Weekly Earnings in Export-Intensive U.S. Services Industries 

by David Riker and Brandon Thurner 

Office of Competition and Economic Analysis 

March 2011 

Manufacturing and Services Economics Briefs are produced by the Office of Competition and Economic Analysis of 

the International Trade Administration’s Manufacturing and Services unit. A complete list of reports, along with 

links to other trade data and analysis, is available at www.trade.gov/mas/ian. 
Manufacturing and Services Economics Brief No. 4 

ii Weekly Earnings in Export-Intensive U.S. Services Industries – March 2011

Acknowledgements 

We appreciate the helpful comments from Justin Pierce, William Powers, and the ERS division 

in the Department of Commerce. The views expressed are those of the authors and do not 

necessarily reflect those of the U.S. Department of Commerce, the Secretary of Commerce, the 

International Trade Administration, or the Undersecretary for International Trade. 

 
International Trade Administration 

Weekly Earnings in Export-Intensive U.S. Services Industries iii 

Table of Contents 

 

Acknowledgements ......................................................................................................................... ii

Table of Contents ........................................................................................................................... iii

Executive Summary ........................................................................................................................ v

1. Introduction ................................................................................................................................ 1

2. We combine data on individual worker characteristics with data on international trade in 

services ....................................................................................................................................... 2

3. There are significant differences in earnings and demographics among the services industries6

4. Regression analysis provides more precise estimates ................................................................ 7

5. The export earnings premium can have important policy implications ................................... 15

6. Conclusions .............................................................................................................................. 16

References ..................................................................................................................................... 17

 
Manufacturing and Services Economics Brief No. 4 

iv Weekly Earnings in Export-Intensive U.S. Services Industries – March 2011

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International Trade Administration 

Weekly Earnings in Export-Intensive U.S. Services Industries v 

Executive Summary 

In this paper, we analyze the weekly earnings of workers in the U.S. services sector. We 

estimate the premium in labor earnings in U.S. services industries that are export-intensive. The 

calculations combine worker-level data on weekly earnings, educational attainment, occupational 

categories, and other demographic characteristics from the Current Population Survey with 

industry-level data on U.S. exports of services from the Bureau of Economic Analysis. We 

estimate that workers in export-intensive services industries earn 15 to 20 percent more than 

comparable workers in other industries. 

 
Manufacturing and Services Economics Brief No. 4 

vi Weekly Earnings in Export-Intensive U.S. Services Industries – March 2011

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International Trade Administration 

Weekly Earnings in Export-Intensive U.S. Services Industries 1 

1. Introduction 

There are a growing number of econometric studies that estimate an export wage 

premium in U.S. manufacturing industries, including Bernard and Jenson (1999) and Bernard, 

Jensen, Redding, and Schott (2007). These studies define the export wage premium as the 

percentage difference in the average wages of workers in plants that export manufactured goods 

and the average wages of workers in plants that do not export. Bernard, Jensen, Redding, and 

Schott (2007) report an export wage premium in 2002 of 6 to 17 percent, depending on whether 

the authors calculate the premium from within-industry variation or also include betweenindustry variation. While the authors are able to link specific plants to exporting, their 

calculation does not control for the level of education and experience of the individual workers 

within each plant. In addition, these estimates are limited to the manufacturing sector. 

It is common for economic studies of international trade and U.S. labor markets to focus 

on the manufacturing sector rather than the much larger services sector.1 One reason is that data 

on international trade in services are less detailed and probably less reliable. A second reason is 

that services are commonly portrayed as non-traded. For example, economics textbooks often 

draw a stark contrast between non-traded haircuts and trade in manufactured goods like steel. In 

fact, for many services, international trade does not require face-to-face interaction. Many U.S. 

services industries have measurable exports, and their volumes are large and growing. In total, 

U.S. exports of private services accounted for $410.8 billion in 2006, $478.1 billion in 2007, and 

$527.8 billion in 2008. 

In this paper, we focus on the U.S. services sector, and specifically on the weekly 

earnings of workers in services industries. We analyze worker-level data on weekly earnings, 

educational attainment, occupational categories, and other demographic characteristics from the 

Current Population Survey (CPS) in 2006, 2007, and 2008 and industry-level data on U.S. 

services exports from the Bureau of Economic Analysis (BEA). We find that workers in exportintensive services industries earn, on average, 15 to 20 percent more than comparable workers in 

other U.S. services industries. 

 

1

 Jensen (2009) estimates that the services sector accounts for approximately 85 percent of U.S. employment. 
Manufacturing and Services Economics Brief No. 4 

2 Weekly Earnings in Export-Intensive U.S. Services Industries

Our estimates update and corroborate the estimated earnings premium for tradable 

services industries in Jensen and Kletzer (2006, 2008). Jensen and Kletzer also analyze 

individual-level data and control for worker characteristics like educational attainment.2

 

However, instead of directly measuring U.S. services trade, they infer whether services industries 

are tradable based on the geographic concentration of each industry’s employment within the 

United States. Despite the differences in methodology, the set of industries that Jensen and 

Kletzer classify as tradable is essentially the same as the set that we classify as export-intensive. 

Many of these are in the export-intensive major business services sectors highlighted in Jensen 

(2009). Despite the difference in the time period that Jensen and Kletzer examine, the export 

earnings premium that they estimate is similar to our estimates for more recent years. They find 

that workers in tradable services industries earned approximately 15 percent more, even after 

controlling for differences in the educational attainment of the individual worker. In addition, 

they find that earnings are almost 20 percent higher in tradable industries within the professional 

services sector. 

2. We combine data on individual worker characteristics with data 

on international trade in services 

We analyze the weekly earnings of approximately 430,000 U.S. workers who responded 

to the CPS in 2006, 2007, or 2008. These data are provided in the NBER’s Merged Outgoing 

Rotation Group extracts. The sample is limited to individuals employed in U.S. services 

industries.3

 The measure of each worker’s compensation is his or her usual weekly earnings, 

converted to constant 2008 dollars. Our statistical analysis takes into account individual 

characteristics that are likely to affect a worker’s earnings, including educational attainment, age, 

occupational category, race, sex, and state of residence. 

The data on U.S. private services exports are published by the Bureau of Economic 

Analysis (BEA) in U.S. International Services: Cross-Border Trade 1986-2008, and Services 

 

2

 They analyze data from the Public Use Micro-Sample of the 2000 Census. 

3

 We define the services sector as Census Industrial Classification codes 0770, 0570-0690, and 4069-9291 in the 

CPS data. 
International Trade Administration 

Weekly Earnings in Export-Intensive U.S. Services Industries 3 

Supplied Through Affiliates, 1986-2007. The data are based on responses to BEA surveys. 

Responding companies reported the dollar value of their sales of selected services and intangible 

assets to foreign entities. BEA started publishing significantly more disaggregated services trade 

data in 2006, and therefore we restrict our econometric analysis of earnings in 2006, 2007, and 

2008. There are 26 distinct types of private services reported for these three years (Table 1). 

We construct a concordance between the industry categories of the BEA services exports 

and the NAICS codes used in the 2007 Economic Census and the Current Employment Survey 

(CES) and to the Census Industry Classification (CIC) codes assigned to individuals in the CPS 

data. In most cases, the concordance is straightforward. For example, the Advertising Services 

category in the BEA data corresponds to NAICS 5818. In other cases, the concordance is more 

ambiguous. For example, there is not a specific match for BEA’s Other Services category. The 

BEA categories that we could clearly match account for $469.2 billion in private U.S. services 

exports in 2008, which is 89.2 percent of total private U.S. services exports in that year. The 

remaining categories cannot be assigned to a specific CIC code and therefore are not matched to 

the CPS data. The largest examples of unmatched export categories are a miscellaneous Other 

Services category (4.7 percent of the total), Installation and Maintanence (2.1 percent of the 

total) and Operational Leasing (1.7 percent of the total). 

For each of the CIC industries that we matched to the BEA services export data, we 

calculated two measures of export intensity, the dollar value of exports per worker and the ratio 

of the value of exports to the total revenues for the corresponding NAICS industry (Table 2). 

We classify industries as Export-Intensive Services Industries (EISIs) if the dollar value of the 

industry’s exports per employee was greater than $5,000 in 2007 or if the ratio of the value of 

exports to the total revenues of the industry was greater than one percent. The classification of 

industries as EISIs is the same under either of these two criteria. 
Manufacturing and Services Economics Brief No. 4 

4 Weekly Earnings in Export-Intensive U.S. Services Industries

Table 1: U.S. Private Services Exports in Millions of Dollars 

Industry 2006 2007 2008 

Accounting auditing and bookkeeping $717 $883 $1,399 

Advertising $3,773 $4,041 $4,019 

Architecture and engineering (1) $8,605 $9,186 $9,964 

Computer services $10,079 $11,638 $12,599 

Construction $739 $1,021 $1,679 

Education $14,647 $15,956 $17,796 

Film and television tape rentals $12,823 $14,422 $13,598 

Financial services $47,647 $61,393 $60,190 

Installation, maintenance, and repair $7,673 $8,946 $9,661 

Insurance services $9,445 $10,184 $10,756 

Legal services $5,256 $6,409 $7,269 

Management and consulting services $21,421 $25,331 $26,942 

Medical services $2,166 $2,306 $2,467 

Mining $721 $2,170 $3,080 

Operational leasing $6,183 $7,363 $7,942 

Other business professional and tech $920 $1,160 $1,352 

Other services $7,737 $8,241 $8,500 

Research and development and testing $12,810 $14,293 $17,139 

Royalties – Industrial $32,415 $36,791 $40,130 

Royalties – Other $38,311 $47,033 $51,469 

Sports and performing arts $431 $635 $755 

Telecommunications $7,105 $8,043 $9,163 

Trade-related services $3,611 $5,216 $6,112 

Training Services $1,284 $1,240 $1,414 

Transportation $68,261 $77,186 $90,568 

Travel (2) $85,789 $97,050 $110,090 

Total Private Services $410,805 $478,136 $527,786 

Source: BEA. U.S. International Services: Cross-Border Trade 1986-2008, and Services Supplied Through 

Affiliates, 1986-2007. 

Notes: (1) this category includes architectural, engineering and other technical services as well as industrial 

engineering; (2) this category includes passenger fares and other transportation services. 
International Trade Administration 

Weekly Earnings in Export-Intensive U.S. Services Industries 5 

Table 2: Export-Intensive Services Industries 

Industry 2007 NAICS 

Codes 

Services Exports 

per Employee 

Exports as a 

Share of 

Revenues 

Classified as 

EISI? (1) 

Accounting auditing 

and bookkeeping 

5412 $943 0.8% No 

Advertising 5418 $8,572 4.5% Yes 

Architecture and 

engineering 

5413 $6,414 3.6% Yes 

Computer services 5182 $43,458 17.0% Yes 

Construction 23 $134 0.2% No 

Education 6113 $10,762 32.5% Yes 

Financial services 5211-5239 $16,431 3.0% Yes 

Insurance services 5241-5242 $4,415 0.6% No 

Legal services 5411 $5,453 2.5% Yes 

Management and 

consulting services 

5416 $26,589 19.2% Yes 

Medical services 6211-6239 $178 0.1% No 

Mining 21 $3,269 0.1% No 

Research and development 

and testing (2) 

5417 $23,735 14.7% Yes 

Sports and performing arts 7111-7115 $1,568 0.8% No 

Telecommunications 5171-5179 $7,804 1.6% Yes 

Transportation 4811-4889 $23,425 17.2% Yes 

Travel 5615, 7211 $47,583 44.8% Yes 

* Note: (1) We classified services industries as EISI if the dollar value of service exports per employee was greater 

than $5,000 in 2007 and exports accounted for more than one percent of total revenues. The same set of industries 

meet both of these alternative criteria. (2) In this table, we did not include royalties in the exports for research and 

development and testing, to be conservative. If we had, research and development and testing would still be 

classified as an EISI.
Manufacturing and Services Economics Brief No. 4 

6 Weekly Earnings in Export-Intensive U.S. Services Industries

3. There are significant differences in earnings and demographics 

among the services industries 

Having separated the industries into two groups, we examine whether there are 

significant different in average earnings of the groups. Usual weekly earnings are approximately 

33% higher for workers in the EISIs than for their counterparts in the other services industries 

(Table 3). To determine whether this difference is statistically significant, we compare the 

difference in the group means to the standard errors of the group means. A formal t-test 

indicates that the group means are statistically different at the one percent significance level. 

Likewise, there are significant differences in the group means of the educational 

attainment, occupation, and demographic measures, and these differences across the two groups 

of industries account for some of the differences in earnings. For example, the share of workers 

with a college degree is 12.8 percentage points higher for the EISIs, and the share of workers 

with a graduate degree is 7 percentage points higher, indicating that workers in the EISIs are, on 

average, more highly educated than workers in the other services industries. The share of 

workers who are at least 35 years old is 5.8 percentage points higher for the EISIs, and the 

average age is approximately 1.75 years older for the EISIs, which suggests that workers in these 

industries are probably more experienced on average. In addition, the share of workers in white 

collar occupations is more than 12 percentage points higher for EISIs. 

If we condition on these differences in individual characteristics, then the differences in 

usual weekly earnings are smaller than the 33% difference that we observed in the unconditional 

means. For example, among college graduates, the average of usual weekly earnings in EISI 

industries is only 28% higher than the average of usual weekly earnings in the other services 

industries. Similarly, if we only look at workers who are at least 35 years old, then the average 

of usual weekly earnings in EISIs (for workers at all levels of educational attainment) is only 

19% higher than the average of usual weekly earnings in the other services industries. In the 

next section, we use a multivariate regression model to quantify the export premium in weekly 

earnings by simultaneously conditioning on a combination of the education, occupation, and 

demographic measures. 
International Trade Administration 

Weekly Earnings in Export-Intensive U.S. Services Industries 7 

4. Regression analysis provides more precise estimates 

We estimate an econometric model of earnings based on the following equation: 

 ௧௜௝ߝ ൅ ௧௜ܼ ߛ ൅ ௝ܫܵܫܧ ߚ ൅ ௧ߙ௧൯ൌ௜௝ܹ൫݈݊

Table 3: Descriptive Statistics for the Two Groups of Services Industries 

 

Export-Intensive 

Service Industries 

All Other 

Service Industries 

Test of the Difference in 

the Group Means 

Average weekly 

earnings in 2008 dollars 

$947.57 

(2.2746) 

$714.76 

(0.9499) 

t = 120.000 

p = 0.000 

Share with a 

college graduate 

0.4146 

(0.0017) 

0.2871 

(0.0008) 

t = 72.3634 

p = 0.000 

Share with a 

graduate degree 

0.1645 

(0.0013) 

0.0935 

(0.0005) 

t = 60.1905 

p = 0.000 

Share with 

Age ≥ 35 

0.6736 

(0.0016) 

0.6155 

(0.0008) 

t = 31.4622 

p = 0.000 

Age 41.6985 

(0.0445) 

39.9512 

(0.0239) 

t = 33.0602 

p = 0.000 

Share with a 

white collar occupation 

0.7381 

(0.0015) 

0.6120 

(0.0008) 

t = 68.9457 

p = 0.000 

Number of observations 85,363 344,165 

Note: Standard errors are reported in parentheses. 
Manufacturing and Services Economics Brief No. 4 

8 Weekly Earnings in Export-Intensive U.S. Services Industries

The variable ܹ௜௝௧ is the usual weekly earnings for individual ݅, employed in industry ݆ in month 

ݐ. ܫܧܵܫ ௝is an indicator variable that is equal to one if industry ݆ is export-intensive, following 

the definition above, and is equal to zero otherwise. ܼ௜௧ represents a set of earnings-relevant 

individual characteristics, including educational attainment, age, occupational category, race, 

sex, state, and year. ߝ௝௜௧ is an error term. 

 The coefficients on the individual characteristics (the ߛ coefficients) measure the 

contribution to the worker’s usual weekly earnings from these characteristics. The coefficient on 

ܫܧܵܫ) ௝the ߚ coefficient) measures the premium in export-intensive services industries relative to 

the other services industries.4 We estimate a common ߚ coefficient across the EISIs. If the 

premium varies across the EISIs, then this pooled estimate is the average premium for the group 

of industries. We estimate the parameters of the model using Ordinary Least Squares.5

 Our 

estimates are based on variation in earnings both within industries and between industries.6

 

 We report the parameter estimates for three alternative specifications that include 

different sets of individual characteristics ܼ௜௧ (Table 4). Workers who have a college education, 

are 35 or older, work in white collar occupations, are male, and are white had higher weekly 

earnings on average. The coefficient on each of these individual characteristics is statistically 

significant at the one percent level, and the positive signs are consistent with the extensive 

 

4

 Technically, it is the mean difference in usual weekly earnings conditional on the set of worker characteristics ܼ௜௧. 

The regression estimate is equivalent to the following three-step process: First, estimate the contribution to the 

weekly earnings from the worker’s individual characteristics like education and age. This is the average percentage 

addition to earnings that is associated with each characteristic. Second, subtract these percentage additions from the 

measure of weekly earnings to calculate the conditional means for each group of industries. Third, calculate the 

difference in the conditional means. 

5

 We weight the individual observations using the CPS sampling weights, and we correct for potential clustering in 

the error terms. 

6

 The studies by Bernard and Jensen that focus on manufacturing industries find that the export premium in earnings 

is smaller when it is estimated using only within-industry variation in earnings. However, it is not possible for us to 

include industry fixed effects in our regression specification, because our measure of export intensity is an industrylevel effect. It would be possible to include additional industry-level explanatory variables in the regression 

specification, and this would change the estimate of ߚ because it would change the definition of the coefficient. We 

are estimating the average wage difference in services industries that are export-intensive conditional on the 

workers’ observable individual characteristics. The alternative specification would also condition on the new 

industry-level variables. This would be an important estimation issue if we were trying to establish the cause of the 

export earnings premium, but we are not. Our export earnings premium, ߚ, is intended to simply measure the 

difference in the conditional means of the two groups of industries. 
International Trade Administration 

Weekly Earnings in Export-Intensive U.S. Services Industries 9 

economics literature on the determinants of wages. Conditional on these individual 

characteristics, the weekly earnings for workers in EISIs are 15.84 to 20.40 percent higher, 

depending on the model specification. Because we utilize a large sample of workers in the CPS, 

this export earnings premium is fairly precisely estimated for each of the model specifications. 

We designate Model 3 as the benchmark specification. It includes the largest number of 

individual characteristics, and they are all statistically significant. Next, we examine the 

sensitivity of the benchmark estimate of ߚ to variations in the sample period and in how the 

individual characteristics are measured. 

When we estimate separate models for each year, we find that the export earnings 

premium varies across the years, but it remains in a small range (14.82 to 16.55) around the 

benchmark estimate (Table 5). The estimated coefficients on the individual characteristics are 

fairly consistent across the three years. 

Next, we estimate the model using alternative measures of age and education. We find 

that the earnings premium in the EISIs again remains close to the point estimate of 15.84 percent 

in the benchmark model (Table 6). First, we replace age with a proxy for experience.7 Second, 

we replace the indicator that a worker is a college graduate with separate indicators for whether 

the college graduate has only an undergraduate degree or has a graduate degree as well. 

We find that adding an indicator of the union status of the individual worker also does not 

have a substantial effect on the estimated earnings premium in the EISIs (Table 7). The indicator 

variable for union status is equal to one if the worker is a member of a union or is covered by a 

union agreement. Otherwise, it is equal to zero. Union status is likely to be a determinant of 

earnings, and it may be correlated with the export intensity of the worker’s industry. 

We also find that the estimated earnings premium is larger when we adopt a stricter 

definition of EISIs. In this sensitivity analysis, we only include industries with services exports 

greater than $15,000 per employee, compared to $5,000 per employee in the benchmark model. 

Using this more restrictive definition of EISIs, we calculate that the weekly earnings for workers 

in export-intense services industries are 19.64 to 24.14 percent higher (Table 8). 

 

7

 The proxy for experience is age minus years of education minus six. 
Manufacturing and Services Economics Brief No. 4 

10 Weekly Earnings in Export-Intensive U.S. Services Industries

Table 4: Baseline Econometric Models 

The dependent variable is the log of usual weekly earnings. 

Regressors Model 1 Model 2 Model 3 

Export Intensive 

Services Industry 

0.2040 

(0.0019) 

0.1610 

(0.0054) 

0.1584 

(0.0053) 

Age ≥ 35 0.3827 

(0.0182) 

0.3898 

(0.0115) 

0.3891 

(0.0112) 

College Graduate 0.5574 

(0.0091) 

0.5234 

(0.0246) 

0.5183 

(0.0254) 

White Collar Occupation 0.1766 

(0.0141) 

0.2926 

(0.0077) 

0.2920 

(0.0079) 

Male 

 

0.4025 

(0.0176) 

0.3981 

(0.0170) 

White 

 

0.0383 

(0.0174) 

0.0562 

(0.0202) 

Constant Included Yes Yes Yes 

Year Fixed Effects Included No No Yes 

State Fixed Effects Included No No Yes 

R2

 Statistic 0.2101 0.2626 0.2678 

Number of Observations 429,528 429,528 429,528 

Note: Robust standard errors are reported in parentheses.
International Trade Administration 

Weekly Earnings in Export-Intensive U.S. Services Industries 11 

Table 5: Sensitivity Analysis for the Econometric Analysis – Separate Estimates for Each Year 

The dependent variable is the log of usual weekly earnings. 

Regressors Benchmark 

Model 

2006 

Only 

2007 

Only 

2008 

Only 

Export Intensive 

Services Industry 

0.1584 

(0.0053) 

0.1655 

(0.0046) 

0.1482 

(0.0044) 

0.1617 

(0.0067) 

Age ≥ 35 0.3891 

(0.0112) 

0.3966 

(0.0073) 

0.3830 

(0.0151) 

0.3879 

(0.0112) 

College Graduate Only 0.5183 

(0.0254) 

0.5135 

(0.0221) 

0.5183 

(0.0261) 

0.5228 

(0.0279) 

White Collar Occupation 0.2920 

(0.0079) 

0.2934 

(0.0080) 

0.2953 

(0.0058) 

0.2878 

(0.0097) 

Male 0.3981 

(0.0170) 

0.4011 

(0.0112) 

0.4059 

(0.0133) 

0.3876 

(0.0260) 

White 0.0562 

(0.0202) 

0.0596 

(0.0145) 

0.0533 

(0.0254) 

0.0561 

(0.0210) 

Constant Included Yes Yes Yes Yes 

Year Fixed Effects Included Yes No No No 

State Fixed Effects Included Yes Yes Yes Yes 

R2

 Statistic 0.2678 0.2738 0.2656 0.2655 

Number of Obs. 429,528 144,524 143,384 141,620 
Manufacturing and Services Economics Brief No. 4 

12 Weekly Earnings in Export-Intensive U.S. Services Industries

Table 6: Sensitivity Analysis for the Econometric Analysis – Alternative Demographics 

The dependent variable is the log of usual weekly earnings. 

Regressors Benchmark Model Alternative 1 Alternative 2 

Export Intensive 

Services Industry 

0.1584 

(0.0053) 

0.1619 

(0.0056) 

0.1552 

(0.0065) 

Age ≥ 35 0.3891 

(0.0112) 

 0.3828 

(0.0106) 

Experience 0.0117 

(0.0004) 

College Graduate 0.5183 

(0.0254) 

0.5728 

(0.0241) 

Undergraduate Degree Only 0.4654 

(0.0226) 

Post Graduate Degree 0.6307 

(0.0419) 

White Collar Occupation 0.2920 

(0.0079) 

0.2958 

(0.0099) 

0.2870 

(0.0085) 

Male 0.3981 

(0.0170) 

0.3974 

(0.0163) 

0.3959 

(0.0181) 

White 0.0562 

(0.0202) 

0.523 

(0.0199) 

0.0564 

(0.0201) 

Constant Included Yes Yes Yes 

Year Fixed Effects Included Yes Yes Yes 

State Fixed Effects Included Yes Yes Yes 

R2

 Statistic 0.2678 0.2533 0.2704 

Number of Obs. 429,528 429,528 429,528 
International Trade Administration 

Weekly Earnings in Export-Intensive U.S. Services Industries 13 

Table 7: Sensitivity Analysis for the Econometric Analysis – Adding Union Status 

The dependent variable is the log of usual weekly earnings. 

Regressors Model 4 Model 5 Model 6 

Export Intensive 

Services Industry 

0.1995 

(0.0003) 

0.1566 

(0.0044) 

0.1544 

(0.0042) 

Age ≥ 35 0.3678 

(0.0171) 

0.3747 

(0.0109) 

0.3746 

(0.0106) 

College Graduate 0.5419 

(0.0031) 

0.5078 

(0.0115) 

0.5042 

(0.0128) 

White Collar Occupation 0.1868 

(0.0082) 

0.3029 

(0.0031) 

0.3018 

(0.0033) 

Union 0.2335 

(0.0284) 

0.2350 

(0.0378) 

0.2302 

(0.0375) 

Male 0.4026 

(0.0204) 

0.3982 

(0.0198) 

White 0.0414 

(0.0196) 

0.0588 

(0.0224) 

Constant Included Yes Yes Yes 

Year Fixed Effects Included No No Yes 

State Fixed Effects Included No No Yes 

R2

 Statistic 0.2181 0.2707 0.2754 

Number of Observations 429,528 429,528 429,528 
Manufacturing and Services Economics Brief No. 4 

14 Weekly Earnings in Export-Intensive U.S. Services Industries

Table 8: Sensitivity Analysis – Stricter Definition of Export-Intensive Services Industries 

The dependent variable is the log of usual weekly earnings. 

The stricter EISI definition includes the industries in Table 2 with exporters per employee above $15,000. 

Regressors Model 7 Model 8 Model 9 

Export Intensive 

Services Industry 

0.2414 

(0.0009) 

0.2000 

(0.0030) 

0.1964 

(0.0031) 

Age ≥ 35 0.3814 

(0.0118) 

0.3884 

(0.0169) 

0.3878 

(0.0166) 

College Graduate 0.5716 

(0.0020) 

0.5344 

(0.0113) 

0.5290 

(0.0122) 

White Collar Occupation 0.1873 

(0.0003) 

0.3015 

(0.0053) 

0.3007 

(0.0054) 

Male 

 

0.4043 

(0.0119) 

0.3999 

(0.0113) 

White 

 

0.0398 

(0.0132) 

0.0576 

(0.0139) 

Constant Included Yes Yes Yes 

Year Fixed Effects Included No No Yes 

State Fixed Effects Included No No Yes 

R2

 Statistic 0.2099 0.2631 0.2682 

Number of Observations 429,528 429,528 429,528 
International Trade Administration 

Weekly Earnings in Export-Intensive U.S. Services Industries 15 

5. The export earnings premium can have important policy 

implications 

At first glance, the earnings premium suggests that the expansion of U.S. services exports 

will not only increase jobs but will increase relatively high-paying jobs. However, this depends 

on whether the export earnings premium is replicable, meaning that additional hires in the 

industry will also earn the premium that was earned by the workers in our 2006-2008 sample. 

Whether the export earnings premium is replicable depends on its cause. 

Economic theory provides some guidance on this issue. Export-intensive industries 

generally offer opportunities for greater productivity and greater compensation, though this is not 

necessarily a consequence of exporting. For example, an industry may be more productive due 

to superior technology that was not developed in response to export opportunities, but by virtue 

of its high productivity, the industry is also successful in export markets.8

 There is an extensive 

economics literature that tries to identify factors that lead to export success, including Bernard 

and Jensen (1999). Helpman, Itskhoki, and Redding (2010) is an important contribution to this 

literature. The authors demonstrate in the context of a general equilibrium model of international 

trade that exporters tend to employ workers with higher average ability than non-exporters, and 

the higher productivity of the employment match is reflected in higher labor compensation.9 

 If the export earnings premium is due to superior technology that does not exhibit 

decreasing returns to scale, then we would expect the earnings premium to persist as U.S. 

services exports expand and the export-intensive industries hire additional workers. On the other 

hand, the export earnings premium may not be replicable.10 

Our econometric analysis does not try to establish the cause of the export earnings 

premium and whether it is replicable; however, as we noted above, the export earnings premium 

in the U.S. services sector appears to be stable in magnitude over the last decade – in the analysis 

 

8

 In this case, higher wages are positively correlated with exporting but they are not a consequence of exporting.

9

 In their model, export opportunities also raise the compensation of workers with a given level of productivity. 

 

10 In theory, the premium could reflect unobservable worker skills that are not possessed by new hires. 
Manufacturing and Services Economics Brief No. 4 

16 Weekly Earnings in Export-Intensive U.S. Services Industries

of 2000 earnings by Jensen and Kletzer and in our analysis of earnings in 2006, 2007, and 2008 – 

despite the substantial expansion of U.S. services exports over this period. The stability of the 

export earnings premium suggests that it will not diminish with future expansion of U.S. services 

exports. 

6. Conclusions 

 Our econometric analysis indicates that relatively higher weekly earnings in exportintensive services industries can be explained in part by higher levels of educational attainment 

of the workers and other individual characteristics. However, even after conditioning on the 

observable differences in individual characteristics, there is a significant difference in earnings 

between services industries that are export-intensive and services industries that are not. 

Workers in export-intensive services industries earn 15 to 20 percent more than comparable 

workers in other U.S. services industries. 

The estimates in this paper are limited by the availability of data on U.S. services exports. 

The data are not reported on an adequately disaggregated industry basis, unlike data on trade in 

manufactured goods, and therefore it is difficult to match the services exports to the detail 

industry data available for the U.S. economy. If the coverage and quality of services trade data 

can be significantly improved, then the analysis of their relation to earnings can be further 

refined. 

 
International Trade Administration 

Weekly Earnings in Export-Intensive U.S. Services Industries 17 

References 

Bernard, A.; Jensen, J.B. (1999): “Exceptional Exporter Performance: Cause, Effect, or Both?” 

Journal of International Economics 47: 1-25. 

Bernard, A.; Jensen, J.B.; Redding, S.; Schott, P. (2007): “Firms in International Trade,” Journal 

of Economic Perspectives 21(3): 105-130. 

Helpman, E.; Itskhoki, O.; Redding, S. (2010): “Inequality and Unemployment in a Global 

Economy.” Econometrica 78(4): 1239-1283. 

Jensen, J.B.; L.G. Kletzer (2006): “Tradable Services: Understanding the Scope and Impact of 

Services Offshoring,” Offshoring White-Collar Work – Issue and Implications, Brooking 

Trade Forum 2005. Washington, DC: Brookings Institution. 

Jensen, J.B.; L.G. Kletzer (2008): “ ‘Fear’ and Offshoring: The Scope and Potential Impact of 

Imports and Exports of Services,” Policy Brief 08-01. Washington, DC: Peterson 

Institute of International Economics.

Jensen, J.B. (2009): “Globalization and Business Services: A Growth Opportunity,” 

GeorgetownCenter for Business & Public Policy working paper. 

 
About the Office of 

Competition and Economic Analysis 

The Office of Competition and Economic Analysis (OCEA), a part of the 

International Trade Administration’s Manufacturing and Services unit, provides 

industry and policy decision makers with information on the impacts of economic 

and regulatory policies on U.S. manufacturing and services industries. Its staff of 

specialists perform in-depth industry analysis on the effects of both domestic and 

foreign policy developments on U.S. business competitiveness. For more 

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