1

Building Career Ladders for the Working Poor 

Through Literacy Training

Authors 

Lee Bruno 

Ying Jin 

Dwayne Norris 

American Institutes for Research

July 2010 

Contents 

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

Working Poor Compared 

With Other Workers............ 2

Demographic and 

Socioeconomic Comparisons.. 2

Prose and Quantitative 

Literacy Levels ........................ 3

Literacy and Occupations...... 4

Literacy’s Role in Career 

Ladders ................................. 4

NAAL Data’s Implications 

for Literacy Training ........... 6

Need for Literacy 

Assessment Tools..................... 8

The Working Poor as a 

Target Group........................... 8

Changes in How 

Postsecondary Schools 

Approach Literacy Training.. 8

Summary............................... 9

References............................. 9

Appendix A: Methodology 

and Technical Notes......... A-1

The 2003 NAAL 

Assessment...........................A-1

Descriptions of Background 

Variables..............................A-1

Statistical Procedures .........A-2

Linking Occupational 

Information Network 

Occupation Descriptors to 

NAAL Literacy Scales ........A-2

Introduction 

Using U.S. Census Bureau data, the U.S. Bureau of Labor Statistics (BLS) determined that 

7.1 million workers were living at or below the official poverty level in 2007, which 

represented 5.1 percent of the total workforce (U.S. Department of Labor [DOL], 2009). 

The Working Poor Families Project, funded by the Annie E. Casey, Ford, and Joyce and 

Mott foundations, claims that 25 percent of working families can be considered low 

income; and, an income level of twice the poverty income was the criterion for working 

poor (Waldron, Roberts, & Reamer, 2004). Although the operational definitions of “poor” 

and “working” vary, both the Census Bureau and the Working Poor Families Project 

document that a willingness to work hard does not equate to a living wage. Beyond the 

implications this situation has for the welfare of the working poor and their families, 

having such a significant share of the Nation’s workforce permanently attached to a 

minimally valued job poses a threat to the Nation’s ability to provide a workforce for 21st 

century high-growth industries. The working poor make up a pool of invisible talent that 

could be the basis for a revitalized workforce that fills high-demand jobs in energy, health, 

technology, and science sectors of the economy. 

In a sense, the working poor might be considered invisible because social safety nets are 

designed for the unemployed person or the person who meets poverty guidelines. At the 

same time, the working poor are an overlooked talent pool that demonstrates through 

individual work histories the essential soft skills that are important for all jobs. Further, the 

working poor demonstrate the employability skills needed to find jobs and meet an 

employer’s behavioral expectations to sustain employment. 

So what accounts for the working poor’s inability to hold jobs with higher salaries, 

especially jobs that are in high-growth industries? Using data collected from the 2003 

National Assessment of Adult Literacy (NAAL),1

 this report compares workers who 

reported an average weekly wage that, if they were employed full time, was less than 

125 percent of official poverty levels for a family of three1

2

 with workers whose hourly 

wages translate into incomes greater than 125 percent of official poverty levels for a family 

of three. All workers represented in the NAAL study are thus divided into the working poor 

and the working nonpoor—or “other workers,” for the purposes of this report. Under 

contract to the DOL’s Employment and Training Administration (ETA), the American 

Institutes for Research (AIR) has used the NAAL data, from a sample of 9,296 workers, to 

address the following research questions: 

 What distinguishes the working poor from other workers? 

 What are the literacy requirements that pose barriers to occupations that would lift 

poor workers to “other” worker status? 

 How can ETA’s workforce investment system3

3

 best address the literacy barriers to 

the upward mobility of the working poor? 

A detailed description of background variables and methodology used in this report is 

provided in Appendix A: Methodology and Technical Notes.

 

1

 The 2003 NAAL study (http://nces.ed.gov/naal/) provided information on the literacy proficiency of nearly 18,000 adults, 16 years of age or older. See the 

appendix of this report for a description of the study. For an interpretation of the literacy scales and performance levels on the NAAL assessment, see Kutner et al. 

(2007). 

2

This is the income requirement for immigrants imposed by the U.S. Immigration and Naturalization Service (INS), which is less restrictive than the Federal 

poverty guidelines. (INS 2009). 

3

 http://www.doleta.gov/etainfo/wrksys/WIMission 
2

Working Poor Compared With Other Workers 

Demographic and Socioeconomic Comparisons 

In addition to assessing the literacy skills of respondents, 

the NAAL gathered extensive background information on 

their demographic and socioeconomic characteristics (e.g., 

age, gender, nativity status, schooling, labor force status, 

household income), along with their practices for gaining 

information. Table 1 compares age, sex, race/ethnicity, 

native language, and educational attainment of the working 

poor with those of other workers and the general 

population of NAAL respondents4

 

4

 Many of the background variables examined in this report are based on 

self-reported data, and because they are also related to one another, 

complex interactions and relationships among them cannot be explored. 

Therefore, readers are cautioned not to draw causal inferences on the sole 

basis of the results presented here. 

Table 1. Percentage distribution of working poor, other workers, and the general population across demographic and 

socioeconomic characteristics 

Characteristic Working poor % Working nonpoor % General population % 

Age 

18–24 years 31* ** 8 14 

25–39 years 29* 38 28 

40–49 years 19* 28 21 

50–64 years 15* ** 24 22 

65+ years 6* ** 2 16 

Sex 

Female 60* ** 40 52 

Male 41* ** 60 48 

Race/ethnicity 

White 62* ** 74 71 

Black 13* 10 11 

Hispanic 19* ** 11 12 

Other 6 5 6 

Language spoken before school 

English only 76* ** 83 81 

English and Spanish (with or without other) 2 2 2 

English and other 3 4 4 

Spanish only or with other 4* ** 7 8 

Other only 4 5 5 

Educational attainment 

0–8 years 7* 2 6 

9–12 years 11* ** 6 9 

GED/equivalency 8* ** 4 5 

H.S. grad 30* ** 22 27 

Vocational degree 6 6 6 

Some college 13 13 12 

A.A./2 years of studies 13 14 12 

College graduate 7* ** 17 12 

Graduate studies/degree 4* ** 17 11 

* Significantly different from other workers at the significance level of 0.05. 

** Significantly different from the general population at the significance level of 0.05. 

Note: Percentages may not sum to 100% because of rounding.

Age. There was a marked difference between the number

of youth 18–24 years of age among the working poor

(31 percent) compared with other workers (8 percent),

suggesting that income may influence whether youth 

entered the workforce rather than postsecondary education

and training. In no other respect was age a feature that

distinguished the two groups of workers. 

Sex. The number of females among the working poor

(60 percent) and the general population (52 percent) was 

greater than among other workers (40 percent). This 

difference may have resulted from two intertwining 

factors—gender wage gaps and the higher probability that

women will be single heads of families. Women earn 

about 80 percent of what men earn for similar types of

jobs (Institute for Women’s Policy Research, 2009).
3

Women also bear the greater responsibility for rearing 

children when families break up. According to another 

report by the Institute for Women’s Policy Research, 

56 percent of all low-income working mothers are single 

mothers and are more likely to have younger children than 

higher income mothers (Lee, 2007). 

Race/ethnicity. The working poor were more likely to be 

white (60 percent) or Hispanic (19 percent) than any other 

race; however, Hispanic workers were overrepresented 

among the working poor and white workers were 

underrepresented. It is important to note that the NAAL 

found that Spanish-only speakers were significant 

segments of the working poor, as well, and were also 

overrepresented among the working poor. 

Educational attainment. Not surprisingly, the working 

poor were less likely to attain college degrees than other 

workers or the general population; however, the working 

poor generally matched the educational experience of the 

general population through high school but then dropped 

behind other workers and the general population in terms 

of college degrees. Of all workers surveyed, 34 percent of 

other workers graduated from college, whereas only 

11 percent of the working poor did. However, 81 percent 

of the working poor graduated from high school including 

those with General Education Development (GEDs). With 

the exception of college degree attainment, the working 

poor in the NAAL sample were very similar to other 

workers and the general population. 

Prose and Quantitative Literacy Levels 

The NAAL measured respondents’ proficiencies on three 

literacy scales: prose, document, and quantitative. For 

each, proficiency was measured on a scale that ranged 

from 0 to 500. Scores on each of the three literacy scales 

were characterized in terms of four literacy proficiency 

levels: Below Basic, Basic, Intermediate, and Proficient. 

Given the scope of this report and the high correlation 

between prose and document literacy, the analyses in this 

section focus on the prose and quantitative literacy scales 

only. 

Average prose and quantitative scores. Table 2 is a 

comparison of the NAAL prose and quantitative literacy of 

the working poor, other workers, and the general 

population of NAAL respondents. The table first 

compares the average scores for each group and then 

presents the percentage distribution of each group across 

the four levels of literacy proficiency. 

Table 2. Average prose and quantitative literacy scores, and percentage in each literacy level of the working poor, other workers, 

and general population 

 Mean Below Basic % Basic % Intermediate % Proficient % 

Prose 

Working poor 266* ** 16* 31* 42* 10* ** 

Other workers 295 7 22 51 21 

General population 275 14 29 44 13 

Quantitative 

Working poor 275* ** 24* 36* ** 31* 9* ** 

Other workers 304 11 29 40 20 

General population 283 22 33 33 13 

* Significantly different from other workers at the significance level of 0.05. 

** Significantly different from the general population at the significance level of 0.05.

The most significant finding is that literacy proficiency 

consistently distinguished the working poor from other 

workers. These data reinforce the relevancy of literacy in 

employment outcomes. For example, the working poor 

had lower average prose and quantitative literacy scores 

than not only other workers but the general population. 

Conversely, other workers had higher average prose and 

quantitative scores than the general population. 

An examination of the percentage distribution of working 

poor, other workers, and the general population across the 

four levels of literacy shows that a disproportionate share 

of the working poor (47 percent) had Basic or Below Basic 

prose literacy, while 60 percent had either Basic or Below 

Basic quantitative literacy. Below Basic means that they 

had no more than the simplest and most concrete prose5

 

5

 Able to locate easily identifiable information in short commonplace 

prose texts. 

and quantitative6 skills, whereas Basic literacy translates 

into abilities to perform simple, everyday activities, such 

as finding information in a newspaper article or calculating 

the cost of a meal based on menu prices. The working 

poor’s distribution across the four levels of literacy closely 

paralleled the distribution of the general population, but at 

a slightly lower level of skill. A large majority of other 

workers (72 percent for prose and 60 percent for 

quantitative) fell into the Intermediate and Proficient levels 

of literacy and exceeded the skill levels of the general 

population by wide margins across all levels in both prose 

and quantitative literacy. 

 

6

 Able to locate numbers and use them to perform simple quantitative 

operations, such as comparing two prices and subtracting. 
4

Literacy and Occupations 

The NAAL data offer an opportunity to compare the 

occupations held by the working poor with those held by 

other workers. Table 3 shows the distribution of working 

poor and other workers among NAAL respondents in 

terms of the type of occupation and the average prose 

scores of respondents holding each occupation. 

Table 3. Average prose literacy scores for working poor and other workers, by occupation 

 Working poor Other workers 

 Overall Prose Overall Prose 

Occupation % Mean % Mean 

Management/business/financial 5 297 17 313 

Professional and related 12 302 25 325 

Service 31 253 11 274 

Sales and related 14 276 7 290 

Office/administrative support 16 283 14 293 

Construction/extraction 5 234 8 259 

Installation/maintenance/repair 2 261 5 281 

Production 9 238 8 265 

Transportation/material moving 7 244 5 259 

The most common occupations held by the working poor 

were the following: 

1. Service occupations where the average prose literacy 

score of workers was 253. Examples are food 

preparation and grounds maintenance workers. 

2. Office and administrative support where the workers’ 

average prose literacy score was 283. Examples are 

file clerks and word processors. 

3. Sales and related where the workers’ average prose 

literacy score was 276. Examples are sales clerks and 

cashiers. 

Among the working poor, 61 percent held occupations in 

these three categories where workers’ average prose 

literacy scores fell in the range of 253 to 283. 

The most common types of occupations held by other 

workers were the following: 

1. Professional and related where workers’ average prose 

literacy score was 325 

2. Management/business/financial where workers’ 

average prose literacy score was 313 

3. Office/administrative support where workers’ average 

prose score was 293 

Among other workers, 56 percent held occupations in 

these three categories where their average prose literacy 

score ranged from 292 to 325. 

If the average prose score of the majority of working poor 

was 266 and the average prose score of the majority of 

other workers was 295, the possibility exists that a 

majority of working poor could hold jobs common to the 

majority of other workers by eliminating a relatively small 

literacy gap. 

Literacy’s Role in Career Ladders 

The NAAL data suggest that literacy proficiency is a 

pervasive factor influencing the types of jobs workers hold 

and the wages they earn. To test this thesis, a comparison 

was made between the average literacy scores of the 

working poor and the literacy requirements of occupations 

that typically pay an hourly wage greater than $10.58, the 

amount required to move a worker classified as poor to the 

classification of nonpoor. To accommodate the likelihood 

that the working poor do not generally have college 

degrees or the financial means to secure college degrees, 

occupations that require either 2-year or 4-year degrees are 

limited. To ensure that these occupations are now, and 

will be in the future, viable opportunities, a final criterion 

requires that the occupations be designated high-growth 

occupations.7

Fifty representative occupations that meet these criteria 

were selected. Based on the multiple regression models 

developed by AIR that integrate Occupational Information 

Network (O*NET)8

8

 data and the NAAL literacy measures,

each occupation’s literacy requirements were derived and 

then compared with the average literacy levels of the 

working poor.9

9

 

Table 4 represents the results of this comparison, 

organized by occupations that have literacy requirements 

that are higher than, lower than, or similar to the working 

poor average for either prose or quantitative literacy. For 

example, about half (52 percent) of the occupations 

(represented by the yellow block) require both higher 

 

7

 www.Careervoyages.gov. This Web site was the primary source for the 

selection of occupations. In some instances (e.g., construction), where 

occupations meeting the criteria were numerous, occupations were 

collapsed into categories that are recognized by the respective industries. 

8

 O*NET is a database of information describing all U.S. occupations. 

See http://online.onetcenter.org/. 

9

 The method used to link the O*NET data and the NAAL literacy 

measures is described in Appendix A. 
5

prose and quantitative literacy levels than the working 

poor average. Another 11 occupations (22 percent) 

(represented by the bright green and tan blocks) require 

higher quantitative literacy levels than the average. Only 

one occupation—rough carpenter—requires a higher prose 

literacy level. In summary, the literacy gaps are sufficient 

to prevent the average working poor from functioning 

adequately in most (76 percent) occupations that could 

raise their incomes above 125 percent of the poverty level. 

Table 4: Comparison of working poor average literacy level with that required by selected high-growth occupations 

Quantitative Higher Same Lower 

Higher 

Industrial engineering technicians 

Mechanical drafters 

Mechanical engineering technicians 

Chemical technicians 

Electrical and electronic engineering 

technicians 

Statistical assistants 

Construction managers 

Licensed practical nurses 

Electronic drafters 

Registered nurses 

Insurance adjusters, examiners, and 

investigators 

Electricians 

Mapping technicians 

Surveying technicians 

Medical equipment repairers 

Food science technicians 

Payroll and timekeeping clerks 

Bookkeeping, accounting, and auditing clerks 

Brokerage clerks 

Police patrol officers (police and sheriff patrol 

officers) 

Food service managers 

Computer, automated teller and office 

machine repairers 

Municipal fire fighters (fire fighters) 

Industrial machinery mechanics 

Plumbers 

Aircraft mechanics and services technicians 

Rough carpenter (carpenter) 

Same 

Customer service representatives 

Bill and account collectors 

Radiological technicians 

Electrical and electronics repairers 

Commercial and industrial equipment 

Physical therapist assistants 

Police, fire, and ambulance dispatchers 

Security and fire alarm systems installers 

Freight and cargo inspectors 

Electrical power-line installers 

and repairers 

Aircraft structure, surfaces, 

rigging 

Systems and assemblers 

 

Prose 

Lower 

Automotive body and related repairers 

Chefs and head cooks 

Dental assistants 

Medical transcriptionists 

Truck drivers, heavy and tractortrailer 

Dental hygienists 

Mates—ship, boat, and barge 

Railroad conductors and 

yardmasters 

Excavating and loading 

machine and dragline 

operators 

Bus and truck mechanics and 

diesel engine specialists 

Pipe layers 

Operating engineers and other 

construction equipment 

operators 

Painters, transportation 

equipment 
6

NAAL Data’s Implications for Literacy Training 

Literacy gaps are not the same for all workers for all these 

occupations, but evidence strongly suggests that literacy 

levels are relevant to economic advancement among 

marginal workers. Although 81 percent of the working poor 

graduated from high school or its equivalent, only 52 percent 

achieved prose literacy levels higher than Basic level and 

only 40 percent achieved above the basic level in 

quantitative literacy. This finding suggests that literacy 

levels may be more indicative of employment capacity than 

educational attainment is. 

Table 5 presents the 50 high-growth occupations that are 

achievable for working poor if increases are made in prose 

and quantitative literacy, and for each occupation, shows the 

following: 

 Differences in prose and quantitative literacy from 

the average for the working poor 

 BLS median wage per hour 

 Type of postsecondary training typical for workers 

in the occupation 

 Percentage of workers in the occupation with high 

school or some college education 

Table 5. High-growth occupations within reach of average working poor 

Occupation 

Prose 

variance 

Quantitative 

variance Hourly wage 

Education/ 

training 

% workers 

HS or less 

% workers 

some college 

Painters, transportation 

equipment 

–31 –24 18.69 Moderate-term 

on-the-job training 

75.4 20.8 

Railroad conductors and 

yardmasters 

–23 –3 29.56 Moderate-term 

on-the-job training 

37.7 51.7 

Operating engineers and other 

construction equipment 

operators 

–21 –20 20.22 Moderate-term 

on-the-job training 

77.8 19.6 

Medical transcriptionists –19 18 15.44 Postsecondary 

vocational award 

30.6 58.9 

Mates—ship, boat, and barge –15 1 30.15 Work experience in a 

related occupation 

54.9 24.2 

Dental assistants –15 7 15.52 Moderate-term 

on-the-job training 

33.6 57.5 

Dental hygienists –14 1 31.21 Associate’s degree 2.9 63.8 

Pipe layers –12 –19 16.7 Moderate-term 

on-the-job training 

67.6 28.5 

Bus and truck mechanics and 

diesel engine specialists 

–10 –9 19.04 Postsecondary 

vocational award 

65.7 31.2 

Truck drivers, heavy and 

tractor-trailer 

–10 –5 18.06 Moderate-term 

on-the-job training 

70.7 25.2 

Excavating and loading 

machine and dragline operators 

–9 –12 17.79 Moderate-term 

on-the-job training 

78.8 17.7 

Chefs and head cooks –8 10 19.57 Work experience in a 

related occupation 

47.7 38.8 

Automotive body and related 

repairers 

–7 6 18.53 Long-term 

on-the-job training 

74.3 22.5 

Security and fire alarm systems 

installers 

–5 13 17.93 Postsecondary 

vocational award 

48.3 44 

Freight and cargo inspectors –4 –2 27.43 Moderate-term 

on-the-job training 

42.8 41.7 

Police, fire, and ambulance 

dispatchers 

–4 34 16.38 Moderate-term 

on-the-job training 

44 45.1 

Physical therapist assistants 1 14 21.32 Short-term 

on-the-job training 

11.7 65.4 

Electrical and electronics 

repairers, commercial and 

industrial equipment 

3 8 22.9 Postsecondary 

vocational award 

46.4 46 

Radiological technicians 3 18 24.59 Associate’s degree 7.2 67.9 

Bill and account collectors 3 15 15.21 Short-term 

on-the-job training 

38.4 48.2 

Electrical power-line installers 

and repairers 

4 1 24.85 Long-term 

on-the-job training 

55.9 38.2 
7

Table 5. High-growth occupations within reach of average working poor 

Occupation 

Prose 

variance 

Quantitative 

variance Hourly wage 

Education/ 

training 

% workers 

HS or less 

% workers 

some college 

Customer service representative 4 16 14.93 Moderate-term 

on-the-job training 

33.8 44.2 

Aircraft structure, surfaces, 

rigging, systems and assemblers 

5 5 21.24 Moderate-term 

on-the-job training 

58.7 35.3 

Aircraft mechanics and services 

technicians 

7 21 23.88 Postsecondary 

vocational award 

32.9 56.6 

Plumbers 7 20 22.76 Long-term 

on-the-job training 

67.6 28.5 

Industrial machinery mechanics 8 19 21.16 Long-term 

on-the-job training 

55.9 38.8 

Rough carpenters (carpenters) 9 3 19.84 Long-term 

on-the-job training 

72.8 21.6 

Municipal fire fighters (fire 

fighters) 

9 17 21.22 Long-term 

on-the-job training 

22.9 58.8 

Computer, automated teller and 

office machine repairers 

10 30 18.61 Postsecondary 

vocational award 

22.5 52.7 

Food service managers 11 23 23.39 Work experience in a 

related occupation 

40 35.8 

Police patrol officers (police 

and sheriff patrol officers) 

12 30 24.36 Long-term 

on-the-job training 

15.5 51.7 

Brokerage clerks 15 20 19.23 Moderate-term 

on-the-job training 

24.4 44.6 

Bookkeeping, accounting, and 

auditing clerks 

15 24 15.76 Moderate-term 

on-the-job training 

33.7 50.3 

Payroll and timekeeping clerks 16 19 16.59 Moderate-term 

on-the-job training 

32.9 49.6 

Food science technicians 16 21 17.08 Associate’s degree 42.7 25.6 

Medical equipment repairers 20 39 20.95 Associate’s degree 28.3 57 

Surveying technicians 20 23 17.26 Moderate-term 

on-the-job training 

42.2 51 

Mapping technicians 23 43 17.26 Moderate-term 

on-the-job training 

42.2 51 

Electricians 24 17 23.12 Long-term 

on-the-job training 

50.7 42.4 

Insurance adjusters, examiners, 

and investigators 

24 32 26.67 Long-term 

on-the-job training 

18.3 35.1 

Registered nurses 24 22 30.04 Associate’s degree 1 42.7 

Electronic drafters 28 31 24.86 Postsecondary 

vocational award 

13 62.4 

Licensed practical nurses 29 39 18.72 Postsecondary 

vocational award 

20.6 72.4 

Construction managers 30 33 41.26 Bachelor’s degree 39.5 31.1 

Statistical assistants 32 49 16.45 Moderate-term 

on-the-job training 

23.7 46.5 

Electrical and electronic 

engineering technicians 

32 42 25.23 Associate’s degree 27.2 54.4 

Chemical technicians 35 39 20.39 Associate’s degree 30.3 32.5 

Mechanical engineering 

technicians 

37 45 23.7 Associate's degree 27.2 54.4 

Mechanical drafters 38 48 22.45 Postsecondary 

vocational award 

13 62.4 

Industrial engineering 

technicians 

42 51 24.72 Associate’s degree 27.2 54.4 
8

This table is a useful guide for an employability 

development plan creating a career ladder that moves 

workers from their current literacy levels to literacy levels 

compatible with high-growth occupations. When combined 

with O*NET’s other resources for conducting a gap analysis 

to define occupational choices, a complete training plan is 

possible that integrates literacy training with occupational 

training to facilitate career transition or movement up a 

career ladder. 

In some cases, this movement may require only a modest 

investment in literacy training. For example, 21 of the 

occupations were 10 points or less above the average prose 

literacy score and 13 occupations were 10 points or less 

above the average quantitative literacy average. A gap 

analysis for each individual client sets the stage for efficient, 

prescriptive training investments that closely target explicit 

employment objectives for advancement on a career ladder. 

This strategy also supports contextual literacy training. 

To capture this level of systemic efficiency throughout the 

workforce development system, a number of issues must be 

addressed. First, One-Stop Career Centers need an efficient 

and practical means of assessing literacy levels. Second, the 

NAAL data suggest that the failure of the least literate of the 

working poor to see literacy as a factor in their employment 

and their urgent need for steady wages will affect their 

response as a group to outreach and recruitment strategies 

and their take-up rates in the workforce investment system 

training programs. Third, where postsecondary training is 

needed, training institutions may need to incorporate more 

efficient literacy training approaches to fit the needs of the 

working poor. Each of these issues is discussed below. 

Need for Literacy Assessment Tools 

If actual literacy levels are substituted for the averages used 

in Table 5, a One-Stop Career Center counselor can easily 

calculate the literacy gap between a client and one or more 

occupations. Literacy gaps have implications, whether the 

objective is employment placement or training. Credibility 

with employers and clients is enhanced when One-Stop 

Career Center staff can make a good match between the two. 

Discussing literacy requirements of the job when a gap is 

suspected may result in a conditional placement where a 

literacy “refresher course” can be negotiated, rather than a 

lost opportunity. The literacy gap also has implications for 

training referrals because workers who lack the literacy 

requirements for the occupation are unlikely to fare well in 

the vocational training for that occupation. 

Literacy assessments that correlate to the literacy 

requirements of O*NET occupations are not available to 

One-Stop Career Centers or approved eligible training 

providers. Without this critical piece of information, 

O*NET is less useful as a training and placement tool, and 

this may deter long-term positive outcomes. 

A literacy assessment tool must meet the following criteria: 

 Measure all levels of literacy for both prose and 

quantitative skills 

 Be administrable by One-Stop Career Center 

professional staff 

 Correlate to O*NET database of occupational 

information 

Such an assessment tool is likely to fall short of a 

comprehensive assessment of literacy but would serve as a 

useful counseling tool for an employment plan that 

minimizes inappropriate job and training referrals. At a 

minimum, it will create system awareness of the importance 

of literacy in employment outcomes. 

The Working Poor as a Target Group 

The NAAL survey also examined respondents’ practices in 

getting information and learned that the working poor 

obtained most of their information about current events, 

public affairs, and the Government from radio and 

television. Only about 20 percent reported getting 

information on the Internet “a lot.” 

On the subject of job training, a large percentage of the 

working poor with low (i.e., Below Basic and Basic) literacy 

did not think that their reading, writing, mathematics, or 

computer skills limited their job opportunities much. Those 

with low literacy were also less likely to participate in any 

kind of training. 

These findings from the study suggest that outreach to the 

working poor by One-Stop Career Centers or training 

institutions should rely on radio or television, or recruitment 

at the workplace. Employers are beginning to understand 

the value of higher literacy levels for their workers and may 

be willing to support literacy programs for their workers. 

Also, convincing the working poor to give up their jobs for 

full-time training is unlikely to happen Their paychecks are 

obviously essential for survival, and the working poor do not 

see the relationship between their low literacy and their 

minimally valued jobs. Yet, small changes in literacy 

proficiency can open doors to better jobs for the working 

poor once their perceptions and needs as a target group 

guide outreach services and training logistics. 

Changes in How Postsecondary Schools Approach 

Literacy Training 

Literacy or basic education classes are common across most 

postsecondary schools. Public high schools frequently offer 

Adult Basic Education (ABE) to assist high school dropouts 

to earn their GEDs. Vocational technical schools and 

community colleges offer English as a Second Language 

(ESL) and ABE programs at night to accommodate working 

students. Federal Pell grants provide financial assistance to 

ABE students. The infrastructure for upgrading the literacy 

levels of the working poor exists in almost every 

community. However, the NAAL data show that the 

working poor are unlikely to benefit from these resources. 

Using the NAAL data in combination with other research 

offers some reasons why this may be the case and may hold 
9

the answer to why these facilities are not in greater use by 

the working poor. 

First, by definition, the working poor may have little time or 

money to access even free educational resources. Situational 

constraints can prevent workers with limited transportation 

resources or contrary job schedules from attending class. 

Mothers without childcare or workers who are working 

several jobs to make ends meet are not available for training 

(Stowe, 1998). Nor is there much likelihood that online 

courses are the answer. According to the NAAL data, 

computer literacy is not common in this group. 

Second, the working poor seem unaware that low literacy is 

a barrier to better jobs. One aspect of low literacy is that 

common sources of important information—information that 

may enlighten low-income workers about the value of 

literacy to employment outcomes—are not accessible. 

Third, even those working poor who have access to literacy 

training and see the need for it experience high dropout rates 

as they battle situational constraints over time. One study 

showed that a job, a job promotion, and admission to job 

training were “compelling motivators” to complete literacy 

training (Connor & Steadman, 2006, p. 6). However, 

literacy learners must be motivated long enough to achieve 

the long list of incremental learning objectives that stand 

between Below Basic or even Basic literacy and a GED. 

Once learners acquire sufficient literacy to be functional in 

their invironments, there are few rewards to making progress 

through the GED teaching materials. Literacy training may 

be particularly frustrating for people with high school 

diplomas.. Training institutions that hope to be successful 

with this target group must alter their training approaches to 

address these issues by doing the following: 

 Tailoring the standard ABE offerings to close 

individual literacy gaps expeditiously: Literacy 

learning must be eminently relevant to the learner, 

and a better paying job is a prize that will motivate. 

Limiting literacy training to skills directly 

associated with a future job reduces the time 

required for career advancement. Reducing the 

time in training reduces the cumulative effects of 

situational constraints. 

 Teaching the required literacy skills 

concurrently with occupational training rather 

than requiring that students gain the needed 

literacy level prior to entering skill training:

ABE seems more palatable when it is taught in 

close association with the skill training which is 

another way to keep literacy learning focused on 

clearly relevant learning objectives and learners 

motivated (Strawn, 2010). 

 Taking literacy training to the workplace:

Training institutions that work with employers to 

upgrade employees’ skills provide a valued service 

to both employers and workers. Frequently, 

employers can provide facilities at the workplace 

for off-hours classes, thus increasing learners’ 

access to training and providing incalculable 

motivation.1

10

Summary 

A significant number of workers are gainfully employed in 

jobs that provide bare subsistence wages. The NAAL data 

provide a basis for comparing data on respondents who are 

among the working poor with data on other workers, to 

determine how they differ. The greatest distinction between 

the working poor and other workers is their literacy levels. 

The NAAL data show that the working poor, on average, fall 

below the general population in their literacy proficiency, 

while other workers fall above the general population. This 

situation suggests that literacy is very relevant to 

employment outcomes. 

When the average literacy levels of the working poor are 

compared with the literacy requirements of 50 selected highgrowth jobs—jobs that could lift the working poor to the 

status of other workers—literacy gaps in most of the jobs 

were found, particularly in quantitative literacy. However, a 

number of occupations require only modest investments in 

literacy improvement to be within reach of the average 

member of the working poor. 

There are sufficient data to suggest that literacy changes, 

even modest ones, can be critical links between minimally 

valued jobs held by the working poor and jobs in highgrowth occupations. With adjustments in One-Stop Career 

Centers counseling and placement activities, and training 

institutions’ approach to teaching ABE, workers may be able 

to move into better paying jobs at minimal cost to public 

resources or themselves. 

References 

Cohen, J. (1988). Statistical power analysis for the 

behavioral sciences (2nd ed.). Hillsdale, NJ: 

Erlbaum. 

Connor, C., & Steadman, S. (2006). Adult basic education: 

Effective classrooms and successful students. 

Tallahassee, FL: Florida State University and 

Florida Center for Reading Research. 

Institute for Women’s Policy Research. (2009). The gender 

wage gap: 2009. (IWPR #C350 Fact Sheet). 

Washington, DC: Author. 

Kutner, M., Greenberg, E., Jin, Y., Boyle, B., Hsu, Y., & 

Dunleavy, E. (2007). Literacy in everyday life: 

Results from the 2003 National Assessment of Adult 

Literacy (NCES 2007-480). Washington, DC: U.S. 

Department of Education, National Center for 

Education Statistics. 

 

10 Although there are numerous examples of employer-based ABE classes, 

Malden Mills is the most famous 

(www.compassionatwork.com/art_malden_mills.html). 
10

LaPolice, C. C., Carter, G. W., & Johnson, J. W. (2008). 

Linking O*NET descriptors to occupational literacy 

requirement using job component validation. 

Personnel Psychology, 61, 405–441. 

Lee, S. (2007). Keeping moms on the job: The impacts of 

health insurance and child care on job retention 

and mobility among low-income mothers. 

Washington, DC: Institute for Women’s Policy 

Research. 

Stowe, P. (1998). Adult education participation decisions 

and barriers: Review of conceptual frameworks 

and empirical studies (Working Paper No. 98-10). 

Washington, DC: U.S. Department of Education, 

Office of Educational Research and Improvement, 

National Center for Education Statistics. 

Strawn, J. (2010). Shifting gears: State innovation to 

advance workers and the economy in the Midwest. 

Chicago: The Joyce Foundation. 

U.S. Census Bureau. (2000). 2000 census industry and 

occupational codes. Washington, DC: Author. 

U.S. Department of Labor, Bureau of Labor Statistics. 

(2009). A profile of the working poor (Report 

1012). Washington, DC: Author. 

U.S. Immigration and Naturalization Service (INS). (2009). 

Requirements for new immigrants: U.S. 

Department of Health & Human Services. Federal 

Register, 4(14), 4199–4201. Washington, DC: 

Author. 

Waldron, T., Roberts, B., & Reamer, A. (2004). Working 

hard, falling short: America’s working families and 

the pursuit of economic security. Baltimore, MD: 

Annie E. Casey Foundation. Available at 

www.aecf.org/initiatives/jobsinitiative 
A-1

Appendix A: Methodology and Technical 

Notes 

This appendix provides more information about the 

methodology and research that are referenced in this report, 

starting with an overview of the 2003 National Assessment 

of Adult Literacy (NAAL). This section also describes the 

background variables and statistical procedures used in this 

report. A final section discusses the methods used to link 

the O*NET occupation descriptors to the NAAL literacy 

scales so that literacy gaps between selected high-growth 

occupations and the working poor could be determined. 

The 2003 NAAL Assessment 

The 2003 NAAL assessed the English literacy of adults (16 

years of age and older) in the United States for the first time 

since the 1992 National Adult Literacy Survey (NALS). 

The NAAL provided information on the literacy 

proficiencies of a nationally representative sample of 

approximately 18,000 adults living in households and 1,200 

prison inmates. In addition to assessing the literacy skills of 

respondents, the NAAL gathered extensive background 

information on their demographic and socioeconomic 

characteristics (e.g., their age, gender, nativity status, 

schooling, labor force status, and household income), as well 

as on how they obtain information. 

The NAAL measured respondents’ proficiencies on three 

literacy scales: 

 Prose literacy. The knowledge and skills needed to 

search, comprehend, and use information from 

continuous texts. Prose examples include 

editorials, news stories, brochures, and instructional 

materials. 

 Document literacy. The knowledge and skills 

needed to search, comprehend, and use information 

from noncontinuous texts. Document examples 

include job applications, payroll forms, 

transportation schedules, maps, tables, and drug and 

food labels. 

 Quantitative literacy. The knowledge and skills 

needed to identify and perform computations using 

numbers that are embedded in printed materials. 

Examples include balancing a checkbook, figuring 

out a tip, completing an order form, and 

determining the amount of interest on a loan from 

an advertisement. 

For each of the literacy scales, proficiency was measured on 

a scale that ranged from 0 to 500. Scores on each of the 

literacy scales were characterized in terms of four literacy 

proficiency levels: Below Basic, Basic, Intermediate, and 

Proficient. For more information on the methodology and 

findings from the NAAL assessment, see Kutner and 

colleagues (2007). 

Descriptions of Background Variables 

Race/Ethnicity 

In 2003, all respondents were asked two questions about 

their race and ethnicity. The first question asked them to 

indicate whether they were Hispanic or Latino. Then all 

respondents, including those who indicated they were 

Hispanic or Latino, were asked to choose one or more of the 

following groups to describe themselves: 

 White 

 Black or African American 

 Asian 

 American Indian or Alaska Native 

 Native Hawaiian or other Pacific Islander 

Individuals who responded “Yes” to the first question were 

coded as Hispanic, regardless of their answer to the second 

question. Individuals who identified more than one group 

on the second question were coded as Multiracial. 

Respondents of Native Hawaiian or Pacific Islander origin 

were grouped with those of Asian origin. 

Language Spoken Before Starting School 

All respondents were asked what language or languages they 

learned to speak before starting school. Their responses 

were then used to divide respondents into three groups: 

English only, English and other language, or Other 

language(s). 

Highest Educational Attainment 

All respondents were asked to indicate the highest level of 

education they had completed. The following options were 

provided: 

 Still in high school 

 Less than high school 

 Some high school 

 General Education Development (GED) or high 

school equivalency 

 High school graduate 

 Vocational, trade, or business school after high 

school 

 College: less than 2 years 

 College: associate’s degree (A.A.) 

 College: 2 or more years, no degree 

 College graduate (B.A. or B.S.) 

 Postgraduate, no degree 

 Postgraduate degree (M.S., M.A., Ph.D., M.D., etc.) 

Respondents who reported less than high school or some 

high school were asked how many years of education they 

had completed. For certain analyses, some of these groups 

were collapsed. 
A-2

Occupation 

Respondents who had held a job within the past 3 years were 

asked to provide the title of their occupation and its most 

important activities and duties. This information was used 

to assign each occupation a 2000 Census Bureau code. The 

occupations were then collapsed into eight major 

occupational groups: 

 Management, business, and financial 

 Professional and related 

 Service 

 Sales and related 

 Office and administrative support 

 Construction and extraction 

 Installation, maintenance, and repair 

 Production 

Statistical Procedures 

Tests of Statistical Significance 

All comparisons discussed in this report have been tested for 

statistical significance using the t statistic. Statistical 

significance was determined by calculating a t value for the 

difference between a pair of means, or proportions, and 

comparing this value with published tables of values at a 

certain level of significance, called the alpha level. The 

alpha level is an a priori statement of the probability of 

inferring that a difference exists when, in fact, it does not. 

The alpha level used in this report is .05, based on a twotailed test. Differences in the means and proportions 

between subgroups were calculated using the following 

t statistic: 

where p1 and p2 are the estimates to be compared and se1 and 

se2 are their corresponding standard errors. 

When a subgroup was compared to a total group, a 

modification of the standard error of difference was made to 

adjust for group dependence. The formula for the adjusted 

standard error of difference was as follows: 

where p is the proportion of the total group contained in the 

subgroup. 

Minimum Sample Sizes for Reporting Subgroup Results 

In the NAAL reports, the sample sizes were not always large 

enough to permit accurate estimates of proficiency and/or 

background results for one or more categories of variables. 

For results to be reported for any subgroup, a minimum 

sample size of 45 was required. This number was arrived at 

by determining the sample size needed to detect an effect 

size of 0.5 with a probability of 0.8 or greater, using a design 

effect of 1.5. This design effect implies a sample designbased variance 1.5 times that of a simple random sample. 

The effect size of 0.5 pertains to the true difference in a 

given mean estimate (e.g., mean proficiency) between the 

subgroup in question and the total population, divided by the 

standard deviation of that estimate in the total population. 

An effect size of 0.5 was chosen following Cohen (1988), 

who classifies effect size of this magnitude as “medium,” 

Linking Occupational Information Network

Occupation Descriptors to NAAL Literacy Scales 

Several studies support the use of the Occupational 

Information Network (O*NET) data to determine job 

requirement levels of employee aptitudes (LaPolice, Carter, 

& Johnson, 2008). To identify the literacy requirements of 

the selected high-growth occupations that potentially offer 

economic independence for the working poor, occupation 

data related to job analysis ratings of knowledge, skills, and 

abilities were collected through the O*NET database. 

However, the ratings of the O*NET occupation descriptors 

are not on the same scale as the NAAL literacy measures. 

Therefore, linkage needs to be established between the two 

measures, so that the average literacy scores of the working 

poor can be compared with the literacy requirements of 

those selected occupations. This section describes the 

method used to link the O*NET occupation descriptors to 

the NAAL literacy scales. 

Following the approach taken by LaPolice and colleagues 

(2008) in their study “Linking O*NET Descriptors to 

Occupational Literacy Requirements Using Job Component 

Validation,” AIR used multiple regression models to 

estimate the respective literacy requirements for the 50 

selected occupations on the NAAL 2003 literacy scale. The 

LaPolice study used a job component validity approach to 

relate O*NET knowledge, skill, ability, and generalized 

work activity descriptor data to literacy test scores on NALS 

conducted in 1992. The study estimated mean NALS 1992 

literacy scores for 902 O*NET–Standard Occupational 

Classification (SOC) (http://www.bls.gov/soc/) occupations 

using multiple regression models. The study also showed 

that the NALS literacy scores were highly predictable from 

the O*NET descriptors. 

Specifically, for the purpose of this report, the prose, 

document, and quantitative literacy scores for 

100 occupations were first estimated on the basis of the 

NAAL database. More than 400 occupations were available 

through the NAAL database, but these 100 occupations had 

sufficient sample sizes that allowed reliable estimates. The 

literacy scores were used as the dependent variable in the 

multiple regression models for each of the prose, document, 

and quantitative scales, respectively. 

The NAAL occupations were classified according to the 

2000 Census Industry and Occupational Codes (U.S. Census 

Bureau, 2000). The O*NET descriptor data were at the level 

of O*NET–SOC code. The Census Occupational Codes 
A-3

were matched to the O*NET–SOC codes before the multiple 

regression analyses were conducted. 

To select the potential predictor variables for the multiple 

regression models, a team of AIR experts first identified the 

O*NET descriptors that were conceptually relevant to prose, 

document, and quantitative literacy. Correlations of the 

selected predictors and literacy scores were checked, and 

those predictors with negative or zero correlations were 

deleted. Then several models were compared in terms of 

how the descriptors could be further combined. The final set 

of predictors was determined on the basis of the published 

O*NET factor model and these predictors are presented in 

table A.1. 

The results of the multiple regression analyses are presented 

in table A.2. Two coefficients in the models are negative, 

which is contrary to what would be expected. This does not 

mean that those two variables are negatively related to the 

criterion; rather, it is very likely due to the effect of 

multicollinearityF

11 (LaPolice et al., 2008). The regression 

coefficients were then applied to the models described above 

in estimating the literacy scores of the 50 selected 

occupations. 

 

11 Multicollinearity is a problem in multiple regression that occurs when 

variables are so highly correlated with each other that it is difficult to 

separate the effects of two (or more) variables on an outcome variable and 

produce reliable estimates of their individual regression coefficients. 

Table A-1. O*NET descriptors identified to predict NAAL literacy scores 

Prose Document Quantitative 

Basic Skills Basic Skills Complex Problem Solving 

English Language English Language English Language 

Getting Information Getting Information Computers and Electronics 

Interacting With Others Performing Administrative Activities Cognitive Abilities 

Cognitive Abilities Cognitive Abilities 

Table A-2. Regression coefficients for each regression models 

Regression coefficients Standardized regression coefficients 

Literacy scale Predictors B 

Std. 

error Beta t p 

Adjusted 

R square 

(Constant) 170 6.2 27.5 0 

BasicSkillsP 12.5 2.7 0.4 4.7 0 

EnglishLanguage 6.7 2 0.2 3.4 0 

GettingInformation –4.9 2.2 –0.1 –2.2 0 

InteractingWithOthers 7.6 2.2 0.2 3.5 0 

Prose

CognitiveAbilitiesP 9.5 3.3 0.2 2.9 0 

0.8 

(Constant) 184.9 5.7 32.6 0 

BasicSkillsD 7.5 2.2 0.3 3.4 0 

EnglishLanguage 8.8 1.7 0.4 5.3 0 

GettingInformation –4.3 2 –0.2 –2.2 0 

PerformingAdministrativeActivities 5 1.4 0.2 3.5 0 

Document

CognitiveAbilitiesD 10 3 0.2 3.4 0 

0.7 

(Constant) 185.1 5.3 34.9 0 

ComplexProblemSolving 5.9 1.4 0.2 4.2 0 

EnglishLanguage 9.1 1.5 0.3 6.1 0 

Quantitative

ComputersandElectronics 3.6 1.3 0.1 2.8 0 

0.8