1

Workers in Declining Industries: 

Literacy’s Role in Worker Transitions 

Authors 

Lee Bruno 

Ying Jin 

Dwayne Norris 

American Institutes for Research

October 2010 

Contents 

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

Workers in Declining 

Occupations Compared With 

the General Population of 

Workers ................................ 2

Demographic and 

Socioeconomic Comparisons.... 2

Prose and Quantitative 

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

Prose.......................................... 4

Quantitative............................... 4

Literacy and Occupations ......... 4

Literacy’s Role in Structural 

Unemployment ..................... 9

Summary............................. 13

References........................... 13

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 O*NET 

Occupation Descriptors to 

NAAL Literacy Scales......... A–2

Introduction 

The research conducted in the 1980s and 1990s on the subject of essential workplace skills1

was based on the recognition that global competition posed two distinct challenges to 

American industry. First, workers in countries with lower labor costs could perform the 

more mundane routine labor of most manufacturing processes just as well as U.S. workers. 

Second, countries with a better educated workforce, particularly in the technology sectors, 

could design and produce better products than American workers. The 1980s saw the 

United States outperformed in the manufacture of steel, automobiles, and durable goods 

such as appliances. The 1990s saw an outmigration of the labor-intensive garment and 

plastics manufacturing industries to Mexico, China, and other parts of Asia.2

 Today two of 

the three American auto manufacturing giants are restructuring following Government 

takeovers, and nearly all U.S. household appliances are manufactured outside the country.3

The U.S. Bureau of Labor Statistics (BLS) shows that some areas of manufacturing 

represent 19 of the top 20 industries in decline, while only miscellaneous manufacturing is 

listed among the top 20 fastest growing industries.4

 BLS projects that between 2006 and 

2016 approximately 1,060,000 workers in the 20 occupations representing the greatest 

decline will have lost their jobs. An analysis of these 20 occupations shows them to be 

either low-skilled clerical or production jobs, or farm work (see Table 1). 

The loss of these jobs can be attributed not only to the lure of cheaper labor in other 

countries but to the greater use of machines, including computer-aided devices that are 

more efficient than people. Many industries are achieving higher production levels using 

fewer workers. These job losses are permanent, the result of structural changes in the 

economy. Without regard to economic cycles, such as the 2008 recession, over 1 million 

people are or will be displaced across these 20 industries in decline. 

Although structural unemployment is a national crisis, solutions to unemployment are 

implemented through interventions that are attuned to the idiosyncrasies of the local 

economies. Declining industries dislocate workers en mass with commensurate adverse 

impact on communities. The sheer scope of the numbers of unemployed and their 

concomitant economic impact requires strategies that address classes of workers rather than 

individual workers one at a time. A national infrastructure, similar to the Workforce 

Innovation in Regional Economic Development (WIRED) initiative5

, is needed to create 

new jobs for local economies—jobs that are attainable by dislocated workers with a 

minimum of training and readjustment services. The WIRED model engages key 

community stakeholders to integrate economic development with workforce development. 

This model could benefit from tools that facilitate large-scale workforce development 

planning. 

In the report “Are They Really Ready to Work?” a significant barrier to creating the 

workforce necessary for the21st century is the lack of work preparedness skills in new

 

1

 Secretary’s Commission on Achieving Necessary Skills (SCANS). (1992). Learning a living: A blueprint for high performance. Washington, DC: U.S. 

Department of Labor. Retrieved from http://wdr.doleta.gov/SCANS/lal/lal.pdf 2

 Pierce, L. (2004). Plastics products, not elsewhere classified (SIC 3089): America and the world. In Encyclopedia of American industries (4th ed., Vol. 1). 

Farmington Hills, MI: Thomson Gale; Gereffi, G., Spener, D., & Bair, J. (2002). Free trade and uneven development: The North American apparel industry after 

NAFTA (p. 24). Philadelphia: Temple University Press. 3

 U.S. Census Bureau. (2009). Annual current industrial report: Major household appliances (Current Industrial Report Series 2008 MA335F). Washington, DC: 

Author. Retrieved from http://www.census.gov/cir/www/instructions/ma335f.pdf. 4

 Bureau of Labor Statistics, U.S. Department of Labor. (2009 November). Industry output and employment projections to 2018. Monthly Labor Review. 

Washington, DC: Author. 

5

 A current initiative in operation supported with funding by the U.S. Department of Labor, Employment and Training Administration www.doleta.gov/WIRED. 
2 

workers, not technical or occupational skills. Work 

preparedness skills are organized into two groups: basic 

knowledge and applied social skills.6

 Basic knowledge 

includes the literacy skills of mathematics, English reading 

comprehension, and English writing, in addition to subjects 

that are standard high school fare—foreign languages, 

economics, history, science, and humanities. Social skills 

are the interactive skills that support teamwork, good 

interpersonal communications, and responsible workplace 

behavior, also commonly referred to as “the work ethic.” 

Workers in declining industries have demonstrated that they 

have the requisite social skills, but do they have the basic 

knowledge skills? 

The American Institutes for Research (AIR), under contract 

to the U.S. Department of Labor, Employment and Training 

Administration (ETA), uses data collected from the 

2003 National Assessment of Adult Literacy (NAAL),7

 to 

examine workers in declining occupations in order to 

address the following questions: 

 What distinguishes workers in declining 

occupations from workers in the general 

population? 

 

6

 The Conference Board, Corporate Voices for Working Families, 

Partnership for 21st Century Skills, & Society for Human Resource 

Management. (2006). Are they really ready to work? Employers’ 

perspectives on the basic knowledge and applied skills of new entrants to 

the 21st century U.S. workforce. New York: Authors. Retrieved from 

http://www.21stcenturyskills.org/documents/FINAL_REPORT_PDF09-29-

06.pdf. 

7

 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). 

 What are the literacy requirements that pose 

barriers to high-growth occupations? 

 How can ETA’s workforce investment system8

expedite workers’ transition from a declining 

industry to expanding industries? 

Workers in Declining Occupations Compared 

With the General Population of Workers 

Demographic and Socioeconomic Comparisons 

The NAAL research is based on a 2003 household sample of 

U.S. adults 16 years of age or older that constitutes a 

nationally representative probability sample. Proper weights 

were applied in all analyses to reflect the general population. 

This allows the data used in this report to accurately reflect 

the general U.S. adult population 16 years of age or older. 

That is not the case in the analysis of workers in declining 

industries; data on workers in declining industries are not 

necessarily representative of all workers in declining 

industries. Nevertheless, the sample size is large enough to 

lend credence to observations discussed in this report. 

In addition to assessing the literacy skills of the general 

working population and those respondents representing 

workers in declining occupations, NAAL gathered extensive 

background information on respondents’ demographic and 

socioeconomic characteristics (e.g., age, gender, 

race/ethnicity, nativity status, schooling, labor force status, 

and household income), along with their literacy practices. 

Table 2 compares age, gender, race/ethnicity, and 

educational attainment of NAAL respondents working in 

 

8

 ETA funds job training, income maintenance, labor market information, 

and job placement services through a system of state and local One-Stop 

Service Centers http://www.doleta.gov/etainfo/wrksys/WIMission. 

Table 1: Projected job losses in declining occupations, 2006–2016 

Occupation Occupation category Job losses (thousands) Percent change 

Farmers and ranchers Farming, Fishing, nursery 90 –8.5 

Farmworkers and laborers—crop, nursery, and greenhouse Farming, Fishing, Nursery 20 –3.4 

Stock clerks and order fillers Office/clerical 131 –7.7 

Computer operators Office/clerical 32 –24.7 

File clerks Office/clerical 97 –41.3 

Order clerks Office/clerical 66 –24.3 

Word processors and typists Office/clerical 21 –11.6 

Packers and packagers, hand Production 104 –12.4 

Sewing machine operators Production 63 –27.2 

Electrical and electronic equipment assemblers Production 57 –26.8 

Cutting, punching, and press machine setters, operators, and 

tenders, metal and plastic 

Production 40 –14.9 

Inspectors, testers, sorters, samplers, and weighers Production 35 –7.0 

First-line supervisors/managers of production and operating 

workers 

Production 34 –4.8 

Photographic processing machine operators Production 25 –49.8 

Machine feeders and offbearers Production 22 –15.2 

Packaging and filling machine operators and tenders Production 21 –5.4 

Paper goods machine setters, operators, and tenders Production 21 –18.2 

Telemarketers Sales 39 –9.9 

Cashiers, except gaming Service 118 –3.4 

Driver/sales workers Transportation 24 –5.3 
3 

Table 2: Demographic comparisons between NAAL sample of workers in declining occupations and U.S. population 

Workers in declining occupations U.S. Population 

Age 23%* 14% 

18–24 30% 28% 

25–39 21% 21% 

40–49 20% 22% 

50–64 7%* 16% 

65+ 48% 52% 

Sex/Gender 52% 48% 

Female 63%* 71% 

Male 14% 11% 

Race/Ethnicity 18%* 12% 

White 5% 6% 

Black 2% 1% 

Hispanic 6% 6% 

Other 13%* 9% 

Education 7%* 5% 

Still in high school 38%* 27% 

0–8 years 5% 6% 

9–12 years 13% 12% 

GED/equivalency** 10% 12% 

High school graduate 6%* 23% 

Vocational degree 23%* 14% 

Some college 30% 28% 

Associate of Arts degree/2 years of studies 21% 21% 

College graduate and Graduate studies/degree 20% 22% 

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

**General Education Development. 

declining occupations with these characteristics of workers 

in the general population of NAAL respondents.9

Age. In terms of age, there is little to distinguish workers in 

declining occupations from workers in the general 

population during the prime working years, between 25 and 

64 years of age. Workers in declining occupations are more 

likely to include more young workers (18–24 years of age) 

and fewer older workers (older than 65 years of age) than the 

general population. Since many of the declining occupations 

call for physical strength or manual dexterity rather than 

education or experience, younger workers have an advantage 

and older workers a distinct disadvantage in gaining jobs in 

these occupations. 

Gender. Workers in declining occupations generally match 

the general population with regard to gender. Women are 

only slightly less represented in declining occupations (48%) 

than they are among workers in the general population 

(52%). Declining occupations are gender neutral. 

Ethnicity. Workers in declining occupations are mostly 

white (63%). However, minorities, consisting of blacks and 

Hispanics, represent a larger proportion of workers in 

declining occupations (32%) than in the general population 

of workers (23%). 

Educational Attainment. Forty-five percent of workers in 

declining occupations and 32 % of the general population 

 

9

 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 based solely on the 

results presented here. 

have high school diplomas or General Education 

Development (GEDs). Workers in both groups show the 

same levels (18%) of postsecondary education in the form of 

either vocational school or “some” college; however, nearly 

twice as many workers in the general population (35%) as 

workers in declining occupations had either a 2-year or 

4-year college degree. It is the variation in college-level 

educational attainment that represents the single greatest 

distinction between workers in declining occupations and 

workers in the general population. 

In the context of the report “Are They Really Ready to 

Work?” according to which high school and 2-year degrees 

were basic qualifications for the 21st century workforce, 

79% of workers in declining occupations and 85% of the 

U.S. general population of workers represented in the NAAL 

study have graduated from high school. Thirty-four percent 

of the workers in declining occupations and 55% of the 

general population of workers have postsecondary 

educations. This would suggest that U.S. workers generally 

are failing to attain the education levels demanded by 

today’s jobs. 

Prose and Quantitative Literacy Levels 

NAAL measured respondents’ proficiencies on three literacy 

scales: prose, document, and quantitative. Because the 

correlation between the prose and document scores reviewed 
4 

Table 3: Comparisons of literacy levels between workers in declining occupations and workers in general U.S. population 

Prose Quantitative 

Mean Scores Mean Scores 

Workers in declining occupations 259.52* 272.78* 

General population 275.00 283.00 

Below basic Basic Intermediate Advanced 

Prose 

Workers in declining occupations 16% 38%* 41% 6%* 

General population 14% 29% 44% 13% 

Quantitative 

Workers in declining occupations 24% 38%* 31% 8%* 

General population 22% 33% 33% 13% 

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

for this report revealed no new information, the analyses in 

this section are based on the prose and quantitative literacy 

scales only. 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. A brief description of the 

four levels of literacy follows.10

Prose 

 Below Basic—locating easily identifiable 

information in short, commonplace prose texts, 

with literacy assessment scores ranging from 0 to 

209 

 Basic—reading and understanding information in 

short commonplace texts, with literacy assessment 

scores ranging from 210 to 264 

 Intermediate—reading and understanding 

moderately dense, less commonplace prose texts, as 

well as summarizing, making simple inferences, 

determining cause and effect, and recognizing the 

author’s purpose, with literacy assessment scores 

ranging from 265 to 339 

 Proficient—reading lengthy, complex, abstract 

prose tests, as well as synthesizing information and 

making complex inferences, with literacy 

assessment scores ranging from 340 to 500 

Quantitative 

 Below Basic—locating numbers and using them to 

perform simple quantitative operations (primarily 

addition) when the mathematics information is very 

concrete and familiar, with literacy assessment 

scores ranging from 0 to 234 

 Basic—locating easily identifiable quantitative 

information and using it to solve simple, one-step 

problems when the arithmetic operation is specified 

 

10 Source: Hauser, R. M., Edley, C. F. Jr., Koenig, J. A., & Elliot, S. W. 

(Eds.). (2005). Measuring literacy: Performance levels for adults, Interim 

report. Washington, DC: National Academies Press; White, S., & Dillow, 

S. (2005). Key concepts and features of the 2003 National Assessment of 

Adult Literacy (NCES 2006-471). Washington, DC: U.S. Department of 

Education, National Center for Education Statistics. 

or easily inferred, with literacy assessment scores 

ranging from 235 to 289 

 Intermediate—locating less familiar quantitative 

information and using it to solve problems when 

the arithmetic operation is not specified or easily 

inferred, with literacy assessment scores ranging 

from 290 to 349 

 Proficient—locating more abstract quantitative 

information and using it to solve multistep 

problems when the arithmetic operations are not 

easily inferred and the problems are more complex, 

with literacy assessment scores ranging from 350 to 

500 

Table 3 presents a comparison of the prose and quantitative 

literacy of workers in declining occupations and workers in 

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. 

In terms of average literacy scores, there is a significant 

difference between the prose and quantitative score means of 

workers in declining occupations and those of the general 

population of workers, with prose score averages showing 

the larger difference. More workers in declining 

occupations had Basic literacy than did those in the general 

population. In contrast, more workers in the general 

population had Proficient literacy than did those in declining 

occupations. 

It is interesting to note that the distribution of literacy levels 

of both groups of workers correlate closely with the groups’ 

distribution across educational attainment. In both instances, 

there is little to distinguish the two groups until they are 

compared at the upper ranges of educational attainment 

(college degrees) and literacy levels (Proficient). This 

suggests that the groups are very similar except when 

individual circumstances make higher education feasible. 

Literacy and Occupations 

The NAAL data offer an opportunity to compare the literacy 

levels of workers in declining industries with the literacy 

requirements of occupations in high-growth industries. To 

do this, 50 occupations that did not require 4-year college 

degrees and were representative of those in high-growth 
5 

industries were selected.11 Next, multiple regression models 

were developed by AIR to integrate the Occupational 

Information Network (O*NET)12 data and the NAAL 

literacy measures, and each occupation’s literacy 

requirements were derived and then compared with the 

average literacy levels of the NAAL respondents who were 

employed in declining occupations.13

 

11 See www.careervoyages.gov. This Web site was the primary source for 

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

occupations meeting the criteria were numerous, occupations were 

collapsed into categories recognized by the respective industry. 12 O*NET is a database of information describing all U.S. occupations. See 

http://online.onetcenter.org/ 

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

measures is described in Appendix A. 

Table 4 compares the average literacy prose (260) and 

quantitative (273) scores of workers in declining occupations 

with the prose and quantitative scores that correspond to 

each of the 50 high-growth occupations. Also included is 

the educational attainment of the distribution of workers 

holding these occupations (i.e., high school, some college, 

and bachelor’s degree). 

Table 4: Literacy and educational requirements of demand occupations 

Percent of workers 25–44 years of age, 

by educational attainment Occupation Industry Prose Quantitative 

Difference 

in prose 

score 

Difference 

in quant 

score H.S. or 

less 

Some 

college 

Bachelor’s degree 

or higher 

Painters, 

transportation 

equipment 

Transportation 235 251 –24.51 –21.55 75.4 20.8 3.8 

Railroad conductors 

and yardmasters 

Transportation 244 272 –15.91 –0.89 37.7 51.7 10.6 

Operating engineers 

and other 

construction 

equipment operators 

Construction 245 255 –14.45 –18.11 77.8 19.6 2.6 

Medical 

transcriptionists 

Health care 248 293 –11.79 20.18 30.6 58.9 10.4 

Mates—ship, boat, 

and barge 

Transportation 252 276 –7.98 2.79 54.9 24.2 20.9 

Dental assistants Health care 252 282 –7.87 9.67 33.6 57.5 8.9 

Dental hygienists Health care 252 276 –7.11 3.60 2.9 63.8 33.3 

Pipelayers Energy 255 256 –5.01 –16.74 67.6 28.5 3.9 

Bus and truck 

mechanics and diesel 

engine specialists 

Transportation 256 266 –3.54 –7.21 65.7 31.2 3.1 

Truck drivers, heavy 

and tractor–trailer 

Transportation 256 270 –3.26 –3.17 70.7 25.2 4.1 

Excavating and 

loading machine and 

dragline operators 

Energy 257 263 –2.57 –9.47 78.8 17.7 3.5 

Chefs and head 

cooks 

Hospitality 258 285 –1.07 11.91 47.7 38.8 13.5 

Automotive body 

and related repairers 

Automotive 260 281 0.38 8.59 74.3 22.5 3.1 

Security and fire 

alarm systems 

installers 

Homeland 

security 

262 288 2.17 15.59 48.3 44.0 7.7 

Freight and cargo 

inspectors 

Homeland 

security 

262 273 2.69 0.19 42.8 41.7 15.5 
6 

Table 4: Literacy and educational requirements of demand occupations continued

Percent of workers 25–44 years of age, 

by educational attainment Occupation Industry Prose Quantitative 

Difference 

in prose 

score 

Difference 

in quant 

score H.S. or 

less 

Some 

college 

Bachelor’s degree 

or higher 

Police, fire, and 

ambulance 

dispatchers 

Homeland 

security 

263 309 3.07 36.22 44 45.1 10.9 

Physical therapy 

assistants 

Health care 267 289 7.58 16.56 11.7 65.4 22.9 

Electrical and 

electronics repairers, 

commercial and 

industrial equipment 

Energy 270 283 10.05 10.54 46.4 46.0 – 

Radiological 

technicians 

Health care 270 293 10.27 20.25 7.2 67.9 24.9 

Bill and account 

collectors 

Financial 

services 

270 290 10.27 17.34 38.4 48.2 13.3 

Electrical power–line 

installers and 

repairers 

Energy 270 276 10.66 3.40 55.9 38.2 6 

Customer service 

representatives 

Retail 271 291 11.24 18.07 33.8 44.2 22 

Aircraft structure, 

surfaces, rigging, 

systems and 

assemblers 

Aerospace 271 280 11.53 6.88 58.7 35.3 – 

Aircraft mechanics 

and services 

technicians 

Aerospace 273 296 13.46 23.37 32.9 56.6 10.5 

Plumbers Construction 274 294 14.00 21.69 67.6 28.5 3.9 

Industrial machinery 

mechanics 

Advanced 

manufacturing 

274 294 14.68 20.99 55.9 38.8 5.3 

Rough carpenters 

(carpenters) 

Construction 275 278 15.59 4.83 72.8 21.6 5.5 

Municipal fire 

fighters (fire 

fighters) 

Homeland 

security 

275 292 15.98 18.79 22.9 58.8 18.3 

Computer, 

automated teller and 

office machine 

repairers 

Information 

technology 

277 305 17.29 31.90 22.5 52.7 24.7 

Food service 

managers 

Hospitality 277 298 17.89 25.24 40.0 35.8 24.2 

Police patrol officers 

(police and sheriff 

patrol officers*) 

Homeland 

security 

279 305 19.05 32.27 15.5 51.7 32.8 

Brokerage clerks Financial 

services 

281 295 21.48 22.61 24.4 44.6 31 

Bookkeeping, 

accounting, and 

auditing clerks 

Financial 

services 

281 299 21.72 25.77 33.7 50.3 16 

Payroll and 

timekeeping clerks 

Financial 

services 

282 294 22.51 21.66 32.9 49.6 17.5 
7 

Table 4: Literacy and educational requirements of demand occupations continued

Percent of workers 25–44 years of age, 

by educational attainment Occupation Industry Prose Quantitative 

Difference 

in prose 

score 

Difference 

in quant 

score H.S. or 

less 

Some 

college 

Bachelor’s degree 

or higher 

Food science 

technicians 

Emerging 

industries 

282 296 22.92 23.57 42.7 25.6 31.6 

Medical equipment 

repairers 

Health care 286 314 26.44 41.55 28.3 57.0 14.7 

Surveying 

technicians 

Energy 287 298 27.19 25.06 42.2 51.0 6.8 

Mapping technicians Emerging 

industries 

290 318 30.33 45.07 42.2 51.0 6.8 

Electricians Construction 290 292 30.71 19.45 50.7 42.4 6.8 

Insurance adjusters, 

examiners, and 

investigators 

Financial 

services 

290 307 30.82 34.30 18.3 35.1 46.7 

Registered nurses Health care 290 297 30.92 23.77 1.0 42.7 56.2 

Electronic drafters Aerospace 294 306 34.75 33.59 13.0 62.4 24.6 

Licensed practical 

nurses 

Health care 296 314 36.39 40.96 20.6 72.4 7 

Construction 

managers 

Construction 297 308 37.12 35.01 39.5 31.1 29.4 

Statistical assistants Emerging 

industries 

299 324 39.20 51.62 23.7 46.5 29.8 

Electrical and 

electronic 

engineering 

technicians 

Advanced 

manufacturing 

299 317 39.21 43.92 27.2 54.4 18.4 

Chemical technicians Advanced 

manufacturing 

301 314 41.90 41.42 30.3 32.5 37.2 

The prose literacy requirements across the 50 occupations 

range from a low of 235 to a high of 308. The quantitative 

literacy scores range from a low of 251 to a high of 326. 

Generally, the workers in declining industries have lower 

literacy scores than those required for the representative 

high-growth occupations. The more surprising finding, 

however, is the lack of correlation between educational 

requirements and literacy requirements of high-growth 

occupations. In other words, high-growth occupations may 

have higher literacy requirements but not necessarily require 

education beyond high school. For this analysis, the 

50 occupations were selected to exclude occupations in 

which most or all the workers had 4-year degrees as 

incremental to an effective transition strategy that was most 

feasible for the target group. Only 2 of the 50 occupations 

show a high distribution of workers with 4-year degrees—

registered nurses (56.2 %) and insurance adjusters (46.7 %). 

The prose literacy score for both occupations is 290, and the 

quantitative literacy scores are 297 and 307, respectively. 

The occupations with the highest literacy scores are 

mechanical and technical occupations common to the 

advanced manufacturing industry, for example, industrial 

engineering technicians, mechanical drafters, technical 

engineering and chemical technicians. None of these 

positions requires a 4-year degree, but all do require some 

college. According to responses to the NAAL, a significant 

portion of workers in these occupations only have a high 

school diploma. 

The lack of correlation between educational attainment and 

literacy may result from the ability of people with higher 

literacy proficiency but not necessarily education beyond 

high school to master the technical training for some 

occupations through on-the-job coaching and employer inhouse special training. Advanced manufacturing typically 

relies on customized training that is driven by the unique 

requirements of its production equipment and processes. By 

contrast, registered nurses are schooled to state credentialing 

requirements implemented by degree-granting institutions. 

Occupational training that is governed by standardized 

requirements generally falls into the domain of 

postsecondary educational institutions. These institutions 

typically have educational prerequisites, for example, high 
8 

school graduation, for entry. However, where employers 

train workers in occupational skills specific to their 

equipment and processes, educational prerequisites are not 

typical. Workers with the literacy skills needed to learn on 

the job but who lack formal education are more likely to 

advance their careers in high-demand occupations that are 

learned on the job. Postsecondary institutions that must 

meet credentialing standards may be unable to admit 

workers from declining industries who have not graduated 

from high school. 

Table 5 compares the average prose and quantitative literacy 

scores of workers in declining occupations with prose and 

quantitative requirements in the high-growth or highdemand occupations. “Higher” means that the occupations’ 

requirements are higher than the average literacy proficiency 

of workers in declining occupations. “Lower” is similarly 

defined. The majority of high-growth occupations (62%) in 

the sample require higher prose and quantitative literacy 

proficiency than the average for workers in declining 

occupations. Only four occupations (8%) require lower or 

similar literacy skills—railroad conductors/yardmasters; 

ship, boat, and barge mates; painters of transportation 

equipment; and operating engineers and other construction 

equipment operators. Only 6 high-growth occupations 

required the same literacy skills—dental hygienists, truck 

drivers, freight and cargo inspectors, railroad conductors and 

yardmasters, and ship, boat, and barge mates. 

The lack of prose literacy skills is less a barrier to highgrowth occupations than is the lack of quantitative literacy 

skills. A deficiency in quantitative skills was an obstacle to 

74% of the high-growth occupations for workers in 

declining occupations, while a deficiency in prose literacy 

was an obstacle to only 62%. 

Table 5: Comparison of working poor average literacy level with that required by selected high-growth occupations 

Quantitative Higher Same Lower 

Higher 

Physical therapy assistants 

Electrical and electronics repairers, commercial 

and industrial equipment 

Radiological technicians 

Bill and account collectors 

Customer service representatives 

Aircraft mechanics and services technicians 

Plumbers 

Industrial machinery mechanics 

Municipal fire fighters (fire fighters*) 

Computer, automated teller and office machine 

repairers 

Food service managers 

Police patrol officers (police and sheriff patrol 

officers) 

Brokerage clerks 

Bookkeeping, accounting, and auditing clerks 

Payroll and timekeeping clerks 

Food science technicians 

Medical equipment repairers 

Surveying technicians 

Registered nurses 

Construction managers 

Statistical assistants 

Chemical technicians 

Mechanical engineering technicians 

Mechanical drafters 

Industrial engineering technicians 

Electrical power-line installers and 

repairers 

Rough carpenters (carpenters) 

Aircraft structure, surfaces, rigging, 

systems and assemblers 

Same Chefs and head cooks Dental hygienists Pipelayers 

Prose 

Lower Medical transcriptionists Railroad conductors and 

yardmasters 

Painters, transportation 

equipment 
9 

Literacy’s Role in Structural Unemployment 

Structural unemployment is an industry issue rather than an 

individual worker issue. When an industry can no longer 

thrive, it shuts down and all its associated employees are 

unemployed—the managers, the highly skilled technicians, 

and the less skilled line workers—regardless of their 

education or skills. Individual differences among workers 

do not provide any protection from job loss; however, 

workers with skills that are transferrable to other 

employment can adapt to new jobs more readily and find 

new jobs more quickly. The NAAL research shows that 

workers’ prose and quantitative literacy skills are critical to 

their transition to high-growth occupations. In terms of 

developing a strategy that can facilitate the shift of workers 

from declining industries to thriving industries, deficiencies 

in literacy is a barrier that is easier and more cost-effective 

to address than are lack of educational and training 

credentials. 

Table 6 compares the average prose and quantitative literacy 

scores of workers holding declining occupations to various 

industries associated with high-growth occupations. Energy, 

transportation, construction, homeland security, and health 

care industries appear to provide the best reemployment 

options for the average displaced worker in a declining 

occupation for the smallest investment in literacy training. 

Financial services, retail, aerospace advanced 

manufacturing, information technology, hospitality, and 

emerging industries would require a greater investment in 

literacy training. If a geographic region faces a loss of a 

declining industry, the best opportunities for large-scale 

reemployment of the displaced workers will be in industries 

in which literacy skills are most similar to the industry lost. 

Therefore, for areas of the country suffering high structural 

unemployment, economic development that targets 

industries matching the literacy levels of the displaced 

workers can offer a compatible pool of employees as a 

strong inducement to attract new employers. 

Table 6: Comparison of working poor average literacy level with that required by selected high-growth occupations 

Quantitative Higher Same Lower 

Higher 

Health care 

Energy 

Financial services 

Retail 

Aerospace 

Construction 

Advanced manufacturing 

Homeland security 

Information technology 

Emerging industries 

Hospitality 

Energy 

Construction 

Aerospace 

Same 

Hospitality 

Automotive 

Homeland security 

Health care 

Transportation 

Homeland security 

Energy 

Transportation 

Prose 

Lower Health care Transportation Transportation 

Construction 

NOTE. “Higher” indicates that the required literacy proficiency of the industry is higher than the existing average literacy scores of workers in 

declining occupations. In the same manner, “lower” indicates that the required literacy proficiency of the industry is lower than the existing 

average literacy scores of workers in declining occupations. 

By using O*NET data to compare the literacy skills required 

for each of the declining occupations with the literacy 

requirements of high-growth occupations listed in Table 4, 

literacy-training investments can be estimated for 

reemployment training. This type of comparison addresses 

the following questions: 

 What high-growth occupation and corresponding 

industries are most compatible with the local 

displaced workers’ prose and quantitative literacy 

proficiencies? 

 What are the skill and literacy gaps between 

declining and prospective new industries? 

 What should be the content for retraining programs 

that facilitate reemployment for dislocated 

workers? 

 Which industries offer the best jobs compared to 

the training costs? 

To demonstrate, Table 7 lists some of the same high-growth 

occupations found in Table 4 but also adds other O*NET 

data that will be useful to planning a reemployment strategy. 
10 

Table 7: Reemployment options for displaced workers on the basis of literacy and education requirements, median wage, training 

methods, and projected occupation demand in 2016 

Occupation Industry Prose Quantitative Projected need 

(in 1,000s) 

BLS hourly 

median wage 

(50th) 

Post-secondary 

education or 

training category 

Educational 

attainment 

cluster* 

Painters, transportation 

equipment 

Transportation 235 251 15 17.31 Moderate-term 

on-the-job training 

HS/SC 

Railroad conductors 

and yardmasters 

Transportation 244 272 19 28.2 Moderate-term 

on-the-job training 

HS/SC 

Operating engineers 

and other construction 

equipment operators 

Construction 245 255 118 18.33 Moderate-term 

on-the-job training 

HS 

Medical 

transcriptionists 

Health care 248 293 26 15.02 Postsecondary 

vocational award 

HS/SC 

Mates—ship, boat, and 

barge 

Transportation 252 276 15 27.5 Work experience 

in a related 

occupation 

HS/SC/C 

Dental assistants Health care 252 282 280 15.17 Moderate-term onthe-job training HS/SC 

Dental hygienists Health care 252 276 82 31.12 Associate’s degree SC/C 

Pipelayers Energy 255 256 20 15.04 Moderate-term onthe-job training HS/SC 

Bus and truck 

mechanics and diesel 

engine specialists 

Transportation 256 266 91 18.58 Postsecondary 

vocational award 

HS/SC 

Truck drivers, heavy 

and tractor-trailer 

Transportation 256 270 523 17.41 Moderate-term onthe-job training HS/ SC 

Excavating and 

loading machine and 

dragline operators 

Energy 257 263 19 16.37 Moderate-term onthe-job training HS 

Chefs and head cooks Hospitality 258 285 23 17.87 Work experience 

in a related 

occupation 

HS/SC 

Automotive body and 

related repairers 

Automotive 260 281 64 17.16 Long-term on-thejob training HS/SC 

Security and fire alarm 

systems installers 

Homeland 

security 

262 288 19 17.02 Postsecondary 

vocational award 

HS/SC 

Freight and cargo 

inspectors 

Homeland 

security 

262 273 13 24.73 Moderate-term 

on-the-job training 

HS/SC 

Police, fire, and 

ambulance dispatchers 

Homeland 

security 

263 309 38 15.7 Moderate-term 

on-the-job training 

HS/SC 

Physical therapy 

assistants 

Health care 267 289 27 21.22 Short-term 

on-the-job training 

SC/ C 

Electrical and 

electronics repairers, 

commercial and 

industrial equipment 

Energy 270 283 33 22.65 Postsecondary 

vocational award 

HS/SC 

Radiological 

technicians 

Health care 270 293 56 24.16 Associate’s degree SC/C 

Bill and account 

collectors 

Financial 

services 

270 290 165 14.42 Short-term 

on-the-job training 

HS/SC 

Electrical power-line 

installers and repairers 

Energy 270 276 43 25.27 Long-term 

on-the-job training 

HS/SC 
11 

Table 7: Reemployment options for displaced workers on the basis of literacy and education requirements, median wage, training 

methods, and projected occupation demand in 2016 continued

Occupation Industry Prose Quantitative Projected need 

(in 1,000s) 

BLS hourly 

median wage 

(50th) 

Post-secondary 

education or 

training category 

Educational 

attainment 

cluster 

Customer service 

representatives 

Retail 271 291 1158 13.96 Moderate-term 

on-the-job training 

HS/SC/C 

Aircraft structure, 

surfaces, rigging, 

systems, and 

assemblers 

Aerospace 271 280 9 21.84 Moderate-term onthe-job training HS/SC 

Aircraft mechanics and 

services technicians 

Aerospace 273 296 25 23.56 Postsecondary 

vocational award 

HS/SC 

Plumbers Construction 274 294 157 21.2 Long-term 

on-the-job training 

HS/SC 

Industrial machinery 

mechanics 

Advanced 

manufacturing 

274 294 67 20.36 Long-term 

on-the-job training 

HS/SC 

Rough carpenters 

(carpenters) 

Construction 275 278 348 18.11 Long-term 

on-the-job training 

HS/SC 

Municipal fire fighters 

(fire fighters) 

Homeland 

security 

275 292 142 20.75 Long-term 

on-the-job training 

HS/SC 

Computer, automated 

teller, and office 

machine repairers 

IT 277 305 26 17.84 Postsecondary 

vocational award 

HS/SC/C 

Food service managers Hospitality 277 298 100 21.43 Work experience 

in a related 

occupation 

HS/SC/C 

Police patrol officers 

(police and sheriff 

patrol officers) 

Homeland 

security 

279 305 243 23.86 Long-term 

on-the-job training 

SC/C 

Brokerage clerks Financial 

services 

281 295 38 17.96 Moderate-term 

on-the-job training 

HS/SC/C 

Bookkeeping, 

accounting, and 

auditing clerks 

Financial 

services 

281 299 594 15.17 Moderate-term 

on-the-job training 

HS/SC 

Payroll and 

timekeeping clerks 

Financial 

services 

282 294 61 16.26 Moderate-term onthe-job training HS/SC 

Food science 

technicians 

Emerging 

industries 

282 296 6 16.17 Associate’s degree HS/SC/C 

Medical equipment 

repairers 

Health care 286 314 38 19.38 Associate’s degree HS/SC 

Surveying technicians Energy 287 298 29 16.17 Moderate-term 

on-the-job training 

HS/SC 

Mapping technicians Emerging 

industries 

290 318 29 16.17 Moderate-term onthe-job training HS/SC 

Electricians Construction 290 292 234 21.53 Long-term 

on-the-job training 

HS/SC 

Insurance adjusters, 

examiners, and 

investigators 

Financial 

services 

290 307 105 25.75 Long-term 

on-the-job training 

SC/C 

Registered nurses Health care 290 297 1001 28.85 Associate’s degree SC/C 

Electronic drafters Aerospace 294 306 11 23.68 Postsecondary 

vocational award 

SC/C 
12 

Table 7: Reemployment options for displaced workers on the basis of literacy and education requirements, median wage, training 

methods, and projected occupation demand in 2016 continued

Occupation Industry Prose Quantitative Projected need 

(in 1,000s) 

BLS hourly 

median wage 

(50th) 

Post-secondary 

education or 

training category 

Educational 

attainment 

cluster 

Licensed practical 

nurses 

Health care 296 314 309 18.24 Postsecondary 

vocational award 

HS/SC 

Construction managers Construction 297 308 152 36.65 Bachelor’s degree HS/SC/C 

Statistical assistants Emerging 

industries 

299 324 12 15.64 Moderate-term 

on-the-job training 

HS/SC/C 

Electrical and 

electronic engineering 

technicians 

Advanced 

manufacturing 

299 317 39 25.07 Associate’s degree HS/SC 

Chemical technicians Advanced 

manufacturing 

301 314 24 19.58 Associate’s degree HS/SC/C 

Mechanical 

engineering 

technicians 

Aerospace 304 320 12 22.73 Associate’s degree HS/SC 

Mechanical drafters Advanced 

manufacturing 

304 323 26 21.51 Postsecondary 

vocational award 

SC/C 

Industrial engineering 

technicians 

Advanced 

manufacturing 

308 326 22 22.83 Associate’s degree HS/SC 

*Key: HS = high school; SC = some college; c = college 

To demonstrate the usefulness of O*NET data for the 

purposes of planning economic development or providing 

individual counseling at One-Stop Career Centers, the 

strategies or processes described above can be applied to a 

scenario involving major layoffs of team assemblers, for 

example, a job held by a large number of autoworkers. For 

this example, the estimated prose literacy score required of 

team assemblers is 250 and the quantitative score is 257. 

These scores represent the minimum literacy proficiency for 

this occupation. Obviously, team assemblers can function at 

higher levels, and this could be determined with a literacy 

assessment tool that correlates to O*NET occupations.14 By 

comparing this assumption of minimum literacy levels, 

one can identify from O*NET data a variety of high-growth 

occupations that fall within or close to these scores. For 

discussion purposes, Table 8, below, presents a comparison 

with four high-growth occupations. 

 

14 There are many literacy tests available, but AIR’s research failed to find 

any that specifically correlated to O*NET occupation descriptions. 

Table 8: Using O*NET to plan re-employment of dislocated team assemblers 

Prose and quantitative 

literacy requirements High-growth occupation 

industry 

Prose Quantitative 

Training content Median hourly 

wage 

Level of 

demand 

Medical transcriptionists—health 248 293 Some quantitative literacy/brief skill 

training—SC 

$15.02 Medium 

Heavy excavation—energy 256 270 Some quantitative literacy—on-the-job 

training 

$17.41 High 

Radiological technician—health 270 293 Some prose and quantitative literacy—2 

to 4 years college 

$24.16 High 

Mechanical drafters—advanced 

manufacturing 

304 323 Extensive prose and quantitative 

literacy—2 to 4 years college 

$21.51 Low 
13 

Each choice above poses tradeoffs, and none of these 

options considers individual preferences, but in terms of 

local economic development planning to address regional 

structural unemployment, this tool provides important 

information for planning responses to anticipated industry 

decline. Specifically, the tool can assist the workforce 

investment system to 

 Identify industries and jobs that match the literacy 

levels of workers in declining occupations to guide 

industry recruitment and economic development 

 Inform One-Stop Career Centers and training 

institutions about potential training needs of 

workers in transition from one industry to another 

 Tailor job placement options to fit individual 

workers’ needs and interests 

Summary 

Structural unemployment requires a response that can 

address large numbers of workers who typically represent 

occupations in a declining industry. The impact of 

large-scale worker displacement has degrading effects on 

local economies. To combat these events, regional 

economic development strategies should compare the 

capabilities and skills of workers in declining industries with 

the requirements of expanding industries and high-growth 

occupations. 

Workers in declining occupations do not differ from workers 

in the general population except in the area of functioning 

literacy levels and college degrees. NAAL data show that 

literacy deficiencies, perhaps more than educational 

attainment, is the critical barrier that prevents workers in 

declining occupations from adapting to the demands of 

high-growth occupations. 

By using O*NET data organized around literacy 

requirements of both declining and high-growth occupations, 

local area planners can affect the structural causes for 

unemployment by attracting industries that minimize the 

retraining needed by the pool of workers dislocated from 

declining industries. Efficiencies at this important planning 

stage of economic development reduce the duration of 

unemployment and the overall adverse effects of layoffs on 

the local economy. 

References 

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

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

Erlbaum. 

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. 

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. 

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. 

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

occupational codes. Washington, DC: Author. 
A–1 

Appendix A: Methodology and Technical 

Notes 

This appendix provides additional 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 Occupational Information Network (O*NET) occupation 

descriptors to the NAAL literacy scales, in order to 

determine the literacy gaps between selected highgrowth/demand occupations and workers in declining 

occupations. 

The 2003 NAAL Assessment 

The 2003 NAAL assessed the English literacy of adults 

(16 years of age or older) in the United States for the first 

time since the 1992 National Adult Literacy Survey. 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, 

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 the way the respondents 

obtained information. 

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 skills, 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 Literacy in 

Everyday Life: Results from the 2003 National Assessment 

of Adult Literacy (Kutner et al., 2007; 

http://nces.ed.gov/Pubs2007/2007480.pdf). 

Descriptions of Background Variables 

Race/Ethnicity 

In 2003, all respondents were asked two questions about 

their race/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. 

Highest Educational Attainment 

All respondents were asked to indicate the highest level of 

education that 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. 

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: 
A–2 

 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 

two-tailed 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 design-based 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,” and to be consistent with what was done 

in the 1992 National Adult Literacy Survey 

(NALS). 

Linking O*NET 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 could 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 workers in declining occupations 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 literacy requirement 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 

Classifications (SOCs) (http://www.bls.gov/soc/) 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 that database, but these 100 occupations had 

sufficient sample sizes to allow reliable estimates. These 

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 Industry and 

Occupational Codes were matched to the O*NET–SOC 

codes before the multiple regression analyses were 

conducted. 
A–3 

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 the 

ways in which the descriptors could be further combined. 

The final set of predictors was determined on the basis of the 

published O*NET factor model is presented in Table A.1.

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 

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, the negative coefficients are very likely due 

to the effect of multicollinearity15 (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. 

 

15 Multicollinearity is a problem in multiple regression that occurs when 

variables are so highly correlated with one another 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.2: Regression coefficients for each regression models 

Regression coefficients Standardized 

Literacy scale Predictors regression coefficients 

B Standard error Beta t p 

Adjusted 

R square 

(Constant) 170 6.2 27.5 0 

Basic skills P 12.5 2.7 0.4 4.7 0 

English language 6.7 2.0 0.2 3.4 0 

Getting information –4.9 2.2 –0.1 –2.2 0 

Interacting with others 7.6 2.2 0.2 3.5 0 

Prose 

Cognitive abilities P 9.5 3.3 0.2 2.9 0 

0.8 

(Constant) 184.9 5.7 32.6 0 

Basic skills D 7.5 2.2 0.3 3.4 0 

English language 8.8 1.7 0.4 5.3 0 

Getting information –4.3 2.0 –0.2 –2.2 0 

Performing administrative activities 5.0 1.4 0.2 3.5 0 

Document 

Cognitive abilities D 10.0 3.0 0.2 3.4 0 

0.7 

(Constant) 185.1 5.3 34.9 0 

Complex problem solving 5.9 1.4 0.2 4.2 0 

English language 9.1 1.5 0.3 6.1 0 Quantitative 

Computers and electronics 3.6 1.3 0.1 2.8 0 

0.8