U.S. Department of Commerce

Economics and Statistics Administration

The Pay Premium for Manufacturing Workers as 

Measured by Federal Statistics

Executive Summary

 

 istorically, manufacturing jobs have offered relatively high pay. However,

 there is not a consensus on the size of the pay premium for manufacturing jobs

relative to the economy as a whole or even whether a premium continues to exist. 

This report turns to evidence to answer those questions, using ten federal datasets, 

each of which allows us to calculate and compare the average pay of manufacturing 

workers and the average pay of workers overall. The following datasets are 

included:

 American Community Survey

 County Business Patterns

 Current Employment Statistics

 Current Population Survey

 Economic Census

 Employer Costs for Employee Compensation

 National Income and Product Accounts

 Occupational Employment Statistics

 Quarterly Census of Employment and Wages

 Quarterly Workforce Indicators

In addition, these datasets allow us to examine comparisons of average pay that 

control for various factors that could affect the estimated pay premium, such as 

whether the data measures pay on an hourly basis or for some other period, and 

which workers are included in the pay premium estimate. Generally, we find 

evidence of a pay premium regardless of which dataset we examine. However, the 

size of the premium is increased or decreased by various factors.

Key findings from the analysis include:

 Based on hourly wages and salaries, manufacturing workers earn more on 

average than the overall average worker: using data for 2013, estimates of 

By 

Jessica R. Nicholson

and 

Regina Powers

ESA Issue Brief

# 05-15

October 2, 2015

H
Page 2 U. S. Department of Commerce, Economics and Statistics Administration 

the hourly pay premium vary from 2 to 9 percent, depending on the dataset used. 

 When hours worked in a week or over the course of a year are taken into consideration, the 

estimated premium increases. Estimated premiums using weekly or annual pay data are as high as 

32 percent. This larger premium is because manufacturing employees work longer hours per week 

and more hours per year on average.

 Because the manufacturing sector has a high proportion of full-time workers relative to other 

private sector industries, the pay premium is smaller when the estimates are restricted to full-time 

workers, in one case declining from 32 percent for all workers to 12 percent for full-time employees.

 Data that distinguishes new hires from all workers finds that both groups enjoy a pay premium in 

the manufacturing sector, with new hires earning a larger premium than other workers.

 The size of the premium also varies greatly depending on the occupation. For some occupations, 

manufacturing workers earn less than workers overall. At the other extreme, manufacturing workers 

in sales occupations earn 64 percent more than their counterparts in non-manufacturing sales 

occupations throughout the economy.

 Estimates of a manufacturing pay premium should use the dataset and comparison groups that best 

suits the particular question at hand. Datasets providing estimates of hourly pay might be more 

appropriate for estimating a premium based solely on wages, but datasets providing estimates of 

weekly, monthly or annual pay might provide greater insights about worker incomes.

 Additional research is needed to better understand the underlying factors that drive the 

manufacturing premium. The heterogeneity of the manufacturing sector and its workforce suggests

that both worker characteristics, such as occupational and educational attainment, and employer 

characteristics, such as firm size and age and specific industry, could play a role in the premium. 

Regression analyses that control for such factors can help identify their importance in the overall 

premium.
U. S. Department of Commerce, Economics and Statistics Administration Page 3

Introduction

Historically, manufacturing jobs have offered 

relatively high pay, good benefits and a certain 

job security for workers in that sector.1

However, as the economy changed, the 

manufacturing sector shed millions of jobs, 

especially in the 1990s, and the manufacturing 

sector’s reputation for offering higher paying 

jobs has been called into question. 

Much of the existing evidence suggests that 

there is some pay premium for manufacturing 

jobs, although there is no consensus estimate of 

the size of the premium. For example, previous 

work by the Economics and Statistics 

Administration’s Office of the Chief Economist 

(OCE) found that, in 2010, manufacturing 

workers earned a 17 percent compensation 

premium over those not working in 

manufacturing,2and OCE recently reported

that, in 2012, new hires in manufacturing 

earned a substantial 38 percent more than new 

hires in other sectors.3

Other researchers have also estimated whether 

there is a manufacturing pay premium. The 

Economic Policy Institute found that, on 

average for 2012 to 2013, non-college educated 

workers in manufacturing made 10.9 percent 

more than similar workers in the rest of the 

economy.4 On the other hand, research by the 

 

1

Langdon, David, and Rebecca Lehrman, “The Benefits of 

Manufacturing Jobs,” U.S. Department of Commerce, 

Economics and Statistics Administration, May 2012, 

available at: 

http://esa.doc.gov/sites/default/files/1thebenefitsofmanu

facturingjobsfinal5912.pdf. 

2

Langdon and Lehrman (2012). 

3

Powers, Regina, and Ryan Noonan, “Earnings of New 

Hires in Manufacturing: Wage Benefits Continue,” U.S. 

Department of Commerce, Economics and Statistics 

Administration, February 2014, available at: 

http://esa.doc.gov/economic-briefings/earnings-newhires-manufacturing-wage-benefits-continue. 

4

Scott, Robert E., “The Manufacturing Footprint and the 

Importance of U.S. Manufacturing Jobs,” Economic Policy 

Institute, January 2015, available at: 

National Employment Law Project (NELP) found 

that certain manufacturing workers earned 7.7

percent less than their public- and privatesector counterparts.5

A recent Bloomberg View 

piece also pointed to lower earnings of 

production and non-supervisory workers in the 

durable-goods manufacturing sector, compared 

to all workers in the private sector.6

One possible explanation for this wide range of 

estimates of the manufacturing pay premium is 

that many of these studies use different 

datasets and different comparison groups as 

the basis for the calculations. Below we test this 

explanation by reviewing evidence of a pay 

premium in 2013 from ten official federal 

government data series.7These data series are: 

 American Community Survey

 County Business Patterns

 Current Employment Statistics

 Current Population Survey

 Economic Census

 

http://www.epi.org/publication/the-manufacturingfootprint-and-the-importance-of-u-s-manufacturing-jobs/. 

5

Ruckelshaus, Catherine, and Sarah Leberstein, 

“Manufacturing Low Pay: Declining Wages in the Jobs that 

Built America’s Middle Class,” National Employment Law 

Project, November 2014, available at: 

http://www.nelp.org/content/uploads/2015/03/Manufact

uring-Low-Pay-Declining-Wages-Jobs-Built-MiddleClass.pdf. 

6

Fox, Justin, “Farewell to the Blue-Collar Elite,” Bloomberg 

View, April 6, 2015, available at: 

http://www.bloombergview.com/articles/2015-04-

06/factory-worker-wages-are-nothing-special-these-days. 

Durable goods manufacturing accounted for 62 percent of 

all production and nonsupervisory employment in 

manufacturing in May 2015 according to the Current 

Employment Statistics survey. Nondurable goods 

manufacturing accounted for the remaining 38 percent. 

7

In this report, “pay” is a general term that refers to the 

monetary compensation paid to or received by employees 

for work performed. Depending on the data source, “pay” 

can refer to wages, an hourly rate as stated by the 

employer; earnings, the actual cash compensation 

received over a given time period; or payroll expenditures 

for wages and salaries. 
Page 4 U. S. Department of Commerce, Economics and Statistics Administration

 Employer Costs for Employee 

Compensation

 National Income and Product Accounts

 Occupational Employment Statistics

 Quarterly Census of Employment and 

Wages

 Quarterly Workforce Indicators

It is important to note that these data sources 

have several methodological differences,

including how pay is defined, whether the 

survey unit is a household or business 

establishment, how long the reference period is

(that is, the period of time the statistics are 

measuring), and what types of employees are 

covered.8These methodological differences 

may help explain why there is a range of 

estimates of the manufacturing pay premium.9

Other methodological issues that can lead to 

different estimates of the premium include the 

fact that most of the datasets rely on sample 

surveys with corresponding sampling

errors.

10 In other words, average pay is an

estimate that approximates a true value; these 

sample-based estimates lie in a confidence 

interval around the true value the survey is 

trying to measure.

11 Second, the sources of pay 

 

8

Appendix 1 provides detail on the definitions and 

methodology for each dataset, as well as how industries 

and class of worker are identified. Appendix 2 discusses 

differences in employee coverage of the estimates.

9 We do not provide an explanation as to why the 

premium exists, nor does this report attempt to pick the 

best dataset for measuring the premium. However, as 

discussed below, we do offer areas for future research 

that might help explain why a manufacturing pay premium 

exists and how to put the size of the premium in context. 

10 Three datasets are not from sample-based surveys; the 

Economic Census, County Business Patterns, and the 

Quarterly Census of Employment and Wages provide data 

from full counts of establishments.

11 A confidence interval expresses the amount of 

uncertainty associated with a statistic drawn from a 

sample. For example, suppose we might estimate, based 

on survey data, that 10.0 to 12.0 percent of a city’s 

population had a certain characteristic. We might then use 

a confidence level to describe the uncertainty associated 

with the estimate of the interval of 10.0 to 12.0 percent. 

data for the datasets can differ. Most of the 

datasets use information from employers’

business payroll records, but others, namely the 

Current Population Survey (CPS) and the 

American Community Survey (ACS), collect 

information from individuals (or households). 

To the extent that individuals rely on recall, 

they may report their earnings with less 

accuracy than that of official employer records. 

Furthermore, in some cases the household 

surveys request information about “usual pay” 

rather than actual pay, which could make the 

estimates even less precise. In addition, these 

data sources can have different methods of

editing and correcting data and handling 

nonresponse. Finally, as discussed in Box 1,

definitions of pay differ across data sources. 

However, in our discussion below, we focus on 

a few crucial aspects that help explain the 

difference in pay premium estimates across the 

various datasets. One key difference is the time 

period over which the pay premium is 

calculated; looking at hourly wages versus 

weekly or annual data generally results in a 

lower estimate of the pay premium. In 

addition, limiting the analysis to full-time 

employees or new hires also leads to a different 

picture of the size of the pay premium: limiting 

the estimates to full-time employees decreases 

the size of the premium, while examining only 

new hires leads to a higher estimate of the pay 

premium.

 

This estimate might be described as a "95% confidence 

interval" which indicates that if we used the same 

sampling method to construct multiple samples and an 

interval estimate was computed for each sample, the true 

population parameter would fall within the interval 

estimates 95% of the time. See Stat Trek for a full 

definition of confidence intervals: 

http://stattrek.com/statistics/dictionary.aspx?definition=c

onfidence_interval.
U. S. Department of Commerce, Economics and Statistics Administration Page 5

Box 1. Defining Pay

One possible reason that pay premium estimates differ is that the definition of “pay” differs by data 

source; earnings data from one source may include different components of pay than earnings data 

from another. Table B-1 lists some common components of pay and indicates which components are 

included in each dataset that provides hourly estimates of pay.AThe table lists the datasets from the 

narrowest measure of pay to the broadest. For example, hourly earnings data from the Current 

Population Survey (CPS) and the Employer Costs for Employee Compensation (ECEC) datasets include 

only the basic wage rates that a worker earns for an hour of work while hourly pay data from the 

National Income and Product Accounts (NIPA) includes the payments for basic wages as well as overtime 

pay, pay for leave, regular bonuses, and other components of pay.B

Generally speaking, including more sources of pay increases the hourly pay; however broader measures 

of pay do not necessarily show a higher pay premium. The size of the manufacturing pay premium 

depends on the prevalence of a specific pay component in manufacturing relative to the private sector 

as a whole. For example, if manufacturing workers are more likely than the average worker to receive 

shift differentials (extra compensation for hours worked outside of standard shift hours), then we would 

expect to see a higher manufacturing pay premium estimate from measures of pay that includes shift 

differentials. Unfortunately, we do not have information on the prevalence of these pay components 

across industries, so we cannot determine to what extent these differences account for the differences 

in the estimated pay premium.

A

See Appendix I for a complete list of how pay is defined for all ten datasets.

B

Some components of supplemental pay are included in CPS estimates of usual weekly earnings, but not estimates of hourly 

earnings, which are intended to capture the stated wages of hourly workers.

Current 

Population 

Survey[1]

Employer 

Costs for 

Employee 

Compensation

Occupational 

Employment 

Statistics

Current 

Employment 

Survey

National 

Income and 

Product 

Accounts 

Base wage/salary rate     

Supplemental pay [2]

Overtime pay  

Pay for leave (sick, vacation, personal)   

Regular bonuses   

Irregular bonuses 

Pay in kind 

Commissions/tips   

Shift differentials  

Table B-1. Components of Hourly Pay for Datasets with Hourly Pay 

Note: [1] Hourly earnings data from the Current Population Survey only covers workers who are paid on an hourly basis, which 

accounts for approximately 3/5 of all wage and salary workers.

 [2] In ECEC, supplemental pay is considered a benefit, not a component of wages or salaries. Supplemental pay does not include 

paid leave, which is considered a separate benefit. 

Source: Department of Commerce, Office of the Chief Economist
Page 6 U. S. Department of Commerce, Economics and Statistics Administration

Estimates of the Manufacturing Pay 

Premium from Ten Federal 

Government Datasets

Table 1 presents the most recently available 

average pay data for the manufacturing sector 

and for private industry, as a whole, from ten 

different federal government data sources.12

The table also includes a simple measure of the 

manufacturing pay premium: the ratio of the 

average pay in manufacturing to the average 

pay in the private sector—without taking into 

account any differences in job or worker

 

12 For the purpose of this report, we focus on private 

sector employees. See Appendix 2 for additional 

information on employee coverage.

characteristics between the average 

manufacturing worker or job and the overall 

average job across the private sector. A ratio 

greater than one indicates that the average pay 

in manufacturing is greater than the average 

pay in the private sector and that, therefore, 

there is a pay premium in manufacturing. Taken 

together, these sources, which all show ratios 

greater than one, suggest that there is a 

manufacturing pay premium. 

In what follows, we explore some of the 

characteristics of the datasets that help explain 

differences in the estimates of the pay 

Coverage Period Manufacturing

Ratio: 

Manufacturing/

All Private 

Sector

Average hourly wages, salaries, or earnings:

Current Employment Statistics [2] Bureau of Labor Statistics August 2015 $ 25.33 $ 25.09 1.01 1.02

National Income and Product Accounts Bureau of Economic Analysis 2013 $ 31.19 $ 29.50 1.06 1.06

Employer Costs for Employee Compensation Bureau of Labor Statistics Q2 2015 $ 24.10 $ 21.82 1.10 1.09

Current Population Survey [3] Bureau of Labor Statistics Q2 2015 $ 17.35 $ 15.59 1.11 1.09

Occupational Employment Statistics Bureau of Labor Statistics May 2014 $ 23.37 $ 22.17 1.05 1.06

Average weekly wages, salaries or earnings:

Current Employment Statistics [2] Bureau of Labor Statistics August 2015 $ 1,033 $ 868 1.19 1.20

Quarterly Census of Employment and Wages [2] Bureau of Labor Statistics Q1 2015 $ 1,275 $ 1,056 1.21 1.23

Current Population Survey [4] Bureau of Labor Statistics Q2 2015 $ 1,164 $ 1,149 1.01 1.07

Average monthly wages, salaries, or earnings:

Quarterly Workforce Indicators [5] Census Bureau Q1-Q4 2013 $ 5,090 $ 4,252 1.20 1.20

Average quarterly wages, salaries, or earnings:

County Business Patterns Census Bureau Q1 2013 $ 13,473 $ 11,934 1.13 N/A

Average annual wages, salaries or earnings:

Quarterly Census of Employment and Wages [2] Bureau of Labor Statistics 2014 $ 62,977 $ 51,295 1.23 1.23

Economic Census [2] Census Bureau 2012 $ 52,686 $ 46,695 [6] 1.13 N/A

County Business Patterns [7] Census Bureau 2013 $ 54,258 $ 47,534 1.14 1.14

National Income and Product Accounts Bureau of Economic Analysis 2014 $ 64,021 $ 51,710 1.24 1.24

American Community Survey [8] Census Bureau 2014 $ 41,880 $ 31,754 [9] 1.32 1.32

Notes: [1] Data are the most recent as of September 17, 2015.

 [2] Data for the most recent period was preliminary at the time of publication.

 [3] Hourly earnings data only covers workers who are paid on an hourly basis, which accounts for approximately 3/5 of all wage and salary workers. 

 [4] Usual weekly earnings of full-time wage and salary workers.

 [5] National QWI dataset, four-quarter averages.

 [6] Excludes the construction sector.

 [7] Annual pay per employee is based on annual payroll and first quarter employment.

 [8] Median earnings are presented. Data is from the 1-year sample.

 [9] Data is for all private employees ages 16 and older, including the incorporated self-employed.

Source: Department of Commerce, Office of the Chief Economist analysis using data from the Bureau of Labor Statistics, the Census Bureau, and the 

 Bureau of Economic Analysis.

Table 1. Pay in Manufacturing and the Private Sector, 2013 and Most Recent Period

Data Source and Measure of Pay

Most Recent Data [1] 2013 Ratio: 

Manufacturing/

All Private 

Sector

All Private Sector Publishing Organization
U. S. Department of Commerce, Economics and Statistics Administration Page 7

premium, and we discuss the importance of 

selecting appropriate comparison groups. To 

facilitate comparability, we focus on annual 

estimates for 2013, the most recent year for 

which data from all datasets except the 5-year 

Economic Census is available.13 The 2013 

estimates are shown in the last column of 

Table 1.

A straightforward way to measure the pay 

premium is to compare the average hourly pay 

in manufacturing to the average hourly pay in 

the private sector overall. 14 The top part of 

Table 1 provides estimates of the hourly pay 

premium in manufacturing from the five 

datasets for which this information is available. 

For 2013, the premium measured in this way 

ranges from 2 percent to 9 percent. 

It is important to note that Table 1 includes only 

monetary earnings in the measurement of pay. 

Additional analysis of the manufacturing pay 

premium could incorporate a broader measure 

of compensation that would include employerprovided (fringe) benefits such as insurance 

(e.g. health, life, disability); contributions to 

retirement plans; and legally-required benefits 

(e.g. unemployment insurance, workers’ 

compensation). Figure 1 displays the hourly pay 

premium for two datasets for which data on 

earnings and total compensation are available. 

When benefits for manufacturing workers and 

private sector workers are considered, the 

estimated total compensation premium for 

 

13 Annual payroll data for 2013 is available from the 

County Business Patterns program, but employment data 

is only available for the first quarter of that year. The latest 

data from the Economic Census is for 2012. 

14 Some datasets allow comparison to non-manufacturing 

in addition to the private sector overall, but the results are 

essentially the same. For example, using Current 

Employment Statistics and National Income and Product 

Accounts data, the estimated hourly premiums for 

manufacturing were one percentage point higher than the 

results presented in Table 1. Since not all datasets allow 

for the calculation of pay for the non-manufacturing 

sector, throughout the rest of this report, we compare 

manufacturing pay to the private sector. 

manufacturing workers increases. This is not 

surprising because manufacturing workers 

typically have greater access to employerprovided benefits than the average private 

sector worker.15

Hours of Work and the Pay Premium

Table 1 shows an hourly pay premium in the 

range of 2 to 9 percent, but other measures of 

pay, such as weekly, monthly, quarterly and 

annual pay, are also available from the data 

sources we examine. Table 1 shows that the 

manufacturing pay premium for weekly pay is in 

the range of 7 to 23 percent, while the annual 

pay premium is in the range of 14 to 32 percent. 

The average manufacturing employee works 

more hours per week and more weeks per year 

than the average private sector employee, so 

the longer hours worked amplifies the hourly 

pay premium when pay is measured over 

weeks, months, quarters, and years. 

Specifically, average weekly hours of 

manufacturing employees in 2013 were 40.8 

hours, or more than 6 hours higher than the 

private sector average of 34.4 hours.16 Even if 

hourly pay were equal across manufacturing 

and the private sector, these differences in 

weekly hours would result in the average 

manufacturing employee earning 19 percent 

more per week than the average private sector 

employee. Furthermore, manufacturing 

workers, on average, worked 48.4 weeks in 

2013, more than the 46.4 worked by the

 

15 See Bureau of Labor Statistics, Employee Benefits in the 

United States March 2014, available at 

http://www.bls.gov/news.release/pdf/ebs2.pdf.

16 Data on average weekly hours is from the Current 

Employment Statistics survey available at: 

http://www.bls.gov/ces/. In July 2015, the latest data 

available, average weekly hours for manufacturing 

employees were 40.7 versus 34.6 for all private sector 

employees.
Page 8 U. S. Department of Commerce, Economics and Statistics Administration

average private sector employee.17 Therefore,

the cumulative effect of more hours worked per 

week and more weeks worked per year would 

increase the pay premium to 24 percent, even 

with no difference in hourly pay.

To further explore the issue of how the 

measurement period affects the estimated pay 

premium, we examine estimates from sources 

that provide both a measure of hourly earnings 

and a measure of earnings over a longer time 

period. These sources are the National Income 

and Product Accounts (NIPAs), the Current 

Employment Statistics (CES) survey, and the 

CPS. 

Figure 2 shows the ratios of pay in 

manufacturing to pay in the private sector, 

calculated using these three different data 

sources. The NIPA and CES estimates, which are 

based on employer payrolls, suggest that the

longer the time period of payment, the larger 

the premium. Using the NIPA data, hourly 

 

17 OCE tabulations of 2014 CPS ASEC data on weeks 

worked per year.

earnings in manufacturing are 6 percent higher 

than in the private sector overall, but annual 

earnings are 24 percent higher. Similarly, the 

CES data shows that average hourly earnings in 

manufacturing and in the private sector overall 

are about equal, but weekly earnings in 

manufacturing are about 20 percent higher 

than the private sector average. 

In contrast, the CPS estimates do not show a 

large difference between the hourly and weekly 

earnings premium. The CPS shows hourly 

earnings about 9 percent higher in 

manufacturing than the private sector, and 

usual weekly earnings about 7 percent higher. 

Thus, while the data shows an earnings 

premium in both cases, the premium per hour 

appears smaller than the premium per week. 

However, these results must be interpreted 

with caution. The CPS hourly earnings statistics 

are based on the wage rate of only those

workers who are paid on an hourly basis, which 

represents about three-fifths of workers 

covered by the survey. In contrast, the statistics 

on usual weekly earnings pertain only to fulltime workers and can include components of 
U. S. Department of Commerce, Economics and Statistics Administration Page 9

pay in addition to the wage (if those 

components are usually paid).18 In short, the 

hourly and weekly earnings data cover different 

sets of workers and therefore are not directly 

comparable even though they come from the 

same data source.19 

Figure 3, while further highlighting the 

importance of hours of work, also shows that 

the size of the earnings premium can vary 

depending on the segment of the workforce 

considered. Specifically, Figure 3 shows 

estimates from three data sources—the NIPAs, 

 

18 In the CPS, respondents are asked for their hourly wage, 

excluding tips, bonuses and overtime, if they are paid by 

the hour, but are also given the option of reporting their 

pay on a different basis, such as weekly, monthly, or 

annually. If respondents choose to report their earnings on 

a basis other than hourly, their responses are not used to 

compute average hourly earnings but are used to compute 

usual weekly earnings. 

19 The Bureau of Labor Statistics does not publish average 

hourly earnings statistics from CPS based on all workers. 

Meisenheimer II, Joseph R., “Real compensation, 1979 to 

2003: analysis from several data sources,” Monthly Labor 

Review, May 2005, p. 21, available at: 

http://www.bls.gov/opub/mlr/2005/05/art1full.pdf. 

ACS, and Employer Costs for Employee 

Compensation (ECEC)—that provide earnings

data for all workers and for full-time workers 

only and show that the manufacturing pay 

premium is lower when part-time workers are 

excluded from the comparisons. According to 

the 2013 NIPA data, manufacturing workers 

have average annual wages and salaries that 

are 24 percent higher than those of all private 

sector workers. However, the pay premium is 

smaller, only 15 percent, when considering the 

earnings of full-time equivalent workers. Data 

for 2013 from the ACS shows a similar result. 

Median annual earnings for all employees were 

32 percent higher in manufacturing than in the 

overall economy; but considering only full-time, 

year-round employees, the median annual 

earnings premium in manufacturing— 12

percent—was less pronounced. 

Excluding part-time workers has the effect of 

raising average annual earnings because parttime workers, by definition, work and,

generally, earn less on an annual basis than fulltime workers. In 2014, almost a quarter (23.7 

percent) of all wage and salary workers worked 
Page 10 U. S. Department of Commerce, Economics and Statistics Administration

part-time (or less than 35 hours a week) 

according to the CPS. For the manufacturing 

sector, the share of workers who typically 

worked part-time was only 10.9 percent. 

Because a relatively small percentage of the 

manufacturing workforce is part-time, taking 

these workers out has a smaller effect on 

average annual earnings in manufacturing than 

it does for all wage and salary workers. For this 

reason, the manufacturing pay premium for 

annual pay is smaller when considering only 

full-time workers.

Like the NIPAs and the ACS, the ECEC data 

shows a higher pay premium when all workers 

are included than when only full-time workers 

are considered. However, the ECEC estimates 

are different because they pertain to hourly 

earnings, defined as the hourly straight-time 

wage rate for workers paid on an hourly basis,

or as the straight-time earnings divided by the 

corresponding hours of work for those not paid 

on an hourly basis. According to the 2013 ECEC 

data, hourly earnings for all employees in 

manufacturing were 9 percent higher in 

manufacturing than in private industry overall. 

However, among full-time workers, average 

hourly earnings were lower in manufacturing:

97 percent of the overall private industry 

average for full-time workers. 

Pay Premiums for Select Workers

As highlighted in the previous discussion, the 

magnitude of the pay premium can depend on 

the population of workers that makes up the 

comparison group. This point is further 

illustrated in Figure 4, which compares pay 

premiums for different worker populations 

from the same data sources. The first set 

distinguishes between the premium for all 

employees and the premium for new hires 

only—as measured by the Quarterly Workforce 

Indicators (QWI), which uniquely identifies 

newly hired workers. The second set 

distinguishes between the premium for all 

employees and the premium for production and 

nonsupervisory workers (PE), a group uniquely 
U. S. Department of Commerce, Economics and Statistics Administration Page 11

identified in the CES. 20 Wages of production 

and nonsupervisory workers are frequently 

cited by observers of pay in the manufacturing 

sector.21

Figure 4 shows that the earnings premium is 

different for new hires than for all workers. The

QWI reports average monthly earnings of all 

 

20 See the CES technical notes for the full definition of 

production and nonsupervisory employees (PE) at: 

http://www.bls.gov/web/empsit/cestn.htm#section4. For 

the manufacturing sector, PE include working supervisors 

and all nonsupervisory employees (including group leaders 

and trainees) engaged in activities closely associated with 

fabricating, processing, assembling, inspecting, receiving, 

storing, handling, packing, warehousing, shipping, 

trucking, hauling, maintenance, repair, janitorial, guard 

services, product development, auxiliary production for 

plant's own use (for example, power plant), 

recordkeeping, and other services closely associated with 

the above production operations.

21 See, for example, Levinson, Marc, “Job Creation in the 

Manufacturing Revival,” Congressional Research Service 

Report R41898, July 2, 2015, available at: 

https://www.fas.org/sgp/crs/misc/R41898.pdf and Fox, 

Justin, “Farewell to the Blue-Collar Elite,” Bloomberg View, 

April 6, 2015, available at: 

http://www.bloombergview.com/articles/2015-04-

06/factory-worker-wages-are-nothing-special-these-days. 

workers employed for a full quarter, and 

separately reports the average monthly 

earnings of new hires (full-quarter employees 

who were newly hired during the previous 

quarter). According to the QWI data, average 

monthly earnings in 2013 were 19 percent 

higher for all employees in manufacturing than 

for all employees across all private industries. 

But among new hires only, average monthly 

earnings were 33 percent higher in 

manufacturing than in all industries. It is not 

clear why the manufacturing pay premium 

would be greater for new hires than for 

employees overall. One possible explanation is 

hours worked: as noted above, workers in 

manufacturing work more hours than workers 

overall. If the greater hours of work observed 

in manufacturing is even more pronounced for 

new hires, it can show up as a larger pay 

premium.

22

 

22 Another explanation is that manufacturers have a twotiered hiring system, with higher-paid workers coming on 

board as employees and therefore counted as new hires, 

and lower-paid workers brought on as temporary workers 

and thus not counted as new hires at all. Additional 

research is needed to explore these issues.
Page 12 U. S. Department of Commerce, Economics and Statistics Administration

Figure 4 also shows the manufacturing 

premium, using the CES, for all employees as 

well as for PE workers only. Both show little or 

no pay premium for manufacturing. However, 

the latter comparison is problematic because 

the CES definition of PE in manufacturing is 

much narrower than the definition of PE in the 

service-providing industries (that is, industries 

other than mining, construction and 

manufacturing). For the manufacturing sector, 

the PE category includes working supervisors 

and all non-supervisory employees engaged in 

activities closely associated with production 

operations; these workers represented 70 

percent of all manufacturing jobs in 2013. For 

service-providing industries, the PE category is a 

broader, more heterogeneous group that

includes most employees that are not 

supervisors, regardless of the type of work they 

perform. For example, this group can include 

office and clerical workers, physicians, teachers, 

lawyers, and salespersons. In 2013, PEs 

represented about 83 percent of all serviceproviding industry jobs. 

It might be more precise to compare 

manufacturing and private sector pay by 

specific occupations because, even across 

industries, workers in a specific occupational 

group are more likely to perform similar work. 

Figure 5 offers a preliminary analysis drawing 

upon Occupational Employment Statistics (OES) 

hourly earnings estimates for each major 

occupational group.23

 

23 See http://www.bls.gov/soc/#classification for more 

information on the Standard Occupational Classification 

system.
U. S. Department of Commerce, Economics and Statistics Administration Page 13

Using the OES, across all occupations, hourly 

earnings for manufacturing workers are about 6 

percent higher than the average across the 

private sector; however, the differences within 

specific occupations are considerable. For six 

occupations, shown in red, workers in 

manufacturing earn less than their private 

industry counterparts. Workers in production 

occupations (as classified by SOC 51-0000) in 

the manufacturing sector earn 2.8 percent 

more, on an hourly basis, than workers in 

production occupations in the private sector as 

a whole. This contrasts with the results 

discussed above which showed a slightly lower 

pay for manufacturing PEs relative to all PEs,

but which are based on different definitions of 

the PE category across the two sectors.24 

Further Research on the 

Manufacturing Pay Premium

Additional research could go beyond an 

examination of how different datasets affect

the premium to shed light on what drives the 

existence of the premium and how it has 

 

24 In a November 2014 report, NELP estimated that 

workers in manufacturing earned 7.7 percent less than all 

workers in the public and private sectors. That report 

compared the median hourly wage of workers in 

production occupations in manufacturing (SOC 51-0000) to 

the earnings of all workers in both the public and private 

sector (all SOC codes, or SOC 00-0000). Because workers in 

production occupations made up just over half of 

employment in the manufacturing sector in 2013, the 

NELP method compared the hourly earnings of only half of 

the workers in manufacturing to all workers in the U.S. 

economy, which is not a proper comparison for estimating 

an earnings premium. As shown in Figure 5, when only 

workers in production occupations are considered for both 

manufacturing and the comparison group, there is a 2.8 

percent earnings premium for manufacturing workers. 

Using median hourly earnings rather than average hourly 

earnings, and including public sector workers in the 

comparison group, as the NELP estimates do, 

manufacturing workers in production occupations earn a 

4.2 percent premium over workers in production 

occupations in other sectors. 

changed over time.25 In particular, a more 

detailed analysis of industries within the 

manufacturing sector would be useful. 

Manufacturing, like other sectors, comprises

industry groups that often have different 

characteristics from the sector average. Within 

manufacturing, for example, pay in the textile 

manufacturing industry varies substantially 

from pay in the transportation equipment 

industry.26 Data on new hires and separations 

within the higher-paying and growing 

manufacturing industries might help explain 

trends in the overall manufacturing premium. 

Like industries in the manufacturing sector, the 

occupational mix of the manufacturing sector is 

heterogeneous, and the average pay in these 

occupations varies. While touched on in this 

report, occupational pay premiums could be 

further explored. The occupational pay 

 

25 Inter-industry pay differentials have been a topic of 

research for some time. Some research points to 

“efficiency wages” as an explanation for manufacturing 

pay premium. Under this line of reasoning, a pay premium 

arises because workers in certain industries are paid 

above-average wages to ensure that they are 

appropriately skilled and motivated. Such skill and 

motivation may be particularly important for 

manufacturing firms, which are generally more capitalintensive and larger and therefore require workers that 

manage themselves effectively. For citations to such 

research, see Helper, Susan, Timothy Krueger, and Howard 

Wial. “Why Does Manufacturing Matter? Which 

Manufacturing Matters? A Policy Framework.” 

Metropolitan Policy Program at Brookings, February 2012 , 

available at 

http://www.brookings.edu/~/media/Research/Files/Paper

s/2012/2/22%20manufacturing%20helper%20krueger%20

wial/0222_manufacturing_helper_krueger_wial.pdf (at 

page 5). Helper et al. also mention other possible 

explanations for a manufacturing pay premium, including 

arguments that (1) manufacturing jobs are less pleasant or 

safe than others and therefore require compensating 

higher pay; (2) unmeasured or unobservable worker or job 

characteristics explain the higher wages; and (3) unions 

bid up wages. However, they suggest that there is little 

evidence to support these explanations.

26 In 2014, workers in the textile manufacturing industry 

were paid $16.02 per hour while workers in the 

transportation equipment manufacturing industry were 

paid $29.85 per hour.
Page 14 U. S. Department of Commerce, Economics and Statistics Administration

variation is likely at least partially the result of 

differences in the education and experience 

required for specific occupations. Previous OCE 

research found that manufacturing workers 

enjoyed wage and benefit premiums even after 

controlling for education and other factors; 27

this research can be updated and expanded to 

examine more recent trends in manufacturing 

pay and to examine other factors, such as firm 

age and firm size, that might be correlated with 

higher earnings. 

Both union membership and coverage are down 

substantially over the past few decades for all 

workers, including manufacturing workers. In 

2014, 7.4 percent of all private sector workers 

were covered by a collective bargaining 

agreement, and 6.6 percent were union 

members.28 For the manufacturing sector, the 

shares are higher—9.7 percent of workers are 

members and 10.5 percent are covered.

Nonetheless, unionization rates remain 

relatively high in some sectors, so the effect of 

unions on hiring practices and pay in different 

manufacturing industries deserves further 

exploration. 

Similarly, the manufacturing sector’s increasing 

use of temporary workers may affect the pay 

premium for manufacturing. Temporary 

workers are typically paid less than permanent 

employees and are less likely to earn benefits.29

If the relative use of temporary workers in 

 

27 Langdon and Lehrman (2012).

28 See http://www.unionstats.com/ section I. U.S. 

Historical Tables: Union Membership, Coverage, Density 

and Employment, 1973-2014. Workers are counted as 

covered by a collective bargaining agreement if they are 

union members or non-members covered by a union 

contract.

29 According to the 2005 Current Population Survey 

Contingent Worker Supplement (the latest available), 8.3 

percent of all temporary workers were covered by 

employer-provided health insurance and 3.8 percent were 

included in an employer-provided pension plan. The 

comparable figures for workers in traditional work 

arrangements were 56.0 percent and 47.7 percent. 

Available at: 

http://www.bls.gov/news.release/pdf/conemp.pdf. 

manufacturing and the private sector were 

taken into account, the manufacturing pay (and 

total compensation) premium could be reduced 

or disappear completely.30 Thus, additional 

research should explore the use of temporary 

workers within manufacturing industries, as 

well as its impact on the hiring and pay of 

traditional employees. 

Conclusion

This report has shown that the size of the 

manufacturing pay premium is a function of 

various factors, including which dataset is used 

to address this question. No single dataset 

stands out as the best one for measuring the 

“true” pay premium. Instead, the choice of 

dataset depends on the specific question at 

hand. Datasets providing estimates of hourly 

pay tend to show a smaller premium than 

measures that capture pay received over longer 

time periods. Further, measures restricted to 

the earnings of full-time workers tend to show a 

smaller premium than those that capture the 

earnings of all workers. Together, these findings 

suggest that an important source of the pay 

premium for manufacturing workers is that they 

work more hours than other workers. 

The reality is that manufacturing is not one 

economic activity, but comprises millions of 

 

30 Federal government surveys that measure employment 

and pay do not ask firms about their use of temporary 

workers in a way that counts these workers as part of the 

firms’ labor force. Instead, temporary workers are 

classified as employees in a separate industry called 

“temporary help services.” All temporary workers are 

included in this industry whether they report to work at a

factory or a doctor’s office or a retail outlet. This method 

of classifying temporary workers makes it impossible to 

identify which industries are using temporary workers and 

whether they are using them as a substitute for traditional 

permanent employees. However, preliminary calculations 

based on reasonable assumptions about the use of 

temporary workers in manufacturing and using May 2014 

OES data suggest that the pay premium declines by about 

two percentage points when some temporary workers are 

included in the manufacturing sector. 
U. S. Department of Commerce, Economics and Statistics Administration Page 15

workers in hundreds of thousands of 

establishments across a multitude of industries 

– so a single number would not capture the 

benefits of working in manufacturing. Further 

research is needed to build on the pay premium 

estimates presented here and to refine our 

understanding of manufacturing jobs and better 

assess the characteristics that have traditionally 

contributed to them being considered good 

jobs.

Additional research could also explore other 

potential reasons that the manufacturing 

premium exists. Analyses that simultaneously 

account for the many factors that affect the pay 

premium would more precisely estimate the 

differential between pay in manufacturing 

versus other sectors. This could be done 

through regression analyses that control for 

variables such as education, age, occupation 

and other characteristics that might affect pay.

By providing a descriptive analysis of the many 

datasets that can be used to analyze 

manufacturing earnings, and providing some 

results based on type of worker and occupation, 

this report represents a first step in that 

direction. 
Page 16 U. S. Department of Commerce, Economics and Statistics Administration

Appendix 1 – Methodological 

Summary for Ten Federal Earnings 

Datasets

The American Community Survey (ACS) is a 

national household survey that uses continuous 

measurement methods. In this survey, a series 

of monthly samples produce annual estimates 

for the same small areas (census tracts and 

block groups) formerly surveyed via the 

decennial census long-form sample. The ACS 

collects information from individuals about 

demographics, employment and earnings, 

educational attainment, housing characteristics, 

social issues, and other topics. Approximately 

3.5 million housing units and group quarters are 

sampled each year. Estimates are released for 

5- and 1-year periods at various levels of 

geographic detail. The ACS collects information 

about earnings during the 12-month reference 

period (the previous 12 months from the date 

the questionnaire is filled out). Estimates are 

published for workers 16 years and older with 

earnings; for all employees; and full-time, yearround civilian employees 16 years and older

with earnings; and other subgroups. Earnings 

are defined as the sum of wage or salary 

income from all jobs and net income from selfemployment. Wage or salary income includes 

total money earnings received for work 

performed as an employee during the past 12 

months. It includes wages, salary, Armed Forces 

pay, commissions, tips, piece-rate payments, 

and cash bonuses earned before deductions 

were made for taxes, bonds, pensions, union 

dues, etc. See 

http://www.census.gov/programs-surveys/acs/

for more information.

The County Business Patterns (CBP) program 

provides subnational economic statistics by 

industry for employer businesses, including the 

number of establishments, employment during 

the week of March 12, first quarter payroll, and 

annual payroll. The universe of employer 

establishments for CBP is the Census Bureau’s 

Business Register, which contains information 

for each known establishment in the U.S. 

Payroll and employment data is tabulated from 

administrative records for single-unit 

companies and a combination of administrative 

records and survey-collected data for multi-unit 

companies. Payroll includes all forms of 

compensation, such as salaries, wages, 

commissions, dismissal pay, bonuses, vacation 

allowances, sick-leave pay, and employee 

contributions to qualified pension plans paid 

during the year to all employees. For 

corporations, payroll includes amounts paid to 

officers and executives; for unincorporated 

businesses, it does not include profit or other 

compensation of proprietors or partners. 

Payroll is reported before deductions for social 

security, income tax, insurance, union dues, etc. 

See http://www.census.gov/econ/cbp/ for 

more information.

The Current Employment Statistics (CES)

provides data based on a monthly survey of 

approximately 143,000 nonagricultural business 

establishments and government agencies 

representing approximately 588,000 worksites 

throughout the U.S. CES average earnings are a 

measure of gross payrolls divided by total hours 

paid during the pay period that includes the 

12th day of the month. Payroll is for full- and 

part-time workers who received pay for any 

part of the pay period, and is reported before 

deductions of any kind, such as those for oldage and unemployment insurance, group 

insurance, withholding tax, bonds, or union 

dues; also included is pay for overtime, holidays 

and vacation, sick leave paid directly by the 

firm, and commissions paid at least monthly. 

Bonuses (unless earned and paid regularly each 

pay period); other pay not earned in the pay 

period reported (such as retroactive pay); and 

the value of free rent, fuel, meals, or other 

payment in kind are excluded. Employee 

benefits (such as health and other types of 

insurance, contributions to retirement, and so 

forth, paid by the employer) also are excluded.

See http://www.bls.gov/ces/ for more 

information. 
U. S. Department of Commerce, Economics and Statistics Administration Page 17

The Current Population Survey (CPS) is a 

probability selected monthly sample survey of

about 60,000 occupied households from all 50 

states and the District of Columbia, 

representing the civilian non-institutional 

population age 15 years and older (although 

published data typically pertains to persons age 

16 and over). Respondents answer questions 

that typically refer to work activities during the 

calendar week that includes the 12th day of the 

month. Earnings data are collected from onefourth of the monthly sample. One of the main 

earnings measures developed from CPS data is 

the usual weekly earnings of full-time wage and 

salary workers, which is based on a wage 

earner’s report of the total gross pay usually 

received before deductions for taxes, social 

security, and other mandatory deductions. 

Gross pay includes any overtime pay, tips, or 

commissions usually received. Data also is

collected on the hourly earnings of workers paid 

by the hour. Hourly earnings for hourly paid 

workers do not include overtime pay, 

commissions, or tips received. See 

http://www.census.gov/cps/ and 

http://www.bls.gov/cps/ for more information. 

The quinquennial Economic Census (conducted 

for years ending in 2 or 7) collects data, under 

the mandatory authority of Title 13 of the U.S. 

Code, from establishments that are located in 

the U.S., have paid employees, and are 

classified in North American Industry 

Classification System (NAICS) sectors other than 

agriculture, forestry, fishing, and hunting 

(Sector 11) and public administration (Sector 

92). Large- and medium-sized singleestablishment companies and all 

establishments of multi-establishment 

companies are surveyed. For small singleestablishment companies, data is based on a 

sample survey or administrative records 

provided to the U.S. Census Bureau by other 

federal agencies. The Economic Census provides 

data on total employment and gross payroll 

earnings of all employees of all operating 

establishments in the calendar year. Gross 

payrolls include salaries, wages, commissions, 

dismissal pay, bonuses, vacation and sick leave 

pay, and compensation in kind, prior to such 

deductions as employees’ social security 

contributions, withholding taxes, group 

insurance, union dues, and savings bonds. See 

http://www.census.gov/econ/census/ for more 

information.

The Employer Costs for Employee 

Compensation (ECEC) program publishes data 

on employers’ expenditure on wages and 

salaries collected through the National 

Compensation Survey. The ECEC provides data 

on hourly costs for employee compensation

(including wages and salaries and benefits) on a 

quarterly basis. Wages and salaries are defined 

as the hourly straight-time wage rate or, for 

workers not paid on an hourly basis, straighttime earnings divided by the corresponding 

hours. Straight-time wage and salary rates are 

total earnings before payroll deductions, 

including production bonuses, incentive 

earnings, commission payments, and cost-ofliving adjustments. This measure does not 

include compensation such as tips. See latest 

data at http://www.bls.gov/ect/ and 

http://www.bls.gov/opub/hom/pdf/homch8.pd

f for more information. 

The National Income and Product Accounts

(NIPA) provides data on hourly wages and 

salaries of employees, measured as payments 

to all employees divided by total hours of work 

of all employees. The NIPAs also provide data 

on annual wages and salaries of employees. 

These payments include monetary 

remuneration to employees and to corporate 

officers at regular intervals, including piecework 

payments; overtime or unusual-hour payments; 

payments for periods away from work due to 

temporary discontinuation of production, such 

as holidays or absences for illness; allowances 

for unusual location or conditions; early 

retirement (buyout) payments; severance pay; 

jury, witness, and other compensatory fees; and 

regular supplementary allowances, such as 

housing allowances. It also includes incentive 

payments, commissions, tips, and bonuses 
Page 18 U. S. Department of Commerce, Economics and Statistics Administration

payable to employees and to corporate officers; 

employee gains from exercising nonqualified 

stock options (NSOs); wages and salaries paid to 

employees of unincorporated enterprises; and 

in-kind payments such as transit subsidies, 

meals, and lodging. The primary source data for 

information on annual wages and salaries and 

number of employees is the Quarterly Census of 

Employment and Wages (QCEW) program.

Other source data is used for certain industries 

for which the QCEW provides little or no 

coverage. The Bureau of Labor Statistics (BLS)

also provides the primary source data for 

information on hours of work. See 

http://www.bea.gov/iTable/index_nipa.cfm and 

http://www.bea.gov/national/pdf/chapter10.p

df for more information. 

The Occupational Employment Statistics (OES) 

program provides data on hourly earnings of 

workers based on employer reports of straighttime wages. The OES program surveys 

approximately 200,000 establishments every six 

months, taking three years to fully collect the 

sample of 1.2 million establishments. Wages 

are straight-time, gross pay, exclusive of 

premium pay and include base rates, 

commissions, cost-of-living allowances, 

deadheading pay, guaranteed pay, hazard pay, 

incentive pay, longevity pay, over-the-road pay, 

piece rates, portal-to-portal rates, production 

bonuses, and tips. Wages do not include 

overtime pay and exclude attendance bonuses, 

back pay, clothing allowances, merchandise and 

other discounts, draws, holiday bonuses, 

holiday premium pay, jury duty pay, meal and 

lodging payments, non-production bonuses, oncall pay, overtime pay, perquisites, profitsharing payments, relocation allowances, 

severance pay, shift differentials, stock 

bonuses, tool/equipment allowances, tuition 

repayment, uniform allowance, weekend 

premium pay, and year-end bonuses. See 

http://www.bls.gov/oes/ for more information. 

The Quarterly Census of Employment and 

Wages (QCEW) program publishes quarterly 

and annual counts of employment and wages 

reported by employers covering 98 percent of 

U.S. jobs, available at the county, MSA, state 

and national levels by industry. The data 

comes from the BLS business register of active 

business establishments in the U.S., which 

serves as the sampling frame and benchmark 

for several BLS establishment surveys. The data 

is derived from summaries of employment and 

total pay of workers covered by state and 

federal unemployment insurance (UI) legislation 

and provided by state workforce agencies. In 

the first quarter of 2014, UI records were 

submitted by 9.4 million establishments. Major 

exclusions from UI coverage include selfemployed workers, most agricultural workers 

on small farms, all members of the Armed 

Forces, elected officials in most states, most 

employees of railroads, some domestic 

workers, most student workers at schools, and 

employees of certain small nonprofit 

organizations. Monthly employment is based on 

the number of workers who worked during or 

received pay for the pay period including the 

12th of the month. Workers on paid vacations 

and part-time workers also are included. 

Average weekly wage values are calculated by 

dividing quarterly total wages by the average of 

the three monthly employment levels (all 

employees, as described above) and dividing 

the result by 13, for the 13 weeks in the 

quarter. Included in the quarterly wage data are 

non-wage cash payments such as bonuses, the 

cash value of meals and lodging when supplied, 

tips and other gratuities, and, in some states, 

employer contributions to certain deferred 

compensation plans such as 401(k) plans and 

stock options. See http://www.bls.gov/cew/ for 

more information. 

The Quarterly Workforce Indicators (QWI) is a 

product of the Longitudinal EmployerHousehold Dynamics program. The QWI

provides tabulations of earnings and 

employment data reported at the employer 

level. The program uses state-reported earnings 

data from administrative records on employers 

(namely, state unemployment insurance 

systems), representing approximately 96 
U. S. Department of Commerce, Economics and Statistics Administration Page 19

percent of employment. The linkage of 

employer and employee information allows for 

the identification of the number of job changes, 

the number of hires and separations, and the 

earnings of hired and separated workers. Total 

quarterly earnings capture straight wages and 

supplemental pay reported by employers. 

Average monthly earnings are based on total 

reported quarterly earnings. Earnings include 

gross wages and salaries, bonuses, stock 

options, tips and other gratuities, and the value 

of meals and lodging, when supplied. In some of 

states, employer contributions to certain 

deferred compensation plans, such as 401(k) 

plans, are included in total wages. Total wages, 

however, do not include employer 

contributions to Old–age, Survivors and 

Disability Insurance (OASDI); health insurance; 

unemployment insurance; workers’ 

compensation; and private pension and welfare 

funds. This gross amount includes cash 

allowances and the cash equivalent of any type 

of remuneration. It includes all lump–sum 

payments for terminal leave, withholding taxes, 

and retirement deductions. See 

http://lehd.ces.census.gov/ for more 

information.
Page 20 U. S. Department of Commerce, Economics and Statistics Administration

Appendix 2 – Worker Coverage of 

Pay Data Used to Calculate the 

Manufacturing Pay Premium

Generally, the pay data used in this report 

pertains to earnings of employees in the 

manufacturing sector and employees in all of 

the private sector. However, as a result of 

numerous methodological differences, as 

discussed throughout the report and in 

Appendix 1, the ten datasets discussed in this 

report cover slightly different groups of 

employees, albeit with large overlap. In 

particular, the American Community Survey 

(ACS) and Current Population Survey (CPS) are 

household-based surveys that capture civilian 

workers, and the establishment-based data 

sources rely on different business registries to 

define their universe of businesses. Therefore, 

the pay data for each of these datasets differs, 

as do the resulting pay premium calculations. 

This Appendix details the employees covered by 

each of the ten datasets.

Most of the datasets provide pay data for 

employees working in the private sector, which 

excludes the government sector (public 

administration), the self-employed, and 

domestic employees working in households. 

Several of the datasets also exclude employees 

working in the agriculture, forestry, fishing, and 

hunting industries. Datasets from the two 

household-based surveys, the ACS and CPS, 

cover a wider range of workers. The specific 

coverage of each of the ten datasets is 

described below.

American Community Survey (ACS): Covers the 

resident population of the United States, 

including the institutional population living in 

residential treatment facilities and, correctional 

facilities, the non-institutional population living 

in college dorms and military barracks, and 

other group quarters. Earnings data are 

available for the employed portion of this 

population age 16 years and older, including the 

self-employed. This report compares earnings 

of the employees of private companies, 

including those self-employed in an 

incorporated business, to earnings of those who 

self-report that they are employed in 

manufacturing. 

County Business Patterns (CBP): Provides 

payroll data for private business establishments 

with employees, including businesses in the 

forestry, fishing, and hunting industries. This 

report compares payroll per employee for all 

private employer businesses to that of 

manufacturing businesses.

Current Employment Statistics (CES): Covers 

employees of business establishments,

excluding businesses in the agriculture, forestry, 

fishing, and hunting industries. This report 

compares payroll per employee for all nonfarm 

private establishments to that of manufacturing 

establishments.

Current Population Survey (CPS): Provides 

hourly wage rates for hourly-paid wage and 

salary workers and weekly earnings estimates 

for full-time wage and salary workers. These 

sub-samples of the civilian non-institutional 

population age 16 and older exclude people 

serving in the armed forces and the selfemployed. This report compares earnings for 

all private wage and salary workers to those 

who self-report that they are employed in the 

manufacturing sector. 

Economic Census: Provides payroll data for 

private business establishments with 

employees, excluding those in the agriculture, 

forestry, fishing, and hunting industries. 

Although data is collected for the construction 

sector, payroll data for the construction sector 

is not published. This report compares data on 

the sum of all included sectors to that of the 

manufacturing sector. 

Employer Costs for Employee Compensation 

(ECEC): Provides data on employer costs for 

wages and salaries, and employee benefits for 

nonfarm private and state and local 
U. S. Department of Commerce, Economics and Statistics Administration Page 21

government workers. The ECEC sample includes 

data by worker and establishment 

characteristics. Data for workers in 

construction, extraction, farming, fishing, and 

forestry occupations is also available.

National Income and Product Accounts (NIPA):

Provides aggregate estimates of employment 

and wage and salary payments based on payroll 

data for private business establishments with 

employees, including businesses in the 

agriculture, forestry, fishing, and hunting 

industries.

Occupational Employment Statistics (OES):

Provides payroll data for private business 

establishments with employees but excludes 

private household employers and most 

agriculture, forestry, fishing, and hunting 

industries, except logging and support activities 

for crop and animal production. 

Quarterly Census of Employment and Wages 

(QCEW): Provides payroll data for private 

business establishments with employees, 

including businesses in the agriculture, forestry, 

fishing, and hunting industries.

Quarterly Workforce Indicators (QWI): The 

program uses state-reported earnings data 

from administrative records on employers 

(namely, state unemployment insurance 

systems), representing approximately 96 

percent of employment across private industry, 

including agriculture, forestry, fishing and 

hunting. Data is reported at the state level and 

the national level.

These differences in employee coverage are 

reflected in the different employment estimates 

for the manufacturing sector and for the private 

sector comparison group from each dataset. 

Table A-1 shows average annual employment 

for 2013 as reported by the datasets covered in 

this report. Employment data is not available 

for two datasets, namely the quinquennial

Economic Census, which only collects data for 

years ending in 2 and 7, and the ECEC, which 

Data source

American Community Survey 15,227 104,074 [1]

County Business Patterns 11,276 118,266

Current Employment Statistics 12,020 114,541

Current Population Survey 14,484 114,120 [2]

Employer Costs for Employee Compensation [3] N/A N/A

Economic Census [4] N/A N/A

National Income and Product Accounts 12,028 117,955

Occupational Employment Statistics 11,983 111,269

Quarterly Census of Employment and Wages 11,995 112,958

Quarterly Workforce Indicators 12,032 112,324 [5]

Manufacturing All Private

Notes: [1] Includes all private employees ages 16 and older, including incorporated self-employed.

 [2] Excludes unincorporated self-employed. Note that the incorporated self-employed are 

 included here but are not included for estimates on earnings presented in the body of this report.

 [3] No employment data is available.

 [4] Data is only available for years ending in 2 and 7.

 [5] National QWI data, 4-quarter averages.

Source: Bureau of Labor Statistics, the Census Bureau, and the Bureau of Economic Analysis.

Table A-1. Average Annual Employment in Manufacturing 

and the Private Sector, 2013 (thousands)
Page 22 U. S. Department of Commerce, Economics and Statistics Administration

does not publish employment data. 

As the table shows, the employment levels 

calculated from each data source do not exactly 

match, as expected, but most are reasonably 

close. The household surveys show higher levels 

and shares of manufacturing employment. For 

all sources with data available, manufacturing 

employment represents 10 to 11 percent of 

total employment, with the exception of the 

CPS, at 13 percent and the ACS, at 15 percent.
U. S. Department of Commerce, Economics and Statistics Administration Page 23

Acknowledgements

Jessica R. Nicholson and Regina Powers are both economists in the Office of the Chief Economist. 

The authors would like to thank the following persons and groups who provided comments, suggestions, 

and other contributions to this report. Any errors in the report are solely the authors’ responsibility.

Economics and Statistics Administration

Susan Helper, Chief Economist, Department of Commerce

Rob Rubinovitz, Deputy Chief Economist

David Langdon, Senior Economist

Sabrina Montes, Senior Economist

Ryan Noonan, Economist

Adji Fatou Diagne, Pathways Intern Economist

Claire Monteiro, Pathways Intern Economist

Many Vansuch, Intern, Office of the Chief Economist

Jasmine Joung, Intern, Office of the Chief Economist

Jane Callen, Editor

Statistical Agencies

American Community Survey Office, U.S. Census Bureau

Economic Directorate (County Business Patterns), U.S. Census Bureau

Economy Wide Statistics Division (Economic Census), U.S. Census Bureau

Division of Current Employment Statistics, Bureau of Labor Statistics

Division of Labor Force Statistics (Current Population Survey), Bureau of Labor Statistics

National Compensation Survey Office (Employer Costs for Employee Compensation), 

Bureau of Labor Statistics

National Income and Wealth Division (National Income and Product Accounts), 

Bureau of Economic Analysis

Office of Occupational Employment Statistics, Bureau of Labor Statistics

Quarterly Census of Employment and Wages program, Bureau of Labor Statistics

Local Employer-Household Dynamics (Quarterly Workforce Indicators), U.S. Census Bureau

 Technical inquiries:

Office of the Chief Economist

 (202) 482-3523

 Media inquiries:

Office of Communications

 (202) 482-3331

U.S. Department of Commerce

Economics and Statistics Administration

1401 Constitution Ave., NW

Washington, DC 20230

www.esa.doc.gov
Page 24 U. S. Department of Commerce, Economics and Statistics Administration