U.S. Department of Labor 

Bureau of International Labor Affairs 

Office of Trade and Labor Affairs 

Contract Research Program

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Government or the U.S. Department of Labor. 
1

Employment Impacts of Globalization 

(The Impact of Service Offshoring on Displaced Workers Post-displacement Outcomes) 

Final Version 

May 15, 2010 

Department of Labor Solicitation # DOL089RP20567 

(Order # DOLB089K27942; Requisition # 121-089K-45) 

 

Ivan T. Kandilov 

Assistant Professor 

Department of Agricultural and Resource Economics 

North Carolina State University 

Raleigh, NC 27695-8109 

Phone: 919-513-3713 

E-mail: ivan_kandilov@ncsu.edu
2

I. Introduction 

The employment effects of service trade is a topic of significant importance, especially in light of 

the recent rise in services offshore outsourcing to low-wage destinations such as India and China. 

In this paper, I study the impact of service trade, separating the effects of service imports 

(offshore outsourcing) from those of service exports, on the unemployment duration and the reemployment wages of workers dislocated from the service sector of the U.S. economy.1,2 

Investigating these outcomes provides implications for policies that deal with displaced workers’ 

transitions from unemployment back to work. Further, analyzing jobless spell duration and re-

 

1

 Outsourcing is typically considered to be purchasing of (contracting out) services from an 

external (to the firm) provider, which can be, but does not have to be, located outside the U.S. 

Offshoring is considered to be purchasing of services from a supplier operating outside the U.S., 

but it can be from an external (to the firm) provider, or from within the firm (in the case of 

multinationals). Offshore outsourcing occurs when firms purchase services from an external (to 

the firm) supplier operating outside the U.S. Offshore outsourcing (international service 

transactions between unaffiliated parties) is the type of international trade I consider in this 

paper. I will use service imports (into the U.S.) and offshore outsourcing (of services by U.S. 

firms) synonymously in the rest of the paper.

2

 As I discuss later, the sample of displaced service workers I consider consists of individuals 

who are on average more educated, earn higher wages, and have slightly longer tenure on the lost 

job than the average worker displaced from the U.S. service industry. Note, however, that the 

sample is quite diverse, including workers with no high school education as well as workers with 

advanced degrees. 
3

employment wages also sheds light on the issue of transferability of skills used by U.S. workers 

as they switch industries and occupations in the face of international trade shocks. In particular, 

recent work by Kandilov (2009), as well as Kambourov and Manovskii (2008a and 2008b), 

shows evidence that U.S. manufacturing workers possess industry and occupation specific 

human capital that is lost in the job transition from one to industry (occupation) to another. 

 The infant empirical literature on services offshore outsourcing includes work on 

measurement issues (Lipsey 2006, Houseman 2007), classification of service activities as 

tradable and at risk of offshoring (Jensen and Kletzer 2006, 2008), as well as evaluation of the 

impact of trade in services on labor markets in the U.S. (Liu and Trefler, 2008; Amiti and Wei, 

2010). Liu and Trefler (2008) use Current Population Survey (CPS) data from 1996 to 2006 to 

examine the impacts of offshore outsourcing of services to China and India on occupational and 

industry switching, weeks unemployed as a share of weeks in the labor force, and earnings for 

workers in the U.S. service sector. Taking into account both imports and exports of services, 

they find a very small, positive impact of services trade on labor market outcomes. This paper 

complements the work by Liu and Trefler (2008) as well as Jensen and Kletzer (2006, 2008) and 

in the spirit of Kandilov (2009) employs data from the Displaced Workers Supplement (DWS) to 

the CPS to consider the impact of service offshore outsourcing on the labor market transition of 

dislocated service sector workers – a population that has been previously understudied.3

 The two 

 

3

 Kandilov (2009) and Kletzer (2004a, 2006b) have examined displaced manufacturing workers, 

who may be eligible for Trade Adjustment Assistance, and the impact of trade on their labor 

market transitions. Liu and Trefler (2008) do not use the DWS, but rather use the regular March 

edition of the CPS. As such, they do not have information on dislocated workers post-
4

most important post-displacement outcomes for this population of workers are the 

unemployment duration and the re-employment wage. Identifying the effects of services 

offshore outsourcing on these two outcomes provides a much deeper understanding of how 

international trade shapes labor market transitions for service-sector workers in the U.S. This 

line of research may be also useful in formulating policies related to transitional unemployment 

of dislocated service sector workers. 

 In this paper, I employ data on displaced workers and U.S. trade in services from 1986 to 

2007, which significantly extends the time span of many previous empirical analysis of the 

impact of service trade on U.S. workers – see, for example, Liu and Trefler (2008), who use CPS 

data from 1996 to 2006.4

 This is a significant advantage because, unlike previous work, my 

sample includes periods of very low services trade activity (late 1980’s) and episodes of much 

higher services offshore outsourcing (2002-2005). The extended time dimension in my analysis 

helps significantly with the identification of the impacts of trade in services on labor market 

transition of U.S. service-sector workers. 

 While I cannot find any statistically significant impact of either service exports or 

imports on unemployment duration, the estimates indicate that total trade in services, as well as 

service trade with India and China, does affect post-displacement earnings. In particular, the 

 

displacement outcomes. To my knowledge, Jensen and Kletzer (2006, 2008) are the only other 

studies that have considered displaced service workers. 

4

 The DWS, just as the CPS, does not provide panel data on displaced individuals. Rather, each 

respondent who has experienced job displacement 3 (or 5) years prior to the DWS interview is 

surveyed only once. Information on pre-displacement wages and unemployment duration is 

collected retrospectively at the time of the survey. 
5

results suggest that service imports in the industry of displacement from India and China are 

associated with a decrease in re-employment wages. The results further suggest that the negative 

impact is also more pronounced for workers with lower levels of education.

II. Literature Review 

This paper links the empirical literature on service offshoring with the empirical literature on 

displaced workers’ post-displacement outcomes. The impact of service offshoring on the U.S. 

labor market is a topic of significant importance, especially in light of the recent rise in service 

offshoring to low-wage destinations such as India and China. The empirical literature on service 

offshore outsourcing includes work on measurement and classification of service activities as 

tradable and at risk of offshoring (Jensen and Kletzer, 2006 and 2008; Moncarz, Wolf, and 

Wright, 2008), as well as evaluation of the impact of trade in services on labor markets in the 

U.S. (Liu and Trefler, 2008; Crino, 2008; Amiti and Wei, 2010).5

 There also exists empirical 

literature on the impact of international trade on displaced manufacturing workers post-

 

5

 Other recent contributions to the service offshore outsourcing debate include Amiti and Wei 

(2004), Samuelson (2004), Bhagwati, Panagariya, and Srinivasan (2004), Brainard and Litan 

(2004), Blinder (2006, 2007), Mankiw and Swagel (2006), Lipsey (2006), and Houseman (2007). 

Antras and Helpman (2004) is a recent theoretical contribution. In the text above, I describe in 

more detail a number of recent empirical contributions that are most relevant for and closely 

related to the topic of this paper, which investigates the impact of offshoring on displaced 

workers post-displacement outcomes. 

 
6

displacement outcomes. In particular, studies relating unemployment duration and reemployment wages of displaced manufacturing workers to industry of displacement import 

competition, overall and from low-wage, developing countries includes work by Addison et al. 

(1995), Kletzer (2001), Kletzer (2002), and Kandilov (2009). I first discuss the literature on 

measurement of service offshoring and its impact on U.S. labor markets. 

 Jensen and Kletzer (2006) contribute to both the literature on measurement of offshoring 

and its impacts on labor markets in the U.S. The authors develop an innovative approach to 

identifying manufacturing and service activities that are potentially tradable internationally 

(offshorable). To accomplish this, they classify activities based on a measure of their geographic 

concentration in the U.S. – a Gini coefficient of geographic concentration. Domestic geographic 

concentration reveals which activities are tradable domestically and therefore potentially tradable 

internationally. To compute the geographic Gini coefficient, Jensen and Kletzer use the 2000 

Decennial Census of Population Public Use Micro Sample (PUMS) individual-level data. 

Employing their measure of offshorable activities (the geographic Gini coefficient), Jensen and 

Kletzer estimate that about 39 percent of all workers in the U.S. economy are employed in 

industries classified as tradable. They further compare demographic characteristics and job 

displacement outcomes of workers in tradable vs. nontradable activities, and also evaluate the 

differences in employment growth between tradable and nontradable industries. Using data on 

individual characteristics from the PUMS, they find that workers in tradable industries possess 

higher skills (education) and have about 7 percent higher wages. Tradable industries are shown 

to have lower employment growth rates, but these results are mostly driven by the manufacturing 

sector. Within services, tradable and non-tradable activities appear to have similar growth rates, 

except for tradable activities with very low-skill intensity, for which employment growth is 
7

actually lower than the employment growth for non-tradable low-skill intensity activities. 

Finally, Jensen and Kletzer use the 2004 Displaced Worker Supplement (DWS) to the Current 

Population Survey (CPS), which supplies them with data on displaced workers from 2001 to 

2003, to show that workers in industries that are potentially tradable internationally experienced 

about 7.7 percentage point higher rate of job loss. 

 Continuing the tradition of Jensen and Kletzer (2006), Jensen and Kletzer (2008) 

introduce another new measure of offshorability (exposure to international trade). They develop 

the measure of tradability because the old one based on U.S. geographic concentration of the 

activity may be problematic in some cases.6

 Their new approach provides additional information 

for classifying activities as tradable and it is based on measuring occupational tasks, activities 

and characteristics, such as high information content, remote from customer, internet-enabled, 

that are associated with offshorability. Using detailed information on the content and context of 

jobs from the publicly available O*Net database, a database of 450 occupations available from 

the U.S. Department of Labor, Jensen and Kletzer (2008) develop the job task content measure 

of tradablility. The authors find that there is substantial overlap between the two measures of 

offshorability – the geographic concentration measure they developed in their earlier work and 

the job task content measure. They compute that 43.8 percent of overall service sector 

employment is potentially tradable. Using data from 2001 to 2005 from DWS (2004, 2006), 

Jensen and Kletzer also show evidence that job displacement rates are higher for tradable service 

sector occupations, whose labor force is more educated and commands higher pre-displacement 

earnings. Re-employment rates are estimated to be higher for tradable, rather than for 

 

6

 For example, if an activity is tradable but not characterized by increasing returns, it may not be 

geographically concentrated (see Jensen and Kletzer, 2008). 
8

nontradable, services. Finally, Jensen and Kletzer (2008) also document that workers displaced 

from tradable service industries face larger earnings losses upon reemployment than workers 

displaced from nontradable service industries. 

Moncarz, Wolf, and Wright's (2008) analysis uses the BLS offshoring scoring system to 

identify 160 service-providing occupations that are susceptible to offshoring. The BLS 

offshoring system identifies characteristics that make an occupation susceptible to being 

offshored and ranks occupations by level of susceptibility. These characteristics are quite 

diverse and do not depend only on geography (e.g. they incorporate the degree to which the work 

can be routinized or handled by following a script). The authors report that these occupations are 

heterogeneous in their job functions, average educational attainment, and wages. 

 Liu and Trefler (2008) estimate the impact of offshore outsourcing from low-wage 

trading partners (India and China) on labor market outcomes for workers in the service sector in 

the U.S. As their measure of outsourcing, the authors use Bureau of Economic Analysis (BEA) 

data on actual (realized) international transactions between U.S. firms and foreign unaffiliated 

parties between 1995 and 2005, and as such they do not consider offshoring within multinational 

firms. Liu and Trefler’s approach is much different than that of Jensen and Kletzer, who do not 

use actual service trade flows as a measure of outsourcing, but rather construct one based on 

geographic concentration of occupations. Liu and Trefler (2008) estimate the impact of what 

they term “inshoring” – the sale of services produced in the U.S. to unaffiliated parties in lowwage countries (India and China). They consider four different labor market outcomes: (1) 

industry switching, (2) occupational switching, (3) annual changes in weeks spent unemployed 

as a share of total weeks in the labor force, (4) changes in earnings. Positive impacts of offshore 

outsourcing on the first two outcomes imply that offshore outsourcing increases job insecurity, 
9

an important consideration, especially if workers possess industry specific or occupation specific 

human capital that is destroyed with changes in industry or occupation (Kambourov and 

Manovskii, 2008a, 2008b). Individual labor market data is obtained from the March Current 

Population Survey (CPS) from 1996 to 2006. The authors match workers’ industry and 

occupation from the labor market data in the CPS to the offshore outsourcing BEA data via 

industry and occupation mapping as in van Welsum and Vickery (2005) and van Welsum and 

Reif (2006b). Liu and Trefler (2008) estimate that both inshoring and offshoring have had very 

modest impacts on all of the four labor market outcomes they consider. For example, workers in 

service industries with higher trade (both inshoring and offshoring) have experienced about 1.5 

percent increase in wages between 1996 and 2005 years. They also find that the overall effect of 

trade in services is somewhat worse for those without a college degree, or those employed in a 

low-skill, white-collar job. The overarching theme of their analysis is that the net effect of 

inshoring and offshoring of services on labor markets in the U.S. is negligible. 

Amiti and Wei (2010) estimate the impact of offshoring on employment in U.S. 

manufacturing industries between 1992 and 2000. Using disaggregate data (450 manufacturing 

industries), they find a small negative effect (less than half of a percent) of service offshoring on 

employment. This negative impact entirely disappears when more aggregated data (96 

manufacturing industries) is used instead, implying that there really is no net job loss from 

service offshoring in the U.S. 

 Crino (2008) estimates the changes in labor demand in the U.S. between 1997 and 2002 in 

response to increased service offshoring. The author employs data on wages and employment 

for a panel of manufacturing industries (144 industries in total) and uses the BEA data on 

imported services (as a share of total non-energy input purchases) by industry as in Amiti and 
10

Wei (2005).7

 He further uses the Occupational Employment Statistics (OES) of the Bureau of 

Labor Statistics (BLS) to consider the impact of offshoring along both the industry and 

occupation dimensions.8 The results show that service offshoring is skill-biased as it raises the 

relative demand for high-skilled occupations in the U.S. Crino (2008) concludes then that 

service offshoring may, in fact, motivate human capital accumulation, as oppose to discourage. 

Last, I briefly discuss the existing literature relating displaced manufacturing workers’ 

post-displacement outcomes to international trade. Previous work in this area includes the 

studies by Addison et al. (1995), Kletzer (2001), Kletzer (2002), and Kandilov (2009). Addison 

et al. (1995) use descriptive statistics and sample correlations from the 1988 DWS to conclude 

that industry trade sensitivity, defined as the import penetration rate, is weakly associated with 

re-employment earnings but it does not affect the jobless spell duration. Kletzer (2001) uses data 

from nine DWS’s (1979-1999) and defines import competition as a time-invariant indicator 

based on the industry change in import penetration over the period 1979-1994. She finds that 

workers displaced from manufacturing industries with high import competition face lower reemployment probabilities and somewhat lower re-employment wages, but the effects are 

economically small and statistically insignificant when worker characteristics are included in the 

regression.9

 Using the same data, Kletzer (2002) uses a time-varying measure of import 

 

7

 He estimates the fraction of imported services for each industry by attributing a share of the 

economy-wade level of service imports. Note also that unlike Liu and Trefler (2008), Crino uses 

both affiliated and unaffiliated parties service import data. 

8

 The data OES data is combined with the proxy for service offshoring at the industry level. 

9

 Kletzer (2001) identifies high import competing industries as those ranking in the top quartile 

of the distribution of changes in import penetration over the period 1979-1994. 
11

penetration and distinguishes between imports from developed and developing countries. She 

relates these two import competition measures to industry employment growth and job 

displacement rates. Her results indicate that rising import penetration, overall or from 

developing countries, is associated with higher displacement rates, but the coefficients are small 

and estimated imprecisely. Rising import penetration, on the other hand, is statistically 

significantly associated with employment decline, and the negative impact of imports from 

developing countries is estimated to be larger. 

 Kandilov (2009) also uses the individual-level data from the DWS and international trade 

flows to extend the previous work by Addison et al. (1995), Kletzer (2001), and Kletzer (2002). 

He broadens the empirical analysis by using an additional Displaced Worker Supplement, which 

provides him with data on displaced workers from 1979 to 2001, and by employing within

industry variation in import penetration from both developed and developing countries to assess 

its impact on both unemployment duration and re-employment wages. Building upon Addison et 

al. (1995), Kletzer (2001), and Kletzer (2002), not only does Kandilov(2009) employ within 

industry variation in the import penetration, but he additionally includes industry, state, and 

personal characteristics specific (e.g. gender specific) time trends to control for any pre-existing 

trends. In contrast to much of the previous work, he uncovers economically and statistically 

significant effects of import penetration from low-wage countries on displaced workers’ postdisplacement outcomes. Last, Kandilov (2009) provides novel evidence that import penetration 

from low-wage, developing countries affects workers displaced from industries with shorter 

quality ladders much more adversely than it affects workers displaced from industries with 
12

longer ladders.10 This large degree of heterogeneity in the impact of imports from low-wage 

countries may explain why previous work ha found small and imprecisely estimated effects. 

III. Data 

To evaluate the impacts of increased services offshore outsourcing on displaced workers’ 

unemployment duration and re-employment wages, I use two key datasets. The first dataset is 

the only large-scale and nationally representative survey of displaced workers – the Displaced 

Workers’ Supplement (DWS), a biennial supplement to the January or February Current 

Population Survey (CPS), administered by the Census Bureau on behalf of the Bureau of Labor 

Statistics (BLS).11 The first DWS was instituted in January of 1984, and I use all the surveys 

from 1986 through year 2008, which supplies data on displaced workers from 1986 to 2007. 

DWS is intended for all workers who have been displaced from their jobs at least once in the 3 

(or 5) years prior to the survey. Some of the workers qualified to take the survey are still 

displaced at the time of the interview, although the majority (about 80 percent) have been reemployed. The DWS is intended for all workers who have been involuntarily displaced from 

their jobs in the 3 (or 5) years prior to the survey. Workers can be displaced due to 6 different 

reasons: (1) establishment closed, (2) insufficient work, (3) position abolished, (4) seasonal job 

ended, (5) self-employed business failed, and (6) other reasons. Prior to the 1994 DWS, 

information on old and new employment, including duration of unemployment, was collected 

 

10 An industry “quality ladder” is the industry's scope for product quality differentiation (see 

Khandelwal (2010), forthcoming). 

11 The Displaced Workers Supplements to the Current Population Survey are available on-line, 

among other places, on the NBER data page at http://www.nber.org. 
13

from workers displaced for any of these six reasons. Many economists consider workers 

displaced for any of the six reasons to be involuntarily displaced (see Farber, 1998). Using data 

from debriefing of respondents to the 1996 DWS, Farber (1998) concludes that a little over 25 

percent of workers displaced for “other reasons” can be classified as involuntarily displaced. 

Starting with the 1994 DWS, information on old and new employment, including duration of 

unemployment, was collected for workers displaced for reasons (1), (2), or (3), but not (4), (5), 

or (6). I consider all displaced workers involuntarily displaced, and note that less than 3 percent 

of workers in my sample are displaced for “other reasons”. 

While each CPS respondent (household) is surveyed 8 times in the span of 16 months, 

they can potentially participate in the DWS only once since the DWS is administered only once 

every two years. Information on the displacement episode (such as pre-displacement wage, 

industry, occupation, and unemployment duration) is collected retrospectively at the time of the 

survey. In addition to personal characteristics found in the regular monthly CPS, DWS collects 

information on both old and new employment for displaced workers – previous and current 

wages, hours, current industry, industry of displacement, reason for displacement, occupation, 

and duration of unemployment, among other things.12

 Unemployment duration data in the 1992 DWS and earlier years were top-coded at 99 

weeks. After 1992, duration was top-coded at 168 weeks, but this is not binding for the 

 

12 Note that the current wage may not necessarily be the re-employment wage that immediately 

followed the jobless spell. Some workers may have experienced other spells of unemployment 

between the one for which they report information and the date of the DWS interview. Workers 

are asked to report wage and duration information for only one previous displacement episode 

even if they experienced more than one. 
14

subsample of service workers I use, as the longest reported duration is my subsample is 158 

weeks (nearly 3 years), and the second longest is 132 weeks. Also, while the CPS does not track 

movers, the DWS includes a question asking the respondents if they have moved and if the move 

occurred to take a job elsewhere. 

 The second key data source is the Bureau of Economic Analysis (BEA), which supplies 

information on U.S. cross-border trade in services. Such international trade data for a number of 

private business services between unaffiliated parties is available from 1986 to 2007 for more 

than 30 partners, including both high- and low-income countries. BEA collects disaggregated 

trade data for a number of different types of services including Financial, Insurance, 

Telecommunication, Advertising, Research and Development, Management Consulting, 

Computer, Legal, and Construction services. Note that data is only available for these 10 service 

industries, so the estimates presented later apply to those industries alone. 

 Combining the two dataset outlined above – the Displaced Worker Survey and the BEA 

data on cross-border trade in services between unaffiliated parties – enables me to assess the 

impact of service imports (offshore outsourcing) and service exports (inshoring) in the worker’s 

industry of displacement on the unemployment duration and the re-employment earnings. 

Because the BEA data on service trade starts from 1986, and the DWS data ends in 2007, the 

effective time period for my investigation is the 22 years from 1986 to 2007. Merging the two 

data sets is not trivial, as they use different industry and occupation classifications. The DWS 

data, for example, classifies workers’ industries either according to the Standard Industrial 

Classification (SIC) (before 2002), or according to the North American Industrial Classification 

System (after 2002), while the BEA data does not strictly use those classifications. This, 

however, is not a major hurdle as a number of authors have already developed reliable 
15

concordances for these classifications (see, for example, Liu and Trefler 2008, van Welsum and 

Reif 2006a, 2006b, 2006c, 2006d, van Welsum and Vickery 2005). Based on these 

classifications (in particular, Table A.2 in Liu and Trefler 2008), I connect the BEA’s data on 

international trade in services to specific industries (or occupations) in the DWS. Subsequently, 

I investigate how the changes (over time and within industry (or occupation)) in service 

offshoring affect displaced service workers’ unemployment duration and their re-employment 

wages. 

IV. Econometric Strategy 

IV.1 Unemployment Duration 

Not all of the displaced workers have completed their jobless spells at the time of the survey, and 

so about one fifth of the jobless spells are right-censored.13 To deal with the right-censored 

unemployment duration observations in the DWS data, I employ maximum likelihood (ML). 

Because the probability of finding a job (i.e. the hazard of leaving the unemployment pool) 

generally declines with the jobless spell duration, I specify a Weibull model for the data on 

unemployment spells.14 More formally, I estimate a Weibull model maximizing the following 

log-likelihood function: 

 

13 Another source of right-censoring in the data for which I accommodate by using maximum 

likelihood is the top-coding of durations in the 1992 DWS and earlier years at 99 weeks. 

14 Typically, the probability of finding employment (the hazard of leaving the unemployment 

pool) on the first day of the jobless spell is higher than the probability of finding employment on 

the 100th day of the spell. The Weibull model accommodates for that by allowing the hazard to 

change (decline) monotonically over the course of the unemployment spell. 
16

log { log[ ( | , )] (1 )log[1 ( | , )]}

1

Zi η i i Zi η

N

i

i i L =  d f t + −d − F t =

 (1), 

where the Weibull distribution has the following conditional density 

( | , ) exp( , ) exp[ exp( , ) ] φ 1 φ

i φ i i f t Z η Z η t Z η t i = i − i

− (2), 

and di is a censoring indicator equal to unity if the unemployment duration of displaced 

worker i is uncensored, and N is the number of displaced workers included in the analysis. The 

vector of parameters to be estimated is η , and Zi is a matrix of covariates. The Weibull hazard 

(of leaving the unemployment pool) at time, or unemployment duration, t, is given by 

1 ( , , ) exp( , ) − = φ φ t Z η Z η φt i i (3). 

It captures a monotonically increasing or monotonically decreasing (in unemployment duration) 

hazard – if φ > 1, the hazard exhibits positive duration dependence, and if φ < 1, it exhibits 

negative duration dependence. This specification accommodates for the negative duration 

dependence visually found in the data. When presenting the results, I employ the accelerated 

failure time (AFT) representation, which allows the interpretation of the estimated coefficients as 

semi-elasticities of the expected unemployment duration with respect to a given covariate in 

Zi .

15 

The matrix of covariates, Zi , can be written as 

[ | | ( ) | | | | | | | ] (4), jt jt k j s i t

RATE

st

pre-displacement

ikjst ikjts ikjst Z X Tenure ln w U ServiceImports ServiceExports | δ λ σ ο τ i =

 

15 When I present one of the robustness checks in the Appendix, I also employ the hazard rate 

representation of the Weibull model. 
17

which includes Xikjst – a vector of personal characteristics for individual i, surveyed in year k (k 

= 1986, 1986, ..., 2008) displaced from sector j in year t (t = 1986, 1980, ..., 2007) and residing 

in state s. Personal characteristics included are education, current age, current age squared, and 

dummies for race, gender, marital status, metropolitan area residence status, and Hispanic origin. 

I use six education categories – no high school, high-school dropout, high-school graduate, some 

college, college graduate, and advanced degree. The omitted category is high-school graduate. I 

also include the worker’s lost job tenure, Tenureikjts , and the logarithm of the pre-displacement 

wage, ( ) pre-displacement wikjst ln , as additional covariates. The state (of residence) unemployment rate at 

the time of displacement, RATE Ust , is included as a proxy for the local labor market condition, 

which affects the likelihood of re-employment. To control for time-invariant state of residence 

characteristics, Zi includes state of residence dummies, σ s . Year of displacement and year of 

the survey dummies, t δ k τ , are added to absorb annual economy-wide shocks in the year of and 

displacement and year of the survey. I include two trade measures – imports, jt ServiceImports , 

and exports, jt ServiceExports , in industry j and year t. In many of the specifications, I 

additionally include service imports, India China

jt ServiceImports + , and exports, 

India China

jt ServiceImports + , from China and India specifically. Finally, Zi also includes dummies 

for industry of displacement, λ j , and pre-displacement occupation, οi . 

 To check for robustness, I also estimate the Weibull model including industry-specific 

education dummies or industry-specific time trends. Further, to relax the Weibull hazard 

assumption, I estimate a model with a very flexible hazard specification. To this end, I specify a 

piecewise-constant proportional hazard, i.e. we allow for period-specific (for each two-week 
18

period during a spell of unemployment) baseline hazard rate (for details see Wooldridge, 2002, 

pp. 706-710). For inference, I calculate robust standard errors clustered by industry of 

displacement.16 

 Unlike the re-employment wage, the jobless spell duration is observed for all displaced 

workers, those re-employed and those still looking for a job at the date of the interview. For the 

latter group, I only observe interrupted (right-censored) spells, which were accommodated in the 

likelihood function. Hence, problems associated with selection based on worker’s reemployment status do not arise in the analysis of unemployment duration. 

IV.2 Re-employment Wage 

Equation (5) below estimates the impact of service trade in the industry of displacement on the 

worker’s post-displacement wage: 

 

 (5),

( ) ( )

Overall

Overall

1

6 jt k j s i t ikjst

5 jt

RATE

4 st

pre-displacement

ikjst 2 ikjts 3 ikjst

re employment

ikjst

β ServiceExports δ λ σ τ ε

ln w β Tenure β ln w β U β ServiceImports

+ + + + + + +

= ′ + + + + + −

ο

X β

where ( ) re employment wikjst ln − is the logarithm of the weekly re-employment wage for an individual i

surveyed in year k, displaced from a service industry j in year t, and residing in state s; Xikjst is 

a vector of personal characteristics, which, as before, includes education, current age and 

current age squared included to proxy for experience, and dummies for race, gender, marital 

status, metropolitan area residence status, and Hispanic origin. Also, as in the unemployment 

duration model, I include the worker’s lost job tenure, Tenureikjts , the logarithm of the pre-

 

16 Note that this can accommodate any pattern of serial correlation within industry over time. 
19

displacement wage, ( ) pre-displacement wikjst ln , and state (of residence) unemployment rate in the year of 

displacement, RATE Ust , as additional covariates in the re-employment regression equation (5). 

Trade measures include both imports, jt ServiceImports , and exports, jt ServiceExports , in 

industry j and year t. Further, regression equation (5) includes fixed effects for industry of 

displacement, λ j , state of residence, σ s , and pre-displacement occupation, οi , fixed effects. 

This implies that identification of the effects of trade on the re-employment wage comes from 

within-industry variation in trade. Year of displacement and year of the survey 

dummies, t δ k τ , absorb annual economy-wide shocks in and the year of displacement and year 

of the survey. Finally, to investigate if service imports and exports from India and China 

specifically have had any impact on the unemployment duration and the re-employment wage, I 

use two additional measures of trade in services, India China

jt ServiceImports + and 

India China

jt ServiceExports + , which only incorporate imports and exports from India and China. 

Some displaced workers are still unemployed at the date of the survey, and consequently, 

I do not have information on their re-employment wage. As a result, I estimate the reemployment wage equation (5) for those who are employed at the time of the interview, but I 

also show that the censoring and the potential selection problem do not affect the results much. 

Note that I include both year of the survey and year of displacement dummies in the reemployment wage equation (5). Because they control for the length of time between the date of 

the survey and the date of displacement, which is intrinsically associated with the re-employment 

censoring mechanism, these dummies alleviate selection concerns.17 In addition to estimating 

 

17 The difference between the year of the survey and the year of displacement is the length of the 

period (in years) since displacement, which is associated with the censoring mechanism as those 
20

equation (5) by Ordinary Least Squares (OLS), in the Appendix, I also employ the Heckman 

(two-step) correction procedure (Heckman, 1979) that delivers consistent estimates in the 

presence of selection. To satisfy the exclusion restriction requirement in the Heckman 

procedure, I follow Addison and Portugal (1989) and drop the year of displacement dummies 

from the re-employment wage equation, but use them in the probit equation to predict the 

likelihood of re-employment by the date of the survey. Additionally, I include dummies for 

reasons for displacement as explanatory variables in the first stage probit equation, but I exclude 

them from the second stage (the re-employment wage equation)18. Given the difficulty of 

finding a good exclusion restriction, however, the Heckman model estimates should be 

interpreted with caution. Nonetheless, the results from this correction procedure are similar to 

the results from equation (5) using displaced workers who are employed at the date of the 

survey. 

 In another check for robustness, I also estimate regression equation (5) additionally 

including industry-specific education dummies or industry-specific time trends. In the first case, 

 

who were more recently displaced would have had less time to locate a job by the time of the 

DWS interview. 

18 I have also estimated the Heckman model using year of displacement dummies in both the 

probit and the re-employment wage equation, with only “reasons for displacement” dummies 

used as additional explanatory variables (exclusion restriction) in the first stage probit equation. 

Reasons for displacement may plausibly affect the probability of re-employment at the time of 

the interview, but they should not affect the re-employment wage rate. The results with this 

alternative Heckman specification are nearly identical to the Heckman model results reported in 

the Appendix. 
21

this allows returns to education to vary across service industries for which trade data is available. 

In the latter case, identification of the impact of trade on the re-employment wage comes from 

within industry deviations in trade from the industry-specific time trend. Finally, I also estimate 

a restricted version of regression equation (5), in which I impose the restriction 1 β3 = . I then 

collect the wage terms on the left-hand side and the dependent variable becomes 

( / ) pre-displacement

ikjst

re employment ln wikjst w − . All other terms on the right-hand side are the same as in equation 

(5). 

V. Results 

Before I discuss the results from the econometric specifications, consider the raw data on service 

trade and the summary statistics of the workers displaced from the service industries in the 

sample. Panels A and B of Table 1 report the temporal evolution of service export and imports 

in the industries with available trade data over the last 20 years. The statistics reveal that total 

service exports from the U.S. have increase five fold over the last 20 years. Similarly, the 

numbers suggest that the U.S. imports of services have risen about 4.5 times their original levels 

in 1986. Note that up to date, the U.S. has always been a net exporter of services. 

 Another striking statistic evident from Panel A of Table 1 is the increase in both exports 

and imports of services to China and India – the numbers imply that both trade flows have 

increased more than 30 times over the last 20 years. In comparison, U.S. trade with G-8 nations 

has increased about 10 times in the same period. Panel B presents the temporal evolution of 

U.S. service trade by industry – it shows that while there is some heterogeneity across industries, 

the growth in overall service trade and service trade with China and India is quite robust. In 
22

particular, note that the largest increase in service trade between the U.S. and China and India is 

in Computer and data processing services. 

Panels A and B of Table 2 detail the DWS (1988-2008) summary statistics for workers 

displaced from all service industries, those displaced from the service industries with available 

trade data, and workers in service industries with trade data for whom information on 

unemployment duration and re-employment wages is provided. I consider workers between the 

ages of 21 and 65 at displaced, who were displaced from a full-time job from a service industry 

between 1986 and 2007. For each of the three subsamples of displaced workers, I report the 

mean and the standard deviations for workers who are re-employment (and for whom reemployment wages are observed) and for the whole subsample, which includes those who are 

still unemployed at the date of the DWS interview. First, focus on Panel A of Table 2. The two 

subsamples of displaced workers in this panel (all services workers vs. those in industries with 

available trade data) differ in important ways. Workers in the subsample with available trade 

data earn more than the average service worker, both in terms of their displacement and reemployment wages ($1,003.86 vs. $725.44 and $820.18 vs. $618.83, respectively). Also, 

workers in industries with available trade data are more highly educated – for example, about 45 

percent of workers in that subsample have a college education, whereas only about 29 percent do 

in the overall service industry subsample. Workers in the former sample are also more likely to 

be female, live in a metropolitan area, and have longer lost job tenure (by about 0.3 years). This 

is not surprising, given that industries for which trade data is available tend to be more high-skill 

intensive such as Legal, Financial, and Insurance services. Note that in both subsamples in Panel 

A of Table 2 about 80 percent of all displaced workers are re-employed at the time of the survey. 
23

Panel B of Table 2 reports the summary statistics for the two subsamples used in the 

unemployment duration model and in the re-employment wage regression – these are workers 

displaced form the service industries with available trade data for whom information on 

unemployment duration and re-employment wages are reported. Naturally, some of the 

displaced workers are still unemployed at the date of the survey and re-employment wage data 

for them is not available, which is why the number of observations in the jobless spell duration 

model is larger than the number of observations in the re-employment wage regression. The 

characteristics of two subsamples in Panel B of Table 2 are not very different at all and they are 

also quite similar to the characteristics of subsample of all workers in the service industries for 

which trade data is available presented in Panel A of Table 2. 

 Table 3 reports the sample correlations among all of the trade measures employed in our 

analysis. Not surprisingly, they are strongly positively correlated. For example, the correlation 

between overall service imports and those from G8 countries is 0.94, while the correlation 

between overall imports and those coming from China and India is only 0.64. 

 Next, we turn to the results from equation (5), which estimates the impacts of overall 

service trade (both exports and imports), and the impacts of trade with China and India on postdisplacement wages. The results are presented in Table 4. While the estimates imply that 

overall trade volumes in the industry of displacement have not had a significant impact on postdisplacement wages, once we control for both overall as well as trade with India and China, the 

results are quite a bit different. The estimates in the second column of Table 4 reveal that higher 

exports to China and India in the industry of displacement lead to a higher re-employment wage, 

while higher imports from China and India lead to a lower re-employment wage. The impact of 

imports is statistically significant at the conventional 5 percent level, while the impact of exports 
24

is not.19 Also, the coefficient on overall imports in the second column of Table 4 is both 

economically and statistically significant, implying that higher overall imports in the industry of 

displacement lead to a higher re-employment wage. Interestingly, in this specification, the effect 

of overall exports is negative, unlike the effect estimated in column (1). Adding the two effects 

(that of overall exports and overall imports) yields a small, positive impact of overall trade on reemployment wages, both in column (1) and column (2) of Table 4. On the other hand, the model 

estimates a small, negative combined impact of trade with China and India. 

 Because the dependent variable (re-employment wage) and the trade flows are in the 

logarithmic scale (see equation (1)), the estimated coefficients can be interpreted as elasticities. 

While the estimated elasticities appear small, -0.07 for service imports from China and India, 

note that these imports have risen about 30 times over the last 20 years. On average, service 

imports from India and China have risen about 20 percent annually, which suggests that reemployment wages may have been driven down by 1.4 percent (1.4 percent = -0.07*20 percent) 

annually as a result of service imports from China and India. Further, the good news is that 

service exports to China and India have the opposite (positive) impact of re-employment wages. 

In particular, the estimated elasticity of exports to China and India implies that the reemployment wages were likely driven up about 1.0 percent annually as a result of the increase in 

exports to these nations.20 The net annual effect from trade with China and India on reemployment wages is then about -0.4 percent. Also, as I already discussed, note that the impact 

 

19 The p-value on the coefficient of exports to China and India term is 0.18. 

20 The growth of service exports to China and India is also about 20 percent annually (see Panel 

A of Table 1). 
25

of overall imports is positive and statistically significant and it outweighs the negative and 

insignificant impact of overall exports. The effects of all other covariates are as expected. 

 To check if trade with low-skilled nations such as China and India has had a larger 

negative impact on less-skilled service workers in the U.S. (this would be an implication of the 

basic Heckscher-Ohlin model of trade, assuming that the U.S. is abundant in skilled labor, and 

China and India are abundant in unskilled labor), I re-estimate the re-employment wage equation 

(5) with two separate subsamples of workers. The first subsample consists only of workers with 

less than high school education, while the second subsample includes college graduates and 

advanced degree holders. The estimates in columns 3 and 4 of Table 4 confirm my expectations. 

Service imports from China and India are associated with a larger decline in re-employment 

wages for less-skilled workers (high-school education or less) – the respective elasticities are 

-0.12 for less-skilled workers and -0.08 for college graduates. Both coefficients are statistically 

significant at the conventional 5 percent level. Further, higher exports to China and India are 

associated with a higher re-employment wage for service workers with college education, but 

they have virtually no impact on re-employment wages for workers with less than high-school 

education. The overall impact of trade with China and India on re-employment wages for more 

educated workers, assuming 20 percent annual growth in both service exports and imports, is 

positive at 1.4 percent annually, while the overall impact on displaced workers with less than 

high-school education is negative at -2.8 percent. These estimates are larger than the impacts 

estimated by Liu and Trefler (2008), but are consistent with their findings that inshoring and 

offshoring have more adverse impacts on less educated workers. 

 Because not all displaced workers are re-employed at the time of the interview, I do not 

observe a post-displacement wage for everyone in the sample. As I discussed in the section on 
26

the Econometric Strategy, I also estimate a Heckman selection correction model, which is 

presented in Table A1 of the Appendix. The re-employment wage equation results from the 

Heckman model are qualitatively similar to those presented in Table 4, although the trade effects 

are less pronounced. While they support the baseline estimates in Table 4, the Heckman model 

estimates should be interpreted with caution due to difficulties with finding plausible exclusions 

restrictions. 

 Further, to check for robustness of the results, I re-estimate the re-employment regression 

equation (5) but additionally include industry-specific education terms. This allows returns to 

education to vary across service industries for which trade data is available. These results are 

presented in the first two columns of Appendix Table A2. The estimated coefficients on the 4 

international trade terms are nearly identical to those in the first two columns of Table 4. 

Another robustness check I perform involves augmenting specification (5) with industry-specific 

time trends. In this case, identification of the impact of trade on the re-employment wage comes 

from within industry deviation (in trade) from the industry-specific time trend. These results are 

in columns 3 and 4 of the Appendix Table A2. The estimated coefficients on the trade variables 

are again no too different from the estimated effects in our baseline specification shown in the 

first two columns of Table 4. Finally, I estimate the restricted version of the re-employment 

wage specification (5), imposing 1 β3 = and using ( / ) pre-displacement

ikjst

re employment ln wikjst w − as a dependent 

variable. The results, which are presented in Appendix A3, show that the estimated effects of 
27

trade on the change (growth) in wages from before displacement to after re-employment are 

quite similar to those implied from the estimates in the baseline specification (5) in Table 4.21 

 I next discuss the unemployment duration results, which are presented in Table 5. First, 

note that the duration dependence parameter φ in the Weibull hazard specification is estimated at 

0.96 with a standard error of 0.01, showing that there is negative duration dependence in the 

data. The estimates in Table 5 reflect the accelerated failure time representation of the Weibull 

model, and as such the estimated coefficients can be interpreted as either semi-elasticities or 

elasticities (if the right-hand side variable is also in the logarithmic scale). The results in 

columns 1 and 2 of Table 5 imply that only overall exports have a small economic impact on 

unemployment duration. The estimates imply that an increase in overall exports leads to an 

increase in unemployment duration, with elasticity of 0.12, but this impact is not statistically 

significant at the conventional 5 percent level. The overall level of imports appears to have no 

effect on jobless spell duration, and neither do exports or imports from China and India. 

Allowing for industry-specific education dummies, see the first two columns of Appendix Table 

A4, produces similar results to those reported in the first two columns of Table 5. On the other 

hand, allowing for industry-specific time trends (Appendix Table A4) reduces the magnitude of 

the estimated effects of trade, especially the impact of overall exports, and it also changes the 

sign of the coefficient on overall exports to a negative one. Note, however, that none of the 

effects of trade in the two augmented models are statistically significantly different from 0. 

 Estimating the impacts of trade separately for workers with less education and more 

education (columns 3 and 4 of Table 5) reveals that service trade with China and India has little 

 

21 The estimates in Table 4 show that 0.55 ˆ

β3 = with a standard error of 0.03. This implies that 

the restriction 1 β3 = is really not reasonable. 
28

impact on unemployment duration for either type of workers. The estimates suggest that overall 

exports have a large positive impact on duration (elasticity of 0.43) for less educated workers, 

and also that overall imports have a large negative impact on duration (elasticity of -0.30) for the 

same group. Taken together, these results imply that total trade (overall exports and overall 

imports) has a small positive impact on unemployment duration (i.e. trade prolongs 

unemployment duration) for less educated workers. While only overall exports have a small 

positive impact on duration for more educated workers, the small positive (prolonging duration) 

total effect of trade (overall exports and overall imports) for this group is quite similar to the total 

effect of trade for less educated workers. Note, however, that none of the estimated effects of 

trade for more educated workers is statistically significant at the 5 percent level. 

 As I discussed earlier, I also estimate a very flexible hazard specification instead of 

imposing the Weibull hazard assumption – I specify a piecewise-constant proportional hazard, 

i.e. I allow for period-specific (for each two-week period during a spell of unemployment) 

baseline hazard rate (for details see Wooldridge, 2002, pp. 706-710). The estimates from this 

flexible specification are presented in Table A5 of the Appendix. The first two columns of 

Appendix Table A5 show the hazard rate representation of the Weibull model (equations (1)-(4), 

but not the accelerated failure time representation as in Table 5), and the last two columns show 

the results from the more flexible specification. Overall, the results are quite similar. Neither of 

the models produces statistically significant impacts of trade in services on the hazard rate 

(probability) of leaving unemployment. Hence, one can conclude that, on average, there are 

likely negligible effects of service trade on displaced workers’ duration of unemployment. 

 Finally, note that recall bias may potentially affect the estimates both in the jobless spell 

duration regressions and in the re-employment wage regressions. If erroneous recall is assumed 
29

to behave as a classical measurement error, in the context of the linear re-employment wage 

regression, the effects of such a bias would be toward zero, which implies that the magnitudes of 

the coefficients in the re-employment wage model may be biased downward and the true effects 

may be even more pronounced than the estimates indicate.22

VI. Conclusion 

I use data from the Displaced Workers Supplement to the Current Population Survey and 

information on foreign trade between unaffiliated parties from the Bureau of Economic Analysis 

 

22 The DWS also collects information on geographic relocation after the job displacement for 

each respondent. Survey takers are additionally asked if the reason for the relocation is to take a 

job elsewhere. However, a large fraction of the respondents in the sample I use (more than 80 

percent) do not provide the latter information. One could argue that movers are more aggressive 

in their search and willingness to accept a job, and they are also less tied to their community, all 

of which may affect unemployment duration and earnings potential. When an explanatory 

dummy variable (Mover) indicating if the respondent moved after displacement is included in the 

duration and the re-employment models (equations (1)-(4) and (5), respectively), all of the 

remaining coefficients change trivially. The coefficient on Mover in the duration model is 0.03 

(with a standard error of 0.06) indicating that respondents who moved experienced a slightly 

longer spell; the coefficient in the re-employment wage regression is 0.03 (with a standard error 

of 0.04), also indicating that moving is associated with higher re-employment earnings. Note, 

however, that geographic relocation (for a job or otherwise) is a choice and including Mover as 

an explanatory variable raises endogeneity concerns, which is why I have chosen to keep it out 

of the baseline specification. 
30

for 10 service industries from 1986 to 2007 to investigate the impact of trade in services 

(offshoring and inshoring) on displaced workers re-employment wages and jobless spell 

duration. Consistent with previous work by Liu and Trefler (2008), I cannot find any 

economically and statistically significant impacts of either service exports (inshoring) or imports 

(offshoring) form China and India on unemployment duration.23 However, the estimates indicate 

that service trade with India and China, does affect service workers’ post-displacement earnings. 

In particular, the results reveal that higher exports (inshoring) to China and India in the industry 

of displacement lead to a higher re-employment wage, while higher imports (offshoring) from 

China and India lead to a lower re-employment wage. Combining the two effects yields a small, 

negative effect (-0.4 percent annually) of service trade with China and India on re-employment 

wages. 

 Further, service imports from China and India are associated with a larger decline in reemployment wages for less-skilled workers (less than high-school education). I also find that 

higher exports to China and India lead to a higher re-employment wage for service workers with 

college education, but they have virtually no impact on the re-employment wage for workers 

with less than high-school education. The overall impact of trade (both exports and imports) 

with China and India on re-employment wages for more educated workers, assuming the 

prevailing in the last 20 years 20 percent annual growth in both service exports and imports from 

China and India, is positive at 1.4 percent annually, while the overall impact on displaced 

 

23 The estimates imply that overall trade tends to increase unemployment duration (by about 2.6 

percent annually) for both less and more educated service workers, but the effect is statistically 

significant only for the former type of workers. 
31

workers with less than high-school education is negative at -2.8 percent.24 These estimates are 

larger than the impacts estimated by Liu and Trefler (2008), but are consistent with their findings 

that inshoring and offshoring have more adverse impacts on less educated workers. 

 The results from this study imply that service trade (inshoring and offshoring) does have 

some impact on re-employment wages, perhaps somewhat larger than previously thought. 

Consistent with the U.S. comparative advantage, highly educated workers tend to benefit from 

service trade more than do workers with less education. Displaced workers with less than high 

school education tend to experience lower wages upon re-employment perhaps as a result of a 

loss of industry- or occupation-specific human capital. Hence, unemployment programs that 

help low-skilled workers displaced from service industries facing increasing international trade 

pressures with job training and skill (education) acquisition may be beneficial. Such programs 

that focus on job training and skill upgrading already exist – the Trade Adjustment Assistance 

(TAA) program has been in place since the mid 1970s, but it has been mostly utilized by 

manufacturing workers, likely because it was the rising trade in manufacturing and not services 

that affected U.S. workers throughout the 1980s and the 1990s. 

 There is clear need for more research on the impacts of international trade on 

employment transitions of (displaced) service workers. An interesting extension of the work 

here would be to examine the path of wage growth of (trade) displaced workers after reemployment and assess if they ever catch up with their non-displaced peers. To address these 

issues, one has to employ a long-run panel data on individuals, which unfortunately is not the 

 

24 Note, however, that while not statistically significant, the impact of overall trade on reemployment wages for less educated workers is estimated to be positive at 1.6 percent annually.
32

type of data supplied by the DWS. Such research would better inform policy-makers on issues 

that concern the impact of trade in services on U.S. labor markets. 
33

TABLES 

Table 1. U.S. Service Exports and Imports (Millions of 2007 U.S. dollars), 1986-2007. 

Panel A: Total Services (Includes Advertising; Computer and data processing; Construction, engineering, 

and architecture; Financial; Industrial Engineering; Insurance; Legal; Management consulting; Research 

and development; Telecommunications). 

Year Overall Exports Exports to G-8 

Exports to 

China and India Overall Imports Imports from G-8 

Imports from 

China and India 

1986 17,279 6,355 195 14,913 6,364 289

1996 31,515 11,848 1,527 26,419 9,641 1,427

2007 87,173 58,105 5,996 66,259 47,614 9,679

Panel B: Services By Industry 

Service Industry Year Overall 

Exports 

Exports 

to G-8 

Exports 

to China 

and India 

Overall 

Imports 

Imports 

from G-8 

Imports 

from China 

and India 

Advertising 1986 178 87 0 146 74 0

Advertising 1996 718 412 48 1,283 883 32

Advertising 2007 1,198 1,510 47 1,335 1,272 93

Computer and data processing services 1986 1,863 272 17 61 44 0

Computer and data processing services 1996 2,137 1,061 63 365 163 11

Computer and data processing services 2007 4,022 3,742 304 2,942 6,128 4,652

Construction, engineering, and architecture 1986 1,436 204 26 569 344 26

Construction, engineering, and architecture 1996 4,695 492 262 614 180 4

Construction, engineering, and architecture 2007 5,078 1,957 668 1,107 787 63

Financial Services 1986 6,245 2,525 0 3,347 2,934 0

Financial Services 1996 10,875 4,361 441 3,842 2,236 119

Financial Services 2007 46,922 23,548 2,713 11,447 11,420 1,315

Industrial engineering 1986 185 59 2 142 38 0

Industrial engineering 1996 1,150 173 79 260 61 5

Industrial engineering 2007 3,189 1,499 271 997 1,183 113

Insurance 1986 2,619 1,957 0 4,162 1,657 79

Insurance 1996 2,182 1,323 27 7,129 3,436 38

Insurance 2007 10,184 5,624 198 41,666 8,430 26

Legal Services 1986 184 72 6 76 30 0

Legal Services 1996 2,568 1,685 132 813 488 52

Legal Services 2007 6,278 3,609 433 1,385 889 88

Management, consulting, and public relations 1986 579 170 13 114 79 2

Management, consulting, and public relations 1996 1,929 581 63 657 325 16

Management, consulting, and public relations 2007 3,091 9,666 898 2,439 9,407 1,683

Research, development, and testing services 1986 533 178 0 144 62 0

Research, development, and testing services 1996 900 436 12 501 213 7

Research, development, and testing services 2007 2,160 4,295 82 2,941 5,979 964

Telecommunications 1986 3,456 831 131 6,154 1,101 182

Telecommunications 1996 4,362 1,323 399 10,955 1,657 1,144

Telecommunications 2007 5,051 2,655 382 - 2,119 682

Source. – Author’s calculations with data from the Bureau of Economic Analysis.
34

Table 2. Summary Statistics for U.S. Workers Displaced from All Service Industries and from Service Industries with Available 

Panel A 

All Service Industries Service Industries w

Variable Displaced and Re-employed Displaced Displaced and Re-employ

Mean St. Dev. Mean St. Dev. Mean St. De

Fraction re-employed at date of survey 1.00 0.00 0.80 0.40 1.00 0.00 

Current weekly wage (constant 2003 dollars) 618.83 452.16 618.83 452.16 820.18 590.4Lost weekly wage (constant 2003 dollars) 725.44 503.10 708.45 499.19 1,003.86 647.9Unemployment duration (two-week intervals) 7.65 9.87 8.54 10.56 7.31 9.06 No high school 0.02 0.14 0.02 0.15 0.00 0.05 High school dropout 0.06 0.24 0.07 0.26 0.01 0.11 High school graduate 0.31 0.46 0.33 0.47 0.20 0.40 Some college 0.31 0.46 0.30 0.46 0.32 0.47 College graduates 0.20 0.40 0.19 0.39 0.33 0.47 Advanced degree 0.09 0.29 0.09 0.28 0.14 0.35 Age (years) 38.76 10.45 38.82 10.60 39.30 10.04 Lost job tenure (years) 4.61 5.77 4.52 5.77 4.92 6.19 Female 0.45 0.50 0.45 0.50 0.49 0.50 Non-white 0.13 0.33 0.14 0.35 0.13 0.34 Married 0.60 0.49 0.58 0.49 0.60 0.49 Metropolitan area 0.78 0.41 0.78 0.41 0.91 0.28 Non-white Female 0.06 0.24 0.07 0.26 0.07 0.26 Married Female 0.23 0.42 0.22 0.42 0.25 0.43 Hispanic Origin 0.07 0.25 0.07 0.26 0.06 0.23 

RATE Ust 0.06 0.02 0.06 0.02 0.05 0.02 

Log Overall Exports - - - - 8.16 1.11 Log Exports to China and India - - - - 4.58 1.44 Log Overall Imports - - - - 7.55 1.46 Log Imports from China and India - - - - 3.96 1.83 No. Obs. 21,250 26,672 3,796 

Source. – Author’s calculations with data from Displaced Workers Supplement (1988-2008) and the Bureau of Economic A

Note. – Workers displaced from a full-time job in the U.S. service sector between 1986 and 2007, ages 21 to 65 at displace

specified. 
35

Table 2 (Cont’d.). Summary Statistics for U.S. Workers Displaced from Service Industries with Available Trade Data used in the 

Panel B 

Service Industries with Available Trade Data and Information on Unemployment Duratio

Variable Displaced and Re-employed Disp

Mean St. Dev. Mean 

Fraction re-employed at date of survey 1.00 0.00 0.80 

Current weekly wage (constant 2003 dollars) 838.62 594.62 846.08 Lost weekly wage (constant 2003 dollars) 999.18 646.04 1,005.20 Unemployment duration (two-week intervals) 7.27 8.97 8.07 No high school 0.00 0.06 0.00 High school dropout 0.01 0.10 0.01 High school graduate 0.20 0.40 0.22 Some college 0.32 0.47 0.32 College graduates 0.33 0.47 0.32 Advanced degree 0.13 0.34 0.13 Age (years) 39.17 10.06 39.46 Lost job tenure (years) 4.89 6.24 4.90 Female 0.50 0.50 0.49 Non-white 0.13 0.34 0.14 Married 0.59 0.49 0.59 Metropolitan area 0.91 0.29 0.90 Non-white Female 0.07 0.26 0.08 Married Female 0.26 0.44 0.26 Hispanic Origin 0.06 0.23 0.06 

RATE Ust 0.05 0.02 0.05 

Log Overall Exports 8.19 1.08 8.19 Log Exports to China and India 4.63 1.41 4.62 Log Overall Imports 7.60 1.44 7.58 Log Imports from China and India 4.01 1.82 4.04 No. Obs. 2,665 3,

Source. – Author’s calculations with data from Displaced Workers Supplement (1988-2008) and the Bureau of Economic A

Note. – Workers displaced from a full-time job in the U.S. service sector between 1986 and 2007, ages 21 to 65 at displace

specified. 
36

 Table 3. Correlations among trade measures, 1986-2007. 

Trade Variable (1) (2) (3) (4) (5) (6) 

(1) Overall Exports 1.00 

(2) Exports to G-8 0.92 1.00 

(3) Exports to China and India 0.90 0.79 1.00 

(4) Overall Imports 0.67 0.72 0.59 1.00 

(5) Imports from G-8 0.70 0.79 0.58 0.94 1.00 

(6) Imports from China and India 0.63 0.66 0.69 0.64 0.66 1.00 

Source. – Author’s calculations with data from the Bureau of Economic Analysis, and Displaced 

Workers Supplement (1988-2008). 

Note. – Correlations are across workers in the Displaced Workers Supplement data based on industry 

trade data matched to worker’s industry of displacement. 
37

Table 4. The Impact of Trade in Services on Displaced Workers Re-employment Wage. 

ln(wre-employment Variable )

All workers All workers Less than 

High-school 

College and 

more 

No High School -0.20 -0.17 0.10 

(0.20) (0.20) (0.54) - 

High-school Drop-out -0.22*** -0.23*** -0.07 

(0.06) (0.06) (0.11) - 

Some College 0.06 0.06 

(0.05) (0.05) - - 

College Graduate 0.15** 0.15** -0.12 

(0.06) (0.06) - (0.08) 

Advanced Degree 0.26*** 0.26*** 

(0.05) (0.05) - - 

Age 0.03*** 0.03*** 0.02 0.03 

(0.01) (0.01) (0.02) (0.02) 

Age Squared -0.0005*** -0.0005*** -0.0002 -0.0005** 

(0.0001) (0.0001) (0.0002) (0.0002) 

Lost Job Tenure -0.00 -0.00 -0.00 -0.00 

(0.00) (0.00) (0.00) (0.01) 

Log Lost Job Weekly Wage 0.55*** 0.55*** 0.51*** 0.58*** 

(0.03) (0.03) (0.11) (0.07) 

Female -0.08* -0.08* -0.21* -0.10 

(0.04) (0.04) (0.11) (0.06) 

Nonwhite -0.11 -0.11 -0.34 -0.03 

(0.07) (0.07) (0.46) (0.06) 

Married 0.09* 0.09* 0.01 0.07 

(0.04) (0.04) (0.10) (0.06) 

Metropolitan Area 0.17** 0.16** 0.01 0.29 

(0.07) (0.06) (0.09) (0.20) 

RATE -1.25 -1.14 -8.63* -2.75 Ust (2.61) (2.56) (4.54) (3.01) 

Female × Nonwhite 0.01 0.02 0.30 -0.00 

(0.10) (0.10) (0.50) (0.09) 

Female × Married -0.20*** -0.20*** -0.02 -0.34*** 

(0.05) (0.05) (0.16) (0.08) 

Hispanic -0.03 -0.02 -0.29** 0.21* 

(0.05) (0.05) (0.11) (0.10) 

Log Overall Exports 0.08 -0.06 -0.06 -0.23 

(0.06) (0.07) (0.17) (0.14) 

Log Exports to China and India 0.05 -0.02 0.15** - (0.03) (0.08) (0.05) 

Log Overall Imports 0.00 0.09** 0.14 0.12** 

(0.03) (0.03) (0.13) (0.05) 

Log Imports from China and India -0.07*** -0.12** -0.08** - (0.02) (0.05) (0.03) 

No. Obs. 2,665 2,665 543 1,265 

R2 0.34 0.34 0.40 0.31 

Note. – Robust standard errors clustered by industry of displacement are reported in parentheses. ***Indicates significance at 1 percent, ** at 5 percent, and * at 10 percent. 
38

Table 5. The Impact of Trade in Services on Displaced Workers Unemployment Duration. 

Variable ln(Unemployment Duration)

All workers All workers Less than 

High-school 

College and 

more 

No High School 0.67** 0.67** 0.98** 

(0.29) (0.29) (0.41) - 

High-school Drop-out 0.17 0.18 0.19 

(0.27) (0.27) (0.37) - 

Some College -0.04 -0.04 

(0.05) (0.05) - - 

College Graduate -0.14*** -0.13*** 0.08* 

(0.05) (0.05) - (0.04) 

Advanced Degree -0.23*** -0.23*** 

(0.06) (0.06) - - 

Age 0.03 0.03 0.06* 0.02 

(0.02) (0.02) (0.03) (0.04) 

Age Squared -0.0001 -0.0001 -0.0005 0.0002 

(0.0002) (0.0002) (0.0003) (0.0005) 

Lost Job Tenure 0.00 0.00 0.01 0.01 

(0.01) (0.01) (0.01) (0.01) 

Log Lost Job Weekly Wage 0.15*** 0.15*** 0.11 0.15* 

(0.04) (0.04) (0.15) (0.08) 

Female -0.06 -0.06 0.28 -0.24 

(0.13) (0.13) (0.25) (0.14) 

Nonwhite -0.02 -0.02 0.07 0.02 

(0.09) (0.09) (0.20) (0.12) 

Married -0.25*** -0.25*** 0.00 -0.25** 

(0.05) (0.05) (0.19) (0.12) 

Metropolitan Area -0.24*** -0.24*** -0.16 -0.37*** 

(0.05) (0.05) (0.19) (0.08) 

RATE 3.66 3.65 5.61 3.42 Ust (4.04) (4.05) (7.27) (3.85) 

Female × Nonwhite 0.30** 0.30** 0.04 0.30** 

(0.13) (0.13) (0.20) (0.12) 

Female × Married 0.24** 0.24** -0.21 0.37** 

(0.12) (0.12) (0.24) (0.15) 

Hispanic 0.12 0.12 0.06 -0.23 

(0.14) (0.14) (0.11) (0.19) 

Log Overall Exports 0.12 0.10 0.43** 0.13 

(0.08) (0.09) (0.18) (0.19) 

Log Exports to China and India 0.03 0.05 -0.08 - (0.07) (0.10) (0.08) 

Log Overall Imports -0.00 -0.02 -0.30* 0.03 

(0.07) (0.04) (0.18) (0.09) 

Log Imports from China and India 0.01 0.01 0.02 - (0.04) (0.08) (0.05) 

No. Obs. 3,162 3,162 712 1,439 

Log Likelihood -4,656.09 -4,655.89 -951.36 -2,085.45 

Note. – Robust standard errors clustered by industry of displacement are reported in parentheses. ***Indicates significance at 1 percent, ** at 5 percent, and * at 10 percent. 
39

Appendix 

Table A1. The Impact of Trade in Services on Displaced Workers Re-employment Wage, Heckman Selection Model. 

Variable All workers

ln(wre-employment) Re-employed ln(wre-employment) Re-employed 

No High School -0.11 -0.78* -0.09 -0.79* 

(0.32) (0.45) (0.32) (0.46) 

High-school Drop-out -0.19 -0.16 -0.20 -0.15 

(0.14) (0.23) (0.14) (0.23) 

Some College 0.05 0.17** 0.05 0.16* 

(0.04) (0.08) (0.04) (0.08) 

College Graduate 0.12** 0.29*** 0.12*** 0.29*** 

(0.05) (0.09) (0.05) (0.09) 

Advanced Degree 0.23*** 0.39*** 0.23*** 0.39*** 

(0.06) (0.12) (0.06) (0.12) 

Age 0.04*** -0.03 0.04*** -0.03 

(0.01) (0.02) (0.01) (0.02) 

Age Squared -0.0005*** 0.0001 -0.0005*** 0.0001 

(0.0001) (0.0003) (0.0001) (0.0003) 

Lost Job Tenure -0.00 0.00 -0.00 0.00 

(0.00) (0.01) (0.00) (0.01) 

Log Lost Job Weekly Wage 0.54*** 0.03 0.54*** 0.03 

(0.03) (0.06) (0.03) (0.06) 

Female -0.12** 0.24** -0.12** 0.24** 

(0.05) (0.10) (0.05) (0.10) 

Nonwhite -0.10 0.01 -0.11 0.01 

(0.07) (0.13) (0.07) (0.13) 

Married 0.06 0.30*** 0.06 0.30*** 

(0.05) (0.09) (0.05) (0.09) 

Metropolitan Area 0.15*** 0.15 0.15*** 0.15 

(0.06) (0.11) (0.06) (0.11) 

RATE -0.98 -5.51 -1.05 -5.63 Ust (1.73) (3.98) (1.72) (3.99) 

Female × Nonwhite 0.07 -0.57*** 0.07 -0.57*** 

(0.09) (0.17) (0.09) (0.17) 

Female × Married -0.16*** -0.27** -0.16*** -0.27** 

(0.06) (0.12) (0.06) (0.12) 

Hispanic -0.01 -0.13 -0.01 -0.13 

(0.07) (0.12) (0.07) (0.12) 

Log Overall Exports 0.11* -0.02 0.01 0.12 

(0.06) (0.12) (0.08) (0.16) 

Log Exports to China and India 0.05 -0.09 - - (0.04) (0.08) 

Log Overall Imports 0.01 0.05 0.05 0.01 

(0.03) (0.07) (0.05) (0.09) 

Log Imports from China and India -0.04 0.03 - - (0.02) (0.05) 

Mills Ratio -0.35*** -0.33*** 

(0.07) - (0.08) - 

No. Obs./No. Uncensored Obs. 3,300/2,665 3,300/2,665 

χ2 9,282.68 3,133.24 

Note. – Robust standard errors clustered by industry of displacement are reported in parentheses. ***Indicates significance at 1 percent, ** at 5 percent, and * at 10 percent. 
40

 Table A2. The Impact of Trade in Services on Displaced Workers Re-employment Wage, Robustness Checks. 

Variable All workers

ln(wre-employment)

No High School -0.20 -0.17 - - (0.20) (0.21) 

High-school Drop-out -0.24*** -0.24*** - - (0.06) (0.06) 

Some College 0.06 0.06 - - (0.05) (0.05) 

College Graduate 0.15** 0.15** - - (0.06) (0.06) 

Advanced Degree 0.26*** 0.26*** - - (0.05) (0.05) 

Age 0.04*** 0.04*** 0.04*** 0.04*** 

(0.01) (0.01) (0.01) (0.01) 

Age Squared -0.0005*** -0.0005*** -0.0005*** -0.0005*** 

(0.0001) (0.0001) (0.0001) (0.0001) 

Lost Job Tenure -0.00 -0.00 -0.00 -0.00 

(0.00) (0.00) (0.00) (0.00) 

Log Lost Job Weekly Wage 0.54*** 0.54*** 0.54*** 0.54*** 

(0.03) (0.03) (0.03) (0.03) 

Female -0.09* -0.09* -0.09* -0.09* 

(0.04) (0.04) (0.04) (0.04) 

Nonwhite -0.09 -0.09 -0.11 -0.11 

(0.07) (0.07) (0.07) (0.07) 

Married 0.08* 0.08* 0.09* 0.10* 

(0.04) (0.04) (0.04) (0.04) 

Metropolitan Area 0.17** 0.16** 0.17** 0.16** 

(0.07) (0.06) (0.07) (0.06) 

RATE -1.41 -1.32 -1.19 -1.07 Ust (2.68) (2.64) (2.56) (2.54) 

Female × Nonwhite -0.01 -0.00 0.02 0.02 

(0.10) (0.10) (0.10) (0.10) 

Female × Married -0.19*** -0.19*** -0.19*** -0.20*** 

(0.05) (0.05) (0.05) (0.05) 

Hispanic -0.03 -0.03 -0.03 -0.03 

(0.05) (0.05) (0.05) (0.05) 

Log Overall Exports 0.07 -0.05 -0.04 -0.10 

(0.06) (0.07) (0.12) (0.12) 

Log Exports to China and India 0.05 0.05* - (0.03) - (0.03) 

Log Overall Imports 0.01 0.08** 0.09 0.14** 

(0.03) (0.03) (0.05) (0.06) 

Log Imports from China and India -0.06** -0.05* - (0.02) - (0.02) 

Educ. Dummies x Ind. Dummies Yes Yes - - 

Ind. Specific Time Trends - - Yes Yes 

No. Obs. 2,665 2,665 2,665 2,665 

R2 0.35 0.35 0.34 0.34 

Note. – Robust standard errors clustered by industry of displacement are reported in parentheses. ***Indicates significance at 1 percent, ** at 5 percent, and * at 10 percent. 
41

Table A3. The Impact of Trade in Services on Displaced Workers Re-employment Wage. 

ln(wre-employment/ wdisplacement Variable )

All workers All workers Less than 

High-school 

College and 

more 

No High School 0.04 0.08 0.29 0.00 

(0.19) (0.20) (0.59) (0.00) 

High-school Drop-out -0.16* -0.17* -0.01 0.00 

(0.09) (0.08) (0.12) (0.00) 

Some College 0.05 0.05 0.00 0.00 

(0.05) (0.05) (0.00) (0.00) 

College Graduate 0.05 0.05 0.00 -0.06 

(0.07) (0.07) (0.00) (0.07) 

Advanced Degree 0.10 0.10 0.00 0.00 

(0.06) (0.06) (0.00) (0.00) 

Age 0.01 0.01 0.01 -0.01 

(0.01) (0.01) (0.02) (0.02) 

Age Squared -0.0002 -0.0002 -0.0001 -0.0001 

(0.0001) (0.0001) (0.0002) (0.0002) 

Lost Job Tenure -0.01** -0.01** -0.01* -0.00 

(0.00) (0.00) (0.00) (0.01) 

Log Lost Job Weekly Wage - - - - 

Female -0.04 -0.04 -0.16 -0.08 

(0.05) (0.05) (0.14) (0.06) 

Nonwhite -0.06 -0.06 -0.23 0.02 

(0.06) (0.06) (0.45) (0.06) 

Married 0.04 0.04 -0.08 0.02 

(0.04) (0.04) (0.09) (0.06) 

Metropolitan Area 0.10 0.10 -0.10 0.22 

(0.08) (0.08) (0.10) (0.21) 

RATE -1.92 -1.81 -8.58 -2.93 Ust (2.94) (2.90) (4.89) (3.00) 

Female × Nonwhite 0.00 0.01 0.24 0.02 

(0.09) (0.09) (0.50) (0.09) 

Female × Married -0.15** -0.15** 0.09 -0.30*** 

(0.06) (0.06) (0.16) (0.08) 

Hispanic 0.04 0.04 -0.20 0.28** 

(0.05) (0.05) (0.18) (0.11) 

Log Overall Exports 0.09 -0.05 0.02 -0.24 

(0.06) (0.07) (0.19) (0.14) 

Log Exports to China and India 0.05 -0.06 0.17** - (0.03) (0.07) (0.06) 

Log Overall Imports -0.01 0.07* 0.14 0.10* 

(0.03) (0.03) (0.15) (0.05) 

Log Imports from China and India -0.07*** -0.11* -0.07** - (0.02) (0.05) (0.03) 

No. Obs. 2,665 2,665 543 1,265 

R2 0.13 0.13 0.30 0.19 

Note. – Robust standard errors clustered by industry of displacement are reported in parentheses. ***Indicates significance at 1 percent, ** at 5 percent, and * at 10 percent. 
42

 Table A4. The Impact of Trade in Services on the Hazard of Leaving Unemployment, Robustness Check.

Variable Hazard of Leaving Unemployment

All workers 

No High School -0.66* -0.66** -0.33* -0.43** 

(0.34) (0.34) (0.19) (0.21) 

High-school Drop-out -0.17 -0.17 -0.12 -0.13 

(0.22) (0.22) (0.16) (0.19) 

Some College 0.04 0.04 0.02 0.01 

(0.05) (0.06) (0.04) (0.04) 

College Graduate 0.13** 0.13** 0.12** 0.09** 

(0.06) (0.06) (0.06) (0.04) 

Advanced Degree 0.23*** 0.23*** 0.18** 0.22*** 

(0.05) (0.05) (0.08) (0.05) 

Age -0.03 -0.03 -0.03*** -0.03*** 

(0.02) (0.02) (0.01) (0.01) 

Age Squared 0.0001 0.0001 0.0001 0.0002 

(0.0002) (0.0002) (0.0001) (0.0001) 

Lost Job Tenure -0.00 -0.00 -0.01 -0.00 

(0.00) (0.00) (0.01) (0.00) 

Log Lost Job Weekly Wage -0.15*** -0.15*** -0.13*** -0.12*** 

(0.05) (0.05) (0.02) (0.03) 

Female 0.06 0.06 0.06 0.05 

(0.08) (0.08) (0.08) (0.09) 

Nonwhite 0.02 0.02 0.01 -0.00 

(0.09) (0.09) (0.08) (0.08) 

Married 0.24*** 0.24*** 0.19*** 0.22*** 

(0.05) (0.05) (0.03) (0.04) 

Metropolitan Area 0.24*** 0.24*** 0.11*** 0.14*** 

(0.08) (0.08) (0.04) (0.05) 

RATE -3.59 -3.59 -7.02** -7.16** Ust (3.78) (3.77) (2.80) (2.87) 

Female × Nonwhite -0.29** -0.29** -0.23* -0.23* 

(0.12) (0.12) (0.13) (0.13) 

Female × Married -0.23** -0.23** -0.24*** -0.25*** 

(0.10) (0.10) (0.07) (0.09) 

Hispanic -0.12 -0.12 -0.05 -0.12 

(0.12) (0.12) (0.08) (0.12) 

Log Overall Exports -0.12 -0.10 -0.02 -0.04 

(0.09) (0.10) (0.04) (0.08) 

Log Exports to China and India -0.03 -0.03 - (0.06) - (0.05) 

Log Overall Imports 0.00 0.02 -0.02 -0.01 

(0.07) (0.06) (0.05) (0.05) 

Log Imports from China and India -0.01 0.00 - (0.03) - (0.03) 

No. Obs. 3,162 3,162 3,162 3,162 

Log Likelihood -4,656.09 -4,655.89 -10,653.08 -8,637.82 

Note. – Robust standard errors clustered by industry of displacement are reported in parentheses. ***Indicates significance at 1 percent, ** at 5 percent, and * at 10 percent. 
43

 Table A5. The Impact of Trade in Services on Displaced Workers Unemployment Duration, Robustness Checks. 

Variable All workers

ln(Unemployment Duration)

No High School 0.73*** 0.73*** - - (0.28) (0.28) 

High-school Drop-out 0.16 0.16 - - (0.27) (0.27) 

Some College -0.04 -0.04 - - (0.05) (0.05) 

College Graduate -0.14*** -0.13*** - - (0.05) (0.05) 

Advanced Degree -0.22*** -0.22*** - - (0.06) (0.06) 

Age 0.02 0.02 0.03* 0.03* 

(0.02) (0.02) (0.02) (0.02) 

Age Squared -0.00 -0.00 -0.00 -0.00 

(0.00) (0.00) (0.00) (0.00) 

Lost Job Tenure 0.00 0.00 0.00 0.00 

(0.01) (0.01) (0.01) (0.01) 

Log Lost Job Weekly Wage 0.15*** 0.16*** 0.15*** 0.16*** 

(0.04) (0.04) (0.04) (0.04) 

Female -0.06 -0.06 -0.07 -0.07 

(0.13) (0.13) (0.13) (0.13) 

Nonwhite -0.04 -0.03 -0.03 -0.02 

(0.09) (0.09) (0.09) (0.09) 

Married -0.23*** -0.23*** -0.25*** -0.25*** 

(0.06) (0.06) (0.05) (0.06) 

Metropolitan Area -0.22*** -0.22*** -0.26*** -0.25*** 

(0.05) (0.05) (0.05) (0.05) 

RATE 3.99 3.98 3.57 3.53 Ust (4.22) (4.21) (3.89) (3.91) 

Female × Nonwhite 0.32** 0.32** 0.29** 0.29** 

(0.13) (0.13) (0.14) (0.13) 

Female × Married 0.22* 0.22* 0.24** 0.25** 

(0.12) (0.12) (0.12) (0.12) 

Hispanic 0.09 0.09 0.12 0.12 

(0.15) (0.15) (0.14) (0.14) 

Log Overall Exports 0.12 0.06 -0.04 -0.05 

(0.08) (0.08) (0.10) (0.08) 

Log Exports to China and India 0.05 0.03 - (0.07) - (0.07) 

Log Overall Imports -0.00 -0.01 -0.02 -0.06 

(0.07) (0.04) (0.12) (0.10) 

Log Imports from China and India 0.00 0.05 - (0.04) - (0.04) 

Educ. Dummies x Ind. Dummies Yes Yes - - 

Ind. Specific Time Trends - - Yes Yes 

No. Obs. 3,162 3,162 3,162 3,162 

Log Likelihood -4,621.33 -4,620.98 -4,648.66 -4,647.81 

Note. – Robust standard errors clustered by industry of displacement are reported in parentheses.

***Indicates significance at 1 percent, ** at 5 percent, and * at 10 percent.
44

References 

Addison, J.T., and P. Portugal. 1989. “Job Displacement, Relative Wage Changes, and Duration 

of Unemployment.” Journal of Labor Economics 7(3): 281-302. 

Addison, J., Fox, D., Ruhm, C. 1995. Trade and Displacement in Manufacturing. Monthly Labor 

 Review 118, 58-67. 

Amiti, M., Wei, S., 2005. “Fear of Service Outsourcing: Is It Justified?” Economic Policy

 20(42):308-347. 

______, 2010. “Does Service Offshoring Lead to Job Losses” in (eds.) Marshall Reinsdorf and 

Mattthew Slaughter, International Flows of Invisibles, NBER, Chapter 7. 

Antras, P., Helpman, E., 2004. “Global Sourcing,” Journal of Political Economy 112: 552-580. 

Bhagwati, J., Panagariya, A., Srinivasan, T. N., 2004. “The Muddles over Outsourcing,” Journal 

of Economic Perspectives 18: 93-114. 

Blinder, A., 2006. “Offshoring: The Next Industrial Revolution?” Foreign Affairs 85: 113-128. 

Blinder, A., 2007. “How Many U.S. Jobs Might Be Offshorable?” CEPS Working Paper No. 

142, Princeton University. 

Brainard, L., Litan, R., 2004. “Offshoring Service Jobs: Bane or Boon – and What to Do?” 

Brookings Institution Policy Brief No. 132. 

Bureau of Economic Analysis – http://www.bea.gov. 

Crino, Rosario, 2008. “Service Offshoring and White-Collar Employment,” Mimeo, University 

 of Milan. 

Houseman, S., 2007. “Outsourcing, Offshoring, and Productivity Measurement in U.S. 

Manufacturing,” International Labour Review, forthcoming. 
45

Farber, H.S. 1998. “Has the Rate of Job Loss Increased in the Nineties?” Proceedings of the 

Fiftieth Annual Winter Meeting of the Industrial Relations Research Association 1:88-97. 

Jensen, J. B., Kletzer, L., 2006. “Tradable Services: Understanding the Scope and Impact of 

Services Offshoring,” in Brookings Trade Forum 2005: Offshoring White-Collar Work, 

S. Collins, L. Brainard, eds., Washington D.C.: Brookings Institution Press. 

______, 2008. “Measuring Tradable Services and the Task Content of Offshorable Services 

Jobs,” in Labor in the New Economy, K. Abraham, M. Harper, J. Spletzer, eds., 

University of Chicago Press, forthcoming. 

Khandelwal, A. 2010. “The Long and Short (of) Quality Ladders. Review of Economic 

Studies, forthcoming. 

Kambourov, G., Manovskii, I., 2008a. “Rising Occupational and Industry Mobility in the 

United States: 1968-1997,” International Economic Review 49(1): 41-79. 

______, 2008b. “Occupational Specificity of Human Capital,” 

International Economic Review, forthcoming. 

Kandilov, I. 2009. “How Import Competition Affects Displaced Workers in the U.S.,” 

forthcoming, Berkeley Electronic Journal of Economics Analysis and Policy (Topics). 

Kletzer, L. 2001. Job Loss from Imports: Measuring the Costs. Washington, DC: Institute for 

 International Economics. 

______. 2002. Imports, Exports, and Jobs: What Does Trade Mean for Employment and Job 

 Loss? Kalamazoo, MI: W.E. Upjohn Institute for Employment Research. 

Lipsey, R., 2006. “Measuring International Trade in Services,” NBER working paper No. 12271. 

Liu, R., Trefler, D., 2008.” Much Ado About Nothing: American Jobs and the Rise of 

Outsourcing of American Jobs to China and India,” NBER working paper No. 14061. 
46

Mankiw, N. G., Swagel, P., 2006. “The politics and economics of offshore outsourcing,” Journal 

of Monetary Economics 53: 1027-1056. 

Moncarz, R.J., Wolf, M.G., Wright, B., 2008. “Service-providing occupations, offshoring, and 

the labor market,” Monthly Labor review, December 2008, 71-86. 

National Bureau of Economic Research (NBER) – http://www.nber.org. 

Samuelson, P., 2004. “Where Ricardo and Mill Rebut and Confirm Arguments of Mainstream 

Economists Against Globalization,” Journal of Economic Perspectives, 18: 135-146. 

van Welsum, D., Reif, X., 2006a. “Potential Offshoring: Evidence from Selected OECD 

Countries,” in Brookings Trade Forum 2005: Offshoring White-Collar Work, ed. Susan 

M. Collins and Lael Brainard. Washington D.C.: Brookings Institution Press. 

______, 2006b. “The Share of Employment Potentially Affected by 

Offshoring: An Empirical Investigation,” DSTI Information Economy Working Paper 

No. DSTI/ICCP/IE(2005)8/FINAL, OECD. 

______, 2006c. “Potential Impacts of International Sourcing on Different 

Occupations,” DSTI Information Economy Working Paper No. 

DSTI/ICCP/IE(2006)1/FINAL, OECD. 

______, 2006d. “We Can Work It Out – The Globalization of ICT-Enabled 

Services,” NBER working paper No. 12799. 

van Welsum, D., Vickery, G., 2005. “Potential Offshoring of ICT-Intensive Using Occupations,” 

DSTI Information Economy Working Paper No. DSTI/ICCP/IE(2004)19/FINAL, OECD. 

Wooldridge, J. 2002. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: 

 MIT Press.