Monthly Labor Review • October 2011 3

Domestic Employment

Elizabeth Weber 

Handwerker,

Mina M. Kim,

and

Lowell Mason

The Bureau of Economic Analysis 

(BEA) collects data on multinational companies based in the United States—firms that have full or partial 

ownership of affiliate companies in foreign 

countries. However, BEA data offer little detail on the characteristics of these firms’ U.S. 

employment. Identifying such firms in BLS

data can show the geographic, occupation, 

and wage distributions of their employees in 

the United States. The popular media sometimes describe U.S. companies that make investments in companies overseas as “exporting good jobs,” but there is little evidence on 

the domestic employment characteristics of 

these firms, either before or after their overseas investments have taken place. 

Typically, firms that own at least a 10-percent interest in a foreign company are described as being engaged in foreign direct investment.

1

 In the economics literature, such 

companies have been shown to be systematically different from firms that focus on the 

domestic market.2

 In particular, firms with 

foreign affiliates tend to be larger and more 

productive than firms that have no foreign 

affiliates, but that sell their products internationally.3

 Exporting firms in turn tend to 

be larger and more productive than firms 

that sell their products only on the domestic market.4

 However, aside from establishing that the firms engaged in foreign direct 

investment tend to be large employers that 

pay higher wages, the literature has uncovered very little about the geographic, occupation, or wage distributions of employment 

Domestic employment in U.S.-based 

multinational companies

Establishments of multinational manufacturing firms in the United States are 

larger, are located disproportionately in the South, employ a disproportionate 

number of engineers, and pay higher wages, on average, than other U.S. 

establishments; these findings hold even after controlling for establishment 

industry, size, and age, and the interaction between industry and size

by U.S.-based multinational companies.

The research presented in this article is 

based on a joint BEA-BLS project that linked 

BEA firm-level data on U.S. multinational 

companies with BLS establishment-level 

data for all U.S. employers. By identifying 

a subset of all domestically located establishments in the BLS data that are the establishments of U.S.-based multinational 

companies, the article presents, for the first 

time, details on the employment, wages, and 

geographical and occupational distributions 

of these companies. 

Background

Beginning with the enactment of the Foreign Direct Investment and International 

Financial Data Improvements Act of 1990, 

BLS and BEA collaborated to combine BEA

data on foreign-owned businesses with BLS

employment data until the funds dedicated 

to the project were eliminated. That project, 

which focused on the composition of domestic employment related to direct investments in the United States by foreign firms, 

produced the tabulations “Employment and 

Wages in Foreign-Owned Businesses in the 

United States” for 1989 through 1992 and 

“Occupations in Foreign-Owned Manufacturing Establishments in the United States” 

for 1989. Nowadays, there is more concern—reflected in both the popular press 

and the economic literature—about the impact of U.S. direct investment overseas on 

domestic employment.

Elizabeth Weber Handwerker is a research 

economist in the Office 

of Employment and Unemployment Statistics, 

Bureau of Labor Statistics, 

and Lowell Mason is an 

economist in the same 

office. Mina M. Kim is a 

research economist in the 

Office of Prices and Living 

Conditions. Email: 

handwerker.elizabeth.

weber@bls.gov
Domestic Employment

4 Monthly Labor Review • October 2011

From 2005 to 2007, a National Academy of Public Administration panel, authorized and funded by Congress, 

studied the definition, available data, and impact of offshoring on the U.S. economy. The panel produced three 

reports,5

 and its efforts led to a memorandum of understanding between BEA and BLS. The memorandum permits relevant data sharing between the two agencies to investigate possible avenues for improving statistics through 

linking their data. The panel recommended that BEA and 

BLS work to link the BEA firm-level data on U.S. multinational companies with BLS establishment-level data for all 

U.S. employers. Following this recommendation, a team 

of researchers at BLS and BEA has been investigating the 

feasibility of linking the two datasets since the panel concluded its work in 2007.

Data and methods

In what follows, BEA data were used to match a pilot group 

of U.S. parent firms of multinational companies with their 

establishments appearing in BLS data. The pilot group consists of the largest 500 U.S.-based multinational manufacturers (by primary industry of the U.S. parent) in the BEA’s 

firm-level data from the 2004 Benchmark Survey of U.S. 

Direct Investment Abroad. The efforts at matching were 

based primarily on the names, locations, and employer 

identification numbers (EINS)

6

 provided in this survey.

The BLS Quarterly Census of Employment and Wages 

(QCEW) is the starting point for identifying the establishments of firms in the pilot group for the years 2004 

and 2005. The QCEW collects information on total employment by month and total wage bills (total wages paid 

by establishments) by quarter for all U.S. establishments 

covered in the Unemployment Insurance program, as well 

as detailed information on the industry of main activity 

(at the six-digit North American Industry Classification 

System (NAICS) level) and geographic location (at the 

Census block level) for each establishment.

These establishments were then matched with those 

sampled in the BLS Occupational Employment Statistics 

(OES) survey from November 2003 to May 2006. This 

survey collects data from a sample of 1.2 million establishments over each 3-year cycle. Sampled establishments 

provide data on the distribution of their employees’ occupations (by the 801 detailed civilian occupations of the 

Standard Occupational Classification (SOC) system) and 

hourly wages (in 12 broad wage bands). To calculate average wages in the OES data, each employee is assigned a 

wage based on the mean wage for these wage bands, following the methods used in OES publications.7

The automatic matching efforts entail matching EINs 

between BEA firm-level data and BLS establishment-level 

data. BEA firm-level data contain only one or two EINs 

per firm, while BLS establishment-level data contain one 

EIN per establishment, and the establishments of each 

firm may report many different EINs in the BLS data. 

Thus, additional EINs for each firm are found by matching 

firm names and addresses with the establishment names 

and addresses in the QCEW, as well as by using company 

family lists (lists of employers that operate under different names but are part of the same company) from other 

BLS programs, company information in the Compustat 

database, and other sources of data on firms. However, 

such automated matching procedures are imperfect: some 

firms are matched with unrelated establishments, while 

other firms appear to be matched with only a fraction of 

their establishments in the QCEW. Accordingly, the lists 

of all establishments found through automated matching 

were reviewed manually, and the establishments matched 

in error were removed. Then, the QCEW was searched for 

additional establishments identified from company Web 

sites, filings from the Securities and Exchange Commission (SEC), and company annual reports.

On the advice of BEA staff, the firms identified in their 

surveys were considered to be “adequately matched” with 

BLS establishment data if the total employment of all 

matched BLS establishments for a particular firm was 

within 20 percent of the total employment reported in the 

BEA survey. Of the 500 firms in the pilot group, 201 were 

considered to be adequately matched with the QCEW data 

with the use of only automated matching methods. After several months of labor-intensive review and “hand 

matching,” 453 of the 500 firms were believed to be adequately matched. The remainder of this article discusses 

results for these 453 firms, which employ 90 percent of 

the workers of the largest 500 manufacturers, as the following tabulation shows: 

Source of data and category of employment Number of workers

BEA data from 2004 Benchmark Survey of U.S. 

 Direct Investment Abroad:

 Total domestic employment of companies in survey....... 22,445,900

 Employment in these companies whose primary 

 industry is manufacturing.......................................... 7,628,500

 Employment in the largest 500 of these 

 companies.............................................................. 6,829,300

 Employment in the 453 matched companies... . 6,444,300

BLS data from QCEW and OES survey:

 Total employment in establishments matching

 with these 453 matched companies, per QCEW.............6,112,919

 Weighted8

 employment in establishments matching

 with these 453 matched companies, per OES survey..... 5,638,849
Monthly Labor Review • October 2011 5

The establishments of the 453 adequately matched firms 

were then linked with establishments in the OES survey 

data. Because the OES sample design always includes all 

large establishments over the 3-year panel, about onefifth of the matched establishments are part of the OES

sample and responded to the survey between November 

2003 and May 2006.

Caveats

Although a large portion of U.S. employment of U.S.-

based multinational manufacturing companies was 

found in the QCEW and OES data, the missing employment is not random. The 453 multinational companies 

that were found to match with the BLS establishment 

data are different from the 47 firms that remained unmatched. For example, it was more difficult to match 

privately owned firms (which generally disclose less information than publicly owned firms and, in particular, 

do not file annual reports with the SEC) and firms that 

have undergone liquidation or reorganization since the 

survey date.

Even the establishments found in the 453 matched 

firms were not randomly distributed among those firms 

in ways that might affect the resulting estimates of wages 

and occupations. For example, in a multinational firm, 90 

percent of whose BEA-estimated employment was found 

in the QCEW, matched establishments in the BLS data 

might include all of the firm’s major manufacturing plants 

and just a few of the firm’s smaller sales offices.

Furthermore, the sample design of the OES survey is 

intended to produce estimates at the State and industry 

levels, not to provide estimates for the unusual subsample 

of multinational firms examined in this article. The OES

survey collects information from a sample of establishments rotating in 3-year panels, with sample probabilities that vary by establishment size. The probability that 

a larger establishment is included in the OES sample over 

the course of 3 years is greater than the probability that 

a small establishment is included. This difference could 

affect the distribution of occupations and wages in the 

subsample of establishments that are matched with multinational firms. In the extreme case, the distribution of 

occupations found in the OES data for a particular firm 

might represent only the large manufacturing plants of 

that firm, excluding the firm’s sales and headquarters establishments. Consequently, the sample and nonsample 

variance of these estimates may be large.

Another concern is the difference in wage reporting 

between the QCEW and the OES survey. Total wage bills 

in the QCEW data include bonuses and overtime pay for 

all employees on the payroll of an establishment for each 

quarter, whereas wage payments in the OES include only 

base hourly or annual wages for employees at the time 

the establishment is contacted by the survey. It was found 

that, among the establishments in the pilot group, those 

included in the OES survey reported monthly wage bills 

per employee in the QCEW data that were about 219 

times the average hourly wages per employee they reported to the OES survey (approximately equivalent to 27 

days’ wages times 8 hours per day), while the typical ratio 

for all establishments was an average monthly wage bill 

per employee in the QCEW data that was about 135 times 

the average hourly wage per employee reported to the OES

survey. The following tabulation gives the breakdown:

 

Category of Average monthly Average OES

establishment QCEW wage bill wages per hour Ratio

 All U.S. 

establishments..... $2,538.53 $18.84 135

Matched 

establishments..... 5,193.16 23.74 219

This different relationship between QCEW and OES

wage data for U.S.-based multinational manufacturers, 

compared with typical U.S. employers, could be due to 

differences in average hours worked, particularly in the incidence of part-time employment, overtime pay, or bonuses. To reduce the impact of bonuses on the comparisons 

of multinational companies with other employers, QCEW

data from the third quarter of 2004 were used instead of 

data from the fourth quarter, during which bonuses typically are largest. However, it is still possible that the multinational companies pay higher bonuses, even in the third 

quarter. It is also possible that some of the difference between the measurement of wages in the QCEW and OES

data is due to underreporting and topcoding of wages in 

the OES for highly paid workers.

Results

Employment in the 453 matched companies among the 

500 largest multinational manufacturers accounts for 4.7 

percent of total U.S. employment measured in the QCEW. 

As one would expect from a matching effort that began 

with firms whose primary industry is manufacturing, 

most establishments of those firms are involved in manufacturing; about 67 percent of the firms’ employment is in 

manufacturing establishments.

Table 1 compares employment and monthly wage bills 

(based on the qcew for the third quarter of 2004) per 

employee, by industry (major sectors and subsectors), for 
Domestic Employment

6 Monthly Labor Review • October 2011

Table 1. Employment and wages for major industry groups and manufacturing subsectors, all United States and matched

 U.S. multinational manufacturing companies, third quarter, 2004

Industry group 

or manufacturing 

subsector

All U.S. establishments Matched U.S. multinational manufacturing companies 

Number of 

establishments 

Average 

monthly 

employment 

Average 

establishment 

employment 

Average 

monthly 

wages 

Number 

of establishments 

Average 

monthly 

employment 

Percentage of 

average 

monthly 

employment 

for all 

U.S. establishments 

Average 

establishment 

employment 

Ratio of 

average 

matched 

establishment size 

to average U.S. 

establishment size 

Average 

monthly 

wages 

Ratio of 

average 

wages in 

matched 

establishments to 

average 

U.S. 

monthly 

wages 

 All industries 8,388,413 129,188,999 15.4 $2,538.53 105,462 6,112,919 4.7 58.0 3.8 $5,193.16 2.0

Agriculture, forestry, 

fishing and hunting 97,661 1,264,864 13.0 1,738.26 721 18,690 1.5 25.9 2.0 2,994.21 1.7

Mining, quarrying, 

and oil and gas

extraction 25,431 525,340 20.7 3,722.63 1,223 64,471 12.3 52.7 2.6 6,207.66 1.7

Utilities 24,650 823,642 33.4 3,735.40 127 3,384 .4 26.6 .8 5,096.16 1.4

Construction 830,965 7,403,064 8.9 2,288.16 3,144 81,201 1.1 25.8 2.9 3,813.43 1.7

Manufacturing 370,645 14,368,451 38.8 2,804.14 19,323 4,098,018 28.5 212.1 5.5 5,254.62 1.9

Food manufacturing 28,583 1,519,225 53.2 2,095.70 1,916 424,300 27.9 221.5 4.2 4,233.34 2.0

Beverage and

tobacco product

manufacturing 4,287 199,578 46.6 2,890.18 501 83,004 41.6 165.7 3.6 6,223.41 2.2

Textile mills 4,538 237,662 52.4 2,786.02 166 33,599 14.1 202.4 3.9 4,016.26 1.4

Textile product mills 7,881 176,074 22.3 1,881.77 102 18,289 10.4 179.3 8.0 3,233.79 1.7

Apparel manufacturing 12,454 284,205 22.8 1,720.40 98 14,306 5.0 146.0 6.4 3,906.29 2.3

Leather and allied

product manufacturing 1,496 42,589 28.5 2,062.75 8 598 1.4 74.8 2.6 3,990.32 1.9

Wood product

manufacturing 17,744 559,338 31.5 2,155.77 554 61,140 10.9 110.4 3.5 3,703.68 1.7

Paper manufacturing 6,536 493,560 75.5 3,640.75 1,324 193,203 39.1 145.9 1.9 4,631.92 1.3

Printing and related 

support activities 38,402 662,736 17.3 2,482.77 654 82,534 12.5 126.2 7.3 4,927.15 2.0

Petroleum and coal

products manfacturing 2,334 114,175 48.9 4,777.08 533 53,368 46.7 100.1 2.0 6,403.45 1.3

Chemical manufacturing 15,413 882,111 57.2 4,098.05 2,168 386,453 43.8 178.3 3.1 6,077.88 1.5

Plastics and rubber

products manufacturing 14,809 806,133 54.4 2,883.31 1,051 194,313 24.1 184.9 3.4 4,022.45 1.4

Nonmetallic mineral 

product manufacturing 17,648 509,399 28.9 2,797.83 879 91,941 18.0 104.6 3.6 3,997.58 1.4

Primary metal 

manufacturing 6,057 467,500 77.2 3,412.04 521 137,663 29.4 264.2 3.4 4,849.14 1.4

Fabricated metal 

product manufacturing 60,794 1,503,397 24.7 2,764.81 1,520 204,347 13.6 134.4 5.4 5,294.56 1.9

Machinery manufacturing 32,166 1,141,544 35.5 3,303.69 1,595 322,836 28.3 202.4 5.7 4,893.30 1.5
Monthly Labor Review • October 2011 7

Computer and 

electronic product 

manufacturing 19,846 1,318,540 66.4 4,535.20 1,767 508,530 38.6 287.8 4.3 8,383.27 1.8

Electrical equipment, appliance, 

and component 

manufacturing 7,371 443,048 60.1 3,389.99 865 185,169 41.8 214.1 3.6 4,844.07 1.4

Transportation

equipment manufacturing 15,390 1,780,375 115.7 3,149.14 2,004 889,093 49.9 443.7 3.8 5,381.64 1.7

Furniture and

related product

manufacturing 24,266 570,882 23.5 2,156.10 420 92,994 16.3 221.4 9.4 3,282.51 1.5

Miscellaneous

manufacturing 32,630 656,380 20.1 2,466.70 677 120,338 18.3 177.8 8.8 5,333.30 2.2

Wholesale trade 593,377 5,661,253 9.5 4,068.79 37,384 678,511 12.0 18.1 1.9 6,356.45 1.6

Retail trade 1,037,755 15,062,368 14.5 1,745.98 18,634 205,966 1.4 11.1 .8 2,714.15 1.6

Transportation and 

warehousing 232,150 5,104,776 22.0 2,475.12 2,180 80,540 1.6 36.9 1.7 4,327.88 1.7

Information 148,334 3,222,535 21.7 4,148.19 1,010 57,529 1.8 57.0 2.6 5,950.71 1.4

Finance and insurance 450,062 5,852,186 13.0 3,776.96 2,097 54,380 .9 25.9 2.0 5,771.11 1.5

Real estate and 

rental and leasing 339,405 2,165,788 6.4 2,499.01 1,507 17,322 .8 11.5 1.8 3,587.29 1.4

Professional, scientific, and technical

services 882,094 6,845,485 7.8 3,484.03 7,097 315,940 4.6 44.5 5.7 6,314.28 1.8

Management of 

companies and 

enterprises 40,667 1,698,843 41.8 5,781.58 1,891 252,113 14.8 133.3 3.2 10,191.68 1.8

Administrative 

and support and 

waste management and remediation services 424,372 8,106,947 19.1 2,250.98 3,114 92,816 1.1 29.8 1.6 5,203.35 2.3

Educational services 142,085 10,001,237 70.4 2,462.54 448 10,606 .1 23.7 .3 4,093.31 1.7

Health care and 

social assistance 685,556 15,788,272 23.0 3,266.01 969 18,827 .1 19.4 .8 3,100.36 .9

Arts, entertainment, 

and recreation 121,375 2,484,259 20.5 3,002.96 345 16,229 .7 47.0 2.3 2,172.59 .7

Accommodation 

and food services 564,739 10,957,166 19.4 1,026.36 601 14,445 .1 24.0 1.2 1,309.41 1.3

Other services 

(except public 

administration) 1,085,435 4,373,574 4.0 1,340.99 2,151 28,950 .7 13.5 3.3 3,619.24 2.7

SOURCE: U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages.

Table 1. Continued—Employment and wages for major industry groups and manufacturing subsectors, all United States and

 matched U.S. multinational manufacturing companies, third quarter, 2004

Industry group 

or manufacturing 

subsector

All U.S. establishments Matched U.S. multinational manufacturing companies 

Number of 

establishments 

Average 

monthly 

employment 

Average 

establishment 

employment 

Average 

monthly 

wages 

Number 

of establishments 

Average 

monthly 

employment 

Percentage of 

average 

monthly 

employment for 

all U.S. 

establishments

Average 

establishment 

employment 

Ratio of 

average 

matched 

establishment size 

to average U.S. 

establishment size 

Average 

monthly 

wages 

Ratio of 

average 

wages in 

matched 

establishments to 

average U.S. 

monthly 

wages 
Domestic Employment

8 Monthly Labor Review • October 2011

all U.S. establishments and for the establishments of the 

matched multinational manufacturing companies. In general, the establishments of these matched multinational 

manufacturers have higher employment per establishment than do all employers. This difference is particularly 

large in certain manufacturing subsectors, such as printing 

and textiles. The establishments of these matched multinational manufacturing companies also have higher wage 

bills per employee than all employers have. Later, regression analysis is used to decompose how much of the difference in wages shown in table 1 can be attributed to 

differences in the geographic composition, industries, 

sizes, and occupational distribution of workers in those 

establishments.

These findings echo those of Mark Doms and J. Bradford Jensen, who reported that U.S. multinational companies were larger and paid higher wages in 1987, on average, than either small or large domestically oriented firms 

or than foreign-owned firms.9

 Controlling for establishment size, industry, age of the plant, and State in which 

the company is located, Doms and Jensen found that production workers in establishments of U.S. multinational 

companies were paid 7 percent more than those in establishments of large domestically oriented firms, about 17 

percent more than those in establishments of small domestically oriented firms, and about 3 percent more than 

those in establishments of foreign-owned plants. These 

same authors found smaller differences in pay for nonproduction workers.

However, some of the differences in wages between all 

U.S. establishments and establishments of the matching 

multinational manufacturing companies are likely due to 

differences in the subsectors of the major industries in 

which the establishments are engaged. For example, the 

142,085 U.S. establishments in educational services are 

largely public school establishments, whereas the 448 educational services establishments of multinational manufacturing companies are not.

Table 2 compares employment (based on the QCEW

for the third quarter of 2004) by census region and division between all U.S. establishments and establishments 

of matched multinational manufacturing firms. The concentration of multinational employment ranges from 3.5 

percent of employment in the West to 6.5 percent in the 

Midwest. More specifically, the concentration of multinational employment for the pilot group is greatest in the 

East North Central census division, where the companies 

in the group employ 6.9 percent of all workers. This census division also has the largest difference in employment 

between multinational employers and all employers: establishments of multinational employers average more 

than 5 times the size of average employers in the division. The difference in wage bills between matched multinational employers and all employers is greatest in the 

West, where the average matched multinational employer 

pays monthly wage bills per employee that are 2.3 times 

the monthly wage bills per employee of average employers overall.

Because the establishments examined in this article are 

in multinational manufacturing firms, some of the geographic distribution of matched establishments will be 

driven by the geographic distribution of the manufacturing industry throughout the United States. Table 2 

shows that the manufacturing establishments—matched 

or not—are located mostly in the South and Midwest regions. Within manufacturing establishments, the fraction 

of employment that is in matched multinational employers ranges from 22.0 percent in the Middle Atlantic census division to 32.3 percent in the West South Central 

census division.

An interesting question that arises is, How is the geographic concentration of the matching multinational employers in the South and the Midwest regions influenced 

by factors such as industry composition? To answer this 

question, a simple linear probability regression may be 

performed for each geographic region. The regression results explain how much of the difference in the geographic 

locations of multinational employers is due only to differences in the industry composition, size classes, and ages of 

their establishments. For example, if large, older manufacturing plants are located predominantly in the Midwest 

and the matched multinational companies are composed 

disproportionately of large, older manufacturing establishments, this set of circumstances would explain the 

location of the matched multinational companies in the 

Midwest. The regressions take the form 

yi

 = α + βMatchi + γXi + εi,

where yi

 is the outcome variable of interest for establishment i (here, an indicator variable for the geographic region in which the establishment is located); Matchi

 is an 

indicator variable for whether establishment i matches 

with the multinational manufacturing parent firms identified in the BEA data; and Xi

 is a vector of establishmentlevel control variables (here, the industry,10 size class, 

interaction of industry with size class, and age11 in the 

QCEW in the third quarter of 2004).

The results of these regressions are given in Table 3. 

The first column of numbers shows the coefficients (and 
Monthly Labor Review • October 2011 9

Table 2. Civilian employment and wages, by census region and division, for all industries and manufacturing industries, 

 all United States and matched U.S. multinational manufacturing companies, third quarter, 2004

Census region 

and division

All U.S. establishments Matched U.S. multinational manufacturing companies 

Number 

of 

establishments 

Average 

monthly 

employment 

Average 

establishment 

employment 

Average 

monthly 

wages 

Number 

of 

establishments 

Average 

monthly 

employment 

Percentage of 

average 

monthly 

employment 

for all 

U.S. establishments 

Average 

establishment 

employment 

Ratio of 

average 

matched 

establishment size 

to average U.S. 

establishment size 

Average 

monthly 

wages 

Ratio of 

average 

wages in 

matched 

establishments to 

average 

U.S. 

monthly 

wages 

All industries

 Total 8,388,413 129,188,999 15.4 $2,538.53 105,462 6,112,919 4.7 58.0 3.8 5,193.16 2.0

Northeast 1,626,703 24,309,685 14.9 2,806.40 17,701 918,587 3.8 51.9 3.5 5,463.88 1.9

New England 476,115 6,754,517 14.2 2,964.37 6,461 286,953 4.2 44.4 3.1 5,812.92 2.0

Middle Atlantic 1,150,588 17,555,168 15.3 2,741.04 11,240 631,634 3.6 56.2 3.7 5,263.25 1.9

South 2,794,527 45,493,523 16.3 2,531.15 41,914 2,192,343 4.8 52.3 3.2 4,906.74 1.9

South Atlantic 1,583,836 24,476,157 15.5 2,554.75 21,319 1,044,412 4.3 49.0 3.2 5,154.00 2.0

East South 

Central 417,113 7,325,001 17.6 2,331.31 7,309 446,813 6.1 61.1 3.5 4,505.47 1.9

West South 

Central 793,578 13,692,366 17.3 2,589.07 13,286 701,118 5.1 52.8 3.1 4,730.74 1.8

Midwest 1,782,245 30,422,738 17.1 2,398.43 24,875 1,974,029 6.5 79.4 4.6 4,996.76 2.1

East North 

Central 1,181,720 20,930,306 17.7 2,465.47 15,847 1,434,707 6.9 90.5 5.1 5,184.99 2.1

West North 

Central 600,525 9,492,431 15.8 2,266.51 9,028 539,322 5.7 59.7 3.8 4,666.35 2.1

West 2,184,938 28,963,053 13.3 2,462.80 20,972 1,027,961 3.5 49.0 3.7 5,770.04 2.3

Mountain 595,144 8,748,932 14.7 2,547.16 9,072 292,659 3.3 32.3 2.2 5,988.24 2.4

Pacific 1,589,794 20,214,121 12.7 2,431.22 11,900 735,302 3.6 61.8 4.9 5,603.69 2.3

Manufacturing 

industries

 Total 370,645 14,368,451 38.8 2,804.14 19,323 4,098,018 28.5 212.1 5.5 5,254.62 1.9

Northeast 70,787 2,379,255 33.6 3,026.49 2,736 547,006 23.0 199.9 5.9 6,190.34 2.0

New England 22,400 753,135 33.6 3,176.94 884 189,121 25.1 213.9 6.4 5,846.08 1.8

Middle Atlantic 48,387 1,626,120 33.6 2,956.84 1,852 357,885 22.0 193.2 5.8 6,354.67 2.1

South 111,719 4,786,027 42.8 2,674.78 7,693 1,459,774 30.5 189.8 4.4 4,717.47 1.8

South Atlantic 55,384 2,241,437 40.5 2,695.06 3,527 660,089 29.4 187.2 4.6 4,704.99 1.7

East South

Central 20,710 1,147,627 55.4 2,510.02 1,646 348,229 30.3 211.6 3.8 4,202.60 1.7

West South 

Central 35,625 1,396,963 39.2 2,739.04 2,520 451,456 32.3 179.1 4.6 5,071.24 1.9

Midwest 101,119 4,524,718 44.7 2,813.61 5,556 1,419,108 31.4 255.4 5.7 4,750.88 1.7

East North 

Central 73,242 3,297,125 45.0 2,915.16 3,981 1,038,622 31.5 260.9 5.8 4,962.15 1.7

West North 

Central 27,877 1,227,593 44.0 2,546.78 1,575 380,486 31.0 241.6 5.5 4,216.88 1.7

West 87,020 2,678,451 30.8 2,778.33 3,338 672,130 25.1 201.4 6.5 6,564.03 2.4

Mountain 22,158 623,233 28.1 2,447.92 896 172,464 27.7 192.5 6.8 5,253.28 2.1

Pacific 64,862 2,055,218 31.7 2,891.21 2,442 499,666 24.3 204.6 6.5 7,044.97 2.4

SOURCE: U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages.

their standard errors) for the Match indicator without any 

controls Xi

, while the second column of numbers shows 

the coefficients (and their standard errors) for the Match 

indicator when controls for establishment industry, size 

class, and age, and for the interaction of industry with size 

class, are added.

Because the matching is incomplete, some unmatched 

establishments may belong to the multinational manu-
Domestic Employment

10 Monthly Labor Review • October 2011

Table 3. Difference in establishment locations for matched

 and unmatched firms

Region Raw probability 

difference

Regression-adjusted 

difference

Northeast:

β

S.E.

1

–0.0264

(.0012)

1

–0.0250

(.0020)

Midwest:

β

S.E.

1

.0236

(.0013)

2

–.0048

(.0022)

South:

β

S.E.

1

.0652

(.0015)

1

.0504

(.0024)

West:

β

S.E.

1

–.0624

(.0014)

1

–.0206

(.0021)

1

 Significant at p <.0001. 2

 Significant at p <.03.

NOTE: β = coefficient; S.E. = standard error of β.

SOURCE: U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages.

facturing companies in the pilot group. Thus, the coefficients on Matchi

 are biased toward zero. Furthermore, 

the incompleteness of the matching is not random, both 

because certain kinds of establishments are missing from 

the firms that were matched and because it was more difficult to match certain kinds of firms. As a result, given 

that the locations of the missing matched establishments 

are not known, neither is the direction of the resulting 

bias in the regression coefficients known, and the reported 

standard errors for these coefficients understate the true 

uncertainty of the estimates.

After adjustment for establishment size class, industry, 

and age, and for the interaction of industry with size class, 

the matched establishments turn out to be less likely than 

other establishments to be located in the Midwest, but 

they are still disproportionately located in the South, not 

in the Northeast or the West.

Table 4 shows the distribution of occupations in the 

OES data collected in the survey’s six panels from November 2003 to May 2006, within the establishments of 

the matched multinational manufacturers. Because the 

OES data come from a sample, the weights from the OES

(which account for sampling probabilities, among other 

factors) are used to weight the entries in the table.

These establishments employ a particularly high fraction 

of their employees in the production occupations group, 

which consists of supervisors of production workers (SOC

51–1000); assemblers and fabricators (51–2000); food 

processing workers (51–3000); metal workers and plastic 

workers (51–4000); printing workers (51–5000); textile, 

apparel, and furnishings workers (51–6000); woodworkers (51–7000); plant and system operators (51–8000); and 

other production occupations (51–9000). Matched establishments employ 33.1 percent of their workers in this 

occupational group, while all U.S. establishments collectively employ 7.7 percent of their employees in the group.

Matched establishments also employ a higher fraction 

of their employees than do all U.S. establishments in the 

following SOC groups: management (SOC 11); business 

and financial (13); computer and mathematical (15); architecture and engineering (17); life, physical, and social 

science (19); installation, maintenance, and repair (49); 

and transportation and material moving occupations (53). 

They employ a lower fraction of their employees in other 

groups: education, training, and library (25); healthcare 

practitioner and technical (29); healthcare support (31); 

protective service (33); food preparation and serving (SOC

35); building and grounds cleaning and maintenance (37); 

personal care and service (39); sales and related (41); office and administrative support (43); and construction and 

extraction occupations (47).

Much of this difference in occupational distribution derives from differences in the business activities of these 

establishments. Matched establishments are concentrated 

in manufacturing industries, which disproportionately 

employ people in the engineering and production occupations. Indeed, within the manufacturing establishments 

that can be matched with the U.S-based multinational 

manufacturing firms, nearly 48 percent of all employees 

are in production occupations and more than 10 percent 

are in architecture and engineering occupations.

To see how much of the difference in occupations between employees in establishments of U.S-based multinational manufacturing firms and those in other establishments stems from differences in the industries, sizes, 

locations, and ages of their employing establishments, 

simple linear probability regressions were conducted. 

These regressions are of the same form as the regressions 

used earlier, in which the outcome variable yi

 is an indicator variable for each occupation.

As in table 3, the first column of numbers in table 5 

shows the coefficients (and their standard errors) for the 

Match indicator without any controls Xi

 while the second column of numbers shows the coefficients (and their 

standard errors) for the Match indicator when controls 

for establishment industry, size class, age, and region, and 

for the interaction of industry with size class, are added. 

The variable of interest in these regressions is the occupational classification of the employees, so the regressions 

are weighted by the number of employees in each establishment, as well as the final benchmark weights from 

the OES. Again, the standard errors calculated for these 

regressions understate the true standard errors because 
Monthly Labor Review • October 2011 11

Table 4. Civilian employment and wages for major occupational groups, all United States and matched U.S. multinational 

 manufacturing companies, all industries and manufacturing industries, fall 2003–spring 2006

Occupational group

(SOC major category)

All U.S. establishments Matched U.S. multinational manufacturing companies

Average 

monthly 

employment

Occupational 

distribution 

(percent)

Average 

hourly 

wages

Average 

monthly 

employment

Percentage of 

average monthly 

employment for 

all U.S. establishments

Occupational 

distribution 

(percent)

Average 

hourly 

wages

All industries 

 All occupational groups 132,614,818 100.0 $18.84 5,638,849 4.3 100.0 $23.74 

Management (11) 5,893,403 4.4 44.20 338,606 5.7 6.0 52.48

Business and financial operations (13) 5,827,125 4.4 28.85 311,334 5.3 5.5 31.91

Computer and mathematical (15) 3,077,193 2.3 33.29 274,778 8.9 4.9 36.84

Architecture and engineering (17) 2,433,326 1.8 31.84 490,716 20.2 8.7 35.52

Life, physical, and social science (19) 1,233,302 .9 28.72 114,595 9.3 2.0 32.53

Community and social services (21) 1,749,233 1.3 18.75 2,526 .1 (

1

) 18.81

Legal (23) 976,764 .7 41.04 10,166 1.0 .2 49.94

Education, training, and library (25) 8,206,455 6.2 21.79 7,938 .1 .1 25.16

Arts, design, entertainment, sports, 

and media (27) 1,727,520 1.3 22.17 41,648 2.4 .7 27.49

Healthcare practitioner and technical

(29) 6,713,823 5.1 29.82 21,707 .3 .4 27.38

Healthcare support (31) 3,483,280 2.6 11.83 6,269 .2 .1 13.14

Protective service (33) 3,025,023 2.3 17.81 14,914 .5 .3 16.09

Food preparation and serving related

(35) 11,029,282 8.3 8.86 20,301 .2 .4 9.06

Building and grounds cleaning and 

maintenance (37) 4,396,269 3.3 10.86 46,302 1.1 .8 12.04

Personal care and service (39) 3,249,766 2.5 11.02 9,952 .3 .2 9.93

Sales and related (41) 14,114,875 10.6 16.51 394,192 2.8 7.0 25.20

Office and administrative support(43) 23,078,144 17.4 14.60 631,016 2.7 11.2 16.87

Farming, fishing, and forestry (45) 450,042 .3 10.49 10,806 2.4 .2 12.20

Construction and extraction (47) 6,680,731 5.0 18.89 136,072 2.0 2.4 22.53

Installation, maintenance, and repair 

(49) 5,352,792 4.0 18.78 357,576 6.7 6.3 21.37

Production (51) 10,268,712 7.7 14.65 1,867,665 18.2 33.1 17.14

Transportation and material moving 

(53) 9,647,759 7.3 14.16 529,770 5.5 9.4 14.73

 Manufacturing industries

 All occupational groups 14,185,767 100.0 19.35 3,746,781 26.4 100.0 22.87 

Management (11) 690,667 4.9 49.47 191,354 27.7 5.1 51.49

Business and financial operations (13) 430,189 3.0 28.58 164,581 38.3 4.4 30.96

Computer and mathematical (15) 266,926 1.9 36.51 120,676 45.2 3.2 38.02

Architecture and engineering (17) 799,489 5.6 32.66 382,292 47.8 10.2 35.00

Life, physical, and social science (19) 149,884 1.1 29.43 66,945 44.7 1.8 31.20

Community and social services (21) 123 (

1

) 23.82 (

2

) (

2

) (

2

) (

2

)

Legal (23) 5,509 (

1

) 54.67 (

2

) (

2

) (

2

) (

2

)

Education, training, and library (25) 1,455 (

1

) 26.73 (

2

) (

2

) (

2

) (

2

)

See footnotes at end of table.
Domestic Employment

12 Monthly Labor Review • October 2011

the matching is incomplete and the incompleteness is not 

random.

After controls are added for establishment industry, size 

class, age, and region, and for the interaction of industry 

with size class, the largest multinational manufacturers 

remain somewhat more likely to employ workers in the 

architecture and engineering; computer and mathematical; and installation, maintenance, and repair occupations. 

However, production workers are less likely to be employed in these matched establishments. These findings 

are consistent with the notion that multinational manufacturing employers have shifted some of their less skilled 

production work to plants overseas while retaining more 

skilled work in the United States. The findings are also 

consistent with the hypothesis that more productive firms 

with more highly skilled employees are more likely to become multinational firms.

The difference in wages between U.S.-based multinational manufacturers and other employers is examined with the 

use of similar controls. The following tabulation shows the 

results of regressions using various control variables Xi

 to 

examine the impact on wages of matching to the BEA firm 

data (all coefficients β are significant at p < .0001): 

 

 Standard

Category β error of β

Raw wage difference................... $2,690.50 (38.36)

Regression adjusted for—

 Census region.......................... 2,697.76 (38.36)

 Size class of establishment....... 2,597.10 (38.36)

 Age of establishment................ 2,455.55 (27.02)

 Industry group.......................... 2,099.36 (38.88)

 Specific industry........................ 1,916.71 (39.69)

 All control variables.................. 1,673.41 (28.15)

In these regressions, the outcome variable yi is the 

monthly wage bill per employee for all establishments. 

The first row of numbers shows the coefficient for the 

Match indicator without any controls Xi

: U.S. parent 

firms of multinational manufacturers pay monthly wage 

bills per employee that are $2,690.50 per month higher 

than those paid by other establishments.

Arts, design, entertainment, sports, 

and media (27) 84,672 0.6 22.96 17,400 20.5 0.5 $27.88

Healthcare practitioner and technical 

(29) 16,641 .1 26.38 6,750 40.6 .2 27.40

Healthcare support (31) 1,100 (

1

) 13.96 (

2

) (

2

) (

2

) (

2

)

Protective service (33) 17,913 .1 15.08 7,401 41.3 .2 17.01

Food preparation and serving related 

(35) 36,993 .3 9.30 1,751 4.7 (

1

) 10.59

Building and grounds cleaning and 

maintenance (37) 94,311 .7 11.57 19,192 20.4 .5 13.53

Personal care and service (39) 1,123 (

1

) 12.73 (

2

) (

2

) (

2

) (

2

)

Sales and related (41) 430,491 3.0 28.04 63,403 14.7 1.7 32.50

Office and administrative support (43) 1,384,539 9.8 15.82 282,440 20.4 7.5 17.64

Farming, fishing, and forestry (45) 36,860 .3 11.79 8,567 23.2 .2 12.13

Construction and extraction (47) 265,280 1.9 19.42 70,537 26.6 1.9 23.58

Installation, maintenance, and repair 

(49) 705,450 5.0 20.33 246,026 34.9 6.6 22.22

Production (51) 7,449,077 52.5 15.00 1,785,768 24.0 47.7 17.11

Transportation and material moving 

(53) 1,317,075 9.3 13.42 307,194 23.3 8.2 14.91

1

 Less than 0.1 percent.

2

 Value does not meet BEA or BLS publication criteria.

SOURCE: U.S. Bureau of Labor Statistics, Occupational Employment 

Statistics survey.

Continued—Civilian employment and wages for major occupational groups, all United States and matched 

 U.S. multinational manufacturing companies, all industries and manufacturing industries, fall 2003–spring 2006

Occupational group

(SOC major category)

All U.S. establishments Matched U.S. multinational manufacturing companies

Average 

monthly 

employment

Occupational 

distribution 

(percent)

Average 

hourly 

wages

Average 

monthly 

employment

Percentage of 

average monthly 

employment for 

all U.S. establishments

Occupational 

distribution 

(percent)

Average 

hourly 

wages

Table 4.
Monthly Labor Review • October 2011 13

Successive rows of numbers show that controlling for 

region has a negligible impact on this wage difference, 

whereas controlling instead for establishment size class, 

age, and industry has a more substantial effect. Controlling for the industry groups shown in table 1 reduces the 

wage difference to $2,099.36, whereas controlling instead 

for specific six-digit NAICS industry categories reduces 

the wage difference even further, to $1,916.71. Adjusting 

for all controls Xi (as well as for the interaction between 

industry groups and establishment size class) shows a 

monthly wage bill per employee that is $1,673.41 higher 

in the establishments of U.S. parent firms of multinational 

manufacturers.

These estimates from regressions of establishment-level data show the differences in monthly wage bills per 

employee between establishments that belong to multinational manufacturing companies and other establishments. However, some establishments have more workers 

than other establishments, so the difference between average wages at matched and unmatched employers is not 

the same as the difference between average wages received 

by employees of matched and unmatched employers. Accordingly, to calculate the difference in monthly wage 

bills per employee between employees of establishments 

that belong to multinational manufacturing companies 

and employees of other establishments, the same regressions are performed, with the establishments weighted by 

their employment.

As shown in table 6, the average employee of a multinational manufacturer is paid a monthly wage bill that is 

$2,290.59 higher than the employees of other employInstallation, maintenance, and repair

(49):

β

S.E.

1

.0241

(.0002)

1

.0103

(.0003)

Production (51):

β

S.E.

1

.2651

(.0003)

1

–.0088

(.0003)

Transportation and material moving

(53):

β

S.E.

1

.0221

(.0003)

1

–.0080

(.0003)

1

 Significant at p < .03. 2

 Significant at p < .0001.

NOTE: β = coefficent; S.E. = standard error of β.

SOURCE: U.S. Bureau of Labor Statistics, Occupational Employment 

Statistics survey.

Table 5. Continued—Difference in occupational 

 distribution between the employees of matched 

 and unmatched firms

Occupational group 

(SOC major category)

Raw 

probability 

difference

Regression 

adjusted 

difference

Table 5. Difference in occupational distribution between 

 the employees of matched and unmatched firms

Occupational group 

(SOC major category)

Raw 

probability 

difference

Regressionadjusted 

difference

Management (11):

β

S.E. 

1

.0163

(.0002)

1

.0064

(.0003)

Business and financial (13):

β

S.E.

1

.0118

(.0002)

1

.0044

(.0003)

Computer and mathematical (15):

β

S.E.

1

.0267

(.0002)

1

.0071

(.0002)

Architecture and engineering (17):

β

S.E.

1

.0717

(.0002)

1

.0229

(.0002)

Life, physical, and social science (19):

β

S.E.

1

.0115

(.0001)

1

.0043

(.0001)

Community and social services (21):

β

S.E.

1

–.0133

(.0001)

–.0002

(.0001)

Legal (23):

β

S.E.

1

–.0058

(.0001)

1

–.0044

(.0001)

Education, training, and library (25):

β

S.E.

1

–.0632

(.0003)

2

–.0007

(.0002)

Arts, design, entertainment, sports, and

media (27:)

β

S.E.

1

–.0059

(.0001)

1

–.0024

(.0002)

Healthcare practitioner and technical

(29):

β

S.E.

1

–.0489

(.0002)

1

–.0018

(.0003)

Healthcare support (31):

β

S.E.

1

–.0263

(.0002)

1

–.0007

(.0002)

Protective service (33):

β

S.E.

1

–.0211

(.0002)

1

–.0016

(.0002)

Food preparation and serving related

(35):

β

S.E.

1

–.0831

(.0003)

1

–.0027

(.0002)

Building and grounds cleaning and 

maintenance (37):

β

S.E.

1

–.0261

(.0002)

1

–.0024

(.0002)

Personal care and service (39):

β

S.E.

1

–.0238

(.0002)

1

–.0020

(.0002)

Sales and related (41):

β

S.E.

1

–.0382

(.0003)

1

.0057

(.0004)

Office and administrative support (43):

β

S.E.

1

–.0649

(.0004)

1

–.0183

(.0005)

Farming, fishing, and forestry (45):

β

S.E.

1

–.0015

(.0001)

1

–.0015

(.0001)

Construction and extraction (47):

β

S.E.

1

–.0274

(.0002)

1

–.0055

(.0002)

See footnotes at end of table.
Domestic Employment

14 Monthly Labor Review • October 2011

ers. After establishment characteristics (region, size class, 

age, specific industry, and industry group interacted with 

size class) are controlled for, this wage premium falls to 

$1,071.41 per month, smaller than the coefficient found 

in the unweighted regression. The difference in coefficients between the weighted and unweighted regressions 

suggests that the difference in wages between matched 

and unmatched establishments is greater in establishments with fewer employees.

The OES data also can be used in wage regressions. 

Here, yi

 is the hourly wage level for each (weighted) employee. Regressions on the OES data show that the U.S. 

parent firms of multinational manufacturers pay hourly 

wages that are $5.11 higher than those of other employers. (See table 6.) However, after controls are added for 

establishment region, size class, age, and specific industry, and for the interaction between industry group and 

size class, the difference falls to $1.57, and after further 

controls are added for workers’ occupations, the difference falls to $1.20.

To compare the results obtained here with those of 

Doms and Jensen, regressions of the logarithm of wages 

were run separately for production and nonproduction 

workers. (For the comparison, production workers were 

defined as those employed in the major occupational categories 33, 37, 43, 47, 49, 51, and 53 in the weighted OES

Table 6. Difference in wage premium between matched and 

unmatched establishments1

Dataset Wage measure Raw wage 

difference

Regressionadjusted 

difference 

(without 

occupation) 

Regressionadjusted 

difference 

(including 

occupation)

QCEW

Monthly wage bill 

per employee:

β

S.E.

2

$2,690.50

(38.36)

2

$1,673.41

 (28.15)

(

3

)

...

Monthly wage 

bill per employee, 

employment

weighted:

β

S.E.

2

2,290.59

(7.07)

2

1,071.41

 (9.72)

(

3

)

...

OES survey

Hourly wages per

employee:

β

S.E.

2

5.11 

(0.02)

2

1.57

 (0.02)

2

$1.20

 (0.02)

1

 Coefficients of the logarithm of wages show a pattern similar to that of the coefficients of wages and are available from the authors upon request. 2

 Significant at p <.0001. 3

 Coefficients cannot be calculated because the QCEW does not collect data on occupations.

SOURCE: U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages 

and Occupational Employment Statistics survey.

data.) With establishment age, specific industry, and census division controlled for, the wage premium for production workers in the establishments of multinational manufacturers compared with that for production workers in 

other large establishments is nearly 5 percent and in other 

small establishments is about 13 percent. Analogous 

wage premiums are smaller for nonproduction workers in 

the establishments of multinational manufacturers. These 

estimates are similar to, but somewhat smaller than, the 

wage differences documented by Doms and Jensen between multinational and domestic manufacturing firms 

in 1987.

Even with establishment characteristics and workers’ 

occupations controlled for, the results presented here 

show that the establishments matched with large multinational manufacturing firms pay their employees higher 

wages than do other establishments in the United States. 

However, the question remains, Why do the establishments of these firms pay higher wages? In one study, Raymond Mataloni found that U.S. establishments of large 

multinational manufacturing firms have higher levels of 

labor productivity than other U.S. establishments have.12

It is also possible that these multinational firms have the 

same global levels of labor productivity as other firms, but 

have systematically moved their lower skilled work—particularly their lower skilled production work—overseas, 

leaving higher skilled, more productive, and 

more highly paid work in the United States.

Future work

The research presented here has combined 

firm identifiers from the BEA 2004 Benchmark Survey of U.S. Direct Investment 

Abroad with BLS microdata on employment 

in establishments of these firms, for a pilot 

group of the largest U.S.-based multinational manufacturing companies. Other information collected in the BEA survey, such as 

the magnitude or the destination countries 

of these companies’ foreign investments, has 

not yet been used. This information would 

facilitate an examination, for example, of 

whether there are differences in the occupational distribution of U.S. employees between multinational manufacturing firms 

with investments overseas in high-wage 

countries and those with investments overseas in low-wage countries. BLS and BEA

researchers are working to combine the BLS
Monthly Labor Review • October 2011 15

operations of these firms, it will be possible to examine 

how the activities of multinational manufacturers correspond to their employment structure in the United States. 

The activities measured by BEA include the magnitude 

and scope of foreign direct investment, the amount of 

intrafirm trade, the destination countries for foreign direct investment, the companies’ degree of “global engagement,” their trade in services, and so forth. The collaboration described here between BEA and BLS will enable 

researchers in both agencies to analyze the occupations 

and wage structures of U.S.-based multinational firms by 

the characteristics of their international activities.

Notes

ACKNOWLEDGMENT: Several current and former BEA staff made 

significant contributions to this article, to constructing the link between BEA data and BLS data upon which the article is based, and 

to developing the memorandum of understanding that established 

the administrative procedures and framework for the link. The BEA

contributors include Maria Borga; Alexis N. Chaves; Christopher J. 

Emond; Ned G. Howenstine; Marilyn Ibarra-Caton; Raymond J. Mataloni, Jr.; Steven J. Muno; Obie G. Whichard; Daniel R. Yorgason; 

Robert E. Yuskavage; and William J. Zeile.

1 See “Direct Investment Concepts,” U.S. International Transaction Accounts: Concepts and Estimation Methods (Bureau of Economic 

Analysis, June 2011), pp. 76–77, http://www.bea.gov/international/

concepts_estimation_methods.htm.

2 Mark E. Doms and J. Bradford Jensen, “Comparing Wages, Skills, 

and Productivity between Domestically and Foreign-owned Manufacturing Establishments in the United States,” in Robert E. Baldwin, 

Robert E. Lipsey, and J. David Richardson, eds., Geography and Ownership as Bases for Economic Accounting (Chicago, University of Chicago 

Press, 1998).

3 See Sourafel Girma, Richard Anthony Kneller, and Mauro Pisu, 

“Exports versus FDI: An Empirical Test,” Review of World Economics,

July 2005, pp. 193–218; Keith Head and John Ries, “Heterogeneity 

and the FDI versus export decisions of Japanese manufacturers,” 

Journal of the Japanese and International Economies, December 2003, pp. 

448–467; Eiichi Tomiura, “Foreign Outsourcing, Exporting, and FDI: 

A Productivity Comparison at the Firm Level,” Journal of International 

Economics, May 2007, pp. 113–127; and Raymond J. Mataloni, Jr., The 

Productivity Advantage and Global Scope of U.S. Manufacturing Firms, 

BEA Working Paper WP2011–02 (Bureau of Economic Analysis, 

March 2011).

4 See Andrew Bernard and J. Bradford Jensen, “Exceptional Exporter 

Performance: Cause, Effect, or Both?” Journal of International Economics, 

February 1999, pp. 1–25; and Mataloni, The Productivity Advantage.

5 “Off-Shoring: An Elusive Phenomenon” (Washington, DC, 

National Academy of Public Administration, 2006), http://www.

napawash.org/publications-reports/off-shoring-an-elusivephenomenon; “Off-Shoring: How Big Is It?” (Washington, DC, 

National Academy of Public Administration, 2006), http://www.

napawash.org/publications-reports/off-shoring-how-big-is-it; and 

“Off-Shoring: What Are Its Effects?” (Washington, DC, National 

microdata collected in the OES Survey with the data 

items collected in the BEA Survey of U.S. Direct Investment Abroad.

Recent economic literature on international trade has 

emphasized the importance of heterogeneity among multinational firms in understanding how those firms structure their international operations.13 Stephen Yeaple, for 

example, shows that multinational firms tend to be more 

productive than other firms and multinational firms that 

are more productive own affiliates in a larger number of 

countries.14 In combining BLS establishment-level data 

with BEA firm-level data on the domestic and foreign 

Academy of Public Administration, 2007), http://www.napawash.

org/publications-reports/off-shoring-what-are-its-effects-2. 

6 The Internal Revenue Service assigns the EIN, or Federal tax ID 

number, to identify a business entity. Most large companies with many 

establishments report more than one EIN to the BLS Quarterly Census of Employment and Wages. The one or two EINs that companies 

report to BEA in the Benchmark Survey of U.S. Direct Investment 

Abroad generally match only a fraction of these large companies’ establishments.

7 The midpoints used for the wage bands in the OES survey are 

based on the exact distribution of wages in the National Compensation Survey. (For more information, see Handbook of Methods (U.S. 

Bureau of Labor Statistics, December 2008), chapter 3, “Occupational 

Employment Statistics,” pp. 1–26, http://www.bls.gov/opub/hom/

pdf/homch3.pdf, especially p. 16.)

8 In the OES survey, smaller establishments are sampled with lesser 

probability than larger establishments and are then given larger weights 

in calculating estimates.

9 Doms and Jensen, “Comparing Wages, Skills, and Productivity.”

10 Using the same aggregation of industry groups as in table 1.

11 The age of each establishment is the number of years that the 

establishment has been observed to have positive employment in the 

QCEW. Because the QCEW data are linked longitudinally back to the 

first quarter of 1990, an indicator variable is added for establishments 

which had positive employment at that time.

12 Mataloni, “The Productivity Advantage.”

13 See Stephen R. Yeaple, “Firm Heterogeneity and the Structure 

of U.S. Multinational Activity: An Empirical Analysis,” Journal of 

International Economics, July 2009, pp. 206–215; Susan E. Feinberg 

and Michael P. Keane, “Intrafirm Trade of U.S. MNCs: Findings and 

Implications for Models and Policies toward Trade and Investment,” 

in Theodore H. Moran, Edward M. Graham, and Magnus Blomström

(eds.), Does Foreign Direct Investment Promote Development? (Washington, DC, Peterson Institute for International Economics, 2005); 

and Andrew Bernard and J. Bradford Jensen, “Firm Structure, Multinationals, and Manufacturing Plant Deaths,” The Review of Economics 

and Statistics, May 2007, pp. 193–204.

14 Yeaple, “Firm Heterogeneity.”