
and outcomes in the years prior to 2001,
we replace the single dierence-in-dierences term in equation (9) with interactions between
AARjP N T R and a full set of year dummies. We also include the interaction of rms' initial

31 Autor et al. (2016) nd that increases in Chinese import penetration negatively aects US manufacturers'
innovative activities.

Examining US manufacturing establishments, Pierce and Schott (2017) nd thatinvestment among continuers with greater exposure to PNTR via the NTR gap exhibits relative declines
after the change in policy, those these declines are relatively moderate for establishments with relatively
high levels of initial labor productivity, skill intensity and capital intensity. Gutierrez and Philippon (2017)
document relative increases in investment and innovation among industry leaders in response to PNTR.

16

(1990) attributes and year dummies,ln(Outcomejt ) =2006
Xδt × AARj +t=1990

2006
X

×Xj βt(10)y=1990+αj + αt + jt .
In all other respects, the estimation of equation 10 resembles that of equation 9.
To conserve space, we focus on specications including all rms (i.e., analogous to column
1 of Table 7) and present results visually in Figure 8. Each panel of the gure focuses ona dierent outcome, and within each panel a series of 95 percent condence intervals traces
out the sequence of δy from 1990 to 2006, with 2000 being the omitted year.
The rst panel of the gure examines employment. Consistent with the requirements ofdierence-in-dierences identication, we nd that estimates of δt are close to zero and are
not statistically signicant at conventional levels prior to 2000. In years following the policychange, however, point estimates are positive and statistically signicant, with coecientsrising to about 0.15. We nd a similar pattern of results for sales, physical capital, intangible
capital and total assets, in panels 2, 4, 5, and 6. Consistent with the post dierence-indierences results in Table 7, estimates of δt are statistically insignicant both before andafter 2000 for the ratio of sales to cost of goods sold, suggesting that rm costs rose intandem with sales.

5Firm Exit

Finally, we explore the likelihood of rm exit in response to PNTR. Exit from the CRSP
dataset signies a de-listing from the rm's stock exchange, which may occur for one ofve reasons: bankruptcy or liquidation, merger, rm contraction (e.g., rm assets, equity,
or capital fall below levels required to be listed), a listing violation (e.g., due to a corporate governance problem, or an insucient number of shareholders or market makers), andother. Table 8 provides a more detailed breakdown of the CRSP de-listing codes.

32

We examine the relationship between PNTR CARs and exit using a rm-level multinomial logit regression,−
P r(Yj = d) = βAARjP N T R + →γ Xji2000 + j ,(11)

where P r(Yj = d) is the probability that rm j exits between 2000 and 2006 due to one ofthe ve de-listing events noted above, indexed by d. The base outcome is survival. As with

32 As noted in the appendix, the number of rms exiting due to these causes is 144, 1615, 467, 404, and205, respectively.

17

our previous rm-level regressions, we include controls for initial (in this case, year 2000)
2000, standardize all variables by subtracting their mean and dividing byrm attributes, Xj
their standard deviations, and winsorize all accounting variables at the 1 percent level.
Results are reported in Table 9, which includes the full set of coecients as well as
P NT Rthe marginal eect of rms' AARj
, evaluated at the mean for all covariates, for eachoutcome.

As indicated in the table, exposure to PNTR has a negative and statisticallysignicantly relationship with the likelihood of exit via three channels: bankruptcy, de-listing
due to contraction, and listing violations. The marginal eects indicate that a one standard
P NT R
deviation increase in AARj
is associated with a relative increase in the probability ofexiting due to bankruptcy (column 3), contraction (column 4), or listing violations (column5) of 0.4, 1.3 and 0.6 percent. These estimates are also economically signicant, given thatthe unconditional probabilities of exit due to bankruptcy, contraction, and listing violationsare 2, 8, and 7 percent. The probability of exit via merger or miscellaneous reasons respondweakly to PNTR exposure, if at all.

6

Conclusion

In this paper we introduce a new method for measuring rm exposure to trade liberalization
based on equity market responses that capture traders' assessment of the net impact of
all avenues of exposure and provide estimates for rms both within and outside goodsproducing industries.

We use this method to investigate rm sensitivity to the UnitedStates' granting of Permanent Normal Trade Relations to China in October, 2000, and ndthat rms' average abnormal returns during key legislative milestones associated with thisliberalization vary widely within industries, that they are correlated with standard variables
used to assess import competition, and that they provide explanatory power beyond thesestandard measures in explaining subsequent rm outcomes.
Further exploration of this approach is warranted. For example, assessing rm exposure
to non-tari barriers is notoriously dicult, as changes in these barriers can be hard to
express in terms of equivalent increases or decreases in tari rates (Goldberg and Pavcnik(2016)). Our approach may also prove useful for evaluating rm sensitivity to other shocks,
such as or changes in technology, to the extent that they can be associated with discrete
events. We are currently exploring applications along these lines.

18References
Acemoglu, D., D. Autor, D. Dorn, G. H. Hanson, and B. Price (2016). Import Competition
and the Great U.S. Employment Sag of the 2000s.

Journal of Labor Economics 34 (S1),S141S198.
Amiti, M. and J. Konings (2007). Trade Liberalization, Intermediate Inputs, and Productivity: Evidence from Indonesia.

American Economic Review 97 (5), 16111638.

Antràs, P., T. C. Fort, and F. Tintelnot (2017). The margins of global sourcing: Theory andevidence from u.s. rms.

American Economic Review 107 (9), 251464.

Autor, D., D. Dorn, G. H. Hanson, G. Pisano, and P. Shu (2016, December).

Foreigncompetition and domestic innovation: Evidence from u.s. patents. Working Paper 22879,
National Bureau of Economic Research.
Autor, D., D. Dorn, G. H. Hanson, G. Pisano, and P. Shu (2017). Foreign Competition andDomestic Innovation: Evidence from U.S. Patents.

NBER Working Paper #22879 .

Autor, D., D. Dorn, G. H. Hanson, and J. Song (2014).
evidence.

Trade adjustment: Worker-level

The Quarterly Journal of Economics 129, 1991860.

Autor, D. H., D. Dorn, and G. H. Hanson (2013). The china syndrome: Local labor marketeects of import competition in the united states.

American Economic Review 103 (6),212168.
Bartelsman, E. J., R. A. Becker, and W. B. Gray (2000). NBER-CES manufacturing industry
database.
Bernard, A. B., J. B. Jensen, S. Redding, and P. Schott (2011). Multi-product rms andtrade liberalization.

Quarterly Journal of Economics 126 (3), 12711318.

Bernard, A. B., J. B. Jensen, S. J. Redding, and P. K. Schott (2018, June). Global rms.

Journal of Economic Literature 56 (2), 565619.
Bernard, A. B., J. B. Jensen, and P. K. Schott (2006). Survival of the best t: Exposure
to low-wage countries and the (uneven) growth of us manufacturing plants.

International Economics .
Bloom, N., M. Draca, and J. V. Reenen (2016).

Trade induced technical change:

impact of chinese imports on innovation, diusion, and productivity.

Studies 83, 87117.

19

Journal of
The

Review of Economic

Bloom, N., M. Draca, and J. Van Reenen (2016). Trade Induced Technical Change? The
Impact of Chinese Imports on Innovation, IT, and Productivity.

Studies (83), 87117.

Review of Economic

Breinlich, H. (2011). Heterogenous Firm-Level Responses to Trade Liberalization: A Test
Using Stock Price Reactions.

Journal of International Economics 2014 (93), 270285.

Bustos, P. (2011). Trade liberalization, exports and technology upgrading: Evidence on theimpact of mercosur on argentinian rms.
Dix-Carneiro, R. (2014).

American Economic Review 101 (1), 304340.

Trade Liberalization and Labor Market Dynamics.

rica 82 (3), 825885.

Economet-

Fama, E. F., L. Fisher, M. C. Jensen, and R. Roll (1969). The Adjustment of Stock Prices
to New Information.

International Economic Review 10.

Feenstra, R. C., H. Ma, and Y. Xu (2017). US Exports and Employment.

Paper No. 24056 .

NBER Working

Feenstra, R. C., J. Romalis, and P. K. Schott (2002). Us imports, exports, and tari data,
1989-2001. Working Paper 9387, National Bureau of Economic Research.
Feenstra, R. C. and A. Sasahara (2017). The China Shock, Exports and U.S. Employment:
A Global Input-Output Analysis.
Fernandes, A. M. (2007).

NBER Working Paper No. 24022 .

Trade Policy, Trade Volumes, and Plant-Level Productivity inColumbian Manufacturing Industries.

Journal of International Economics 71 (1).

Golberg, P. K., A. K. Khandelwal, N. Pavcnik, and P. Topalova (2010). Imported Intermediate Inputs and Domestic Productivity Growth: Evidence From India.

of Economics 125 (4), 17271767.

Quarterly Journal

Goldberg, P. K. and N. Pavcnik (2016). The eects of trade policy. mineo.
Gutierrez, G. and T. Philippon (2017, July). Declining competition and investment in theu.s. Working Paper 23583, National Bureau of Economic Research.
Gutierrez, G. and T. Phillipon (2017). Declining Competition and Investment in the U.S.
Hakobyan, S. and J. McLaren (2016). Looking for Local Labor Market Eects of NAFTA.

The Review of Economics and Statistics 98 (4), 728741.
Handley, K. and N. Limão (2017).
Evidence for China and the U.S.

Policy Uncertainty, Trade, and Welfare: Theory andAmerican Economic Review 107 (9), 273183.
20

Huang, Y., C. Lin, S. Liu, and T. Heiwai (2018). Trade Linkages and Firm Value: Evidence
from the 2018 U.S.-China Trade War .

working paper .

The long and short (of ) quality ladders.

Review of Economic

Khotari, S. and J. B. Warner (2006). Econometrics of Event Studies.

Handbook of Corporate

Khandelwal, A. (2010).

Studies 77, 14501476.
Finance: Empirical Corporate Finance A(1).
Kogan, L., D. Papanikolau, A. Seru, and N. Stoman (2017).
Resource Allocation, and Growth.

Technological Innovation,

Quarterly Journal of Economics 132 (2).

Mobarak, A. M. and D. P. Purbasari (2006). Protection for Sale To Firms: Evidence fromIndonesia.

Yale mimeo .

Mosder, C. and A. Rose (2014). Who Benets from Regional Trade Agreements? The View
from the Stock Market.

European Economic Review 68, 3147.

Peters, R. H. and L. A. Taylor (2017). Intangible Capital and The Investment-

Journal of Financial Economics 123 (2), 251272.
Pierce, J. R. and P. K. Schott (2012).

q Relation.

A concordance between ten-digit u.s. harmonizedsystem codes and sic/naics product classes and industries.

Social Measurement 37, 6196.

Journal of Economic andPierce, J. R. and P. K. Schott (2016a). The Surprisingly Swift Decline of U.S. Manufacturing
Employment.

American Economic Review 106 (7), 16321662.

Pierce, J. R. and P. K. Schott (2016b). The Surprisingly Swift Decline of U.S. Manufacturing
Employment.

American Economic Review 106 (7), 16321662.

Pierce, J. R. and P. K. Schott (2017). Investment responses to trade liberalization: Evidence
from u.s. industries and establishments.

NBER Working Papers #24017 .

Sharpe, W. F. (1964). Capital Asset Prices: A Theory of Market Equilibrium Under Conditions of Risk.
Topalova, P. (2010). Factor Immobility and Regional Impacts of Trade Liberalization: Evidence on Poverty from India.

American Economic Journal: Applied Economics 2 (4),141.
Topalova, P. and A. K. Khandelwal (2011).
The Case of India.

Trade Liberalization and Firm Productivity:

The Review of Economics and Statistics 93 (3), 9951009.

21

Wagner, A. F., R. J. Zeckhauser, and A. Ziegler (2018). Company Stock Price Reactions to
the 2016 Election: Trump, Taxes, and Trade.

Journal of Financial Economics Forthcom-

ing.
Wang, Z., S.-J. Wei, X. Yu, and K. Zhu (2018). Re-examining the Impact of the China Trade
Shock on US Labor Market: A Value Chain Perspective.

Working Paper .

Wolfers, J. and E. Zitzewitz (2018). What Can We Learn From Financial Market Response to
the 2016 Election? The Standard Error of Event Studies: Lessons from the 2016 Election.

AEA Papers and Proceedings 108, 584589.
Figure 1: Count of Articles Mentioning "Permanent Normal Trade Relations"

Source: Noted media outlets and authors' calculations. Figure reports the number of unique articles which
mention PNTR during calendar year 2000 from the following sources: the Associated Press, BBC Monitoring
Inernational Reports, the Boston Globe, the Chicago Tribune, CNN Transcripts, the Financial Times, theLos Angeles Times, the New York Times, the Washington Post, PR Newswire and the the Wall Street
Journal. The ve bold segments of the time series indicate the ve legislative event windows considered in
our analysis: the introduction of the bill in the House , the House vote, the Senate vote to bring the bill tothe oor, the Senate vote and Clinton's signing, in that order.

22

Figure 2: PNTR Average Abnormal Returns, By Event

Source: CRSP and authors' calculations. Figure
displays distributions of AARj across 5 PNTR
legislative events and overall. Values below -7.5
and above 7.5 percent are dropped to improvereadability.

23

Figure 3: PNTR Average Abnormal Returns, By Type of Firm

Source: CRSP and authors' calculations. Figure plots
distribution of

AARjP N T R for three mutually exclu-

sive rm types: those which only produce goods, thosewhich only produce services, and those which produce
both.

Values below -7.5 and above 7.5 percent are

dropped to improve readability. The means and standard deviations for the three groups of rms are -0.48
and 1.06 percent, -0.28 and 1.01 percent, and -0.30 and
1.02 percent, respectively.

24

Figure 4: Firm- versus Industry-Level Average Abnormal Returns

Source: CRSP and authors' calculations. Figure com-

AARjP N T R to the market-capitalization
P NT Rweighted average AARjof their major 6-digit
pares rms'

NAICS industry . Values below -7.5 and above 7.5 percent are dropped to improve readability. The means
and standard deviations for the three groups of rmsare -0.48 and 1.06 percent, -0.28 and 1.01 percent, and-0.30 and 1.02 percent, respectively.

Figure 5: Distribution of the NTR Gap

Source: Feenstra et al. (2002). Figure displays the dis-Own

tribution of N T R Gapi

across goods-producing 6-

digit manufacturing industries populated by rms oursample. Goods-producing sectors are dened as: Manufacturing (NAICS 31-33), Mining (NAICS 21), andAgriculture, Forestry, Fishing and Hunting (NAICS
11).

25

Figure 6: US Manufacturing Employment by

P NT RN T R GapOwn
versus AARi
i

Source: NBER-CES Manufacturing Industry Database, CRSP and authors' calculations. Figure displays the evolution of US manufacturing employment from1990 to 2006 according to two dierent groupings of 6-digit NAICS industries. In

Own

the left panel, industries are separated according to whether their N T R Gapi

are above or below the median across all industries. In the right panel, industries

P NT Rare grouped according to whether their AARi

26

lie above or below the median.

Figure 7: AAR and Firm Outcomes (Post Specication)

Source: CRSP, COMPUSTAT and authors' calculations. Figure displays results of rm-level OLS panel

P NT R

regressions of noted rm outcomes on rms' PNTR average abnormal returns (AARj) interactedwith an indicator variable for years after 2000 (P ost) and a series of initial (1990) rm characteristics
also interacted with P ost. Coecients and standard errors for all right-hand side varaibles other thanthe dierence-in-dierences terms of interest are suppressed. Each 95 percent condence interval comesfrom a seperate regression. There are seven results for each outome, corresponding to the specications
reported in columns 1 to 3, 6, 7, 10 and 11 from Table 7. Condence intervals 1 to 3 in each panel
report baseline results for all rms, goods-producing rms and service rms, respectively. Condence

Ownintervals 4 and 5 also include N T R GapiUp

, N T R Gapi

Down

and N T R Gapi

as additional right-handside variables. Condance intervals 6 and 7 also include 2-digit NAICS by year xed eects. Sample
period is 1990 to 2006. Initial rm attributes are market capitalization, market to book value of equity,
return on assets, and growth in total capital. Right-hand side variables also include rm and year xedeects. All variables have been have been normalized by dividing them by their standard deviations,and winsorized at the 1 percent level.

Regressions are weighted by the initial (1990) value of thedependent variable. Standard errors used to construct condence intervals are clustered at the NAICS
4-digit level. Note that y-axis scales vary across panels.

27

Figure 8: AAR and Firm Outcomes (Annual Specication)

Source: CRSP, COMPUTSTAT and authors' calculations. Figure displays results of rm-level OLS

P NT R

panel regressions of noted rm outcomes on rms' PNTR average abnormal returns (AARi) inter-

acted with a full set of year dummy variables and a series of initial rm characteristics also interactedwith year dummies. Coecients and standard errors for all right-hand side varaibles other than thedierence-in-dierences terms of interest are suppressed.

Each 95 percent condence interval comesfrom a seperate regression. There are two results for each outome, representing estimation on rms ingoods-producing and service rms, respectively. Sample period is 1990 to 2006. Initial rm attributes
are are market capitalization, market to book value of equity, return on assets, and growth in total
capital. Right-hand side variables also include rm and year xed eects. All variables have been havebeen normalized by dividing them by their standard deviations and all accounting ratios have been
winsorized at the 1 percent level. Regressions are weighted by the initial (1990) value of the dependent
variable.

Standard errors used to construct condence intervals are clustered at the NAICS 4-digit

level.

28

Figure 9: AAR and Firm Outcomes (Post Specication)

Source: CRSP, COMPUSTAT and authors' calculations. Figure displays results of rm-level OLS panel

P NT R

regressions of noted rm outcomes on rms' PNTR average abnormal returns (AARj) interactedwith an indicator variable for years after 2000 (P ost) and a series of initial (1990) rm characteristics
also interacted with P ost. Coecients and standard errors for all right-hand side varaibles other thanthe dierence-in-dierences terms of interest are suppressed. Each 95 percent condence interval comesfrom a seperate regression. There are seven results for each outome, corresponding to the specications
reported in columns 1 to 3, 6, 7, 10 and 11 from Table 7. Condence intervals 1 to 3 in each panel
report baseline results for all rms, goods-producing rms and service rms, respectively. Condence

Ownintervals 4 and 5 also include N T R GapiUp

, N T R Gapi

Down

and N T R Gapi

as additional right-handside variables. Condance intervals 6 and 7 also include 2-digit NAICS by year xed eects. Sample
period is 1990 to 2006. Initial rm attributes are market capitalization, market to book value of equity,
return on assets, and growth in total capital. Right-hand side variables also include rm and year xedeects. All variables have been have been normalized by dividing them by their standard deviations,and winsorized at the 1 percent level.

Regressions are weighted by the initial (1990) value of thedependent variable. Standard errors used to construct condence intervals are clustered at the NAICS
4-digit level. Note that y-axis scales vary across panels.

29

Table 1:

AARjP N T R versus the NTR Gap and Firm Attributes
(1)

(2)

(3)

(4)

(5)

(6)

House IntroHouse Vote

Sentate Intro

Senate Vote

Clinton Sign

Overall

0.007-0.164∗∗∗-0.158∗∗∗-0.045∗∗-0.188∗∗∗-0.223∗∗∗(0.035)(0.042)(0.027)(0.021)(0.044)(0.050)0.094∗∗∗-0.115∗∗-0.086∗∗-0.027-0.038-0.075(0.032)(0.053)(0.039)(0.024)(0.041)(0.051)NTR Gap

Constant
Observations

2640

2640

2640

2640

2640

2640

R2

0.000

0.025

0.025

0.002

0.032

0.050

Source: CRSP and authors' calculations. This table presents rm-level OLS regressions of average abnormal returns during key PNTR legislatove milestones on the NTR
Gap. The regression sample is restricted to rms in goods-producing industries. Allvariables are de-meaned and divided by their standard deviation. Accounting variables
are winsorized at the 1 percent level. Standard errors are reported below coecient estimates and are clustered by 4-digit NAICS industries. *, ** and *** indicate statisticalsignicance at the 10, 5 and 1 percent levels, respectively.

Table 2:

AARjP N T R versus the NTR Gap and Firm Attributes

NTR Gap

(1)

(2)

(3)

(4)

(5)

AARP N T R

AARP N T R

AARP N T R

AARP N T R

AARP N T R-0.224∗∗∗-0.202∗∗∗-0.167∗∗∗-0.132∗∗∗-0.132∗∗∗(0.052)(0.053)(0.039)(0.031)(0.031)0.199∗∗∗

0.178∗∗∗

0.095∗∗∗

0.094∗∗(0.039)(0.043)(0.036)(0.038)0.098

0.105∗

0.105∗(0.060)(0.054)(0.054)0.230∗∗∗

0.230∗∗∗(0.022)(0.022)

Ln(Size)

PPE per Worker

Return on Assets

GP (2017) Leader Firm

0.021
(0.065)Constant-0.071-0.085-0.097∗-0.082∗∗-0.083∗∗(0.041)(0.052)(0.057)(0.052)(0.041)

Observations

2615

2615

2615

2615

2615

R2

0.049

0.089

0.095

0.149

0.149

Source: CRSP and authors' calculations. This table presents rm-level OLS regres-

P NT R

sions of AARjon the NTR Gap and rm attributes. Firm attributes are for 2000.

The regression sample is restricted to rms in goods-producing industries.

All vari-

ables are de-meaned and divided by their standard deviation. Accounting variables are
winsorized at the 1 percent level. Standard errors are reported below coecient estimates and are clustered by 4-digit NAICS industries. *, ** and *** indicate statisticalsignicance at the 10, 5 and 1 percent levels, respectively.

30

Table 3:

AARjP N T R versus Chinese Import Growth
(1)

(2)

(3)

AARP N T R

AARP N T R

AARP N T R-0.088∗∗-0.088∗∗-0.082∗∗(0.044)(0.043)(0.036)∆ ln(Imports 2000-6)∆ ln(Imports 1991-00)-0.000-0.015(0.042)(0.040)0.089∗∗∗

Ln(Size)(0.025)
PPE per Worker

0.048(0.036)0.228∗∗∗

Return on Assets(0.031)
GP (2017) Leader Firm

0.136
(0.084)-0.084-0.084-0.102∗∗(0.055)(0.055)(0.048)Observations

1882

1882

1882

R2

0.008

0.008

0.098

Constant

Source:CRSP and authors' calculations.

This table
P NT Ron US im-presents rm-level OLS regressions of AARj

port growth from China in the rm's major industry andrm attributes. Firm attributes are for 2000. The regression sample is restricted to rms in industries for which
imports are observed. All variables are de-meaned and divided by their standard deviation. Accounting variables are
winsorized at the 1 percent level.

Standard errors are re-ported below coecient estimates and are clustered by 4digit NAICS industries.

*, ** and *** indicate statisticalsignicance at the 10, 5 and 1 percent levels, respectively.

31

AARjBelgrade versus the NTR Gap

Table 4:(1)

(2)

BelgradeAAR
NTR Gap

Belgrade

AAR

∗∗

0.051

(0.038)(0.034)0.095

Ln(Size)

0.030(0.037)

PPE per Worker-0.051∗(0.028)Return on Assets-0.109∗∗∗(0.025)
GP (2017) Leader Firm

-0.359∗∗∗(0.075)Constant

0.048(0.037)0.073∗∗(0.032)

Observations

2488

2488

R2

0.007

0.024

Source: CRSP and authors' calculations.

This table presents

rm-level OLS regressions of

AARjBelgrade on the NTR Gap.

Firm attributes are for 2000.

"Goods" and "Service" refer torms producing only NAICS 1 to 3 and only NAICS 4 to 9,
respectively.

All variables are de-meaned and divided by theirstandard deviation.
1 percent level.

Accounting variables are winsorized at theStandard errors are reported below coecientestimates and are clustered by 4-digit NAICS industries.

*, **and *** indicate statistical signicance at the 10, 5 and 1 percent
levels, respectively.

32

Table 5:(1)(2)

P NT R

AAR

Belgrade

AAR

AARjP N T R versus AARjBelgrade∗∗∗-0.069(0.020)(3)

P NT R(4)P NT R

AAR

AAR

∗∗

(5)

P NT RAAR

P NT R

AAR-0.052-0.105∗∗-0.036∗-0.013-0.083(0.021)(0.046)(0.021)(0.023)(0.046)

Ln(Size)

0.097

∗∗∗

(0.026)
PPE per Worker

0.084

∗

(0.050)Return on Assets

0.265

∗∗∗

(0.023)
GP (2017) Leader Firm

Constant(6)

P NT R

AAR

∗∗

0.081

(0.034)

∗

∗∗∗

0.119

(0.038)

∗∗∗

0.010

(0.061)(0.043)

0.163

∗∗∗

0.258

(0.025)

∗∗∗

0.262

(0.052)

0.058

0.068

0.003

(0.061)(0.069)(0.097)-0.034-0.0690.022-0.044-0.095∗∗0.008(0.055)(0.074)(0.063)(0.042)(0.048)(0.061)

Observations

4119

2557

1562

4119

2557

1562

R2

0.005

0.003

0.008

0.112

0.142

0.084

All

Goods

Services

All

Goods

Services

Firm TypeP NT R
Source: CRSP and authors' calculations. This table presents rm-level OLS regressions of AARjon
BelgradeAARj
and a series of rm attributes. Firm attributes are for 2000. "Goods" and "Service" refer torms producing only NAICS 1 to 3 and only NAICS 4 to 9, respectively. All variables are de-meaned anddivided by their standard deviation. Accounting variables are winsorized at the 1 percent level. Standard
errors are reported below coecient estimates and are clustered by 4-digit NAICS industries. *, ** and ***indicate statistical signicance at the 10, 5 and 1 percent levels, respectively.

33

Table 6: PNTR AAR versus 2016 Presidential Election AAR

AAR

T rump

(1)

(2)

(3)

(4)

All

All

Goods

Services-0.160**-0.177**-0.233*-0.162*(0.0598)(0.0648)(0.103)(0.0826)-0.138-0.302***0.0348(0.114)(0.0221)(0.167)-0.0753-0.1090.0247(0.0624)(0.0769)(0.0757)

0.268***

0.374**

0.238***(0.0754)(0.157)(0.0657)

0.134**

0.278***

0.0298(0.0589)(0.0786)(0.0505)

0.01280.0302

0.06150.00372(0.0958)(0.116)(0.132)(0.121)

Observations

377

371

203

168

R-squared

0.026

0.076

0.175

0.046

P NT RAAR

Ln(Size)

EquityM arket
EquityBook

ROA%∆ Investment

Constant

Source: CRSP and authors' calculations.

Table presents 6-digit-NAICS-

level OLS estimates from regressing average abnormal returns surrounding

T RU M P

the 2016 Presidential election (AARi) on average abnormal returns

P NT R

during key legislative events associated with PNTR (AARi) and a seriesof year-2000 industry-level control variables that are standard in the asset
pricing literature:market capitalization, market to book value of equity,

return on assets, and growth in total capital. In each case, these controls
represent market-capitalization weighted averages across the rms in eachindustry. All variables have been have been normalized by dividing them by
their standard deviations. Accounting ratios have been winsorized at the 1
percent level. Standard errors are clustered at the NAICS 4-digit level andare reported below coecient estimates. *, ** and *** indicate statisticalsignicance at the 10, 5 and 1 percent levels, respectively.

34

Online Appendix (Not for Publication)
This online appendix contains additional empirical results noted in the main text.

35

36

0.171***(0.0412)

CashF lows
Assets-0.0397(0.0455)

Cash
Assets

Yes
No
49612
0.045

Yes
No
28480
0.034

10.24***(0.0421)0.0177(0.0999)-0.0673(0.0697)-0.103**(0.0398)

0.163**(0.0659)-0.139(0.180)0.118*(0.0638)

(2)
Goods

Yes
No
21132
0.112

10.94***(0.0501)-0.0660(0.0512)0.0786**(0.0353)-0.114***(0.0425)0.188***(0.0706)

0.212***(0.0750)

0.134***(0.0477)(3)
Services

Yes
No
28480
0.036

10.24***(0.0427)

0.0285(0.101)-0.0716(0.0698)-0.107***(0.0402)0.167**(0.0646)-0.118(0.176)-0.0431(0.0309)0.116*(0.0630)

(4)
Goods

Yes
No
28480
0.037

10.24***(0.0429)0.0249(0.102)-0.0770(0.0692)-0.109***(0.0365)0.170**(0.0676)-0.106(0.174)-0.0162(0.0332)

0.0186(0.0378)-0.0485(0.0348)

0.113*(0.0647)

(5)
Goods

Yes
No
20425
0.123

10.97***(0.0489)-0.103(0.0629)0.0765**(0.0375)-0.119***(0.0404)0.243***(0.0794)

0.216***(0.0791)-0.115**(0.0456)-0.0571(0.0582)

0.127***(0.0476)(6)
Services

Yes
Yes
28480
0.231

10.13***(0.142)-0.0156(0.0698)-0.0610(0.0465)-0.102***(0.0301)

0.125**(0.0581)0.149(0.0915)-0.0667**(0.0318)

0.119***(0.0423)

(7)
Goods

Yes
Yes
28480
0.234

10.12***(0.140)-0.0241(0.0740)-0.0693(0.0462)-0.109***(0.0310)0.124**(0.0598)

0.164*(0.0924)-0.0368(0.0239)0.0206(0.0320)-0.0702**(0.0332)

0.111**(0.0449)

(8)
Goods

Yes
Yes
20425
0.310

11.67***(1.749)-0.0273(0.0766)

0.0426(0.0310)-0.129***(0.0390)0.236**(0.0974)

0.206***(0.0689)-0.113(0.0764)-0.0501(0.0540)0.115***(0.0418)(9)Services

P NT R. Sample period is 1990 toStandard errors are clustered at the NAICS 4-digit level and are reported below coecientestimates. *, ** and *** indicate statistical signicance at the 10, 5 and 1 percent levels, respectively.

(1990) value of the dependent variable.

their standard deviations, and accounting variables have been winsorized at the 1 percent level. Regressions are weighted by the initial

Right-hand side variables also include rm and year xed eects. All variables have been have been normalized by dividing them by

2006. Initial rm attributes are are market capitalization, market to book value of equity, return on assets, and growth in total capital.

P NT Ris displayed as AARj

) and a series of initial rm characteristics. All right-hand side variables have been

multiplied by a dummy variable indicating years after 2000, i.e., P ost × AARjrms' PNTR average abnormal returns (AARj

P NT R

Source: CRSP, COMPUSTAT and authors' calculations. Table presents rm-level OLS panel regressions of noted rm outcomes on

N
R2

Year-FE
NAICS -2 × Year - FE

Constant

10.64***(0.0395)

0.0350(0.0387)

Book Leverage× Post× Post-0.113***(0.0319)

Ln(Size)× Post× Post0.138*(0.0765)30.119***(0.0422)

Tobins Q
× PostNTR GapDown
× PostNTR GapU p
× Post3

NTR GapOwn
× PostAARP N T R
× Post(1)
All

Ln(Employment)

Table 7: AAR and Firm Employment (Baseline Specication)

Table 8: CRSP De-Listing Codes
Delist CodeFrequency

Code Description

Merger231-233

1423

When merged shareholders receive stock, ADR's, or cash.

241-261

192

When merged, shareholders receive other compensation

Bankruptcy or Liquidation
450-470

10Issue Liquidated.

574

137

Bankruptcy, declared insolvent.

Firm Contraction
552

281

Price fell below acceptable level.

560

112

Insucient capital, surplus, and/or equity.

561

74

Insucient (or non-compliance with rules of ) oat or assets.

Listing Violation
550

3

Insucient number of market makers.

551

16

Insucient number of shareholders.

580-587

385

Violation of listing requirements including corporate governance violations

Other De-listing500

1Issue stopped trading on exchange - reason unavailable.

520

99

Issue stopped trading current exchange - trading Over-the-Counter

570,573,575

245De-listed by current exchange - company request.

Source: CRSP and authors' calculations. Table presents presents the CRSP de-listing codes used
for categorizing the rm exits between 2000 and 2006 among the rms for which we are able to

P NT R

calculate AARj

.

37

38

0.21

Listing Violation

-0.15

0.08

(0.00309)-0.0130***-3.831***(0.276)(0.0803)(0.156)-1.015***-0.354**0.0981(0.0995)(0.0822)0.657***

0.0641(0.130)(0.0549)-0.332**(0.0497)(0.116)-0.288***-0.556***0.0454(0.584)(0.0558)-1.138*(0.122)(0.140)(0.0457)-0.0927-0.245*(0.110)-2.662***(0.101)-0.0922(0.0966)

0.366***(0.118)-1.441***(0.0852)-0.563***(0.103)0.348***(0.0704)-0.328***(0.140)-2.686***(0.113)-0.0291(0.0668)

0.328***(0.106)-1.459***(0.100)-0.481***(0.100)0.373***(0.0728)-0.170**

-0.08

0.07

(0.00281)-0.00606**

Multinomial Logit Coecients

-0.15

0.02

(0.00211)-0.00367*

0.0131

0.04

Firm Contraction

Marginal Eect at Mean of Covariates

Bankruptcy(0.192)-3.536***(0.202)-0.106(0.110)0.119(0.165)-1.330***(0.106)-0.333***(0.339)-0.374(0.0898)-0.00967-0.0070.03(0.00158)

0.000235

Other De-listing

and *** indicate statistical signicance at the 10, 5 and 1 percent levels, respectively.

percent level. Standard errors are clustered at the NAICS 4-digit level and are reported below coecient estimates. *, **been normalized by dividing them by their standard deviations, and accounting controls have been winsorized at the 1

capitalization, market to book value of equity, return on assets, and growth in total capital. All variables have been haveanother for the same rm) are omitted from the analysis. Right-hand side variables include initial (year 2000) market

through the end of 2006. Firms exiting due to equity exchange (i.e., a particular security is retired and replaced with

2000 to 2006 sample period. Potential reasons for exit are listed in Table 8. The base outcome (column 1) is survival

Source: CRSP and authors' calculations. Table presents results of rm-level multinomial logit model of exit during theConstant

Cash
AssetsBook LeverageLn(Size)

CashF lows
AssetsTobins Q

AAR

P NT R

0.58
0.02(0.00760)(0.00976)%∆ in Probability

0.008250.0143

Unconditional Probability

MergerSurvival

Table 9: AAR and Firm Exit

39
21027
0.519

0.303

(0.122)-0.0904(0.0722)-0.120*

0.476

28859

(0.0375)-0.0750**(0.0448)-0.0704(0.0337)20425(0.0749)-0.122(0.0554)-0.0444

0.228

28480

(0.0254)-0.0577**(0.0313)0.00388(0.0345)-0.0457

0.471-0.0649*

28859

0.224

(0.0363)-0.0614*28480(0.0341)-0.0665*

0.450

20625

(0.103)-0.0367(0.0371)-0.0333

0.323

28790

(0.0224)-0.0440*(0.0301)

0.00902

0.403

27926

(0.0448)-0.0565(0.0422)-0.0845**(0.0316)0.0233

Panel B: Goods

0.397

27926

(0.0298)

(5)

0.291

20954

(0.0788)0.0145(0.0522)-0.0454

0.384

28535

(0.0284)-0.0378(0.0379)-0.0829**(0.0333)-0.124***

0.378

28535

(0.0418)-0.147***

Ln(Sales)

(6)

0.255

20938

(0.0160)-0.0155(0.0165)

0.0333**

0.163

28516

(0.0130)-0.0234*(0.0116)-0.0112(0.0111)0.0145

0.159

28516

(0.00986)

0.0122Ln(Markup)

(7)

0.275

20893

(0.0849)

0.0314(0.0602)-0.0768

0.349

28295

(0.0282)-0.0146(0.0377)-0.0719*(0.0363)-0.138***

0.345

28295

(0.0438)-0.159***Ln(COGS)Own

the 10, 5 and 1 percent levels, respectively.

at the NAICS 4-digit level and are reported below coecient estimates. *, ** and *** indicate statistical signicance atpercent level. Regressions are weighted by the initial (1990) value of the dependent variable. Standard errors are clustered

All variables have been have been normalized by dividing them by their standard deviations, and winsorized at the 1of equity, return on assets, and growth in total capital. Right-hand side variables also include rm and year xed eects.

characteristics. Sample period is 1990 to 2006. Initial rm attributes are are market capitalization, market to book value)) and and a series of initial rm

Table presents rm-level OLS panel regressions of noted

0.392

17864

(0.134)-0.0109(0.132)-0.422***

Panel C: Services-0.0767**(0.0266)-0.0593**

0.317

28790

(0.0311)(4)
Ln(Intangibles)
Panel A: Goods-0.0857***

CRSP, COMPUTSTAT and authors' calculations.

Down3

U p3

Down3

U p3

Own

Own

(3)
Ln(PPE)rm outcomes on the NTR Gap associated with rms' major industry ((N T Ri

Source:N
R2

× PostNTR Gap× PostNTR Gap

R2

× PostNTR Gap× PostNTR Gap× PostNTR GapN
R2

× PostNTR Gap(2)
Ln(Assets)(1)
Ln(Employment)

Table A.1: NTR Gap and Firm Outcomes (Baseline Specication)