Occupation Switching Behavior and the Wage Impact of
Trade-Related Displacement
Vasilios D. Kosteas1 and Jooyoun Park2
April 23, 2013
Preliminary
Abstract
This paper investigates the cross-occupation effect of offshorability on wage rates by examining the
occupation switching behavior of workers previously employed in highly offshorable occupations. Instead
of looking at the labor market for occupations that are traditionally vulnerable to import competition, we
study the destination occupations for the workers displaced from import-competing sectors. We observe
the pattern of occupation switching using the data on participants of the Trade Adjustment Assistance
(TAA) program of the U.S. Department of Labor. Using the NLSY dataset, we find that inflows of
workers who were previously employed in highly offshorable occupations exerts significant downward
pressure on wages for workers who are already employed in the receiving occupations.
JEL classification: F13, F16
Key words: offshorability, wages, displacement
1
2
Cleveland State University (tel) 216–687–4526 b.kosteas@csuohio.edu
Kent State University,
(tel) 330-672-1086 (fax) 330-672-9808 jpark8@kent.edu 1 I. Introduction
Growth in international trade has increased throughout the second half of the 20th Century and
early into the 21st Century. This trend is growing more prominent as offshore outsourcing becomes a
more common business practice. Trade theory predicts, and mass media outlets assert that workers in the
import-competing sectors have been adversely affected. Existing studies on the US labor market impacts
of global market integration focus on employment (see Kletzer 2002 and 2004) and wage effects
(Kosteas 2007) of higher import competition for workers in the manufacturing sector or the service sector
(Jensen and Kletzer 2006 and 2010). Ebenstein et al. (2011) examine the effects of both trade and
offshoring at the occupation level while Baumgarten et al (2010) investigate the effects of offshoring on
wages German workers employed in the manufacturing sector. Each of these studies focuses on the
within industry (Jensen and Kletzer 2006 and 2010, Kletzer 2002 and 2004, Kosteas 2007, Baumgarten et
al 2010) or within occupation (Ebenstein et al 2011) effects of foreign competition on worker outcomes,
ignoring the potential impact of trade induced job displacement on workers in occupations and industries
receiving these workers. The emphasis on within industry and within occupation wage and employment
effects of globalization is likely to understate the true impact of trade and offshoring on wages as it
ignores the general equilibrium effects associated with the movement of trade displaced workers into
industries and occupations which are relatively insulated from trade and offshore outsourcing.
This paper investigates the occupation switching patterns of workers displaced from offshorable
occupations. Rather than investigating which occupations are the most vulnerable, we ask which
occupations they move to and examine the impact of this flow of workers on the wage rates for workers
in the receiving occupations. We use two datasets: the Panel Study of Income Dynamics (PSID) and the
National Longitudinal Surveys of Youth 1979 cohort (NLSY79). NLSY79 dataset has the advantages of
a longitudinal, detailed survey tracking individuals for over 30 years. Its primary drawback is that it
tracks individuals within a narrow age range (between fourteen and twenty-two years of age in 1979).
The PSID, on the other hand, contains observations on individuals in a wide range of ages. The
differences in sample methodology and questionnaires make them interesting comparison groups for the
2 purpose of this project. Performing the same set of empirical exercises using both datasets allows for
stronger statements about the external validity of the results.
We first find the occupations that are the most vulnerable to import competition or offshore
outsourcing (Jensen and Kletzer, 2010). We follow workers who are displaced from these occupations
into new occupations to identify the occupations that receive a large inflow of these workers and compare
the changes in employment and wage rates of these occupations to those in the occupations that are the
most insulated from their occupation switching behavior. The focus of previous work on industries and
occupations that are directly competing in global setting overlooks these secondary labor market impacts
of import competition.
Then we go deeper into this secondary wage effect by looking into their choice of occupational
training prior to the new employment. Workers displaced due to import competition are eligible for
various benefits through the Trade Adjustment Assistant (TAA) program. The most important benefits are
occupational training provision and the income support during training. The data on participant
characteristics, services rendered and outcomes are collected under the Trade Act Participant Report
(TAPR) by the U.S. Department of Labor (USDOL). This dataset contains occupation codes of training
and reemployment. Information on their choices of training occupation provides the unobservable forces
on the supply side in the local labor market.
Park (2012) finds only 37% of occupational trainees of the TAA program find a job in the
training occupation. This implies that looking at the changes in employment in one occupation does not
represent the supply of labor in that occupation. For instance, approximately 20% of the TAA participants
receive training in healthcare-related occupations (healthcare practitioners/ technicians/ assistants), but
only 10% of the TAA participants find a job in those occupations (Park, 2012). If there is a bias in
training choice, it generates a large increase in the supply of workers with qualification in those
occupations, suppressing down the wage rates regardless of the size of employment which tends to be
determined by demand size. The training information obtained from the TAPR dataset provides
3 information on unobservable labor supply changes which potentially influence the wage rates of an
occupation.
II. Background
Existing studies on wages using individual level data
There are relatively few studies using individual-level data to investigate the effects of rising
imports and offshoring on wages as older studies in this area have generally relied on industry-level data.
Kosteas (2008) finds a negative effect of rising imports on wages for blue-collar workers in the US
manufacturing sector from the late 1980s through the mid-1990s. Ebenstein et al (20011) extend the
literature by looking at the relationship between both wages and both imports and offshoring by
constructing occupation level measures of trade and offshoring. The authors find industry level measures
of trade tend to understate the effect of rising foreign competition on wages. Bertrant (2004) finds wages
for US workers in the manufacturing sector become more sensitive to the unemployment rate as foreign
competition increases. As with the present analysis, each of these studies merges industry-level trade data
with individual-level data using either NLSY (Kosteas 2008) or CPS data (Bertrand 2004, Ebenstein et al
2011). These studies share two features which limit the analysis: 1) they focus on observed levels of
imports or offshoring, which generally limits the analysis to workers employed in the manufacturing
sector, and 2) each of these studies analyses the within industry or occupation effects of trade and
outsourcing on the returns to labor. For both of these reasons, existing studies likely understate the
potential impact of globalization on wages.
Focusing on actual offshoring and imports rather than offshorability or tradability ignores the fact
that wages are determined endogenously as part of the system. [More]
Papers trying to measure offshorability
In order to address the second shortcoming of the existing literature, researchers have recently
started looking into ways of measuring offshorability (Blinder 2009, Blinder and Krueger 2009) and
4 tradability Jensen and Kletzer (2010). Blinder (2009) develops an offshorability index using job
descriptions from O*NET. The index is created by assigning offshorability values depending on the
researchers’ answers to certain questions, such as whether the occupation requires the individual to be
physically close to a work location in the US, whether the individual must be physically present and
whether the work unit must be located in the US. Occupation level wage regressions show a negative
correlation between offshorability and wages. They also show that a more objective measure of
offshorability based on O*NET data have a very low correlation with their index. Blinder and Krueger
(2009) develop three different measures of offshorability based on 1) the respondent’s own perception of
her job’s offshorability, 2) the assessment of professional coders, and 3) an index constructed by the
researchers based on individual’s responses to questions about the nature of their work. They argue for
the use of worker-level data to develop measures of offshorability since there may be significant withinoccupation variation. However, individual-level measures are based, necessarily on the individual’s own
perceptions and reports regarding job tasks and, specifically, the relative importance of attributes such as
whether their job can be done remotely or needs to be performed in a specific location and whether their
job requires personal interaction with individuals other than co-workers.
This paper employs job information from the O*NET database to construct a measure of
offshorability based on the importance of complex thinking and face-to-face contact in performing the
job. We will perform each set of analyses using various measures of offshorability as a robustness check.
Ultimately, the move towards a more accurate proxy for offshorability based on the ability of various job
tasks to predict observed offshoring is the next step in this area of research.
III. Data
1. TAPR and TAA petition data
The Trade Act Participant Report (TAPR) is the data set that the DOL collects on the participants
of the TAA program. The data collection began in the third calendar quarter of 1999. We acquired the
5 data set through the Freedom Of Information Act (FOIA). We utilize the observations reported between
2005 occupational code revision and 2009.3 There are 143,300 participants reported.
The TAPR consists of three parts. Identification and Participant Characteristics covers
individual characteristics such as gender, ethnicity, and education, and pre-participation earnings. Activity
and Service Record summarizes various services the participant receives since participation such as types
of training chosen, occupation of training, and receipt of income support. Outcomes reports employment
and earnings for three quarters from the program exit along with the occupation of reemployment. In this
paper, we do not analyze the wage effect using the TAPR. We only utilize the information on occupations
of training and reemployment. For detailed statistics on TAA program, see Park (2012) and Barnette and
Park (2013).
2. Individual-Level Data
Panel Study of Income Dynamics (PSID)Main Family Data
PSID data collection began in 1968 with 4,802 families. It’s collected every year up to 1997,
biannually thereafter in odd-numbered years. When a child of a subject family forms his/her own family,
that family is added to the pool of families to be survey. For this reason, the sample size increased over
time and 8,690 families were interviewed in 2009. The data contains very detailed information on every
aspect of a family amounting up to 5,012 variables in 2009 survey including employment information of
the head and the wife, receipt of federal assistance, health, savings and loans, and personal background of
the head and the wife. In this paper, we utilize the data collected in 2003, 2005, 2007 and 2009. 2003 is
the first year that they recorded the occupation codes using 2000 census occupational classification
system. We mainly make use of personal background and employment information of the head and the
3
The total sample size is 314,964 participants from July 1st, 1999. Prior to the revision in 2005, occupational codes
are reported using various coding system which makes correspondence tricky.
6 wife. The four files include information on 11,207 families together. 5,601 families show up in all four
years, 1,547 families in three years, 1,693 families in 2 years and 2,366 families in only one year.4
National Longitudinal Surveys of Youth 1979 Cohort
The NLSY79 contains information on individuals who were between the ages of fourteen and
twenty-two in 1979. It is a rich dataset with detailed information on work history and participation in
training events in addition to other variables of interest. The survey was conducted annually beginning in
1979 through 1994 and in even numbered years thereafter. The original sample contains 12,686
individuals who were fourteen to twenty-two years old in 1979. This full sample includes oversamples of
minorities and the poor in addition to a supplemental sample representative of individuals enrolled in the
four branches of the military. The military sample was dropped after the 1984 survey while the
oversampling of poor whites was dropped in 1991. The present study uses data only for individuals in the
nationally representative cross-section, which originally contained 6,111 individuals. Due to attrition and
missing information for key variables, the estimation sample is considerably smaller.
The major drawback with the dataset is the narrow age range of the participants; in the 2010 wave
respondents were between the forty-five and fifty-three years of age. While the analysis performed using
these data can provide insight on the effects of offshorability and trade induced displacement on middleaged workers, caution should be exercised when extrapolating these results to the broader population,
particularly younger workers who may not have built up significant stocks of occupation-specific human
capital. The analyses performed in this paper make use of data from the 2002 through 2010 waves of the
NLSY; 2002 is the first year in which the occupation of employment is recorded according to the 2000
census occupational classification system. The use of lagged information restricts the sample to
observations from 2004-2010 (see the methodology section for more details).
4
The public-use files for the PSID are available with states of residency. We acquired the restricted use PSID data which is geocoded with zip codes and census tracts. Using the geographical information will allow us to construct worker flow variables at
the occupation by local area (MSA or county) level. However, splitting the sample into smaller geographical area reduce the
the number of observations in each observation cell drastically. The analysis cannot be done using geographical restriction at
this point. 7 3. Tasks data from O*NET
The O*Net site provides information on the importance of various tasks and attributes for each
occupation such as abilities, skills, and work activities. We use information on the following
characteristics: 1) the importance of complex problem solving skills and 2) the importance of face-to-face
contact. O*Net data is coded with Standard Occupational Classification (SOC) while both PSID and
NLSY are coded with Census Occupational Classification (COC). We use a crosswalk table to convert
O*Net data to COC-level variables.
4. Other Data Sources
In order to construct occupational-level import competition index, we combine various data
sources. First, we use industry-level import and export data from Schott (2008) for 2000. We merge the
trade data to NBER-CES Manufacturing Industry Database to obtain the total value of shipment for each
industry. Lastly, we use Occupational Employment Statistics (OES) for the industry-level occupational
employment in 2000.
IV. Offshorability, Import-Competition, and Data Statistics
1. Offshorability Index
Out of numerous attributes of each occupation, we use two characteristics of occupations that are
the most relevant in making a certain occupation hard to offshore: (1) the importance of complex problem
solving skills and (2) the importance of face-to-face contact. Both variables are measured on a scale of
zero to one-hundred. To construct our simple proxy for offshorability, we sum the two values for each
occupation and subtract that value from two-hundred. This way, larger numbers reflect greater
offshorability. Our approach is similar to that taken by Blinder (2009) and Blinder and Krueger (2009).
The index, as it currently stands is a rough first attempt at proxying for offshorability.
8 Both PSID and NLSY79 use the Census Occupational Classification (COC) 2000 whereas the
O*NET data on job tasks employs the Standard Occupational Classification (SOC) 2010. The latter data
are provided for over 900 separate occupational classifications while the former contain over 400
occupation categories. While many SOC 2010 occupations mapped one-for-one with a counterpart in the
COC 2000 classification, merging the tasks data into the NLSY data required aggregation for several of
the SOC occupation categories. The aggregation was performed by taking the simple arithmetic mean of
the tasks variables across all SOC categories being merged into a single COC occupation code. While
this aggregation will introduce a degree of measurement error into the offshorability variable, most
aggregated SOC occupations show significant within group correlation in the values of the individual
tasks variables. Using this information, we define an occupation as being highly offshorable if its
offshorability index places it in the top quartile of all occupations.
2. Import Competition Index
The occupation-level import competition index is calculated by combining the NBER trade data
(Schott, 2008), NBER Industry Productivity data, and OES data for 2000. First the industry-level import
competition, Mi, is calculated as total imports divided by domestic gross output at 3-digit SIC level.5
Then we take a weighted average of this industry-level import competition measure using the weight, Wik,
calculated using the OES data. Wik is the total employment of occupation k in industry i divided by total
employment in occupation k.
3. Summary Statistics
Before turning to the results, we discuss some of the important basic statistics of the data. Table 1
shows the employment and wage growth of the 20 most and least offshorable occupations. It is very clear
5
We also use import penetration as an alternative measure. 9 that the most offshorable occupations have suffered from a drastic decline in their employment between
2001 and 2008. The employment fell by an average of 3.18% per year, while the total employment in the
U.S. grew by 0.78% annually. 14 out of 20 most offshorable occupations displayed a negative
employment growth during this time period. The size of employment reduction varies greatly. For
instance, hand sewers experienced 16.6% annual loss of employment while molding, coremaking, and
casting machine operators went through .31% annual loss. The average wage growth for the 20 most
offshorable occupations (2.03%) is not even half of that of the 20 least offshorable occupations (4.58%).
Only one out of 20 most offshorable occupations shows the wage growth rate above the total average rate
of 3.10% while only one out of 20 least offshorable occupations shows a below-average rate.
Table 2 shows 20 occupations with the highest degree of import competition. Panel (a) ses
imports from all trade partners. Panel (b) uses imports from low income countries whose GDP per capita
was 10% or lower than that of the U.S. in 2000. The list of occupations are quite similar for all imprts and
low-income country imports.6 Another thing to notice is that other than two occupations in both lists, all
occupations are production-related. The first two digits of SOC identify job families, and 51 is production
job family.7 The employment losses in these occupations are remarkable. Note that these are annual
changes. While the overall average employment growth in the U.S. was around 0.78%, the average
employment loss in these import-competing occupations are more than 5.5%. Some occupations suffer a
very large employment loss such as 16.63% loss for timing device assemblers, 16.5% loss for hand
sewers, and 10.72% loss for textile knitting and weaving machine operators.
Tables 3 show the receiving end of occupation transition of the trade-displaced workers. Panel (a)
shows 20 occupations that are the most frequently chosen as an occupation of training under the TAA
program. Panel (b) shows the 30 occupations that are the mostly frequently chosen as the occupation of
reemployment. These two tables reveal a very interesting aspect of the trade-displaced workers
transitions. Panel (a) shows that TAA participants choose a promising occupation for their occupational
6
7
The share of low-income country imports in the total US imports is 17.34% in 2000.
See Park (2012) for the entire liest of 23 SOC job families.
10 skills training. The top training occupations show above-average employment growth during the perioed
observed. The average offshorability also is similar to the overall average. However, panel (b) shows that
the people tend to go back to the
Panel (b) provides very interesting facts about the occupation transition of the workers displaced
due to import competition. First of all, many of the participants went back to production occupations. The
first two digits of 51 indicate production occupations. This is noteworthy since only one occupation out of
the 20 most frequent training occupations is in production. This implies that workers after a period of job
search tend to go back to their old occupation or similar ones. The occupations that they end up certainly
are much more vulnerable to import competition and offshoring again based on the offshorability indices.
The offshorability indices for the reemployment occupations are visibly higher than those of training
occupations.
V. Methodology
Identification of the destination occupations
In order to identify the destination occupations, first we need to identify the source of such a labor
flow. For this, we utilize the offshorability and import competition indices to select the set of occupations
that are the most vulnerable to import competition and offshoring. We choose the top 25% most
offshorable (or import competing) occupations as the source occupations. In PSID and NLSY data, we
identify workers in these occupations for each survey year. Then we identify the workers in this group
who get displaced. As they move into their first job after displacement, we identify the occupation of
reemployment as the destination occupation. Using this information, we build the occupation-level
destination index, Destjt, as follows:
(1a)
.
11 This destination index captures the popularity of occupation j as a reemployment destination among the
trade-displaced workers. We build three separate destination indices using offshorability, import
competition, and low-income country import competition. Table 4 shows the 20 most popular destination
occupations. As one can see, many of the destination occupations are not in offshorable occupations. This
shows that focusing on the trade-affected occupations in the analysis of the wage effect of import
competition and offshoring misses a big part of the general equilibrium wage effect in the U.S. Table 5
provides summary statistics of all indices that are introduced so far.
For worker-level analysis, we focus more on the added supply pressure in each occupation. As
these trade-displaced workers enter occupation j, this adds to the pool of existing workers which could
suppress the wages in that occupation. First, we construct an indicator variable which takes a value of
one when the individual was employed in a highly offshorable occupation in period t-1 and is reemployed
in a not highly offshorable occupation in period t, and zero otherwise (Switchit). We then create the
occupation-level switching variable by summing up the Switch variable across all individuals employed
in industry j at time t (excluding the individuals own contribution to this sum) and dividing by the total
number of individuals employed in industry j at time t. Thus, the worker flow measure for each
occupation is constructed as follows:
(1b)
∑
.
This measure takes values between zero and one. Both the NLSY and PSID are biennial surveys.8 Thus,
it is possible an individual may change employment multiple times between surveys. Furthermore, the
panel is not balanced as individuals may not be employed in every period or observations may be missing
key information needed to perform the estimation. To simplify the analysis, we restrict the estimation
sample to individuals who have usable observations in consecutive surveys. The NLSY collects the most
8
PSID is collected every year between 1968 and 1997, then in odd-numbered years up to 2009. NLSY is collected
in all years from 1979 through 1994, then in even numbered years.
12 job related information for the current or most recent job at the time of the interview (with a subset of this
information collected for up to an additional four jobs per survey). We make use of the information on
the primary job in all worker level analysis using both PSID and NLSY data. Since the data contain
information on 483 occupations, the number of individuals in any given occupation is relatively small.
While the index provides the sample estimates for the true index, the relatively small number of
observations in each occupation could bias the results. Therefore, we estimate the same specifications
with only the observations that are in occupations with at least five workers employed in that period.
A. Wage Effects
We are interested in analyzing the impact of this inflow of workers on the wage growth and
employment growth of the industry. The average wage for each occupation is from the PSID and NLSY
data. We estimate the following equation:
(2a)
WGt is the wage growth rate between the current survey year and the past survey year (two years earlier).
Dt is the year fixed effect. We can run the same estimation for employment growth replacing wage
growth as the dependent variable. If the wage rate is pressed down due to the inflow of labor supply into
that occupation, the wage growth rate will be negatively associated with the destination index. On the
other hand, employment growth is not determined by the labor supply inflow. Where there is excess
supply of labor, the actual size of employment will depend primarily on the demand side. The coefficient
on the employment growth rather informs us about the choice of reemployment occupation. If the
coefficient is positive, the workers are entering the occupation with promising employment projection.
For the worker-level analysis, we examine the effect of having a large number of displaced
workers in your occupation by estimating an augmented Mincerian wage equation.
(2b)
,
13 Where w is the log wage for worker i in occupation j at time t, Off is the offshorability index for the
worker’s current occupation of employment, , X is a vector of individual characteristics including years of
schooling completed, AFQT score percentile (a standard measure of ability employed in studies which
analyze the NLSY data), age, and demographic indicators. Z is a vector of job specific characteristics
including an indicator variable for whether the employment relationship is governed by a collective
bargaining agreement, the log of the individual’s tenure with the current employer, and the log number of
people employed at the individual’s location. T, O and I are year, 2-digit occupation and 2-digit industry
indicators, respectively. While workers are likely to seek reemployment in occupations providing the
highest wages available to them, these workers are likely restricted to relatively low paying occupations.
The occupation indicators are included to address the bias that might be introduced by this sorting. The
model is estimated via ordinary least squares. The sample is restricted to individuals whose hourly wage
was between five and three-hundred dollars, who worked at least twenty hours per week, worked more
than twenty weeks in the past calendar year and for whom we have complete observations in consecutive
surveys (so that the time between surveys is approximately two years).
We estimate a couple of alternative models to address two potential data issues. First, it is
possible the estimated effect of the inflow of displaced workers from highly offshorable to non-highly
offshorable industries may simply reflect the wage pressures of increased labor supply and is not specific
to the fact these workers have come from highly offshorable occupations. To address this concern, we
include a variable measuring the share of workers employed in the occupation at time t who were
employed in one of the least offshorable occupations at time t-1. This variable is analogous to our
primary occupation switching variable and is constructed in similar fashion (see equation 1b). We label
this variable PercentSwitch4. If an influx of displaced workers from highly offshorable occupations
exerts greater downward pressure on wages than general worker inflows, then the coefficient on the
PercentSwitch variable should be greater in absolute terms (i.e. more negative) than the coefficient for
PercentSwitch4. The second concern is that the results may be driven by one or two workers moving into
14 occupations which have a very low total employment in the sample. To address this concern, we estimate
both models using a restricted sample, including only observations if there are at least five respondents
employed in that individuals occupation at time t.
B. Wage Replacement Rates
As a worker is displaced from one job and moves on to the next job, workers often experience a
reduction in their wage rates. If a worker’s skill set is generally applicable, the worker will have more
options and be more likely to find a job with comparable pay. The workers displaced from the low-skilled
manufacturing sector tend to have limited skill sets with narrow work experience. These workers might
have a harder time finding a job with a comparable pay. We explore the hardship of job search after
trade-related displacement by looking at individual wage replacement rate using PSID and NLSY.
For each year t of available data, we identify people who change jobs. For the subset of individuals who
switched occupations between observations, we examine determinants of the change in log wage between
the previous and the current job. We estimate the following specification:
(3)
is the change in the log wage between the current and the previous job. Xit is various individual
characteristics such as age, educational attainment, and gender. Zit is a vector of employer characteristics
and Tt are a vector of year fixed effects.
IV. Results
Occupation - Level Wages and Employment: Direct effect and Destination effect
Table 6 shows the direct effect offshoring ad import competition have on the occupations in terms
of wage and employment growth. Offshorable occupations suffer from a significant adverse wage effect.
One standard deviation increase in offshorability index reduces the annual wage growth rate by 0.40
15 percentage points.9 Import competition from all partners and low income countries have much smaller
effect (reduction of 0.11 and 0.01 percentage points respectively) and the coefficients are not significant.
Employment effects are generally statiscially insignificant. But the impacts are much bigger for import
competition. One standard deviation increase in import competition reduces the employment growth by
2.57 percentage points for all imports and 2.06 percentage points for low income imports.10
Table 7 shows the destination effect of offshoring and import competition on wage and
employment growth. The dependent variables here are the destination indices using offshorability, import
competition, and low-income country import competition. According to panel (a), all destination indices
show a significantly negative impact on wage growth rate. One standard deviation increase in
offshorability destination index reduces the wage growth rate by 0.224 percentage points. Import
competition reduces 0.24 percentage points for all imports and 0.26 percentage points for low-income
country imports. Panel (b) shows that again the employment effects are generally statistically
insignificant. The size of the effects are not negligible. One standard deviation increase in each I ndex
reduces the annual employment growth by 0.40 percentage points for offshorability, 0.33 percentage
points for import competition from all partners, and 0.26 percentage points for low-income country
imports. In both panels (a) and (b), TAA training frequency has essentially no impact on the wage or
employment growth. All coefficients are highly insignificant. However, the signs are consistent with
panel (b) of Table 3 that occupations with high TAA training frequency display a higher employment
growth. This is potentially because TAA trainees choose occupations with promising employment
projection for their training occupations.
Occupation switching and wages
Table 8 presents estimates for the worker level analysis (equation 2b) using the NLSY data11. The
results for the full sample show that a one standard deviation increase in the share of displaced workers in
9
The arithmetic mean of the hourly wage growth rate is 5.99 percent per year. 10
11
The arithmetic mean of the employment growth is 0.09 percent per year.
Table 11 shows the same worker-level analysis on the wages using PSID sample.
16 the occupation (std dev = 0.06) is associated with a decrease in the hourly wage of approximately 1.48
percent. By comparison, a one standard deviation increase in the occupational offshorability index (std
dev= 15.7) results in a 11.4% wage decline.12 Moving from a non-highly offshorable occupation into a
highly offshorable occupation is associated with a 9.8 percent wage decrease. Each of these results
confirm our prior expectations: greater competition from workers leaving highly offshorable industries, a
high degree of offshorability and moving from non-highly offshorable to highly offshorable occupations
are all associated with wage decreases. The coefficients on (most) of the other control variables also have
the expected signs: wages increase with a collective bargaining agreement, tenure, firm size, ability
(AFQT score percentile) and education. Being female or Hispanic is associated with lower wages.
Interestingly, the results indicate being black is associated with higher average wages. The only result
which may seem contradictory to previous findings is the positive coefficient on the black indicator
variable. This result is due to the inclusion of the AFQT score variable and is driven by the female
portion of the sample. Similar results using the NLSY have been documented in previous research (see
Kosteas 2012).
To check whether the coefficient estimate on the occupation switching variable is measuring
wage impacts due specifically to the influx of workers from highly offshorable industries or whether it is
reflecting greater competition for jobs in general, we include the percentage of workers employed in the
occupation who came from the least offshorable occupations (model 2) and an indicator variable for
whether the individual moved into one of the least offshorable occupations. The results show that an
influx of workers from the least offshorable occupations does not have a significant impact on wages in
the receiving occupation. As indicated by the F-test, the difference between the two coefficients is highly
statistically significant, supporting the hypothesis the influx of workers from highly offshorable
occupations exerts downward pressure on wages that does not result from an influx of workers from less
offshorable occupations. However, we do see that workers who moved into a low offshorability
12
One should be careful when interpreting the coefficient for the offshorability index since it likely reflects the
returns to the skills/tasks included in the index.
17 occupation on average experienced a 6.45 percent wage increase. The remaining coefficients are very
similar to those presented in model 1.
Model 3 restricts the sample to those individuals who did not switch occupations from the
previous interview. The coefficient on the share of workers coming from highly offshorable occupations
is still negative, but smaller in magnitude. A one standard deviation increase in the occupation switching
variable is associated with a one percent drop in the hourly wage rate.
Columns 4-6 estimate models 1-3 on the sample resticted to occupations containing at least five
observations in that year. In each case, the coefficient on the occupation switching variable actually
increases in magnitude compared to the estimate on the full sample. Using the results for non-occupation
switchers (column 6), a one standard deviation increase in the share of workers coming from highly
offshorable occupations results in a 1.64 percent wage decrease. Overall, the results show the movement
of workers displaced from highly offshorable occupations into less offshorable occupations exerts
downward wage pressure on workers employed in the receiving occupations.
Occupation switching and hours worked
Table 9 presents results for the average hours worked per week models. The model specifications
and sample construction are the same as for the wage estimates. Here, the results are less robust. The
models estimated over all workers (regardless of their own occupation switching behavior) show a
negative and highly statistically significant relationship between hours worked and the share of workers
coming from highly offshorable occupations (models 1-2). However, the coefficient decreases greatly in
magnitude and is no longer statistically significant when we restrict the sample to only those individuals
who have not changed occupations since the last interview. Thus, the results in models 1-2 seem to be
driven by occupation switchers.
Wage growth
Finally, Table 10 presents the results of our preliminary analysis on wage changes and worker flows.
These models are estimated over the sample of occupation switchers. Moving into a more highly
18 offshorable occupation is associated with a modest wage decrease. Using the results for the restricted
sample (column 2), a one standard deviation increase in the change in the offshorability index (std. dev. =
12.19) results in an average wage decrease of 1.87 percent. This result holds while controlling for
changes in employer size and the employee’s tenure with the firm, in additional to ability, education and
demographic variables. Thus, we can feel fairly confident the estimated wage impact is not simply due to
the loss of tenure. Using the wage ratio between the current and previous job yields mixed results. The
restricted sample estimates (column 4) do show results similar to those for the change in log wage
estimates. These results are preliminary; we have not separated voluntary job leavers from involuntary
job losers. Inclusion of the job leavers is likely to bias our results towards zero since they are more likely
to experience wage increases when switching jobs, while job losers are more likely to experience wage
losses. Furthermore, we have not separated occupation changes that occur without changing employers
(i.e. due to promotion or demotion) from those that occur as a result of changing employers. The next
step is to include controls for whether the individual has changed employers and whether the job loss was
voluntary or involuntary.
V. Conclusions
This paper estimates the wage effects that result from an influx of workers displaced from highly
offshorable occupations. The results show a strong, negative impact of these worker flows on wages for
workers already employed in the receiving occupations. While several papers have investigated the
within-industry or within-occupation wage and employment effects of rising imports and offshoring, this
is the first study to estimate the potential cross-occupation effects of these events. More work is needed
to check the robustness of our primary results and to test whether these effects are uniform or vary across
worker type. We also plan to check these findings using the PSID data.
19 References
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20 Table 1. 20 Most and Least Offshorable Occupations (O*Net data)
RANK SOC Offshorability index occ_title a. Most Offshorable Occupations Employment growth (%) (2001‐2008) Hourly wage growth (%) (2001‐2008) 1 512041 Structural Metal Fabricators and Fitters
121 3.000
2.09
2 516021 Pressers, Textile, Garment, and Related Materials
119 ‐4.537
1.95
3 454021 Fallers 113 ‐4.549
0.68
4 516042 Shoe Machine Operators and Tenders
112 ‐3.875
3.52
5 452041 Graders and Sorters, Agricultural Products
110 ‐5.238
2.51
6 359021 Dishwashers 110 0.934
2.33
7 514193 Metal /Plastic Plating & Coating Machine Setters, Operators, Tenders 110 ‐1.871
2.08
8 516063 Textile Knitting and Weaving Machine Setters, Operators, & Tenders
109 ‐9.257
1.74
9 435041 Meter Readers, Utilities 108 ‐2.190
2.17
1.32
10 519022 Grinding and Polishing Workers, Hand
108 ‐1.275
11 514022 Forging Machine Setters, Operators, and Tenders, Metal & Plastic
108 ‐7.812
1.95
12 373011 Landscaping and Groundskeeping Workers
107 2.520
2.48
13 537061 Cleaners of Vehicles and Equipment
107 1.186
2.46
14 152091 Mathematical Technicians 106 ‐6.386
‐0.76
15 519198 Helpers‐‐Production Workers 105 1.205
1.90
16 516051 Sewers, Hand 105 ‐16.610
2.59
17 452092 Farmworkers and Laborers, Crop, Nursery, and Greenhouse
105 1.980
2.91
18 514072 Metal/Plastic Molding, Coremaking, Casting Machine Setters, Operators
104 ‐0.314
2.53
19 514032 Metal/Plastic Drilling & Boring Machine Tool Setters, Operators, Tenders
104 ‐7.835
1.95
20 513023 Slaughterers and Meat Packers 104 ‐2.738
2.16
108.75 ‐3.18
2.03
AVERAGE Most offshorable occupations
b. Least Offshorable Occupations 1 111011 Chief Executives 16 ‐5.88
5.69
2 532021 Air Traffic Controllers 20 0.76
3.71
3 291064 Obstetricians and Gynecologists 25 1.95
5.25
4 272022 Coaches and Scouts 27 13.42
.
5 119032 Education Administrators, Elementary and Secondary School
27 1.27
.
6 291067 Surgeons 28 ‐0.55
5.87
7 171011 Architects, Except Landscape and Naval
28 3.81
3.61
8 231023 Judges, Magistrate Judges, and Magistrates
28 ‐1.29
3.33
9 532011 Airline Pilots, Copilots, and Flight Engineers
29 ‐2.02
.
10 291062 Family and General Practitioners 29 ‐3.45
5.48
11 211013 Marriage and Family Therapists 29 2.64
4.09
12 291063 Internists, General 29 ‐1.80
4.72
13 291061 Anesthesiologists 30 4.66
5.79
14 119081 Lodging Managers 31 0.63
5.07
15 231021 Administrative Law Judges, Adjudicators, and Hearing Officers
32 ‐11.79
5.05
16 231011 Lawyers 32 1.74
4.36
17 119033 Education Administrators, Postsecondary
33 0.49
4.72
18 211021 Child, Family, and School Social Workers
35 0.96
2.66
19 119151 Social and Community Service Managers
35 1.51
3.98
20 291066 Psychiatrists 35 0.33
4.57
AVERAGE least offshorable occupations
28.9 0.369
4.58
62.5 0.782 3.105 All Occupations 21 Table 2. Occupations with the highest import competition
RANK SOC Occupations a. All Imports Employment growth (%) (2001‐2009) Hourly wage growth (%) (2001‐2009) 1 516042 Shoe Machine Operators and Tenders ‐5.98 3.66 2 516041 Shoe and Leather Workers and Repairers ‐4.08 2.80 3 519071 Jewelers and Precious Stone and Metal Workers ‐2.68 2.53 4 516031 Sewing Machine Operators ‐7.76 2.61 5 512093 Timing Device Assemblers, Adjusters, and Calibrators 6 516092 Fabric and Apparel Patternmakers 7 519141 Semiconductor Processors 8 516051 Sewers, Hand 9 516062 10 ‐16.63 1.94 ‐7.98 4.30 ‐8.55 2.02 ‐16.50 2.89 Textile Cutting Machine Setters, Operators, and Tenders ‐9.16 2.32 512022 Electrical and Electronic Equipment Assemblers ‐5.58 3.13 11 512021 Coil Winders, Tapers, and Finishers ‐10.64 2.60 12 512023 Electromechanical Equipment Assemblers ‐1.55 2.43 13 514051 Metal‐Refining Furnace Operators and Tenders ‐1.45 2.85 14 271022 Fashion Designers 7.17 3.47 15 519195 Molders, Shapers, and Casters, Except Metal and Plastic ‐1.34 1.65 16 499045 Refractory Materials Repairers, Except Brickmasons ‐5.95 2.51 17 512031 Engine and Other Machine Assemblers ‐6.16 2.83 18 519197 Tire Builders 19 519022 Grinding and Polishing Workers, Hand 20 519031 Cutters and Trimmers, Hand ‐5.54 0.91 AVERAGE of most import competing occupations
‐5.53 2.48
b. Low Income Country Imports (10% of US per capita GDP or less) 0.67 1.50 1 516042 Shoe Machine Operators and Tenders ‐5.98 3.66 2 516041 Shoe and Leather Workers and Repairers ‐4.08 2.80 3 516031 Sewing Machine Operators ‐7.76 2.61 4 516092 Fabric and Apparel Patternmakers ‐7.98 4.30 5 516051 Sewers, Hand ‐16.50 2.89 6 519071 Jewelers and Precious Stone and Metal Workers ‐2.68 2.53 7 516062 Textile Cutting Machine Setters, Operators, and Tenders ‐9.16 2.32 7.17 3.47 ‐16.63 1.94 8 271022 Fashion Designers 9 512093 Timing Device Assemblers, Adjusters, and Calibrators 10 519195 Molders, Shapers, and Casters, Except Metal and Plastic ‐1.34 1.65 11 519123 Painting, Coating, and Decorating Workers ‐1.37 1.80 12 519141 Semiconductor Processors ‐8.55 2.02 13 519031 Cutters and Trimmers, Hand ‐5.54 0.91 2.18 14 519194 Etchers and Engravers ‐1.83 15 512022 Electrical and Electronic Equipment Assemblers ‐5.58 3.13 16 514051 Metal‐Refining Furnace Operators and Tenders ‐1.45 2.85 17 516063 Textile Knitting and Weaving Machine Setters, Operators, and Tenders ‐10.72 1.66 18 516021 Pressers, Textile, Garment, and Related Materials ‐5.35 2.00 19 499045 Refractory Materials Repairers, Except Brickmasons ‐5.95 2.51 20 519022 Grinding and Polishing Workers, Hand ‐3.85 1.50 AVERAGE of most low income country import competing occupations ‐5.76 2.44 All Occupations 0.782 3.105 22 3.55 ‐3.85 Table 3. Most Frequent Training/Reemployment Occupations (TAPR data)
RANK SOC % of Training/Reemp occupation Occupations Offshorability Index Employment growth (%) (2001‐2009) Hourly wage growth (%) (2001‐2009) a. Most Frequent Training Occupations 1 533032 Truck Drivers, Heavy and Tractor‐Trailer 5.784
84 1.101
1.989
2 439061 Office Clerks, General 4.239
67 0.578
2.198
3 319092 Medical Assistants 4.140
57 4.558
2.521
4 311012 Nursing Aides, Orderlies, and Attendants
3.572
1.205
3.086
5 499021 Heating, Air Conditioning, Refrigeration Mechanics, Installers
3.344
54.5 3.705
2.483
6 439011 Computer Operators 3.215
57 ‐7.210
2.653
7 151041 Computer Support Specialists 2.143
44 1.439
1.435
8 292061 Licensed Practical and Licensed Vocational Nurses
2.099
41 0.944
3.453
9 436013 Medical Secretaries 2.063
59 4.392
2.422
10 292071 Medical Records and Health Information Technicians
1.927
73 2.440
3.739
11 433031 Bookkeeping, Accounting, and Auditing Clerks
1.668
83 1.264
2.776
12 436011 Executive Secretaries and Administrative Assistants
1.589
64 1.066
3.135
13 493023 Automotive Service Technicians and Mechanics
1.416
61.5 ‐1.094
2.343
14 395012 Hairdressers, Hairstylists, and Cosmetologists
1.382
47 1.086
3.168
15 151021 Computer Programmers 1.263
63 ‐3.440
2.223
16 132011 Accountants and Auditors 1.237
63 3.595
3.734
17 399011 Child Care Workers 1.123
55.5 4.702
2.602
18 514121 Welders, Cutters, Solderers, and Brazers
1.102
81.5 0.373
2.232
19 436014 Secretaries, Except Legal, Medical, and Executive
1.100
62 0.553
2.202
20 472111 Electricians 1.088
72 0.169
2.067
62.58 1.07
2.62
AVERAGE of most frequent training occupations
b. Most Frequent Reemployment Occupations 1 533032 Truck Drivers, Heavy and Tractor‐Trailer 3.629
84 1.10
1.98
2 519198 Helpers‐‐Production Workers 3.046
105 1.20
1.90
3 311012 Nursing Aides, Orderlies, and Attendants
2.683
1.20
3.08
4 319092 Medical Assistants 2.142
57 4.55
2.52
5 516031 Sewing Machine Operators 1.942
99 ‐6.88
2.62
6 519061 Inspectors, Testers, Sorters, Samplers, and Weighers
1.834
71 ‐1.68
2.12
7 439061 Office Clerks, General 1.664
67 0.57
2.19
8 499021 Heating, Air Conditioning, and Refrigeration Mechanics, Installers
1.614
54.5 3.70
2.48
9 537062 Laborers and Freight, Stock, and Material Movers, Hand
1.563
75 1.53
2.19
10 514041 Machinists 1.388
79 1.02
1.87
11 512022 Electrical and Electronic Equipment Assemblers
1.249
83 ‐4.86
2.96
12 412011 Cashiers 1.222
78 0.65
2.15
13 292061 Licensed Practical and Licensed Vocational Nurses
1.210
41 0.94
3.45
14 435081 Stock Clerks and Order Fillers 1.172
78.25 1.55
1.07
15 372011 Janitors and Cleaners, Except Maids and Housekeeping Cleaners
1.013
94 0.49
2.44
16 433031 Bookkeeping, Accounting, and Auditing Clerks
0.968
83 1.26
2.77
17 499042 Maintenance and Repair Workers, General
0.960
68 0.82
2.34
18 514081 Metal/Plastic Multiple Machine Tool Setters, Operators, Tenders
0.958
71 ‐2.07
1.11
19 537064 Packers and Packagers, Hand 0.928
93 ‐2.89
2.50
20 514121 Welders, Cutters, Solderers, and Brazers
0.902
81.5 0.37
2.23
76.96 0.128
2.299
62.5 0.78 3.10 AVERAGE of most frequent reemployment occupations
All Occupations 23 Table 4. The most popular destination occupations of the trade-displaced workers (PSID sample)
Rank Destination index Offshorability Ranking 25% Most Offshorable? Janitors and Cleaners, Except Maids/ Housekeeping Cleaners 0.096 27 Y Construction Laborers 0.093
247 178 COC Occupation title 1 422 2 626 3 913 Driver/Sales Workers 0.091
4 962 Laborers and Freight, Stock, and Material Movers, Hand 0.089
119 Y
64 Y
5 960 Industrial Truck and Tractor Operators 0.078
6 620 FirstLine Supervisors of Construction Trades and Extraction Workers 0.069
281 7 896 Recycling and Reclamation Workers 0.066
157 8 425 Landscaping and Groundskeeping Workers 0.062
91 9 775 Fiberglass Laminators and Fabricators 0.055
117 92 Y
10 562 Stock Clerks, Sales Floor 0.051
Y
11 470 FirstLine Supervisors of Retail Sales Workers 0.049
375 344 105 Y
Y
12 770 FirstLine Supervisors of Production and Operating Workers 0.046
13 402 Cooks, Fast Food 0.046
14 423 Maids and Housekeeping Cleaners 0.045
69 311 15 401 FirstLine Supervisors of Food Preparation and Serving Workers 0.044
16 734 Maintenance and Repair Workers, General 0.043
248 317 187 17 476 Retail Salespersons 0.042
18 472 Cashiers 0.041
19 420 FirstLine Supervisors of Housekeeping and Janitorial Workers 0.040
219 20 360 Home Health Aides 0.040
198  COC refers to the Census Occupation Code.
Table 5. Summary Statistics for all indices
Mean Source Indices Max Offshorability 63.43 17.20 121 Import Competition – all imports 0.066 0.203 3.57 Import Competition – low income country imports 0.017 0.107 2.31 Offshorability 0.198 0.470 4.73 Import Competition – all imports 0.198 0.438 4.87 Import Competition – low income country imports 0.198 0.426 4.87 TAA Training Frequency 0.174 0.571 6.15 Destination Indices 24 Standard Dev. Table 6: Occupation-level Analysis: Direct Effect
a. Wage Growth Offshorability Index Import Competition I II III I II III ‐0.023** ‐0.023** ‐0.885 ‐0.565 ‐0.564 (0) (0.002) (0.002)
(0.094)
(0.279)
(0.279)
0.018 0.006 0.006 0.053 0.016 0.016 (0.529) (0.84) (0.842)
(0.053)
(0.573)
(0.572)
0.179* 0.178* 0.294** 0.294** (0.013) (0.014)
(0)
(0)
Wage Growth t‐1 Average Edu (yrs) Unionized 2007 2009 Cons Number of obs Prob > F R‐squared Adj R‐squared Import Competition ‐0.032** Index Low Income Country 0.002 ‐0.002 (0.957)
(0.965)
1.242** 1.261** 1.262** 1.238** 1.263** 1.263** (0) (0) (0)
(0)
(0)
(0)
0.233 0.3 0.3 0.261 0.358 0.357 (0.375) (0.25) (0.25)
(0.328)
(0.174)
(0.175)
7.465** 4.529** 4.524** 5.259** 1.486 1.489 (0) (0) (0.001)
(0)
(0.075)
(0.076)
1167 0 0.0453 0.042 1151 0 0.052 0.0478 1151
0
0.052 0.047
1154
0
0.0258 0.0225
1138
0
0.0449 0.0407
1138
0
0.0449 0.0398
I II III ‐0.647 ‐0.080 ‐0.093 (0.517) (0.935)
(0.935)
0.054 0.015 0.015 (0.05) (0.594)
(0.594)
0.302** 0.302** (0)
(0)
‐0.002 (0.968)
1.24** 1.266** 1.266** (0) (0)
(0)
0.258 0.356 0.356 (0.334) (0.176)
(0.177)
5.207** 1.345 1.348 (0) (0.105)
(0.106)
1154 0 0.0238 0.0204 1138
0
0.0439 0.0397
1138
0
0.0439 0.0388
b. Employment Growth Offshorability Index Import Competition Import Competition I II III Index 0.037 ‐0.035 ‐0.037 (0.89) (0.915) (0.911)
(0.63)
(0.591)
(0.591)
(0.697) (0.664)
(0.665)
Emp Growth t‐1 ‐0.004 ‐0.004 ‐0.004 ‐0.004 ‐0.005 ‐0.005 ‐0.004 ‐0.005 ‐0.004 (0.879) (0.882)
(0.88)
(0.874)
(0.877)
(0.882) ‐1.227 ‐1.197 ‐1.257 ‐1.218 (0.707)
(0.639)
(0.652)
(0.882) Average Edu (yrs) (0.7) Unionized Low Income Country I II III ‐11.168 ‐12.661 ‐12.666 ‐0.242 I II III ‐17.055 ‐19.245 ‐19.22 (0.876)
(0.879)
‐1.195 ‐1.157 (0.655)
‐0.25 (0.907)
‐26.037* ‐25.828* (0.905)
‐26.084* ‐25.832* (0.907)
‐25.766* (0.025) (0.026) (0.026)
(0.026)
(0.026)
(0.026)
(0.026) (0.026)
(0.026)
2009 ‐33.32** ‐33.50** ‐33.55** ‐33.36** ‐33.50** ‐33.54** ‐33.36** ‐33.51** ‐33.55** (0.003) (0.004) (0.004)
(0.004)
(0.004)
(0.004)
(0.004) (0.004)
(0.004)
Cons 26.734 48.007 48.578 29.858** 47.001 47.37 29.41** 45.656 46.012 (0.16) (0.406) (0.402)
(0)
(0.205)
(0.203)
(0) (0.215)
(0.213)
1187 0.0505 0.008 0.0046 1171 0.0907 0.0081 0.0039 1171 0.1468 0.0081 0.003 1175 0.0493 0.0081 0.0047 1159 0.0865 0.0083 0.004 1159 0.1408 0.0083 0.0032 1175 0.051 0.008 0.0046 1159 0.0898 0.0082 0.0039 1159 0.1456 0.0082 0.0031  P-values are in the parentheses.
25 ‐26.058* (0.668)
‐0.244 2007 Number of obs Prob > F R‐squared Adj R‐squared ‐26.011* ‐26.064* ‐26.089* Table 7: Occupation-level Analysis: Destination Effect
a. Wage Growth From Offshorable Occupations Desination II III Number of obs Prob > F R‐squared Adj R‐squared II III I II III TAA Training Frequency I II III ‐0.461* ‐0.478* ‐0.582* ‐0.519* ‐0.544* ‐0.625* ‐0.583* ‐0.610** 0.065 0.087 0.078 (0.031) (0.025)
(0.047)
(0.021)
(0.016)
(0.04)
(0.012)
(0.009)
(0.779)
(0.173)
(0.653) 0.011 0.016 0.016 0.015 0.017 0.015 0.016 0.017 0.016 0.023 0.025 0.025 (0.735) (0.568) (0.576)
(0.64)
(0.55)
(0.558)
(0.627)
(0.546)
(0.554)
(0.482)
(0.366)
(0.368) 0.26** 0.274** 0.276** 0.295** (0)
(0)
(0)
(0) 0.262** 0.015 0.277** 0.015 0.278** 0.015 0.296** 0.004 (0) (0.753)
(0)
(0.752)
(0)
(0.745)
(0)
(0.928) 1.565** 1.272** 1.273** 1.559** 1.268** 1.269** 1.557** 1.267** 1.268** 1.595** 1.281** 1.281** (0) (0) (0)
(0)
(0)
(0)
(0)
(0)
(0)
(0)
(0)
(0) 0.479 0.301 0.304 0.469 0.295 0.298 0.469 0.295 0.299 0.535 0.282 0.283 (0.142) (0.25) (0.245)
(0.15)
(0.258)
(0.253)
(0.15)
(0.258)
(0.253)
(0.105)
(0.285)
(0.284) 5.3** 1.976* 1.96* 5.269** 1.79* 1.771* 5.273** 1.788* 1.768* 5.054** 1.323 1.317 (0) (0.021) (0.023)
(0)
(0.032)
(0.034)
(0)
(0.031)
(0.034)
(0)
(0.111)
(0.114) 1336 0 0.0285 0.0256 1150 0 0.0472 0.0431 1150
0
0.0474
0.0424
1336
0
0.0276
0.0247
1150
0
0.0483
0.0441
1150
0
0.0484
0.0434
1336
0
0.0277
0.0247
1150
0
0.0487
0.0445
1150
0
0.0488
0.0438
1308
0
0.0222
0.0192
1136
0
0.0437
0.0395
1136 0 0.0437 0.0387  P-values are in the parentheses.
26 Low‐Income Country Import Comp (0.021) Unionized Cons I ‐0.631* Average Edu (yrs) 2009 Wage Growth t‐1 2007 From Import‐competing Occupations I b. Employment Growth From Offshorable Occupations
Destination Emp Growth t‐1 I II III ‐0.465 0.847 (0.619) (0.607) From Import Competing Occupations
I II III 0.845 ‐1.458* ‐0.746 (0.607) (0.671) (0.641) From Low‐Income Country Import Competition
I II III ‐0.751 ‐1.252 ‐0.598 (0.642) (0.695) (0.663) TAA Training Frequency
I II III ‐0.603 0.409 0.595 0.592 (0.664) (0.515) (0.483) (0.484) 0 0 0 0.001 0 0 0.001 0 0 0 0 0 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) 1.407** 1.406** 1.311** 1.308** 1.321** 1.317** 1.376** 1.372** (0.173) (0.174) (0.168) (0.169) (0.168) (0.168) (0.166) Average Edu (yrs) Unionized 0.011 0.024 (0.13) 0.961 (0.13) 0.963 0.969 (0.13) (0.74) (0.739) (0.74) (0.739) (0.739) (0.74) (0.739) (0.739) (0.74) (0.739) (0.738) (0.738) 2009 ‐5.963** ‐6.311** ‐6.306** ‐5.968** ‐6.297** ‐6.288** ‐5.967** ‐6.297** ‐6.289** ‐6.252** ‐6.434** ‐6.423** (0.731) (0.729) (0.731) (0.73) (0.729) (0.731) (0.731) (0.73) (0.732) (0.731) (0.727) (0.729) Cons 1.83** ‐17.10** ‐17.12** 2.03** ‐15.44** ‐15.48** 1.99** ‐15.60** ‐15.64** 1.85** ‐16.47** ‐16.53** (0.539) (2.406) (2.417) (0.541) (2.332) (2.344) (0.542) (2.324) (2.336) (0.532) (2.285) (2.298) 1342 0 0.0765 0.0737 1160 0 0.1357 0.1319 1342 0 0.0794 0.0766 1160 0 0.1352 0.1315 1342 0 0.0783 0.0756 1160 0 0.1348 0.1311 1160 0 0.1348 0.1303 1314 0 0.0833 0.0805 1146 0 0.1408 0.137 1146 0 0.1408 0.1363 1160 0 0.1352 0.1307  P-values are in the parentheses.
27 1.111 0.963 (0.13) 1.123 1160 0 0.1357 0.1312 1.11 (0.167) 0.032 2007 Number of obs Prob > F R‐squared Adj R‐squared 0.958 0.023 0.968 1.038 0.889 0.896 Table 8. Wage impact for workers in occupations receiving displaced workers (NLSY)
Share(from top 25 Offshorability): PS1 I Full Sample II ‐0.182** ‐0.250** ‐0.171* (0.0639) (0.0656) (0.0739) ‐0.0244 0.0187 (0.055) (0.0689) Share(from bottom 25 Offshorability): PS4 Switched into Top25 Switched into Bottom 25 Offshorability Index III ‐0.101** ‐0.0937** (0.0189) (0.0192) 0.0645** (0.0189) ‐0.0050 ‐0.0073 ‐0.0074 III ‐0.347** ‐0.348** (0.0829) (0.083) ‐0.273** (0.0986) ‐0.0903 0.0462 (0.0729) (0.0878) ‐0.0918** ‐0.0871** (0.0195) (0.0196) 0.0614** (0.0198) ‐0.0075 ‐0.0074 ‐0.0076 (0.0004) (0.0004) (0.0005) (0.0005) (0.0005) (0.0006) Collective bargaining 0.0655** 0.0667** 0.0794** 0.0688** 0.0687** 0.0764** (0.0163) (0.0164) (0.0186) Log of tenure with employer 0.0574** 0.0616** 0.0560** (0.0032) (0.0032) (0.0046) Log(No. local employed) 0.0365** 0.0369** 0.0377** (0.0021) (0.0021) (0.0024) AFQT score percentile 0.0030** 0.0031** 0.0029** (0.0002) (0.0002) (0.0002) Highest grade completed 0.0504** 0.0520** 0.0543** Age (0.0021) (0.0021) (0.0025) ‐0.0026 ‐0.0026 0.0007 (0.0016) (0.0016) (0.0019) Female ‐0.187** ‐0.196** ‐0.174** (0.0091) (0.0091) (0.0108) Black 0.0425** 0.0435** 0.0326** (0.0105) (0.0106) (0.0124) ‐0.0309** ‐0.0330** ‐0.0420** (0.0093) (0.0093) (0.0112) (0.0169) (0.0192) 0.0608** 0.0541** (0.0033) (0.0033) (0.0046) 0.0370** 0.0370** 0.0378** (0.0021) (0.0021) (0.0025) 0.0032** 0.0032** 0.0029** (0.0002) (0.0002) (0.0002) 0.0517** 0.0515** 0.0543** (0.0022) (0.0022) (0.0026) ‐0.0025 ‐0.0025 0.0010 (0.0017) (0.0017) (0.0019) ‐0.197** ‐0.197** ‐0.173** (0.0094) (0.0094) (0.0111) 0.0428** 0.0431** 0.0345** (0.0109) (0.0108) (0.0127) ‐0.0286** ‐0.0374** (0.0096) (0.0096) (0.0115) Test S1=S4 6.77** 3.48+ 5.14* Observations 12070 12087 8609 11443 11443 R‐squared 0.6125 0.6038 0.6085 0.6057 0.6062  Log hourly wage is the dependent variable
 Robust standard errors are in parentheses.
 Models 1-2 use the full estimation sample.
 Models 4-6 use observations only for individuals in occupations with at least five observations in that year.
 Models 3 and 6 only use observations for individuals who did not switch occupations between observations.
 All models contain year and 2-digit industry and occupation indicator variables.
5.63* 8169 0.6115 28 (0.0169) 0.0594** ‐0.0290** Hispanic Restricted Sample I II Table 9. Hours employed for workers in occupations receiving displaced workers (NLSY)
Share(from top 25 Offshorability): PS1 Full Sample I II ‐4.004** ‐4.167** (1.441) (1.933) (1.933) (2.155) ‐1.6 ‐1.182 (2.113) (1.968) (1.908) Collective bargaining Log of tenure with employer Log(No. local employed) AFQT score percentile Highest grade completed Age Female Black Hispanic Test S1=S4 Observations R‐squared 





(1.575) 1.607 2.943 (1.641) ‐0.52 ‐0.4 (0.583) (0.584) (0.509) (0.534) ‐0.0563** ‐0.0606** ‐0.0660** ‐0.0588** ‐0.0590** ‐0.0627** (0.011) (0.011) (0.013) (0.012) (0.012) (0.013) 0.682 0.692 0.667 0.723 0.722 0.677 (0.435) (0.436) (0.499) (0.449) (0.45) (0.513) ‐0.190* ‐0.145+ ‐0.377** ‐0.172* ‐0.133 ‐0.361** (0.081) (0.082) (0.119) (0.084) (0.085) (0.122) 0.222** 0.221** 0.218** 0.190** 0.190** 0.191** (0.049) (0.049) (0.055) (0.05) (0.05) (0.056) ‐0.0158** ‐0.0163** ‐0.0159** ‐0.0174** ‐0.0177** ‐0.0172** (0.005) (0.005) (0.006) (0.005) (0.005) (0.006) 0.202** 0.197** 0.197** 0.232** 0.228** 0.241** (0.053) (0.053) (0.061) (0.055) (0.055) (0.062) 0.117** 0.116** 0.0924+ 0.115** 0.115** 0.0860+ (0.042) (0.042) (0.048) (0.043) (0.043) (0.049) ‐4.185** ‐4.180** ‐3.938** ‐4.149** ‐4.136** ‐3.921** (0.228) (0.229) (0.266) (0.236) (0.236) (0.274) ‐0.637* ‐0.637* ‐0.371 ‐0.712** ‐0.705** ‐0.419 (0.261) (0.261) (0.298) (0.267) (0.267) (0.303) 0.462+ 0.470+ 0.302 0.489+ 0.498+ 0.378 (0.263) (0.263) (0.306) (0.27) (0.269) (0.313) 12024 0.1415 6.78** 12024 0.1427 3.74* 8577 0.1566 11387 0.1418 1.04 11387 0.1428 0.02 8140 0.1564 ‐0.64 ‐0.527 (0.566) (0.567) Switched into Bottom 25 Offshorability Index (1.444) 1.713** 1.623** Hours worked per week is the dependent variable
Robust standard errors are in parentheses.
Models 1-2 use the full estimation sample.
Models 4-6 use observations only for individuals in occupations with at least five observations in that year.
Models 3 and 6 only use observations for individuals who did not switch occupations between observations.
All models contain year and 2-digit industry and occupation indicator variables.
29 III ‐1.545 Share(from bottom 25 Offshorability): PS4 Switched into Top25 III ‐2.331 Restricted Sample I II ‐4.384* ‐4.469* Table 10. Replacement wage for workers switching occupations (NLSY)
Change in offshorability index Change in Log Wage Full Restricted ‐0.0013* ‐0.0015** (0.0005) (0.0004) 0.0399** 0.0390** (0.0067) (0.005) 0.0107* 0.0116** (0.0051) (0.0037) ‐0.0010* ‐0.0002 (0.0005) (0.0003) 0.0321** 0.0024 (0.0078) (0.0034) Age ‐0.0059 ‐0.0056* (0.0044) (0.0029) Female 0.0444* 0.0128 (0.0186) (0.0123) 0.0080 ‐0.0051 (0.0278) (0.0182) ‐0.0089 ‐0.0080 (0.0245) (0.0168) 3318 0.0393 3283 0.0462 Change in log tenure with employer Change in log number of employees AFQT score percentile Highest grade completed Black Hispanic Observations R‐squared  Robust standard errors are in parentheses.
 All models contain year indicators.
30 Wage Replacement Ratio Full Restricted 0.0252 ‐0.0015** (0.026) (0.0005) 0.4000 0.0244** (0.415) (0.0057) ‐0.0428 0.0156** (0.0868) (0.0045) 0.0086 ‐0.0006 (0.0333) (0.0004) 0.630** 0.0095* (0.211) (0.0044) 0.0091 ‐0.0021 (0.0957) (0.0035) 1.684* 0.0300* (0.855) (0.0149) 0.5590 ‐0.0004 (0.693) (0.0212) ‐0.1190 ‐0.0100 (0.469) (0.0212) 3318 0.0089 3194 0.0273 Table 11. Log Weekly Earnings (PSID Sample)
All Sample All Observations II I Share from top 25% offshorable occs I ‐0.1064* ‐0.2258** ‐0.2995** ‐0.2848** ‐0.2565** ‐0.2497** ‐0.3668** ‐0.344** (0.051) (0.0512) (0.0492) (0.1062) (0.1065) (0.0657) (0.0657) (0.0642) (0.1211) 0.2124** 0.2903** 0.1703 0.3376** 0.4093** 0.2961** (0.0477) (0.0461) (0.0886) (0.0578) (0.0565) (0.0999) 0.0723** 0.0576** 0.0694** (0.0171) (0.0171) (0.0177) Switched into bottom 25% Switched from Top 25% Switched from bottom 25% Restricted Sample All Observations Non‐switch Observations II III I II ‐0.1175* Share from bottom 25% offshorable occs Switched into Top 25% III Non‐switch Observations I II ‐0.32** (0.1212) 0.0528** (0.0177) ‐0.0821** ‐0.0978** (0.0156) (0.0161) ‐0.1496** ‐0.13** ‐0.1494** ‐0.127** (0.0151) (0.0152) (0.0153) (0.0155) 0.1079** 0.1187** (0.0142) (0.0145) ‐0.009** ‐0.0087** ‐0.0076** ‐0.0103** ‐0.0099** ‐0.0093** ‐0.0087** ‐0.0076** ‐0.0103** ‐0.0096** (0.0005) (0.0005) (0.0005) (0.0008) (0.0009) (0.0005) (0.0005) (0.0005) (0.0009) (0.0009) ‐0.2594** ‐0.2568** ‐0.2562** ‐0.2341** ‐0.234** ‐0.2579** ‐0.2557** ‐0.2548** ‐0.2393** ‐0.2399** (0.0114) (0.0114) (0.0114) (0.0202) (0.0202) (0.0118) (0.0117) (0.0118) (0.0208) (0.0208) Age 0.0074** 0.0072** 0.0074** 0.003** 0.003** 0.0074** 0.0072** 0.0074** 0.0032** 0.0031** (0.0003) (0.0003) (0.0003) (0.0006) (0.0006) (0.0004) (0.0004) (0.0004) (0.0006) (0.0006) Edu: High School Grad 0.0928** 0.0927** 0.0934** 0.0928** 0.092** 0.0897** 0.0892** 0.0899** 0.0928** 0.0914** (0.0124) (0.0123) (0.0124) (0.0205) (0.0205) (0.0127) (0.0126) (0.0127) (0.021) (0.021) Edu: Some College 0.1331** 0.1308** 0.1382** 0.1523** 0.1518** 0.1302** 0.1263** 0.1342** 0.1569** 0.1553** (0.0132) (0.0132) (0.0132) (0.0222) (0.0222) (0.0136) (0.0135) (0.0136) (0.0227) (0.0227) Edu: College Grad 0.2766** 0.2659** 0.2818** 0.2752** 0.2727** 0.2779** 0.2652** 0.2822** 0.2825** 0.2778** (0.0158) (0.0158) (0.0159) (0.0259) (0.0259) (0.0163) (0.0163) (0.0163) (0.0266) (0.0266) Edu: Post Grad 0.3105** 0.3008** 0.3168** 0.2866** 0.2848** 0.3039** 0.2917** 0.3087** 0.286** 0.2822** (0.0198) (0.0198) (0.0198) (0.0311) (0.0311) (0.0205) (0.0205) (0.0205) (0.0321) (0.0321) constant 7.2515** 7.2343** 7.1727** 7.7696** 7.7408** 6.3262** 6.195** 6.1473** 7.4697** 7.3998** (0.1898) (0.1895) (0.1902) (0.5328) (0.5329) (0.2837) (0.2833) (0.2845) (0.5401) (0.5403) 18053 0 0.4002 0.3961 18053
0
0.4036
0.3994
18053
0
0.3981
0.394
6315
0
0.4503
0.4396
6315
0
0.4506
0.4399
17064
0
0.4038
0.3997
17064
0
0.4083
0.4041
17064
0
0.4022
0.3981
6042
0
0.4516
0.4409
6042 0 0.4524 0.4417 Offshorability Index Female Number of obs Prob > F R‐squared Adj R‐squared  All models contain race, year, state, 1-digit SIC industry, and 2-digit Occupation family indicators.
Robust standard errors are in parentheses.
 Restricted Samples utilizes the observations that are in occupations with at least 5 total employment. Non-switching observations are the people who stayed in the same
occupation from the previous survey year.
31