U.S. Department of Commerce

Economics and Statistics Administration

U.S. CENSUS BUREAU

Education and Synthetic Work-Life 

Earnings Estimates 

American Community Survey Reports

By 

Tiffany Julian

and 

Robert Kominski

ACS-14

Issued September 2011

INTRODUCTION

The relationship between education 

and earnings is a long-analyzed topic 

of study. Generally, there is a strong 

belief that achievement of higher levels 

of education is a well established path 

to better jobs and better earnings.1 This 

report provides one view of the economic value of educational attainment 

by producing an estimate of the amount 

of money a person might earn over the 

course of their working life, given their 

level of education. These estimates are 

“synthetic,” that is, they are not the actual

dollars people earned over the complete 

working life of the person (which would 

require us to have retrospective earnings 

data for the 40 years of their work-life). 

Instead, they are estimated using data 

from a one point-in-time cross-sectional 

survey. Median annual earnings estimates 

are computed for the point in time of the 

survey for all ages (5-year age groups 

are used), education, gender, and race/

ethnicity groups. The age group-specific 

medians are then summed across the category of interest (say, Black females with 

a Master’s degree) to construct expected 

lifetime earnings of that group if all earnings patterns observed in the cross section were to remain unchanged.

In this report, the Synthetic Work-life 

Earnings (SWE) estimates are first used to 

explore the basic relationship between 

education and earnings. The report then 

delves deeper into differences between 

race and gender groups with regard to 

1 Card, David. 1998. “The Causal Effect of

Education on Earnings” in: O. Ashenfelter & D. Card 

(Ed.), Handbook of Labor Economics, pp. 67–86.

this relationship. We also consider other 

factors that might influence earnings, 

such as citizenship, English-speaking 

ability, and geographic location. 

The data for this research comes from 

the Multiyear American Community 

Survey (ACS) data file for the period 

2006 to 2008. The ACS represents 

a part of the U.S. Census Bureau’s 

revised approach in how it conducts 

the federally-mandated decennial census 

of the population of the United States. 

The ACS is a large, monthly, national 

survey of the U.S. population that is 

sent to about a quarter million households each month in order to provide 

nationally-representative data on the 

equivalent of the full long-form content 

on a yearly basis (instead of once every 

10 years). In order to provide estimates 

for very small pieces of geography and 

subpopulations, the Census Bureau takes 

sequential yearly files and combines and 

weights them to produce multiyear files 

with much larger samples. This analysis 

uses the multi-year file for the 2006 to 

2008 period in order to provide sufficient 

characteristic detail for the analysis. We 

include residents from all 50 states plus 

the District of Columbia. All estimates 

are presented in 2008 dollars and represent the amount of money that might be 

expected to be earned over the course of 

a work-life from ages 25 to 64 for different gender and race/ethnicity groups.

An earlier Census Bureau report on this 

topic used data taken from the Current 
2 U.S. Census Bureau

Population Survey (CPS).2 The methodology of that report was similar 

to that used in this report. However, because the 3-year dataset 

from the CPS is about one-tenth 

the size of the 3-year ACS dataset, 

this report allows detailed analysis 

of gender cross-classified by race/

ethnicity groups. Additionally, this 

report uses more exact 5-year age 

intervals for all groups, whereas 

the CPS report relied on less exact 

10-year age cohorts for race and 

gender estimates. Finally, the ACS 

data, because of its content scope 

allows for the investigation of

factors such as language ability, 

which is not a part of the CPS

data collection.

EDUCATION, EMPLOYMENT, 

AGE, AND EARNINGS IN THE 

UNITED STATES

The level of education has risen 

steadily in America over the last

70 years (see Figure 1). In the

1940 Census, 24.5 percent of 

people aged 25 and over had at 

least a high school diploma. In 

2008, 85 percent of this group 

had at least a high school diploma, 

and 27.7 percent had a bachelor’s 

degree or higher. In addition, 10.2 

percent of people aged 25 and over 

had advanced degrees. 

Table 1 shows the median annual 

earnings for 9 distinct levels of 

education. With the exception of 

professional and doctorate degrees, 

annual earnings increase with each 

successive degree. Annual earnings 

ranged from around $11,000 a

2 Day, Jennifer Cheeseman and

Eric C. Newburger. 2002. “The Big

Payoff: Educational Attainment and

Synthetic Estimates of Work-Life Earnings.” 

U.S. Census Bureau, Current Population 

Reports, P23–210. U.S. Census Bureau, Washington, DC.

year for less than full-time, yearround workers without a high 

school degree to around $100,000 

for full-time, year-round workers 

with a professional degree.3 This 

demonstrates there is a strong

relationship between education

and earnings.

Occupation is often the mechanism 

by which education is related to 

earnings. Higher levels of education allow people access to more 

specialized jobs that are often 

3 None through eighth grade earned 

$11,237 and ninth through twelfth grade 

earned $11,274. They are not significantly 

different from each other. 

associated with high pay. Degrees 

in many occupations are treated as 

job training that may be required 

for a position or earn the employee 

more pay within that position. 

While this report does not focus 

on the specific occupations individuals hold, it does consider the 

degree of labor force involvement. 

Another possible factor is the field 

of training in which a degree is 

received. Beginning with the 2009 

data collection of the ACS, the field 

of bachelor’s degree is being collected. Future reports may be able 

to examine the effect of this variable on work-life earnings as well.

Source: U.S. Census Bureau, Decennial Census of Population, 1940–2000, and

the American Community Survey, 2006–2008. 

0

10

20

30

40

50

60

70

80

90

100

1940 1950 1960 1970 1980 1990 2000 2008

Figure 1. 

Educational Attainment of the Population 25 Years 

and Over: 1940 to 2008 

High school graduate or more

Percent

Bachelor's degree or more
U.S. Census Bureau 3

Figure 2 shows that, in addition Table 1 shows that many people both the result of higher likelihoods 

to higher earnings, people with with very low levels of educa- of full-time employment and the 

higher levels of education are tion had no work (and therefore higher levels of education required 

more likely to be employed full- no earnings) in the previous year, for that employment.

time, year-round, that is, they while this was much less likely As with employment status, educa- held a job for the entire year and for people with professional or tion and earnings are also played worked in a full-time capacity. In doctorate degrees. At every level out through time in one’s working fact, 68 percent of people with a of education, people working less life. Years of experience play a role doctorate are employed full-time, than full-time, year-round have in earnings levels, but without this year-round compared with 38 per- earnings that are lower than those explicit variable measured, we can cent of people with less than a who have full-time, year-round use age as a proxy. Figur high school diploma.4 e 3 shows Conversely, employment. These data help to the median annual earnings for better understand the interaction 4 Thirty-eight percent of none through various levels of education taken between education, employment, eighth grade and 38 percent of ninth through across the age categories used in twelfth grade (no diploma). and earnings—higher earnings are 

Table 1.

Annual Earnings by Level of Education and Work Status

Characteristic

Population 

aged 25–64

All people Full-time, year-round 

workers

Less than full-time, 

year-round workers Did not work

Median 

earnings

Standard 

error

Median 

earnings

Percent of 

persons

Median 

earnings

Percent of 

persons

Median 

earnings

Percent of 

persons

 Total . . . . . . . . . . . . 159,814,440 $27,455 $22 $42,850 55 $16,786 26 $0 19

Education

None–8th grade . . . . . . . . . . . 7,815,325 $10,271 $73 $23,277 38 $11,237 24 $0 37

9th–12th grade . . . . . . . . . . . 12,972,423 $10,996 $53 $27,470 38 $11,274 28 $0 35

High school graduate . . . . . . . 45,408,258 $21,569 $22 $34,197 53 $13,790 25 $0 22

Some college . . . . . . . . . . . . . 33,450,090 $27,361 $25 $40,556 56 $15,604 27 $0 17

Associate’s degree . . . . . . . . 13,299,842 $32,602 $60 $44,086 60 $18,957 26 $0 14

Bachelor’s degree . . . . . . . . . 30,138,179 $42,783 $42 $57,026 62 $25,074 26 $0 12

Master’s degree . . . . . . . . . . . 11,825,602 $53,716 $55 $69,958 60 $38,962 30 $0 11

Professional degree . . . . . . . . 3,152,004 $79,977 $345 $103,411 67 $49,187 25 $0 8

Doctorate degree . . . . . . . . . . 1,752,717 $73,575 $263 $88,867 68 $50,275 25 $0 7

Gender

Male . . . . . . . . . . . . . . . . . . . . 79,365,902 $36,422 $40 $48,387 65 $20,905 22 $0 13

Female . . . . . . . . . . . . . . . . . . 80,448,538 $20,050 $23 $36,904 45 $14,665 30 $0 25

Race/Ethnicity

Hispanic . . . . . . . . . . . . . . . . . 22,222,265 $19,934 $42 $30,609 54 $13,870 26 $0 20

White alone, not Hispanic . . . 107,892,275 $31,461 $23 $46,941 56 $18,206 26 $0 18

Black alone, not Hispanic . . . 18,663,853 $21,239 $45 $35,658 50 $13,979 26 $0 24

Asian alone, not Hispanic . . . 7,671,544 $30,265 $73 $49,164 55 $20,099 26 $0 19

Other, not Hispanic . . . . . . . . 3,364,503 $21,699 $105 $38,985 48 $14,631 29 $0 23

Age

25–29 years . . . . . . . . . . . . . . 20,684,074 $22,885 $61 $33,202 53 $13,857 33 $0 14

30–34 years . . . . . . . . . . . . . . 19,441,898 $27,759 $67 $39,740 57 $16,255 28 $0 15

35–39 years . . . . . . . . . . . . . . 21,055,841 $31,140 $45 $44,098 59 $17,442 26 $0 15

40–44 years . . . . . . . . . . . . . . 22,084,838 $31,589 $44 $45,287 60 $17,502 26 $0 15

45–49 years . . . . . . . . . . . . . . 22,860,068 $32,041 $39 $46,677 60 $18,252 25 $0 16

50–54 years . . . . . . . . . . . . . . 21,005,699 $31,558 $40 $47,411 58 $19,385 24 $0 18

55–59 years . . . . . . . . . . . . . . 18,210,745 $26,411 $40 $47,310 50 $18,949 24 $0 25

60–64 years . . . . . . . . . . . . . . 14,471,277 $9,272 $82 $44,922 35 $14,952 24 $0 42

Note: Median earnings shown for the population aged 25–64, not the total population .

Source: U .S . Census Bureau, American Community Survey, 2006–2008 .
4 U.S. Census Bureau

this report. As the figure demonstrates, there are different trajectories of earnings across the 40-year 

work-life period. Educational levels 

certainly vary, but even the trajectories themselves take different 

shapes. Thus, measuring earnings 

at various points in the working 

life is important for better overall 

synthetic estimates.

SYNTHETIC WORK-LIFE 

EARNINGS ESTIMATES

Computing the SWE estimates relies 

on the construction of a large table 

of annual median earnings for 

every combination of age, gender, 

race/ethnicity, and education. In 

this report we use:

• Eight 5-year age groups: 25 to 

29, 30 to 34, 35 to 39, 40 to 44, 

45 to 49, 50 to 54, 55 to 59,

and 60 to 64.

• Two gender groups: male and 

female.

• Nine education levels: none 

through eighth grade, ninth 

through twelfth grade (no 

degree), high school graduate, 

some college, associate’s degree, 

bachelor’s degree, master’s 

degree, professional degree, and 

doctorate degree.

• Five, nonoverlapping race/

ethnicity groups: Hispanic;

White alone, not Hispanic; Black 

alone, not Hispanic; Asian alone, 

not Hispanic; and Other,

not Hispanic).5

• This table is created for each 

of the three employment status 

groups—full-time, year-round 

workers; all workers with earnings; and all persons. This yields 

5 This report will refer to the White alone, 

not Hispanic population as White; the Black 

alone, not Hispanic population as Black; 

the Asian alone, not Hispanic population as 

Asian; and the Some Other Race alone, not 

Hispanic and the Two or More Races, not

Hispanic population as Other. Use of the 

single-race population does not imply that

it is the preferred method of presenting or 

analyzing data. The Census Bureau uses a 

variety of approaches. In this report, the

term Hispanic refers to people who are

Hispanic of any race. These groups have

been chosen to provide complete and unduplicated coverage of the total population. 

The “Other, not Hispanic” group covers a 

wide range of small and distinct race groups, 

including American Indian and Alaska Native 

groups, as well as all persons reporting 

multiple races. We have chosen to consolidate 

this overall small proportionate group rather 

than exclude it from the analysis.

0 10 20 30 40 50 60 70 80 90 100

$0

$20,000

$40,000

$60,000

$80,000

$100,000

$120,000

Figure 2. 

Education, Work Status, and Median Annual Earnings 

Source: U.S. Census Bureau, American Community Survey, 2006–2008.

Professional degree

Doctorate degree

Master’s degree

Bachelor’s degree

Associate’s degree Some college

High school graduate

9th–12th grade

None–8th grade

Percent employed full-time, year-round

2008 inflation adjusted dollars 
U.S. Census Bureau 5

three, 720-cell tables. For any ($33,202×5) + ($39,740×5) + 2-B and 2-C show similar patterns 

gender-race/ethnicity-education ($44,098×5) + ($45,287×5) + of variability.7

combination within a given work ($46,677×5) + ($47,411×5) + 

status, simply sum across the ($47,310×5) + ($44,922×5) = DEMOGRAPHIC VARIATION 

IN SYNTHETIC WORK-LIFE eight age categories (each mul- $1,743,235

EARNINGS ESTIMATES tiplied by 5) to yield the SWE for Tables 2-A, 2-B, and 2-C give the that group.6 The panels of Tables 2-A, 2-B, and result of these calculations for each 2-C demonstrate broad differences 

(Earnings25–29×5) + (Earnings30–34×5) + combination of race/ethnicity, gen- in the SWE estimates by demo- (Earnings35–39×5) + (Earnings40–44×5) + der, and work status category. graphic characteristics such as (Earnings45–49×5) + (Earnings50–54×5) + As Table 2-A shows, there is gender and race. Figure 4 graphi- (Earnings55–59×5) + (Earnings60–64×5) substantial variability in SWE cally shows the education/SWE 

Table 1 shows the median earnings estimates across the 90 gender- relationship for the ten different 

for all full-time, year-round workers race/ethnicity-education groups. gender and race/ethnicity combinain each age group. We can calculate For full-time, year-round work- tions from Table 2-A for full-time, 

the SWE estimate for all full-time, ers, the median SWE ranges from year-round workers. While the 

year-round workers using these a low of $704,005 for Hispanic form of the education and earnings 

numbers, as demonstrated in females with education of none relationship is quite similar across 

the following. through eighth grade, to a high of groups, the overall levels of SWE 

$4,754,930 for White males with vary between groups. 6 Additional details about the a professional degree (not statisti- computation of these estimates are 

available in Kominski and Julian. 2010. cally different from Asian males 

“Developing Synthetic Worklife Earnings with a professional degree). Tables Estimates.” <www.census.gov/hhes 7 Detailed, 720-cell tables available by 

/socdemo/education/data/acs/index.html>. request.

$0

$30,000

$60,000

$90,000

$120,000

25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64

Figure 3. 

Median Annual Earnings by Age and Educational Attainment

(Full-time, year-round workers)

Source: U.S. Census Bureau, American Community Survey, 2006–2008.

Professional degree

Age

2008 inflation adjusted dollars

Doctorate degree

Master’s degree

Bachelor’s degree

Associate’s degree

Some college

High school graduate

9th–12th grade

None–8th grade
6 U.S. Census Bureau

Table 2-A.

Median Synthetic Work-Life Earnings by Education, Race/Ethnicity, and Gender:

Full-Time, Year-Round Workers

Characteristic

Male Female

Synthetic work-life

earnings

Standard

error

Synthetic work-life

earnings

Standard

error

Hispanic

None–8th grade . . . . . . . . . . . . . . . $976,727 $3,152 $704,005 $3,573

9th–12th grade . . . . . . . . . . . . . . . $1,136,694 $5,576 $811,885 $4,993

High school graduate . . . . . . . . . . . $1,306,747 $6,144 $1,021,242 $4,202

Some college . . . . . . . . . . . . . . . . . $1,679,364 $7,618 $1,301,068 $7,222

Associate’s degree . . . . . . . . . . . . $1,837,607 $14,849 $1,446,134 $11,693

Bachelor’s degree . . . . . . . . . . . . . $2,080,558 $14,046 $1,701,767 $11,850

Master’s degree . . . . . . . . . . . . . . . $2,791,370 $31,625 $2,255,883 $22,522

Professional degree . . . . . . . . . . . . $3,120,466 $107,267 $2,334,295 $67,399

Doctorate degree . . . . . . . . . . . . . . $3,109,666 $121,372 $2,624,329 $94,510

White Alone, Not Hispanic

None–8th grade . . . . . . . . . . . . . . . $1,351,121 $9,733 $932,641 $11,554

9th–12th grade . . . . . . . . . . . . . . . $1,443,984 $4,354 $947,568 $4,205

High school graduate . . . . . . . . . . . $1,690,285 $1,993 $1,183,917 $1,304

Some college . . . . . . . . . . . . . . . . . $1,985,967 $2,080 $1,406,249 $1,940

Associate’s degree . . . . . . . . . . . . $2,086,488 $4,038 $1,607,609 $3,052

Bachelor’s degree . . . . . . . . . . . . . $2,847,953 $3,827 $2,028,096 $2,958

Master’s degree . . . . . . . . . . . . . . . $3,318,658 $6,793 $2,366,374 $4,053

Professional degree . . . . . . . . . . . . $4,754,930 $24,973 $3,200,311 $18,546

Doctorate degree . . . . . . . . . . . . . . $3,692,684 $19,536 $2,967,826 $18,805

Black Alone, Not Hispanic

None–8th grade . . . . . . . . . . . . . . . $1,045,580 $19,926 $863,231 $16,216

9th–12th grade . . . . . . . . . . . . . . . $1,124,778 $9,985 $861,353 $5,715

High school graduate . . . . . . . . . . . $1,340,407 $5,031 $1,070,827 $3,720

Some college . . . . . . . . . . . . . . . . . $1,601,729 $7,010 $1,308,183 $4,590

Associate’s degree . . . . . . . . . . . . $1,724,599 $12,357 $1,463,652 $9,495

Bachelor’s degree . . . . . . . . . . . . . $2,107,728 $12,238 $1,859,380 $8,642

Master’s degree . . . . . . . . . . . . . . . $2,530,574 $25,295 $2,310,171 $12,090

Professional degree . . . . . . . . . . . . $3,521,784 $77,518 $2,847,709 $53,871

Doctorate degree . . . . . . . . . . . . . . $2,912,750 $69,795 $2,881,587 $67,031

Asian Alone, Not Hispanic

None–8th grade . . . . . . . . . . . . . . . $1,003,783 $19,132 $864,579 $16,235

9th–12th grade . . . . . . . . . . . . . . . $1,159,638 $16,524 $942,418 $14,461

High school graduate . . . . . . . . . . . $1,292,822 $10,420 $1,059,678 $7,315

Some college . . . . . . . . . . . . . . . . . $1,678,196 $14,528 $1,394,305 $11,002

Associate’s degree . . . . . . . . . . . . $1,843,014 $18,282 $1,600,797 $17,984

Bachelor’s degree . . . . . . . . . . . . . $2,437,516 $15,225 $2,061,186 $11,656

Master’s degree . . . . . . . . . . . . . . . $3,454,087 $18,621 $2,735,465 $26,871

Professional degree . . . . . . . . . . . . $4,700,782 $91,225 $3,680,543 $106,135

Doctorate degree . . . . . . . . . . . . . . $3,601,577 $40,889 $3,134,482 $87,894

Other, Not Hispanic

None–8th grade . . . . . . . . . . . . . . . $1,228,762 $32,412 $848,544 $28,385

9th–12th grade . . . . . . . . . . . . . . . $1,320,118 $27,908 $902,420 $19,700

High school graduate . . . . . . . . . . . $1,478,622 $12,851 $1,135,015 $10,849

Some college . . . . . . . . . . . . . . . . . $1,757,852 $21,149 $1,321,789 $10,330

Associate’s degree . . . . . . . . . . . . $1,857,056 $26,447 $1,513,536 $18,165

Bachelor’s degree . . . . . . . . . . . . . $2,381,770 $25,746 $1,866,935 $20,691

Master’s degree . . . . . . . . . . . . . . . $2,954,449 $53,872 $2,217,916 $49,229

Professional degree . . . . . . . . . . . . $4,086,575 $246,403 $2,889,210 $160,628

Doctorate degree . . . . . . . . . . . . . . $3,318,995 $160,809 $2,678,873 $151,809

Note: Synthetic work-life earnings represent expected earnings over a 40-year time period for the population aged 25–64 based on annual earnings from a 

single (cross-sectional) point in time . The estimate was calculated by adding median earnings for eight 5-year age groups, multiplied by five .

Source: U .S . Census Bureau, American Community Survey, 2006–2008 .
U.S. Census Bureau 7

Table 2-B.

Median Synthetic Work-Life Earnings by Education, Race/Ethnicity, and Gender:

All Workers

Characteristic

Male Female

Synthetic work-life

earnings

Standard

error

Synthetic work-life

earnings

Standard

error

Hispanic

None–8th grade . . . . . . . . . . . . . . . $870,275 $3,330 $540,148 $2,653

9th–12th grade . . . . . . . . . . . . . . . $1,008,029 $4,079 $620,212 $3,703

High school graduate . . . . . . . . . . . $1,165,432 $4,648 $798,769 $3,491

Some college . . . . . . . . . . . . . . . . . $1,494,563 $6,582 $1,033,088 $5,381

Associate’s degree . . . . . . . . . . . . $1,654,826 $13,782 $1,174,274 $8,814

Bachelor’s degree . . . . . . . . . . . . . $1,878,411 $12,405 $1,442,172 $11,418

Master’s degree . . . . . . . . . . . . . . . $2,500,793 $30,265 $2,021,623 $17,882

Professional degree . . . . . . . . . . . . $2,687,056 $77,031 $1,831,512 $59,076

Doctorate degree . . . . . . . . . . . . . . $2,777,200 $65,619 $2,296,287 $72,378

White Alone, Not Hispanic

None–8th grade . . . . . . . . . . . . . . . $1,056,523 $7,959 $574,928 $6,880

9th–12th grade . . . . . . . . . . . . . . . $1,186,229 $3,675 $639,647 $3,088

High school graduate . . . . . . . . . . . $1,510,442 $1,748 $911,031 $1,432

Some college . . . . . . . . . . . . . . . . . $1,790,985 $2,292 $1,090,437 $1,617

Associate’s degree . . . . . . . . . . . . $1,916,932 $3,453 $1,303,304 $2,608

Bachelor’s degree . . . . . . . . . . . . . $2,587,591 $4,130 $1,612,414 $2,359

Master’s degree . . . . . . . . . . . . . . . $2,957,361 $5,815 $2,006,950 $3,198

Professional degree . . . . . . . . . . . . $4,449,503 $22,669 $2,576,982 $12,187

Doctorate degree . . . . . . . . . . . . . . $3,403,123 $14,212 $2,547,199 $14,236

Black Alone, Not Hispanic

None–8th grade . . . . . . . . . . . . . . . $765,997 $18,279 $590,014 $11,581

9th–12th grade . . . . . . . . . . . . . . . $821,293 $7,235 $610,917 $4,536

High school graduate . . . . . . . . . . . $1,138,313 $3,882 $868,305 $3,263

Some college . . . . . . . . . . . . . . . . . $1,383,964 $7,215 $1,088,714 $4,217

Associate’s degree . . . . . . . . . . . . $1,544,448 $10,388 $1,249,944 $7,123

Bachelor’s degree . . . . . . . . . . . . . $1,913,538 $10,720 $1,660,787 $7,342

Master’s degree . . . . . . . . . . . . . . . $2,325,767 $19,805 $2,108,617 $11,885

Professional degree . . . . . . . . . . . . $3,114,131 $75,505 $2,515,271 $53,365

Doctorate degree . . . . . . . . . . . . . . $2,589,390 $61,045 $2,629,772 $52,547

Asian Alone, Not Hispanic

None–8th grade . . . . . . . . . . . . . . . $837,888 $13,103 $662,282 $9,712

9th–12th grade . . . . . . . . . . . . . . . $999,866 $11,915 $735,906 $10,703

High school graduate . . . . . . . . . . . $1,157,460 $8,579 $855,045 $6,214

Some college . . . . . . . . . . . . . . . . . $1,483,683 $10,867 $1,127,116 $11,556

Associate’s degree . . . . . . . . . . . . $1,632,577 $18,194 $1,306,873 $16,294

Bachelor’s degree . . . . . . . . . . . . . $2,179,639 $12,005 $1,677,965 $9,296

Master’s degree . . . . . . . . . . . . . . . $3,125,091 $21,828 $2,176,211 $28,256

Professional degree . . . . . . . . . . . . $4,420,816 $82,257 $3,092,045 $65,518

Doctorate degree . . . . . . . . . . . . . . $3,351,721 $25,214 $2,642,467 $59,556

Other, Not Hispanic

None–8th grade . . . . . . . . . . . . . . . $932,343 $34,659 $613,666 $20,749

9th–12th grade . . . . . . . . . . . . . . . $949,258 $19,399 $594,242 $12,410

High school graduate . . . . . . . . . . . $1,222,863 $10,056 $854,512 $8,570

Some college . . . . . . . . . . . . . . . . . $1,466,827 $12,812 $1,030,573 $10,508

Associate’s degree . . . . . . . . . . . . $1,596,203 $26,626 $1,213,828 $15,519

Bachelor’s degree . . . . . . . . . . . . . $2,079,016 $29,050 $1,525,190 $17,299

Master’s degree . . . . . . . . . . . . . . . $2,550,093 $50,572 $1,888,242 $28,330

Professional degree . . . . . . . . . . . . $3,556,540 $199,271 $2,268,518 $96,249

Doctorate degree . . . . . . . . . . . . . . $2,935,274 $154,895 $2,411,461 $159,437

Note: Synthetic work-life earnings represent expected earnings over a 40-year time period for the population aged 25–64 based on annual earnings from a 

single (cross-sectional) point in time . The estimate was calculated by adding median earnings for eight 5-year age groups, multiplied by five .

Source: U .S . Census Bureau, American Community Survey, 2006–2008 .
8 U.S. Census Bureau

Table 2-C.

Median Synthetic Work-Life Earnings by Education, Race/Ethnicity, and Gender:

All Persons

Characteristic

Male Female

Synthetic work-life

earnings

Standard

error

Synthetic work-life

earnings

Standard

error

Hispanic

None–8th grade . . . . . . . . . . . . . . $758,198 $3,306 $148,106 $3,546

9th–12th grade . . . . . . . . . . . . . . $810,681 $6,129 $204,586 $5,110

High school graduate . . . . . . . . . . $997,242 $5,189 $466,935 $3,351

Some college . . . . . . . . . . . . . . . . $1,302,446 $8,733 $724,707 $6,718

Associate’s degree . . . . . . . . . . . $1,476,446 $14,727 $893,341 $11,441

Bachelor’s degree . . . . . . . . . . . . $1,713,881 $13,141 $1,102,840 $10,320

Master’s degree . . . . . . . . . . . . . . $2,333,141 $26,701 $1,758,151 $26,348

Professional degree . . . . . . . . . . . $2,456,926 $82,629 $1,186,925 $58,880

Doctorate degree . . . . . . . . . . . . . $2,566,943 $64,680 $2,019,318 $111,559

White Alone, Not Hispanic

None–8th grade . . . . . . . . . . . . . . $319,264 $8,414 $76,408 $418

9th–12th grade . . . . . . . . . . . . . . $766,007 $4,575 $138,443 $3,302

High school graduate . . . . . . . . . . $1,254,473 $1,868 $570,784 $1,613

Some college . . . . . . . . . . . . . . . . $1,567,250 $2,569 $774,963 $2,136

Associate’s degree . . . . . . . . . . . $1,730,550 $4,184 $1,031,254 $3,209

Bachelor’s degree . . . . . . . . . . . . $2,387,048 $3,873 $1,256,771 $2,657

Master’s degree . . . . . . . . . . . . . . $2,760,733 $6,313 $1,738,309 $3,803

Professional degree . . . . . . . . . . . $4,266,106 $19,510 $2,228,909 $12,427

Doctorate degree . . . . . . . . . . . . . $3,273,496 $14,916 $2,360,189 $15,318

Black Alone, Not Hispanic

None–8th grade . . . . . . . . . . . . . . $86,828 $903 $81,243 $889

9th–12th grade . . . . . . . . . . . . . . $128,997 $6,770 $123,372 $4,657

High school graduate . . . . . . . . . . $725,592 $4,897 $547,531 $3,256

Some college . . . . . . . . . . . . . . . . $1,032,421 $8,514 $801,444 $5,850

Associate’s degree . . . . . . . . . . . $1,254,105 $11,898 $1,022,889 $9,948

Bachelor’s degree . . . . . . . . . . . . $1,688,325 $13,249 $1,431,940 $9,607

Master’s degree . . . . . . . . . . . . . . $2,134,790 $20,916 $1,892,687 $16,661

Professional degree . . . . . . . . . . . $2,827,172 $78,864 $2,226,001 $54,828

Doctorate degree . . . . . . . . . . . . . $2,364,483 $64,407 $2,370,166 $62,954

Asian Alone, Not Hispanic

None–8th grade . . . . . . . . . . . . . . $596,056 $13,211 $228,381 $13,061

9th–12th grade . . . . . . . . . . . . . . $799,743 $13,472 $345,055 $12,427

High school graduate . . . . . . . . . . $993,799 $7,808 $496,563 $6,700

Some college . . . . . . . . . . . . . . . . $1,313,253 $13,024 $747,663 $12,606

Associate’s degree . . . . . . . . . . . $1,459,483 $18,331 $897,533 $17,887

Bachelor’s degree . . . . . . . . . . . . $1,951,381 $11,866 $1,132,591 $10,724

Master’s degree . . . . . . . . . . . . . . $2,897,024 $22,101 $1,558,365 $26,994

Professional degree . . . . . . . . . . . $4,137,925 $86,176 $2,528,510 $71,279

Doctorate degree . . . . . . . . . . . . . $3,227,523 $36,644 $2,283,537 $63,649

Other, Not Hispanic

None–8th grade . . . . . . . . . . . . . . $371,945 $33,060 $79,947 $1,606

9th–12th grade . . . . . . . . . . . . . . $419,778 $23,032 $100,666 $7,263

High school graduate . . . . . . . . . . $828,292 $10,467 $440,540 $9,586

Some college . . . . . . . . . . . . . . . . $1,120,436 $17,432 $652,351 $10,938

Associate’s degree . . . . . . . . . . . $1,245,553 $32,332 $877,069 $25,750

Bachelor’s degree . . . . . . . . . . . . $1,824,856 $23,195 $1,197,324 $15,788

Master’s degree . . . . . . . . . . . . . . $2,325,529 $49,460 $1,618,260 $34,519

Professional degree . . . . . . . . . . . $3,235,951 $159,141 $1,834,824 $105,483

Doctorate degree . . . . . . . . . . . . . $2,669,137 $160,794 $1,938,912 $126,441

Note: Synthetic work-life earnings represent expected earnings over a 40-year time period for the population aged 25–64 based on annual earnings from a 

single (cross-sectional) point in time. The estimate was calculated by adding median earnings for eight 5-year age groups, multiplied by five.

Source: U.S. Census Bureau, American Community Survey, 2006–2008.
U.S. Census Bureau 9

In Figure 4, colors represent different race/ethnicity groups while 

the dotted and solid lines represent 

females and males, respectively. 

The general pattern is that the 

dotted lines are often below the 

solid lines. What this tells us is 

that, particularly at lower levels of 

education, even women in the most 

advantaged race groups usually 

earn less than men in the most 

disadvantaged race groups. Asian 

women with at least a bachelor’s 

degree are competitive with some 

male groups, but at no point do 

women’s earnings come close to 

White or Asian men’s earnings at 

the same education level.

Table 3 shows the ratio of each 

race/gender group’s SWE to that of 

White males of the same education 

level for completed degrees. No 

group has a SWE estimate comparable to that of White men with the 

exception of Asian men with master’s (who earn more) and professional degrees. Generally, Hispanic 

females have one of the lowest 

ratios when compared to White 

men with the same level of education. For those Hispanic females 

with professional degrees, they 

make as little as half of what their 

White male counterparts make. 

For those whose highest level

of education is a high school 

diploma, the difference between 

Black, Hispanic, or Asian work-life 

earnings is not as large as other 

education levels. Men in these race 

groups make between 75 percent 

and 80 percent, and women make 

between 60 percent and 65 percent 

of White men’s earnings. However, 

for higher levels of education this 

is not the case. Asian men and 

women with a bachelor’s degree or 

higher seem to find much greater 

returns to higher education than 

Black or Hispanic men and women. 

Figure 4. 

Synthetic Work-Life Earnings for Gender/Race-Ethnicity Groups by Education Level

(Full-time, year-round workers)

Source: U.S. Census Bureau, American Community Survey, 2006–2008. 

Median SWE in millions of dollars

White male

Asian male

Other male

Asian female

Black male

White female

Hispanic male

Other female

Black female

Hispanic female

$0

$1

$2

$3

$4

$5

Doctorate

degree

Professional

degree

Master's

degree

Bachelor's

degree

Associate's

degree

Some

college

High school

graduate

9–12th

grade

None–8th

grade
10 U.S. Census Bureau

ESTIMATING THE IMPACT 

OF OTHER FACTORS ON 

SYNTHETIC WORK-LIFE 

EARNINGS ESTIMATES

One of the main questions

raised in an analysis such as

this is the extent to which factors 

other than education play a role 

in the earnings of individuals. The 

cross-tabulation method employed 

to compute the SWE estimates 

requires that in each cell of the 

large age-by-gender-by-race/ethnicity tabulation, we have sufficient 

data cases to obtain reasonable 

estimates. A different approach to 

estimating the SWE is to develop a 

regression model to predict earnings, and then use the parameter 

values from the model to estimate 

an overall SWE. The regression 

results help to show the relative 

level of impact attributable to each 

of the three demographic factors 

of gender, race/ethnicity, and age. 

However, a second value of this 

approach is that it easily allows us 

to include other possible factors 

and assess their overall impact on 

the SWE estimate as well as the 

basic demographic factors. 

Since the parameter values in the 

models represent dollars, one simple way to understand the overall 

impact of a given dimension is to 

look at the range of variability the 

categories of a given factor provide 

in the estimate. Table 4, Model 2 

represents the model based on the 

three demographic factors from 

the original SWE tabulation. For 

example, the range of the effect of 

gender is $12,618 a year, since that 

is the male effect, holding all else 

constant. The range for race/ethnicity is somewhat smaller, since 

the largest single race/ethnicity 

parameter effect is for Hispanics at 

–$6,285, holding all else constant.8

The lowest age group of 25 to 29 

has been used as the omitted category in the regressions; the remaining age categories have a range of 

up to $13,051 (for people 45 to 49 

years old). The actual variability in 

the age categories from 40 to 44 to 

60 to 64 is relatively small, with a 

total range of about $2,000 a year 

($13,051 minus $11,078). 

Returning to the main relationship of this analysis, however, 

none of these demographic characteristics demonstrate the kind 

of variability in range that the 

education levels demonstrate. The 

parameters in Model 2 range from 

a low of –$8,639 (none through 

eighth grade) to a high of $64,753 

(professional degrees), holding all 

else constant. The range across 

8 Not statistically different from the Black 

coefficient of –$6003.

the education variable is about 

$72,000—over five times the range 

exhibited by the demographic 

factor of gender. Thus, from this 

simple evaluation of relative 

impact, it is clear that the demographic factors supplement, but do 

not displace education as a critical 

component in understanding variation in earnings.

Apart from the basic education/

earnings relationship we have 

estimated, and the contribution of 

demographic context factors such 

as gender, age, and race/ethnicity, 

there are other additional factors 

that might mediate the earnings of 

individuals. In this last section we 

look at three additional factors for 

their possible impact on earnings—

citizenship status, English language 

ability, and geographic region of 

the country. Model 3 of Table 4 

shows the results of inclusion of 

these three additional factors and 

their relative impact on estimated 

earnings for the full-time, yearround worker population. These 

results are graphically depicted in 

Figure 5, showing both the relative effect of various variables and 

the changes that result in overall 

impact, as demographic and other 

characteristics are added to the 

basic education/earnings model.

Table 3.

Ratio of Synthetic Work-Life Earnings to White Males by Level of Education

Characteristic High school 

graduate

Bachelor’s

degree

Master’s

degree

Professional 

degree

Doctorate

degree

Female

Hispanic . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.60 0.60 0.68 0.49 0.71

White alone, not Hispanic . . . . . . . . . . . . . 0.70 0.71 0.71 0.67 0.80

Black alone, not Hispanic . . . . . . . . . . . . . 0.63 0.65 0.70 0.60 0.78

Asian alone, not Hispanic . . . . . . . . . . . . . 0.63 0.72 0.82 0.77 0.85

Other, not Hispanic . . . . . . . . . . . . . . . . . . 0.67 0.66 0.67 0.61 0.73

Male

Hispanic . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.77 0.73 0.84 0.66 0.84

Black alone, not Hispanic . . . . . . . . . . . . . 0.79 0.74 0.76 0.74 0.79

Asian alone, not Hispanic . . . . . . . . . . . . . 0.76 0.86 1.04 * 0.99 0.98

Other, not Hispanic . . . . . . . . . . . . . . . . . . 0.87 0.84 0.89 0.86 0.90

* Group not significantly different from White males of same education level.

Source: U.S. Census Bureau, American Community Survey, 2006–2008.
U.S. Census Bureau 11

Table 4.

Regression Models of Median Annual Earnings for the Full–Time, Year–Round Population

Parameter Model 11

Standard

error Model 21

Standard

error Model 31

Standard

error

Intercept . . . . . . . . . . . . . . . . . . . . . . . . . $34,170 $1,125 $19,935 $148 $15,747 $82

Education

None–8th grade . . . . . . . . . . . . . . . . . . . –$10,873 $197 –$8,639 $79 –$4,494 $78

9th–12th grade . . . . . . . . . . . . . . . . . . . –$6,512 $185 –$5,379 $63 –$4,281 $61

High school graduate . . . . . . . . . . . . . . . (R) (R) (R) (R) (R) (R)

Some college . . . . . . . . . . . . . . . . . . . . . $6,278 $289 $6,902 $51 $6,261 $39

Associate’s degree . . . . . . . . . . . . . . . . $9,845 $163 $10,468 $65 $9,862 $55

Bachelor’s degree . . . . . . . . . . . . . . . . . $22,861 $820 $23,391 $76 $22,607 $60

Master’s degree . . . . . . . . . . . . . . . . . . . $35,679 $745 $35,318 $89 $34,276 $82

Professional degree . . . . . . . . . . . . . . . . $69,213 $832 $64,753 $263 $63,643 $269

Doctorate degree . . . . . . . . . . . . . . . . . . $54,600 $582 $51,019 $346 $50,313 $303

Gender

Male . . . . . . . . . . . . . . . . . . . . . . . . . . . . (X) (X) $12,618 $34 $12,741 $37

Female . . . . . . . . . . . . . . . . . . . . . . . . . . (X) (X) (R) (R) (R) (R)

Race/Ethnicity

Hispanic . . . . . . . . . . . . . . . . . . . . . . . . . (X) (X) –$6,285 $142 –$3,415 $68

White alone, not Hispanic . . . . . . . . . . . (X) (X) (R) (R) (R) (R)

Black alone, not Hispanic . . . . . . . . . . . (X) (X) –$6,003 $143 –$5,757 $56

Asian alone, not Hispanic . . . . . . . . . . . (X) (X) –$2,152 $164 –$760 $127

Other, not Hispanic . . . . . . . . . . . . . . . . (X) (X) –$3,895 $148 –$4,383 $119

Age

25–29 years . . . . . . . . . . . . . . . . . . . . . . (X) (X) (R) (R) (R) (R)

30–34 years . . . . . . . . . . . . . . . . . . . . . . (X) (X) $5,267 $60 $5,337 $62

35–39 years . . . . . . . . . . . . . . . . . . . . . . (X) (X) $9,845 $63 $9,782 $63

40–44 years . . . . . . . . . . . . . . . . . . . . . . (X) (X) $11,796 $103 $11,687 $57

45–49 years . . . . . . . . . . . . . . . . . . . . . . (X) (X) $13,051 $114 $12,808 $63

50–54 years . . . . . . . . . . . . . . . . . . . . . . (X) (X) $12,793 $128 $12,499 $66

55–59 years . . . . . . . . . . . . . . . . . . . . . . (X) (X) $12,007 $118 $11,691 $66

60–64 years . . . . . . . . . . . . . . . . . . . . . . (X) (X) $11,078 $137 $10,625 $77

Citizenship

Native-born . . . . . . . . . . . . . . . . . . . . . . (X) (X) (X) (X) (R) (R)

Naturalized . . . . . . . . . . . . . . . . . . . . . . (X) (X) (X) (X) $1,210 $77

Not a citizen . . . . . . . . . . . . . . . . . . . . . . (X) (X) (X) (X) –$2,446 $85

Language

Speak English only . . . . . . . . . . . . . . . . (X) (X) (X) (X) (R) (R)

Speak English “very well” . . . . . . . . . . . (X) (X) (X) (X) –$989 $66

Speak English less than “very well” . . . . (X) (X) (X) (X) –$8,349 $103

Region

New England . . . . . . . . . . . . . . . . . . . . . (X) (X) (X) (X) $8,503 $102

Middle Atlantic . . . . . . . . . . . . . . . . . . . . (X) (X) (X) (X) $7,495 $82

East North Central . . . . . . . . . . . . . . . . . (X) (X) (X) (X) $4,370 $70

West North Central . . . . . . . . . . . . . . . . (X) (X) (X) (X) $1,073 $96

South Atlantic . . . . . . . . . . . . . . . . . . . . (X) (X) (X) (X) $4,415 $70

East South Central . . . . . . . . . . . . . . . . (X) (X) (X) (X) (R) (R)

West South Central . . . . . . . . . . . . . . . . (X) (X) (X) (X) $3,435 $77

Mountain . . . . . . . . . . . . . . . . . . . . . . . . (X) (X) (X) (X) $4,365 $77

Pacific . . . . . . . . . . . . . . . . . . . . . . . . . . (X) (X) (X) (X) $9,516 $91

Variance explained . . . . . . . . . . . . . . . . 0.135 0.181 0.185

(R) Reference group.

(X) Not applicable.

1

 All estimates are significant at the p<.05 level.

Note: Median earnings shown for the population aged 25–64, not the total population.

Source: U.S. Census Bureau, American Community Survey, 2006–2008.
12 U.S. Census Bureau

Figure 5. 

Relative Dollar Value of Education and Other Factors on Median Annual Earnings

(Full-time, year-round workers)

Source: U.S. Census Bureau, American Community Survey, 2006–2008.

Model 1

Model 2

Model 3

–$20,000 $0 $20,000 $40,000 $60,000 $80,000

(Reference: East South Central)

Pacific

Mountain

West South Central

South Atlantic

West North Central

East North Central

Middle Atlantic

New England

Region

(Reference: Speak English only)

Speak English less than “very well”

Speak English “very well”

Language

(Reference: Native-born)

Not a citizen

Naturalized

Citizenship

(Reference: 25–29 years)

60–64 years

55–59 years

50–54 years

45–49 years

40–44 years

35–39 years

30–34 years

Age

(Reference: White, not Hispanic)

Other, not Hispanic

Asian, not Hispanic

Black, not Hispanic

Hispanic

Race/Ethnicity:

(Reference: Female)

Male 

Gender

(Reference: High school graduate)

Doctorate degree

Professional degree

Master's degree

Bachelor's degree

Associate's degree

Some college

9–12th grade

None–8th grade

Education

Median regression estimates
U.S. Census Bureau 13

While all of these factors are highly 

significant, they add only a small 

amount to the explained variance 

in the model—these results are 

likely a function of the extremely 

large sample size of the model. 

Naturalized citizens see a small 

yearly increase over native-born 

persons ($1,210), holding other 

factors constant, but persons who 

are not citizens show a negative 

impact (–$2,446).

The impact of English-speaking 

ability is sizable. People who speak 

a language other than English at 

home show a negative effect. Even 

those who report speaking another 

language at home, but who report 

that they speak English “very well” 

have a yearly impact of –$989, 

holding other factors constant. 

Persons who report English ability 

below this level show a very large 

effect, with a yearly loss in earnings of –$8,349, relative to persons 

who are English-only speakers. 

Regional effects are also evident, 

with a range of $9,516 across the 

nine census-defined divisions, 

holding other factors constant. In 

general, earnings are highest in 

the Pacific and New England, and 

lowest in the East South Central, 

controlling for the other factors in 

the model.

Taken as a whole, the results in 

Model 3 show that other factors 

beyond basic demographics can 

have a sizable impact on estimated 

lifetime earnings. However, as Figure 5 graphically shows, even the 

addition of these various factors 

dampen the impact of education 

only marginally. While effects for 

education levels are relatively small 

(and some negative) the impact of 

degrees, beginning with the bachelor’s degree level, are substantial 

with most of them far larger than 

any of the other social, demographic, and geographic components in the model.

SUMMARY

How much money will any of us 

earn in our lifetime? The answer 

to that question is complex and 

uniquely individual, resting on a 

vast variety of conditions and factors, some of which may be purely 

circumstantial or random. The SWE 

estimates presented in this report 

do not act as a proscription of what 

one should come to expect in life. 

Instead, they constitute a defined 

analytic explanation and disaggregation of current earnings patterns, 

based on a large nationally representative sample.

The results of this analysis demonstrate that there is a clear and 

well-defined relationship between 

education and earnings, and that 

this relationship perseveres, even 

after considering a collection of 

other personal and geographic 

characteristics. When synthetically expanded across 40 years 

of a working life, the implications 

of varying educational levels can 

be quite large—literally millions 

of dollars in variation. While large 

variations are apparent across 

ascribed demographic dimensions 

of gender and race/ethnicity, the 

attainable dimension of education 

does, at some levels, exceed and 

overwhelm these other dimensions. 

Of course, other factors not considered in this report, such as occupation or time period, may also act 

to mediate the effects of education 

and, ultimately, the earnings that 

accrue over time. Because the focus 

of this analysis has been fundamentally focused on the impact of 

education, we have not introduced 

occupational effects.

COMPUTATION ISSUES FOR 

SYNTHETIC WORK-LIFE 

EARNINGS ESTIMATES

There are a number of technical 

and computational issues associated with the calculation of SWE. 

Several are discussed here; others 

are detailed in the working paper 

on this topic (see footnote 6).

The SWE estimates were

constructed by calculating

medians within a basic, five-way 

cross-classification table. This

consisted of a nine category 

education variable crossed by two 

genders; five race/ethnicity groups; 

and eight age groups for a total 

of 720 cells for each of the three 

work universes. An example of this 

calculation is provided earlier in the 

report under “Synthetic Work-Life 

Earnings Estimates” and discussed 

in detail in the previously cited 

working paper.

Standard errors for these estimates 

utilized the 80 replicate weight factors provided in the ACS dataset. A 

simple explanation of this method 

is that the replicate weights are 

used to compute 80 different 

estimates as well as their standard errors with slightly different 

weights each time (reflecting the 

complex sampling design of the 

survey). The average of these 80 

estimates constitutes a better, less 

biased estimate than conventional 

direct estimation provides.9 Once 

medians were calculated for each 

cell, the values were multiplied by 

five and summed for each of the 8 

age groups to represent a full life 

of earnings for that education level 

for each specific race/ethnicity 

by gender by work status group. 

These results are shown in Tables 

2-A, 2-B, and 2-C. 

Adding more variables to the model 

becomes too cumbersome for 

the tabular method. A regression 

method allowed us to explore more 

factors without encountering small 

cell size problems associated with 

a very large table. 

9 See Chapter 12 of the Design and 

Methodology Report for more information at 

<www.census.gov/acs/www/SBasics

/desgn_meth.htm>.
14 U.S. Census Bureau

The regression modeling of earnings used the SAS QUANTREG 

procedure to produce coefficients 

at the 50th percentile and take into 

account the replicate weights and 

complex sampling design of the 

ACS. These models were estimated 

using dummy variables for each 

category of each variable. Model 1 

accounts for the basic relationship 

between education and earnings 

only. Model 2 adds gender, race/

ethnicity, and age to mimic the 

estimates produced in the tabular 

method. Model 3 takes everything 

in Model 2 then adds citizenship, 

English-speaking ability, and geographic location. 

SOURCE OF THE DATA

Estimates in this report are from 

the ACS, 2006 to 2008. The population represented (the population 

universe) in the 2006 to 2008 ACS 

includes both the household and 

the group quarters populations 

(that is, the resident population). 

The group quarters population 

consists of the institutionalized 

population (such as people in correctional institutions or nursing 

homes) and the noninstitutionalized population (most of whom are 

in college dormitories).

ACCURACY OF THE ESTIMATES

Statistics from sample surveys 

are subject to sampling error and 

nonsampling error. All comparisons 

presented in this report have taken 

sampling error into account and 

are significant at the 90 percent 

confidence level. This means the 

90 percent confidence interval for 

the difference between estimates 

being compared does not include 

zero. Nonsampling error in surveys 

may be attributed to a variety of 

sources, such as how the survey 

was designed, how respondents 

interpret questions, how able and 

willing respondents are to provide 

correct answers, and how accurately answers are coded and classified. To minimize these errors, 

the Census Bureau employs quality control procedures in sample 

selection, the wording of questions, 

interviewing, coding, data processing, and data analysis.

The final ACS population estimates 

are adjusted in the weighting procedure for coverage error by controlling specific survey estimates to 

independent population controls by 

sex, age, race, and Hispanic origin. 

This weighting partially corrects for 

bias due to over- or undercoverage, 

but biases may still be present, for 

example, when people who were 

missed differ from those interviewed in ways other than sex, age, 

race, and Hispanic origin. How this 

weighting procedure affects other 

variables in the survey is not precisely known. All of these considerations affect comparisons across 

different surveys or data sources. 

For information on sampling and 

estimation methods, confidentiality protection, and sampling and 

nonsampling errors, please see the 

“2006–2008 ACS 3-Year Accuracy 

of the Data” document located at 

<www.census.gov/acs/www

/Downloads/data_documentation

/Accuracy/accuracy2006-

2008ACS3-Year.pdf>.

FOR MORE INFORMATION

Further information from the

2006 to 2008 ACS is available 

from the American FactFinder on 

the Census Bureau’s Web site at 

<http://factfinder.census.gov

/home/saff/main.html?_lang=en>.

Measures of ACS quality—

including sample size and

number of interviews, response 

and nonresponse rates, coverage 

rates, and item allocation rates—

are available at <www.census.gov

/acs/www/methodology

/sample_size_and_data_quality/>.

Additional information about 

educational attainment is available 

on the Census Bureau’s Web site at 

<www.census.gov/hhes/socdemo

/education/index.html>.

CONTACT

Contact the U.S. Census Bureau 

Customer Services Center at

1-800-923-8282 (toll free) or

visit <ask.census.gov> for

further information.

SUGGESTED CITATION

Julian, Tiffany A. and

Robert A. Kominski. 2011.

“Education and Synthetic WorkLife Earnings Estimates.” American 

Community Survey Reports,

ACS-14. U.S. Census Bureau,

Washington, DC.

USER COMMENTS

The Census Bureau welcomes the 

comments and advice of users of 

our data and reports. Please send 

comments and suggestions to: 

Chief, Housing and Household 

Economic Statistics Division

U.S. Census Bureau

Washington, DC 20233-8500