U.S. Department of Labor (DOL) 

Employment and Training Administration (ETA) 

Workforce Investment Act Training for Older Workers: 

Toward a Better Understanding of 

Older Worker Needs during the Economic Recovery

Prepared by: 

Ting Zhang, Ph.D. 

Public Policy 

George Mason University 

Fairfax, Virginia 

This paper was funded, either wholly or in part, with Federal funds from the U.S. Department of Labor, 

Employment and Training Administration (ETA) under Contract Number DOLJ061A20380. The contents of 

this publication do not necessarily reflect the views or policies of the Department, nor does mention of trade 

names, commercial products, or organizations imply endorsement of same by the U.S. Government. This 

paper was one of seven prepared under the 2009 ETA Research Papers Program, which competitively 

awarded doctoral and post-doctoral students funding to conduct original research and prepare scholarly papers 

on topics relating to the workforce investment system. 
1 

Introduction 

“With about two-thirds of their 401(k) portfolios invested in equities, older workers should recognize 

that the only way to compensate for their decimated assets [in the current economic recession and 

financial crisis] is to remain in the workforce longer” (Munnell, et al., 2009, p. 6). 

The ongoing economic recession has increased older workers’ incentives to continue working or return 

to work, while multiplying the challenges they face to do so. This stark reality contrasts with earlier research 

reports projecting stable retirement rate trends (National Institute on Aging, 2007; Taeuber and Graham, 2007). 

At this historical moment, the long-term demographic trend of an aging population means more older workers 

in the labor force (Zhang, 2008a, 2008b). In the meantime, the current recession similarly pushes older workers 

to remain in or return to the labor force. As a result, better equipping older workers for the labor market is 

becoming increasingly important. 

Older workers’ and retirees’ demand for training to remain or regain labor market competitiveness is 

certain to increase. This has policy implications for Workforce Investment Act (WIA) planning. Building on a 

book and literature review previously completed (Zhang, 2008a), this research will examine and help inform 

investments in future WIA services for older worker training. 

Many employers are concerned about older workers’ obsolete skill sets, high health insurance and 

benefit costs. In the meantime, many employers also recognize that older workers or retirees have accumulated 

years of still valuable experiences, work ethic, mentoring and language skills (Collison, 2003). It is therefore 

critical to deliver the appropriate level and mix of WIA services for older workers who need training to continue 

working or to return to work. 

Relying on the Workforce Investment Act Standardized Record Data (WIASRD) 2007 data and Bureau 

of Labor Statistics (BLS) unemployment data, this paper adopts a panel data model and empirically examines 

the impacts of unemployment rates and various WIA services on older workers’ Entered Employment Rate 

(EER)1

. An innovative two-sector unemployment rate measure is adopted. Older workers’ demographic and 

socioeconomic conditions are controlled for in the investigations. Following the finding interpretation and 

discussions, this paper concludes with policy implications. 

Literature Review 

To understand the uniqueness in training older workers, it is critical to understand the demographic shift, 

older workers’ vulnerability, productivity, unique assets, potential training methods for older audiences, and 

available resources related to the training. This section reviews literature from the above perspectives. 

Aging Resulted Labor Changes

Older workers’ share in the labor force is rising quickly. Currently over 18 percent of the labor force is 

aged 55 and above (Bureau of Labor Statistics, 2009), up from nearly 12 percent in 1995. This figure was 

projected by the Bureau of Labor Statistics (BLS) to be 21 percent in 2014 (U.S. Department of Labor, 2005). 

The actual number of older labor force participants could be even greater than official projections suggest (Rix, 

2006). 

 

1

 For WIA adult program, of those who are not employed at registration, it is defined as the number of adults who have entered 

employment by the end of Q1 after the exit quarter divided by the number of adults who exit during the quarter. For WIA dislocated 

worker program, it is defined as the number of dislocated workers who have entered employment by the end of Q1 after the exit 

quarter divided by the number of dislocated workers who exit during the quarter.
2 

Older Workers’ Vulnerability

However, older workers are particularly vulnerable in the economy, not just due to their shrunken 

retirement assets affected by the current financial crisis. According to Heidkamp (2009), older workers: 1) are 

increasingly more likely than younger workers to be laid off; 2) take longer to find a new job; 3) are less likely 

overall than younger workers to find another job; 4) and also tend to earn less income when able to find another 

job. Given this background, it is critical for government programs to empower older workers and help them 

overcome these vulnerabilities when seeking a new job. 

Many employers are concerned about the higher costs and lower productivity of hiring older employees. 

Older workers are generally perceived to be more vulnerable to skill obsolescence and tend to be at odds with 

technological innovations that are associated with increased training needs (Crown and Longino, 2000). For 

those older people who have retired for a while or who have been working for an employer for a long time and 

do not change jobs as often as younger workers, lacking job hunting techniques is another structural 

disincentive for older people (Hooyman & Kiyak, 2005). Even the “Seniority Principle” (Thurow, 1975) which 

increases salary with experience tends to discourage hiring seniors. 

There is also general doubt about older workers’ trainability as well as employability. Many cognitive 

abilities such as working memory, the capacity to pay attention, and spatial cognition decline with age, 

especially when a task is complex or represents an unfamiliar knowledge/skill area (Charness and Czaja, 2006). 

Seniors are typically slower to acquire skills than younger adults, though some of the slowing in learning new 

tasks may be explained by older adults’ preference for accuracy over speed, with the reverse holding true for 

younger adults. Charness and Czaja (2006) indicated the importance to allow extra time to train an older adult 

(1.5 to 2 times the training time expected for a young adult). In addition, older workers have a shorter work 

life over which to recoup the costs of training. 

Older Does Not Mean Less Productive

Older workers may have more difficulty than younger ones for learning new knowledge, but it does not 

necessarily mean they are less productive. People’s intelligence is composed of crystallized intelligence and 

fluid intelligence. Crystallized intelligence is a form of acquired knowledge and is usually stable until very late 

life; fluid intelligence refers to ability to quickly solve novel problems and shows declines from the 20s or 30s 

(Schulz and Salthouse, 1999). Although older workers may have a disadvantage in fluid intelligence, they may 

not have a disadvantage in crystallized intelligence. For example, older workers tend to have better language 

skills. 

Actually, various studies show that age is a poor indicator of work performance and that variations in 

performance within the same cohorts far exceed the average differences between cohorts (Sterns and McDaniel, 

1994; Human Resources Development Canada, 1999). Using data on U.S. General Motor employees, the 

Florida State University Psychology Department and the Pepper Institute on Aging and Public Policy also found 

that older workers were not less productive or valuable in the workplace, despite their longer learning processes 

(Charness, 2004). 

Even for new skills related to high technology, research also shows that older people’s deficiencies in 

computer skills, for example, are really a function of socially driven motivation (Friedberg, 2003; Resnick et al., 

2004). Social factors may be more important in modulating motivation to train (Colquitt et al., 2000; Maurer et 

al., 2003). 

Although marketing and training for new information and communication technologies has focused 

mainly on younger people, studies (such as Resnick et al, 2004) demonstrated that older adults could learn to 

use the Internet, and that web use could improve elders’ quality of life. A number of other studies (e.g., Elias et 

al., 1987; Gist et al., 1988; Czaja et al., 1989; Charness et al., 1992; Morrell et al., 1995; Mead et al., 1997) have 

examined the ability of older adults to learn to use a variety of computer applications with perspective training 

strategies. Based on those studies, there were no discernable differences in performance for different age 
3 

groups at the end of training, and performance was often better among older novices than that achieved by 

younger novices, though the older group requires more time. 

Older Workers’ Valuable Assets

On the other hand, older workers can bring many unique qualities to the workplace. The wisdom of 

older people is typically represented by experience, guidance, leadership, and comfort (Peterson, 1999) and 

older workers typically bring maturity, dependability, and years of relevant experience to the workplace (Eyster, 

et al., 2008). As identified by the 2003 SHRM/NOWCC/CED2 Older Workers Survey, older workers have 

invaluable experience, established business ties, a strong work ethic, loyalty to their company, diversity of 

thoughts and approaches, and can provide additional support to younger workers through mentoring (Collison, 

2003). 

Older workers possess unique skills that younger workers might not have. Older workers can also be 

effectively trained. Facing different economic conditions3, job training that aid older workers to obtain and 

succeed in a job could play a necessary and pivotal role in workforce development and economic growth. 

Methods to Train Older Workers

Understanding older workers’ vulnerability and values would help to understand training methods that 

are suitable for older workers. Previous literature also addresses some clues on how to train older workers. 

Generally, what is best for young adults works for older adults; however, specific approaches could be 

particularly helpful for seniors. Previous knowledge has been found to be a strong and positive predictor of 

learning performance both for in-class instruction and homework learning activities (Beier and Ackerman, 

2005). This mirrors Schmidt and Hunter’s (2004) research that indicated experience as a better predictor of job 

performance than intellectual abilities. This situation hints at the advantage of training older workers based on 

their previous job experience and skills. Also, certain physical or other accommodations can be made to 

enhance older workers’ productivity. With seniors’ decreasing vision, high contrast settings for print or 

computer screens can effectively stimulate their vision, and similarly important are adjustment settings for 

audio equipment (see Fisk et al., 2004). It is also important to match the instructional technique and medium 

(e.g., text, voice, animation) to the type of material that is being presented. Older adults might also have greater 

difficulty ignoring irrelevant information than younger adults based on basic experimental work on attention 

spans (Carlson et al., 1995); though Schneider, et al. (2000) disagree. Previous literature also found that there 

were greater gains for older adults when performing procedural (action or hands-on) activities, compared to 

conceptual training using automated teller machines (Mead and Fisk, 1998). A similar differential advantage 

was shown on training to search the Internet (Mead et al., 1997). 

Older Workers’ Training Resources

Publicly-funded training programs have been serving older workers for decades. The Workforce 

Investment Act (WIA), the federally-funded employment and training program, provides skill upgrades to 

 

2 This survey is a combination of a team effort between the Society for Human Resource Management (SHRM), the National Older 

Worker Career Center (NOWCC) and the Committee for Economic Development (CED). A sample of HR professionals was randomly 

selected from SHRM’s membership database, which consists of more than 170,000 members. In November 2002, 2,500 randomly 

selected SHRM members received an e-mail invitation containing a link that directed them to the online survey. Of these, 2,143 e-mails 

were successfully delivered to respondents, and 428 HR professionals responded, yielding a response rate of 20 percent.

3 The aging workforce is projected to result in potential labor force shortages, Social Security fund bankruptcy and other related fiscal 

pressure. Older individuals’ participation in the labor force can be a possible solution to those above socioeconomic problems (Zhang, 

2008). The current recession and decimated retirement assets are also pushing seniors to remain or return to the labor force, either for 

those who have retired or those who are about to retire. Retaining more seniors in the labor force becomes a necessity for both older 

individuals and the economy. 
4 

workers regardless of age, and allows states and local areas to give priority to special populations, such as older 

workers, for training funds allocation. Some states have recently begun to focus on older workers’ training 

needs and to tailor some services and funding to serve older workers. Programs funded through WIA serve a 

growing number of older workers. Shown in Table 1, according to the Workforce Investment Act Standardized 

Record Data (WIASRD) Data Book, comparing Program Year (PY) 2005, 2006, and 20074

, among all WIA 

program exiters, adult workers aged 55 and above increased from 6 percent in 2005, to 9 percent in 2006, and to 

11 percent in 2007; and dislocated workers who are aged 55 and above increased from 12 percent in 2005, to 13 

percent in 2006, and to 15 percent in 2007 (U.S. Department of Labor Employment and Training 

Administration, 2007a, 2008a, and 2008b). As the population ages and with growing financial pressure from 

the current recession, the use of WIA-funded training by older workers will likely continue to grow. 

Table 1: Percentage of Older Workers (Aged 55 and above) among all WIA Program Exiters, PY 2005-

2007 

WIA Exiter Groups 

Program Year 

2005 2006 2007 

Older Adults 6% 9% 11% 

Older Dislocated Workers 12% 13% 15% 

Data sources: U.S. Department of Labor Employment and Training Administration (2007a, 2008a, and 2008b). 

In addition to WIA funding, the U.S. Department of Labor (DOL) funds Senior Community Service 

Employment Program (SCSEP) sites, a dedicated employment and training program offering job search 

assistance, training and work experience to lower-income seniors. SCSEP provides many grants to state 

governments and national nonprofit organizations to train workers ages 55 and older (U.S. Department of Labor 

Employment and Training Administration, 2007b). Some community colleges are also leading efforts to 

develop job training opportunities for older workers to meet local labor market needs. 

Unfortunately, based on the WIA National Summary of Annual Performance Data, the key WIA 

program performance measure, Entered Employment Rate (EER) has been declining over recent years. As 

shown in Table 2, from 2005-2007, the annual EER for older adults are respectively 69.6 percent, 63.3 percent, 

63.2 percent, and the EER for older dislocated workers5

 are respectively 74.1 percent, 68.2 percent, 61.5 

percent (U.S. Department of Labor Employment and Training Administration, 2009). This measure is an 

aggregate measure for all WIA programs, for all locations in the country, and for all industry sectors. When 

 

4

 The Program Years span respectively from April 2005 to March 2006, April 2006 to March 2007, and April 2007 to March 2008. 

5 A dislocated worker is defined as a person who (A) (i) has been terminated or laid off from their job, or who has received a notice of 

termination or layoff, from their employer; (ii) (I) is eligible for or has used up their unemployment payments; or (II) has been 

employed for long enough to show, to a program at a One-Stop Career Center referred to in section 134(c), attachment to the 

Workforce Investment Act, but who cannot get unemployment payments because of low earnings or having done work for an 

employer that is not covered under a State unemployment compensation law; and (iii) is not likely to return to the same type of work; 

(B) (i) has lost his or her job, or has received a notice of termination or layoff, from their job because of a permanent closure of, or a 

big layoff at a plant, facility, or company; (ii) is working at a facility where the employer has announced that it will close within 180 

days; or (iii) in order to receive services besides the training services described in section 134(d)(4), intensive services described in 

section 134(d)(3), or supportive services, is employed at a place where the employer has made an announcement that the facility will 

close;(C) was self-employed (including employment as a farmer, a rancher, or a fisherman) but is unemployed because of the local 

economy where the individual lives or because of natural disasters; or (D) is a displaced homemaker. 
5 

tional nuances. 

investigated down to program, location, and industry or occupation sector details, this performance indicator 

might introduce addi

Table 2 Older Workers’ (Aged 55 and above) Entered Employment Rates, PY 2005-2007 

WIA Exiter Groups 

Program Year 

2005 2006 2007 

Older Adults 69.6% 63.3% 63.2% 

Older Dislocated Workers 74.1% 68.2% 61.5% 

Data sources: U.S. Department of Labor, Employment and Training Administration (2009). 

Research Questions and Hypotheses 

With a rising share of older workers enrolled in federally-funded WIA programs but a decreasing EER, 

it is necessary to examine what programs and services are more effective than others. What level and mix of 

WIA employment and training programs will be needed to serve older workers? When investigating EER, it is 

also necessary to determine if WIA program effectiveness is sensitive to cyclical changes in labor market 

conditions considering the heterogeneity of demographics, socioeconomic background, location, time of 

enrollment and even occupation preferences. Answers to the above questions may differ depending upon the 

timing of the cyclical trough of a recession and the speed and magnitude of a recovery. 

While previous literature on this investigation is thin, this study is designed to address these above 

questions and test the following two hypotheses: 

1. Some WIA training and related service combinations can be identified to inform strategic decisionmaking about future allocations of WIA funds to serve older workers. 

2. WIA program success with older workers is sensitive to cyclical changes in labor market 

conditions. 

The following sections introduce the methodology adopted in this study and then observations based on 

descriptive statistics and empirical research. Findings are discussed and policy implications are indicated where 

appropriate. 

Methodology and Data Sources 

This paper uses a panel data model and adopts an innovative measure of unemployment rate to test the 

impacts of unemployment rates and various WIA programs and services on older workers’ EER. WIASRD 

2007 data and BLS unemployment data series are used. This research starts with descriptive statistics using the 

WIASRD 2007 data to describe older workers’ demographic and socioeconomic background, as well as WIA 

program participation. 
6 

The WIASRD 2007 data is used as a longitudinal dataset, and the unit of analysis is WIA program 

exiting date (temporal unit) at the Workforce Investment Area level (geographical unit). Post-estimation 

regression diagnostics is conducted to address the model fit and specifications. Older workers in this study are 

defined as workers of age 55 and above. This age divide is often used as a definition for older workers, and 

represents the front edge of an older worker cohort with employment problems that are generally regarded to be 

particularly acute (Rix, 1990). 

Data and Variable Measurements

 The BLS unemployment data used in this study refer to non-seasonally adjusted unemployment rates. 

This variable captures regional cyclical labor market conditions. In this research, an innovative method to 

measure unemployment is used. Previous literature has identified the dubious role of unemployment rates in 

entrepreneurship related employment growth (Audretsch, et al., 2000). Similarly, unemployment rates could 

have a dubious role in employment growth. Higher unemployment rates on the one hand mean more 

unemployed persons and lower employment growth; higher unemployment rates on the other hand push more 

people to the job-hunting labor pool, and ultimately result in an upcoming rising employment growth. One way 

to explain the dubious role of unemployment rates is to observe the timing of unemployment rates. To further 

clarify the role of unemployment rates, this paper innovatively adopts a two-vector measurement to capture the 

change of unemployment rates. These two vectors are the unemployment rate level and the unemployment rate 

trend. The unemployment rate level (U) captures the magnitude of unemployment rate changes and the 

unemployment rate trend (dU) captures the direction of unemployment rate changes, whether it is going 

upward or going downward. In this study, the trend measure captures the directional difference (which can be 

negative or positive) of unemployment rates between two neighboring time periods. In specific, the

unemployment rate trend (dU) is measured by the difference between unemployment rate of the current month 

(m) and the previous month (m-1), i.e., dU=Um- Um-1. The month (m) is corresponding to each exit date (t). 

At the same magnitude, U, there could be two totally opposite economic situations: one is when the 

unemployment rate is rising (i.e. a slowing down economy) and the other is when the unemployment rate is 

decreasing (i.e. a recovering or growing economy). Explaining the unemployment phenomenon using these two 

vectors could capture both magnitude and direction and thus introduce additional nuances that the traditional 

unemployment rate magnitude measure did not achieve. 

The WIASRD 2007 data are used to extract data for the number of older exiters6

 who were employed

by the end of first quarter after exiting a WIA training program, i.e. Entered Employment Level (N) in a 

reference Workforce Investment Area (w) and for a specific program exit date (t). This is represented by 

Ntw. This measure is an important component of the dependent variable or output variable for the statistical 

models in this study. In this study, the WIASRD 2007 data used for the regression models covered exit dates 

from January 01, 2006 through September 30, 2007 (see Appendix B and Appendix C). The Entered 

Employment Level by the WIA definition is a one quarter lagged value taken after a WIA program participant’s 

exit date, i.e., N(t+q)w, where subscript t denotes the program exit date, q denotes a quarter’s lag after exiting, 

and w denotes a Workforce Investment Area . This one quarter lagged value allows a quarter’s time for workers 

to find a job after exiting WIA programs and services; this also avoids simultaneous causation and endogeneity 

issues in statistical inference. 

Entered Employment Rate (EER) is a common measure for WIA program performance. For WIA 

adult program, of those who are not employed at registration, EER is defined as the number of adults who have 

entered employment by the end of the first quarter after the exit quarter divided by the number of adults who 

exited during the quarter. For the WIA dislocated worker program, EER is defined as the number of dislocated 

workers who have entered employment by the end of the first quarter after the exit quarter divided by the 

number of dislocated workers who exited during the quarter. This paper uses EER as the dependent variable for 

 

6

 Older exiters refer to older workers who are aged 55 and above and exited WIA training programs. 
7 

the following empirical tests. Although there are concerns on using EER as a measure for WIA program 

performance, EER is still a legitimate and best available direct measure when this research is conducted7

. 

The WIASRD 2007 data also offers older workers’ background information and information by 

program, training, and occupation types. The older workers’ background information includes demographic, 

socioeconomic, and WIA program participation information details of older workers who have participated in 

WIA programs. The demographic and socioeconomic information includes gender, age, disability, education 

attainment, veteran status, employment status at participation, whether the participant had limited English 

skills, whether the participant was a low-income resident, whether the participants accepted services or 

assistance from Temporary Assistance to Needy Families (TANF) and other public assistance. The WIA 

program participation information includes whether the participants was served by the National Emergency 

Grant program, whether the participant received services or benefits financially assisted by Wagner-Peyser Act

or Trade Adjustment Assistance (TAA) programs. Additionally, program types, training types and occupational 

categories of training are also offered in the dataset. To be more specific, the program types (P) includes the 

following: Supportive Services (except needs-related payments), Needs-Related payments, Core Self-Services 

and Informational Activities, Workforce Information Services, Established Individual Training Accounts (ITA), 

Pell grants and Pre-Vocational Activities. The training types (T) include On-the-Job Training, Skill 

Upgrading and Retraining, Entrepreneurial Training, Adult Basic Education (ABE) or English as A Second 

Language (ESL) in Combination with Training, Customized Training and Other Occupational Skills Training. 

The occupational categories of training (O) include (1) Agricultural, forestry, fishing and related workers, 

construction and extractive workers, (2) Managerial, administrative, professional or technical, (3) Sales, 

clerical and administrative support, (4) Service workers, and (5) Mechanics, installers, repairers, precision 

workers, machine setters, set-up operators, operators, tenders, assemblers, hand workers, transportation and 

related workers, and military. 

With program participation date and exit date, this paper computes a variable, program participation 

length or training length (L), to capture how long it took a participant to finish the services. The service 

participation length is determined by program design as well as individual motivation, efforts, and access. This 

variable is believed to affect an individual’s EER. 

Please note that the WIASRD data is informative, but using it for statistical tests is not without 

participation bias. The WIASRD 2007 data only offers information related to or reported by the participants. 

This situation results in possible participation bias of the data8

. It is therefore necessary to be cautious about the 

statistical inference based on this data. 

Unit of Analysis

 

7 There were concerns about using Entered Employment Rate as the dependent variables due to two issues related to this variable as a 

performance measure. First, the measure is based on program exit. It was noted that performance measures that focuses on exiters is 

related to selection on both who enters and who exits the program in practice. Focusing exclusively on exiters could introduce

selection bias. One suggestion was to focus on program entry as a better measure. However, using entry as a measure offers no clue 

on when exit happens and therefore cannot directly test the economic impact on participants’ employment following accepting 

training and services. However, with an appropriate measure of training length as a variable and if entry is not selected, future 

sensitive study comparing using entry and exit as a measure for performance could be interesting. Second, there was a concern that the 

outcome measure, EER, is measured only one quarter after program exit. The previous literature shows that many individuals do not 

receive benefits from job-training programs until several quarters after program entry / exit. However, at the moment, the one-quarter 

lag performance measure, EER, is still a commonly used measure for WIA services and that is how the WIASRD data is designed. 

The WIASRD data made this research possible. Using one-quarter lag already serves the purpose of this paper. If a longer-lag is 

permitted in the dataset, future investigation on the sensitivity of length of lags could a possible extension of the research. 

8

 There was a concern about the Ashenfelter’s dip (Ashenfelter, 1978) in earnings and employment prior to program participation 

(called dip). However, this research is not comparing employment rate before and after the training, but comparing those participated 

to those not participated in a specific service, across specific Workforce Investment Areas, and across different exit dates. For a 

specific exiting date, the comparison is cross-sectional, not before-and-after comparison. Therefore, the Ashenfelter’s dip is not 

particularly concerned in this paper. 
8 

As noted above, this study uses Workforce Investment Area as the geographical unit of analysis and a 

worker’s exiting date as the temporal unit of analysis. Considering that the effects of WIA training/service 

programs are examined in this study, Workforce Investment Areas would be the best geographic units. The 

WIASRD 2007 data does not offer data at the metropolitan area or labor market level. Individual level test was 

originally preferred; however, due to too much software and computer processing speed capacity would be 

needed to handle individual level data, this study collapsed the data into Workforce Investment Area rather than 

at the individual level9

. However, the BLS unemployment information is not offered at the workforce 

investment area level, neither is the BLS unemployment information measured by date. The BLS 

unemployment measures are monitored and evaluated monthly at the state level. The sub-state level 

unemployment rates, though available, are mostly estimated data under certain assumptions. 

This paper adopts unemployment rates monthly and at the state level. For the temporal unit, it is 

impossible and unnecessary to monitor unemployment rates daily. For the spatial unit, although states are 

broader geographic units than workforce investment areas, local economy is determined by residents in and 

around the neighborhood and under the state and local legislation. Workforce investment areas are not by 

themselves labor markets or residential areas or legislative districts. A number of employment dynamics occur 

across borders of workforce investment areas, but remain within a state legislative boundary. It is for these 

above reasons that the paper adopts monthly state unemployment rates as a general cyclical economic 

background measure. 

Considering the fact that Entered Employment is measured based on a participant’s exit date, exit date is 

used as the temporal dimension of the paper. Therefore, the unit of analysis is a workforce investment area on a 

specific program exit date. 

Serial Autocorrelation

The longitudinal WIASRD dataset makes temporal dynamics and serial autocorrelation a natural 

concern affecting statistical estimates. Therefore, correlegram plots and Portmanteau (Q) tests for white noise 

are used to detect potential serial autocorrelations. If serial autocorrelation is detected, the nature of the serial 

autocorrelation needs to be investigated to adopt appropriate temporal variables and model specific adjustments. 

In this study, no evident serial autocorrelation is detected in each spatial panel (i.e., each Workforce Investment 

Area). A major reason is that the temporal unit is an older worker’s exit date from the WIA programs and 

services. Since the exit dates are not necessarily continuous in many cases, the date distributions sometimes 

have gaps in between. Also, a few days’ exiting timing difference does not necessarily result in EER change 

and is not necessarily related to differences in individual demographics, socioeconomic status or 

program/service participation situations. Therefore, the panel specific serial autocorrelation concern was 

eliminated after the tests. 

The Panel Data Model

Using longitudinal data that incorporates both temporal and spatial dimensions, the paper adopts a 

panel data model. The Hausman specification tests and Breusch and Pagan LM tests for random effects 

are conducted to choose between the random effect and fixed effect models for the panel data. 

The following equation briefly exhibits the base panel data model10. Please note that the left hand side 

of the model is measuring older workers’ EER. The right hand side of the equation measures in turn 

 

9 If using the individual level data, the data would be multi-level panel data which would make the sample size extremely large when 

running regression models and thus bring a major challenge to the computing power. Using the Workforce Investment Area level 

actually made the data more balanced. The author is willing to conduct the individual level study for the future when the software and 

computer power is not an issue. 

10 Please note that for categorical variables that have several categories and the sum is the total population, one category was 

omitted to avoid multicollinarity. 
9 

unemployment rates, demographic-socioeconomic-program participation attributes, program types, training 

types, training length, and occupational category of training. 

N(t+q)w / Etw= β0+ β1Utw+ β2dUtw+ ∑γlDltw +∑δiPitw +∑ζjTjtw+ ηLtw + ∑θmOmtw

+ f(t-k) + εtw (1

where 

N(t+q)w is the number of older workers who entered employment a quarter q after the 

WIA program exiting date t at Workforce Investment Area w. 

Subscript t specifies the exiting date. 

Subscript q specifies a quarter. 

Subscript w specifies the Workforce Investment Areas. 

β0, β1, β2, γl, δi, ζj, η, and θm are the regression parameters. 

Etw represents the number of older WIA program exiters. 

N(t+q)w / Etw captures the EER(t+q)w.

Utw is the unemployment rate level measure. The exit date t is corresponding to the 

corresponding current month m for this variable. 

dUtw is the unemployment rate trend measure. The exit date t is corresponding to the 

current month m and previous month m-1 to compute this variable. 

Dltw represents the combination of demographic, socioeconomic, as well as the 

background WIA program participation information. These variables are all 

proportions of a special group among all relevant participants. 

Subscript l denotes a specific demographic, socioeconomic, or program participation 

background attribute. 

Pitw represents the proportion of WIA program participants in a specific program type 

i. These are all proportions of a special group among all relevant participants. 

Subscript i denotes a specific program type. 

Tjtw represents the share of WIA program participants in a specific training type j. 

These are all proportions of a special group among all relevant participants. 

Subscript j denotes a specific training type.

Ltw represents the duration of the training or service. 

Omtw captures the occupational skills by Standard Occupational Classification (SOC) 

broad occupation categories. These occupation skill variables are all proportions for 

participants of a specific occupation skill category among relevant participants of all 

occupation skill categories. 

Subscript m denotes the mth occupation skill category. 

f(t-k) is a vector to capture the time effect, if there is any. If the time effect exists, this 

item represents any or any combination of kth lagged autoregressive terms, 

differenced series, and forecast errors (moving average terms). Since no serial 

autocorrelation is detected in each spatial panel, the time effect vector eventually 

disappears in this study. 

εtw is the regression error term. 

) 

Dislocated Workers vs. Adults
10 

Considering the fact that dislocated workers are exclusive and different from adult participants in many 

perspectives, such as wage levels and prior attachment to the labor market, the modeling separates dislocated 

workers from adults. Therefore, the above model is transformed into two separate models below, for older 

dislocated workers (2) and older adults (3) respectively. The superscript D and A respectively denote the model 

for older dislocated workers and the model for older adults. 

ND(t+1)w / EDtw= β0+ β1Utw+ β2dUtw+ ∑γlDDltw +∑δiPDitw +∑ζjTDjtw+ ηLDtw +

∑θmODmtw + fD (t-k) + εDtw (2

NA(t+1)w / EAtw= β0+ β1Utw+ β2dUtw+ ∑γlDAltw +∑δiPAitw +∑ζjTAjtw+ ηLAtw +

∑θmOAmtw + fA (t-k) + εAtw (3

 

) 

) 

These above two equations adopt the same model, but extract two separate and independent datasets. 

Each equation uses a different and unrelated dataset due to the very different and unrelated attributes between 

dislocated worker participants and adult participants in the WIA programs. Equations (2) and (3) are therefore 

estimated separately11. 

Older Workers’ WIA Program Participation 

 This section describes the key attributes of older workers’ WIA program participation through the 

WIASRD 2007 data. To be more specific, this section delineates older workers’ WIA program participation, 

age distribution, gender, health situation, racial composition, training types in which they participated, 

educational attainments, and their occupational category of training. Figure 1 first exhibits older workers’ 

overall demographic composition by age in the WIA program. 

As Figure 1 shows, according to the WIASRD 2007 data, among those WIA older worker participants 

who exited WIA programs in 2006 and 2007, there were more older adult participants than older dislocated 

worker participants, particularly by age 80. By age 60, there were evidently more adult participants than 

dislocated worker participants in the dataset12. Unsurprisingly, this dataset also shows the number of older 

adults’ Entered Employment (please note, not EER in this figure) is higher than older dislocated workers. 

As age increases, program participation sharply decreases. There could be several reasons. First, as age 

increases, the total population of older people drops. Second, as age increases and health conditions decline, 

older workers are less likely to participate in the labor market and less likely to seek training for a potential 

future job or job change. Appendix A shows that seniors’ labor force participation decreases with age, 

according to Current Population Survey 2009 data. According to this data set, seniors’ total population is 

declining by age, but seniors’ labor force declines even more quickly; as a result, seniors’ labor force 

participation rate drops with age. Third, as age increases, accessibility becomes a rising concern for some 

seniors to physically access local Workforce Investment Board offices, one-stop centers, and other facilities for 

11 The author initially considered using Seemingly Unrelated Regression model (SUR) developed by Arnold Zellner (1962). SUR is a 

technique for analyzing a system of multiple equations with cross-equation parameter restrictions and correlated error terms. In this 

study, the model contains two independent equations. Each of those two equations used a different and unrelated dataset, due to the 

very different attributes between dislocated worker participants and adult participants in the WIA programs. For example, participants 

in the adult workers program have extremely low wages and very little prior attachment to the labor market, while participants in the 

dislocated worker program have had strong attachment to the labor market and relatively high wages. These two participant groups do 

not have necessary relationships. In this case, the SUR assumption that error structures of the two models are similar does not 

necessarily hold. There is no need to estimate SUR and the above regression equations (2) and (3) are estimated separately. 12 Please note that this total numbers of older dislocated workers and older adults do not represent the numbers of observations in the 

regression models. The data used in the regression models do not use individual worker as the unit of analysis. 
11 

training. Last, the WIA program was in place for only a few years at the time the WIASRD data was collected. 

This could result in limited awareness of training options among older workers. This limited awareness could 

be even more limited as age increases for older workers. 

Figure 1 also shows several other perspectives of older worker participants. Among all older workers, 

only a very small fraction of them reported disabilities13. A larger proportion of older workers’ WIA program 

participants are male. In Figure 1, the line representing male older workers is above the middle division 

between older adult participants and older dislocated workers. This is particularly so for workers in their 60s. 

 Overall, training length drops with age. Although there are a few high spikes for older workers over 

age 90 (not shown in Figure 1), the total number of observations drops sharply for the very old ages. The 

limited number of observations for older workers aged over 90 could result in extreme fluctuations of training 

length. 

Figure 1 Senior WIA Program Participants’ Demographic Overview by Age14

Data Source: WIASRD 2007 Data 

 

13 Reporting disability and recognizing or defining disability is often tricky. Psychological factors could play a role in this reporting. 

Since most WIASRD data is self reporting based, this fraction of disability could be underreported. 14 Please note that not data of all ages are shown. When age increases to over 80s, the limited observations distract the main message 

delivered in the figure. 
12 

 

As shown in Figure 2, among older participants in the WIASRD 2007 data, 66 percent of them are NonHispanic Whites, 19 percent are Non-Hispanic Blacks, 10 percent are Hispanics, and 4 percent are NonHispanic Asians. 

Figure 2 Racial Compositions of WIA Participants 

Data Source: WIASRD 2007 Data 
13 

Figure 3 shows that among those older workers who accepted training reported in this dataset, almost 

three quarters (74 percent) accepted Other Occupational Training, 12 percent accepted Skill Upgrading and 

Retraining, 7 percent accepted On-the-Job Training, about 3 percent accepted Customized Training, 3 percent 

accepted ABE or ESL in combination with training, and only less than 0.5 percent of older worker participants 

accepted Entrepreneurial Training. Please note that this low participation rate for Entrepreneurial Training

does not necessarily imply that older workers lack entrepreneurial ambition. Instead, it could relate to older 

workers who turn to workforce training might not necessarily have or indicate entrepreneurial ambition as their 

first priority. Older workers participated in the WIA programs and services do not necessarily representing all 

American older workers. This dataset is subject to participation bias, as noted earlier. The relatively low rate of 

accepting Entrepreneurial Training could also relate to how the entrepreneurial training program is designed, 

accessed, and promoted by the One-Stop Career Center. 

Figure 3 Training Type Distributions among Senior WIA Participants 

Data Source: WIASRD 2007 Data 
14 

Figure 4 shows older WIA program participants’ educational attainment by age based on the WIASRD 

2007 data. As age increases and the number of older worker participants sharply decreases, the educational 

attainment becomes less distinguishable. Among younger older participants, a predominantly high proportion 

of them enrolled in an occupational skills certificate/credential/licensure; a relatively limited number of them 

reported college or full-time technical or vocational school education. This number, though limited relative to 

those with occupational skill credentials, is still higher than older workers who reported high school diploma 

(including General Equivalency Diploma or GED) or who reported Bachelor’s degree. Equally limited 

numbers of older worker participants reported either only a high school diploma (including GED) or a 

Bachelor’s degree. 

Figure 4 Education Attainments among Older WIA Program Participants by Age15

Data Source: WIASRD 2007 Data 

 

15 Please note that not data of all ages are shown. When age increases to over 70s, the limited observations distract the main message 

delivered in the figure. 
15 

Figure 5 describes the distribution of occupational categories of training. Among older WIA program 

participants reported in the WIASRD 2007 dataset, 30 percent of those who reported occupational categories of 

training were from Managerial, Administrative, Professional or Technical Occupations, 27 percent were from 

Sales, Clerical and Administrative Support; another 27 percent were from Mechanics, Installers, Repairers, 

Precision workers, Machine setters, Set-up operators, Operators, Tenders, Assemblers, Hand workers, 

Transportation and Related Workers, and Military; 12 percent from Service Workers; and only 3 percent were 

from Agricultural, Forestry, Fishing and Related Workers, Construction, and Extractive Workers. 

Figure 5 Distributions of Occupational Categories of Training 

Data Source: WIASRD 2007 Data 
16 

Regression Model Estimates 

This section focuses on testing which factors affect older workers’ EER after exiting the WIA programs. 

The models discussed in the methodology section are estimated. Appendix B and C exhibit the descriptive 

statistical details of each variable used in the regression models16. Table 3 displays the model estimates for 

older WIA program participants who are dislocated workers, and Table 4 displays the model estimates for older 

WIA program participants who are adults. The results presented in the tables are the final estimates after using 

Hausman tests and Breusch and Pagan LM tests for random effects to select an appropriate model for each 

panel data set17. 

Model Estimates for Older Dislocated Workers

 According to Table 1, when controlling for other variables, unemployment rate level (i.e. the magnitude 

measure of underemployment rate) does not have statistically significant (p=0.1) association with older 

dislocated workers’ EER, but unemployment rate trend (i.e. the directional measure of unemployment rate) 

matters. When unemployment rate trends up, the fixed-effect panel data model shows a lower EER among 

older dislocated workers a quarter after they exited from the WIA programs and services. To be more specific, 

when the unemployment rate increase of a state where a Workforce Investment Area situates rises by 1 

percentage point, this Workforce Investment Area’s older dislocated workers’ EER drops by 0.03 of a 

percentage point, holding other variables constant. This makes sense. When unemployment rate keeps trending 

up, it normally means an economic downturn or even a recession. With an economic downturn when the 

unemployed are increasing and the employed are threatened by an increasingly large laid-off worker pool, it 

increases the difficulty for older workers to find a job. It therefore naturally affects older workers’ EER. This 

is a typical example where the traditional magnitude unemployment rate measure does not show as sensitive 

and direct effect as a trend measure of unemployment rate. It is for this exact reason that this paper particularly 

adopts this innovative compound two-vector unemployment rate measure that integrates both unemployment 

rate magnitude and unemployment rate trend. 

Among demographic information, as expected, the EER decreases with older dislocated workers’ age

18. 

When the mean age of a Workforce Investment Area increases by 1 year, this Workforce Investment Area’s 

older dislocated workers’ EER decreases by 1.4 percentage points, holding other variables constant. This 

situation could be related to older workers’ or their family members’ declining health conditions, a more limited 

access to program and services, and more limited awareness of information when age increases. This implies 

that older cohorts among older dislocated workers particularly need help if they need to or chose to stay in the 

labor force. 

Race and ethnicity factors do not seem to make much difference. Among older dislocated workers, 

compared to non-Hispanic Blacks, only American Indian or Alaska Natives show a higher and statistically 

significant (at p=0.1) EER19. Compared to a Workforce Investment Area’s non-Hispanic Black older dislocated 

 

16 Please note that Appendix B and Appendix C show the statistics across all observations used in the two regression model 

estimations. The regression models’ unit of analysis is not individual workers; therefore, the statistics showing in the tables are not 

necessarily consistent with what was shown in the figures of the previous section. 

17 Please also note that many older workers reported in the WIASRD data did not offer occupational information. Therefore, when 

estimating our models incorporating occupational categories, many observations were dropped. However, this situation happened not 

just to this variable; a few other variables faced the challenge of missing values as well. This occupational training variable is one of 

the key observing variables and it is an important control variable due to different nature of occupational trainings. Even with dropped 

observation, it is still perceived to be necessary to be included. 

18 The author tried some simple non-linear form of age in preliminary analysis, but did not find major difference of regression 

estimates. It was therefore not reported to simplify the coefficient interpretation. Future exploration for more complicated non-linear 

model would be necessary. It was also one of the future research directions the paper mentioned. 

19 The relatively limited occurrence of an older worker to be an American Indian or Alaska Native (only 0.6% among older dislocated 

workers in the statistical test, shown in Appendix B) could have helped to augment the statistical significance to some extent, though 

the opposite situation could occur as well. Non-Hispanic Blacks are a bigger population group and possibly more heterogeneous.
17 

workers, when the area’s share of American Indian or Alaska Native older dislocated workers rises by 1 

percentage point, this area’s older dislocated workers’ EER becomes 0.12 percentage point higher, controlling 

for other variables. 

Unexpectedly, disability status is statistically insignificant at 0.1 level for older dislocated workers. This 

could be related to the fact that a very limited number of older dislocated workers reported disabilities, as noted 

earlier and shown in Figure 1. According to Appendix B, an average of 3.9% of older dislocated workers 

reported disabilities across all Workforce Investment Areas and program exiting dates for the regression model, 

compared to the other 96.1% of older dislocated workers who did not report disabilities. Considering the fact 

that disability status is highly relevant for older workers’ labor condition, this variable, though with relatively 

limited observations, deserves to be kept in the regression modeling. 

Workforce Investment Areas with higher proportions of older dislocated workers reporting to have 

occupational skill credentials or to have college or full-time technical or vocational school education have a 

higher EER. Controlling for other variables and compared to a Workforce Investment Area’s older dislocated 

workers with lower than high school education or with unreported education attainments, when this area’s 

share of older dislocated workers with college or full-time technical or vocational school education and with 

occupational skill credentials goes up by 1 percentage point, the area’s older dislocated worker EER becomes 

0.18 or 0.10 of a percentage point higher. The effect of those older dislocated workers reporting to have high 

school diploma (including GED) and Bachelors’ degrees are not statistically significant (p=0.1). This could be 

related to the fact that occupational skill credentials as well as many college or technical or vocational school 

education programs on the one hand prepared the workers with better knowledge set than only high school 

(including GED) education, and on the other hand are often more career-oriented with more applied skill set 

training than Bachelors’ degree education. The insignificant effects from high school diploma (including GED)

and Bachelors’ degrees could also be partially related to the fact that only a relatively limited number of older 

workers reported to have high school diploma (including GED) and Bachelors’ degrees, as illustrated in Figure 

4. Appendix B also shows that on average only 0.4 percent and 0.3 percent of older dislocated workers reported 

to have high school diploma (including GED) and Bachelors’ degrees respectively across all units of the 

regression analysis. 

Socioeconomic conditions, such as veteran status, employment status at participation, English levels, 

low income status, participation status of TANF and other public assistance, do not seem to matter, based on 

this testing result. Among those variables, a few variables’ insignificant statistical effect (p=0.1) could be 

partially related to their limited occurrence in the dataset used in the regression. According to Appendix B, 

among older dislocated workers across all units of the regression analysis, on average only 1.7 percent reported 

as low-income residents, only 0.03 percent reported to have received TANF and only 0.3 percent reported to 

have received other public assistance. 

Among program participation information variables, training length and receiving services or benefits 

financially assisted by the Trade Adjustment Assistance (TAA) are tested to matter to older dislocated workers’ 

EER with statistically significant (p=0.05) impacts. The longer an older dislocated worker participated in the 

training, the less likely this person would be employed within a quarter after exiting the WIA programs and 

services. When a Workforce Investment Area’s older dislocated workers’ average training duration increases 

by 1 day, this area’s older dislocated workers’ EER drops by 0.03 percentage point, holding other variables 

constant. Therefore adopting certain policies to more effectively monitor the training length might be helpful to 

enhance the cost-effectiveness of WIA programs. Although this training length effect is not strong, the 0.03 of 

a percentage point is relative to each additional day, not week or month or year, of training. According to 

Appendix B, the standard deviation of training length across all units of analysis is as long as 341 days. Older 

dislocated workers who received services or benefits financially assisted by the TAA program reported a lower 

EER, based on the test results. This could be related to structural unemployment impact for older dislocated 

workers assisted by the TAA program. The impacts of National Emergency Grant and Wagner-Peyser Act are 

not statistically significant at p=0.1 level. 

Among services and other related assistance, Workforce Investment Areas with a higher share of older 

dislocated workers receiving Supportive Services (except needs-related payments) and Core Self-Services and 
18 

Informational Activities tend to have a higher EER. Controlling for other variables, when a Workforce 

Investment Area’s shares of older dislocated workers who received Supportive Services (except needs-related 

payments) and Core Self-Services and Informational Activities increase by 1 percentage point, the area’s older 

dislocated workers’ EER increases respectively by 0.04 and 0.05 of a percentage point. This situation shows 

that those two WIA services matter and work for older dislocated workers. Therefore, strengthening those two 

types of services would help to stabilize older dislocated workers’ EER. 

However, it does not mean all services work well. Workforce Investment Areas with a higher share of 

older dislocated workers whose services were purchased utilizing an Individual Training Account (ITA) have a 

lower EER among older dislocated workers after exiting the WIA programs and services. When a Workforce 

Investment Area’s share of older dislocated workers whose services were purchased utilizing an Individual 

Training Account (ITA) increases by 1 percentage point, the area’s older dislocated workers’ EER drops by 0.03 

of a percentage point, controlling for other variables. This ITA effect warrants further investigations for older 

dislocated workers. Other WIA services, such as Needs-Related Payments, Workforce Information Services, 

Pell Grant, and Pre-Vocational Activities, do not have statistically significant impact at p=0.1 level. 

As for types of training services, On-the-Job Training, Skill Upgrading & Retraining, and Customized 

Training are effective to older dislocated workers’ EER. Compared to Other Occupational Skills Training, 

Workforce Investment Areas with a higher share of older dislocated workers who participated in the three types 

of training noted above have a higher EER, and these effects are statistically significant at 0.05 level. To be 

more specific, when a Workforce Investment Area’s share of older dislocated workers who received On-the-Job 

Training, Skill Upgrading & Retraining, and Customized Training increase by 1 percentage point, the area’s 

older dislocated workers’ EER becomes 0.06, 0.05, and 0.12 of a percentage point higher respectively, 

compared to Other Occupational Skills Training and controlling for other variables. Those training types are 

mostly job specific with more focused targets. The effect from Customized Training is relatively stronger than 

others. Customization helps older dislocated workers to get more direct training, and it is not surprising that 

there is a better training effect. Therefore, strengthening On-the-Job Training, Skill Upgrading & Retraining

and Customized Training is advisable for older dislocated workers in the WIA programs. Entrepreneurial 

Training and ABE or ESL in Combination with Training do not have a statistically significant effect at p=0.1 

level, compared to Other Occupational Skills Training. As illustrated previously in Figure 3, there are a very 

limited number of seniors who accepted entrepreneurial training. Appendix B also shows that, on average for 

the data used in the regression model, only 0.8 percent of older dislocated workers across all units of analysis 

received entrepreneurial training, as well as ABE or ESL training. This partially explains why the coefficients 

on these two variables are statistically insignificant. Please note that all training type effects are relative to 

Other Occupational Skills Training. 

Among occupational categories of training, compared to Agricultural, Forestry, Fishing and Related 

Workers, Construction, and Extractive Workers, only Service Worker seems to be predictive of older dislocated 

workers’ EER. Compared to older dislocated workers who are in Agricultural, Forestry, Fishing and Related, 

Construction, and Extractive occupations, Workforce Investment Areas with a higher share of older dislocated 

worker who received trainings as a Service Worker have a higher EER. When a Workforce Investment Area’s 

share of older dislocated workers who received Service Worker training increases by 1 percentage point, the 

area’s older dislocated workers’ EER becomes 0.06 percentage point higher, compared to older dislocated 

workers who are in Agricultural, Forestry, Fishing and Related, Construction, and Extractive occupations and 

controlling for other variables. 

Table 3 Fixed Effect Panel Data Model Estimates for Older WIA Program Participants Who Are 

Dislocated Workers 

POPULATION GROUP: DISLOCATED WORKERS

DEPENDENT VARIABLE: ENTERED EMPLOYMENT RATE

Fixed-effects (within) regression 
19 

INDEPENDENT VARIABLES Coef. Std. Err. t P>|t|

UNEMPLOYMENT RATE 

Unemployment Rate Level 0.0168149 0.0130652 1.29 0.198

Unemployment Rate Trend ‐0.0287712 0.0143462 ‐2.01 0.045

DEMOGRAPHIC INFORMATION 

Age at Registration ‐0.0137948 0.0015805 ‐8.73 0.000

Male 0.0028158 0.0140803 0.2 0.842

Hispanic 0.0288499 0.028227 1.02 0.307

Asian (not Hispanic) ‐0.0259991 0.0368624 ‐0.71 0.481

Native Hawaiian or Pacific Islander (not 

Hispanic) 0.0343185 0.1212326 0.28 0.777

American Indian or Alaska Native 0.1163797 0.0678828 1.71 0.087

White(not Hispanic) 0.023401 0.0185467 1.26 0.207

Mixed not Hispanic ‐0.0412238 0.0568056 ‐0.73 0.468

HEALTH STATUS 

Disabled ‐0.0228398 0.0274906 ‐0.83 0.406

EDUCATION ATTAINMENT 

High School Diploma /GED ‐0.1434284 0.0923218 ‐1.55 0.120

AA or AS Diploma/Degree 0.1841377 0.0334348 5.51 0.000

BA or BS Diploma/Degree ‐0.0173378 0.110996 ‐0.16 0.876

Occupational Skills 

Licensure/Certificate/Credential 0.0972123 0.0149796 6.49 0.000

SOCIOECONOMIC STATUS 

Veteran 0.0045801 0.0152199 0.3 0.763

Employed at Participation 0.0362872 0.0268083 1.35 0.176

Limited English ‐0.0285468 0.031234 ‐0.91 0.361

Low Income 0.060984 0.0511892 1.19 0.234

Temporary Assistance to Needy Families 

(TANF) 0.1137865 0.2699375 0.42 0.673

Other Public Assistance Recipient ‐0.1783591 0.1161673 ‐1.54 0.125

PROGRAM PARTICIPATION DATA 

Training Length ‐0.0002823 0.0000187 ‐15.1 0.000

National Emergency Grant 0.0106345 0.0317255 0.34 0.737

Trade Adjustment Assistance (TAA) ‐0.043001 0.0189468 ‐2.27 0.023

Wagner-Peyser Act 0.0198991 0.0211108 0.94 0.346

SERVICES AND OTHER RELATED ASSISTANCE DATA 

Received Supportive Services (except 

needs-related payments) 0.0425163 0.0146239 2.91 0.004

Needs-Related payments 0.0303082 0.0608594 0.5 0.619

Received Core Self-Services and 

Informational Activities 0.0506986 0.018824 2.69 0.007
20 

Received Workforce Information Services 0.0284463 0.020002 1.42 0.155

Established Individual Training Account 

(ITA) ‐0.031655 0.0185049 ‐1.71 0.087

Pell Grant Recipient ‐0.0614274 0.0386687 ‐1.59 0.112

Received Pre-Vocational Activities ‐0.0081501 0.023164 ‐0.35 0.725

 Type of Training Service 

On-the-Job Training 0.0617694 0.0283098 2.18 0.029

Skill Upgrading & Retraining 0.0534248 0.0319535 1.67 0.095

Entrepreneurial Training ‐0.0328344 0.0625962 ‐0.52 0.600

ABE or ESL in Combination with Training ‐0.0117201 0.0709926 ‐0.17 0.869

Customized Training 0.1229962 0.0503191 2.44 0.015

 Occupational Category of Training 

Managerial, administrative, professional 

or technical 0.019298 0.033341 0.58 0.563

Sales, clerical and administrative support 0.0183524 0.0343191 0.53 0.593

Service workers 0.0603768 0.0362665 1.66 0.096

Mechanics, installers, repairers, precision 

workers, machine setters, set-up 

operators, operators, tenders, 

assemblers, hand workers, transportation 

and related workers, and military 

0.0142394 0.032695 0.44 0.663

constant 1.488055 0.1202214 12.38 0.000

sigma_u 0.1938907

sigma_e 0.36502747

rho 0.22005326 (fraction of variance due to u_i)

Number of Observations: 5780

Group Variable: WIB

Number of Groups: 438

Obs per Group: min= 1

 avg= 13.2

 max= 105

R-sq: within= 0.0864

 between= 0.0535

 overall= 0.0915

corr(u_i, Xb): ‐0.1833

F(41,5301) = 12.23

 Prob > F =  0.0000

F test that all u_i=0:   F(437, 5301) =1.30; Prob > F = 0.0001

Model Estimates for Older Adult Workers

The testing results for older adult workers share some similarities with that for older dislocated workers, 

but there are important differences as well. Several demographic, educational, socioeconomic, and 
21 

occupational background variables that were shown not to be statistically significant (at p=0.1 level) for older 

dislocated workers now are statistically significant for older adult workers. This could be related to the general 

fact that workers who participate in WIA adult programs and services tend to be more homogeneously low 

income residents with limited prior attachment to the labor market, compared to dislocated workers. Similarly, 

participants in the dislocated worker programs have relatively higher wages. On the other hand, several WIA 

program and service factors and unemployment rates that were statistically significant for older dislocated 

workers in Table 3 now are not statistically significant for older adult workers. This could be related to the fact 

that adult program participants tend to have little prior attachment to the labor market, while participants in the 

dislocated worker program have had strong attachment to the labor market. Stronger prior attachment to the 

labor market better prepares workers for employment, helps workers target specific WIA programs and 

enhances certain WIA program and service sensitivity to EER. 

When controlling for other factors, unemployment rates, no matter for the level measure or the trend

measure, are not statistically significant at p=0.1 level for older adult workers’ EER. Since adult workers were 

previously not strongly attached to the labor market, it is understandable that the change of market conditions 

represented by unemployment rates does not show significant effects for older adult workers. 

Among demographic variables, the age variable again shows a negative effect to older adult workers’ 

EER. Older workers tend to be associated with a lower EER. Controlling for other variables, when the mean 

age of a Workforce Investment Area’s older adult workers increases by 1 year, those older adult workers’ EER 

drops by 0.93 of a percentage point. As older adult workers get older, it becomes more difficult for them to get 

a job after exiting the WIA programs and services, as explained earlier in the paper. In this case, older workers 

with the oldest ages who want or need to find a job would need special attention from policy makers and WIA 

program designers. 

 Similar to older dislocated workers, the gender effect is not statically significant (p=0.1). For races and 

ethnicities, Workforce Investment Areas with a higher share of Hispanic and non-Hispanic White older adult 

workers tend to have a higher EER, compared to non-Hispanic Black older adult workers. Holding other 

variables constant, when a Workforce Investment Area’s shares of Hispanic and non-Hispanic White older 

adult workers rise by 1 percentage point, the area’s older adults’ EER becomes respectively 0.11 and 0.07 of a 

percentage point higher than that of non-Hispanic Black older adult workers. Other races and ethnicities do not 

have statistically significant difference from non-Hispanic Blacks in EER. This implies that policy attention 

could be directed to give some focus on certain minority older adults, including non-Hispanic Blacks, nonHispanic Asians, Native Hawaiian or Pacific Islander (not Hispanic), American Indian or Alaska Native, and 

those with non-Hispanic mixed races. 

Although disability does not show statistically significant effect on older dislocated workers’ EER, 

disability reduces older adult workers’ EER with statistical significance at p=0.05 level. Controlling for other 

variables, when a Workforce Investment Area’s share of disabled older adults increases by 1 percentage point, 

the area’s older adults’ EER drops by 0.07 of a percentage point. Disability could result in more difficulty for 

older workers to find a job. The fact that a higher share of older adults than older dislocated workers reported 

disability could have helped enhance this factor’s statistical significance among older adults. According to 

Appendix B and Appendix C, averagely 3.9 percent of dislocated workers reported disability across all units of 

analysis, but averagely 7.7 percent of older adults reported disability. The lower income levels of older adults, 

as compared to older dislocated workers, is another contributing factor that could deepen the negative impact of 

disability with more statistical significance. Therefore, special policy attention should be paid to older adult 

workers with disabilities. 

For education attainments, college or full-time technical or vocational school education and education 

with occupational skills and credentials are associated with a higher EER among older adults. Compared to 

older adults with lower than high school education or with no reported education, when a Workforce 

Investment Area’s shares of older adults with college or full-time technical or vocational school education and 

education with occupational skills and credentials increase by 1 percentage point, the areas’ older adults’ EER 

becomes respectively 0.15 and 0.12 of a percentage point higher, controlling for other variables. This is 

consistent with the test results for older dislocated workers. Those two types of education attainments are more 
22 

 

career oriented and therefore could increase EER. Different from older dislocated workers, high school 

education (or GED) shows a statistically significant (p=0.05) effect on older adult workers’ EER. Areas with a 

higher share of older adult workers who only reported high school (including GED) as education attainment 

have a lower EER, compared to older adults with lower than high school education or with no reported 

education and holding other variables constant20. 

 Among socioeconomic variables, Workforce Investment Areas with a higher share of low income older 

adult workers have a lower EER. When a Workforce Area’s share of low income older adults increases by 1 

percentage point, the area’s older adults’ EER drops by 0.04 of a percentage point, controlling for other 

variables. This low income effect is statistically significant (p=0.05), though this was not the case for older 

dislocated workers. This low income effect difference reflects the different natures between dislocated workers 

and adults. While dislocated workers have relatively high income, adults have very low incomes. Among all 

older workers subject to the regression tests across all units of analysis, only 1.7 percent of older dislocated 

workers are low income residents, however 56 percent of older adults are low income residents. Other 

socioeconomic variables, such as veteran status, employment status at participation, English levels, and TANF 

and other public assistance reception situation, are again not statistically significant for older adult workers 

(p=0.1). 

Training length again shows a negative effect on EER for older adult workers, as well as for older 

dislocated workers. Workforce Investment Areas with an averagely longer training length among older workers 

are associated with a lower EER. When the training length of a Workforce Investment Area’s older adults 

increase by 1 day, those older adult workers’ EER drops by 0.02 of a percentage point, controlling for other 

variables. This effect is again highly significant (p=0.001). 

Different from older dislocated workers, receiving services or benefits financially assisted by Trade

Adjustment Assistance (TAA) programs do not show statistically significant (p=0.1) impact on older adult 

workers’ EER. The relatively small number of older adults who were assisted by TAA could have reduced the 

statistical significance. Among older dislocated workers, 20 percent of them were assisted by TAA, but for 

older adults, this percentage dropped to only 1.1 percent, according to Appendix B and Appendix C. Being 

served by an NEG program or receiving services or benefits financially assisted by Wagner-Peyser programs 

has no statistically significant impact for either older dislocated workers or older adult workers (p=0.1). 

Among WIA services and other related assistance variables, receiving Supportive Services (except 

needs-related payments) boosts older adult workers’ EER (p=0.1). Controlling for other variables, when a 

Workforce Investment Area’s share of older adult workers who received Supportive Services (except needsrelated payments) increases by 1 percentage point, the area’s older adult workers’ EER increases respectively 

by 0.04 of a percentage point. This effect for older adult workers is consistent with that for older dislocated 

workers. Although receiving Core Self-Services and Informational Activities and establishing Individual 

Training Account (ITA) have statistically significant impact on older dislocated workers’ EER, those two factors 

do not have significant impact on older adult workers’ EER at p=0.1 level. This could be related to the major 

differences between adults and dislocated workers, such as their prior attachment to the labor market, as 

explained earlier in this section. None of the following WIA services or related assistance, including NeedsRelated payments, Workforce Information Services, Pell grants, and Pre-Vocational Activities, have a 

significant impact on older adult workers or older dislocated workers’ EER. 

20 Compared to older adults with lower than high school education or with no reported education, when a Workforce Investment 

Area’s shares of older adults with high school (including GED) education increase by 1 percentage point, the areas older adults’ EER 

becomes 0.42 of a percentage point lower, controlling for other variables. It is not surprising that older workers with relatively low 

education attainment like high school (including GED) or less (i.e., lower than high school education) tend to have more difficulty in 

finding a job and those two levels of education attainment could have undistinguishable impact on EER. Therefore, for older 

dislocated workers, the impact of high school education (or GED) was not statistically significant, compared to older dislocated 

workers with lower than high school education or with no reported education. For older adults, however, this impact of high school 

education (or GED) becomes statistically significant and negative, relative to that of lower than high school education or with no 

reported education. This could be related to the better-off nature of dislocated workers in the job market and might hint at the lower 

average job quality for older adults, compared to the job quality for older dislocated workers. Further investigation on this could be an 

extension of this research. 
23 

 

As for the types of training services, On-the-Job Training has a positive and statistically significant 

impact on older adult workers’ EER, as well as on older dislocated workers’ EER. Workforce Investment 

Areas with a higher share of older workers who received On-the-Job Training have a higher EER, controlling 

for other variables. Compared to Other Occupational Skills Training, when a Workforce Investment Area’s 

shares of older adult workers who received On-the-Job Training increase by 1 percentage point, the area’s older 

adult workers’ EER becomes 0.12 of a percentage point higher, controlling for other variables. 

However, Customized Training has a positive and significant impact on older dislocated workers’ EER, 

and has significant but negative effect on older adult workers’ EER. Compared to Other Occupational Skills 

Training, when a Workforce Investment Area’s shares of older adult workers who received Customized 

Training increase by 1 percentage point, the area’s older adult workers’ EER becomes 0.14 of a percentage 

point lower, controlling for other variables. As mentioned before, dislocated workers have a stronger prior 

attachment to the labor market. This strong prior attachment to the labor market makes it easier for dislocated 

workers to identify their training needs and help to design customized training for themselves. However, for 

older adults with limited prior attachment to the labor market, customized training could be less helpful to them 

and therefore even show a negative impact when compared to the impact of Other Occupational Skills Training. 

Similarly, Skill Upgrading & Retraining has a significant and positive effect on older dislocated workers EER, 

and does not have a significant effect on older adult workers’ EER. Neither of the Entrepreneurial Training or 

ABE/ ESL in Combination with Training have significant impact on older workers’ EER, for either older 

dislocated workers or for older adults. As indicated earlier, there are a limited number of older workers who 

participated in the Entrepreneurial Training and ABE/ESL training, which helps explain the lack of statistical 

significance. Entrepreneurship is possibly not the first priority for most older workers in the WIA program, and 

nor is English a major concern to many older workers. Most people who need ABE or ESL training are 

immigrants; most immigrants who are looking for a job tend to be younger, not older. 

Among occupational categories of training, compared to Agricultural, Forestry, Fishing and Related 

Workers, Construction, and Extractive Workers, all other four occupational categories of training21

 are 

associated with higher EERs for older adult workers. Compared to Agricultural, Forestry, Fishing and Related 

Workers, Construction, and Extractive Workers and controlling for other variables, an area’s older adult 

workers’ EER becomes respectively 0.14, 0.10, 0.16, and 0.13 of a percentage point higher, when the 

Workforce Investment Area’s shares of older adult workers increase by 1 percentage point for the following 

occupational categories of training: (a) Managerial, Administrative, Professional or Technical Workers, (b) 

Sales, clerical and administrative support Workers, (c) Service Workers, and (d) Mechanics, Installers, 

Repairers, Precision Workers, Machine Setters, Set-up Operators, Operators, Tenders, Assemblers, Hand 

Workers, Transportation and Related Workers, and Military Employees. For dislocated workers, only impacts 

from Service Worker training have a significant impact (p=0.1), while other occupation categories have positive 

impacts that were not significant. Compared to Agricultural, Forestry, Fishing and Related Workers, 

Construction, and Extractive Workers, the other four occupational categories of training tend to be knowledgebased or service related jobs that require relatively less physical labor and strength. This situation could benefit 

older workers; therefore training in those occupational categories could enhance their EER. As adults tend to be 

a more homogeneously low-income group with limited resources, the training effect could show up more 

evidently than that for dislocated workers. This might partially help explain the difference in statistical 

significance of those occupational categories for older adults versus for older dislocated workers. 

Table 4 Random Effect Panel Data Model Estimates for Older WIA Program Participants Who Are 

Adults 

POPULATION GROUP: ADULTS

21 Those occupational categories include (1) Managerial, administrative, professional or technical, (2) Sales, clerical and 

administrative support, (3) Service workers, and (4) Mechanics, installers, repairers, precision workers, machine setters, set-up 

operators, operators, tenders, assemblers, hand workers, transportation and related workers, and military occupations. 
24 

DEPENDENT VARIABLE: ENTERED EMPLOYMENT RATE

Random effects u_i ~ Gaussian 

INDEPENDENT VARIABLES Coef. Std. Err. t P>|t|

UNEMPLOYMENT RATE 

Unemployment Rate Level 0.0018059 0.0081674 0.22 0.825

Unemployment Rate Trend ‐0.0352783 0.0215427 ‐1.64 0.102

DEMOGRAPHIC INFORMATION 

Age at Registration ‐0.0093111 0.0020708 ‐4.5 0.000

Male ‐0.0055131 0.0221473 ‐0.25 0.803

Hispanic 0.1053346 0.0326824 3.22 0.001

Asian (not Hispanic) ‐0.0022945 0.0579831 ‐0.04 0.968

Native Hawaiian or Pacific Islander (not Hispanic) 0.1594613 0.1579125 1.01 0.313

American Indian or Alaska Native ‐0.0088495 0.0991271 ‐0.09 0.929

White(not Hispanic) 0.0679957 0.0225965 3.01 0.003

Mixed not Hispanic 0.1243167 0.0860148 1.45 0.148

HEALTH STATUS 

Disabled ‐0.0713425 0.0315089 ‐2.26 0.024

EDUCATION ATTAINMENT 

High School Diploma /GED ‐0.4162823 0.1779513 ‐2.34 0.019

AA or AS Diploma/Degree 0.1538826 0.0778017 1.98 0.048

BA or BS Diploma/Degree ‐0.0266752 0.1392866 ‐0.19 0.848

Occupational Skills Licensure/Certificate/Credential 0.1170023 0.0187078 6.25 0.000

SOCIOECONOMIC STATUS 

Veteran ‐0.0077172 0.0237544 ‐0.32 0.745

Employed at Participation 0.1386255 0.0921702 1.5 0.133

Limited English 0.0399606 0.0451793 0.88 0.376

Low Income ‐0.0397435 0.0180465 ‐2.2 0.028

Temporary Assistance to Needy Families (TANF) ‐0.0286952 0.0786419 ‐0.36 0.715

Other Public Assistance Recipient ‐0.0208225 0.029699 ‐0.7 0.483

PROGRAM PARTICIPATION DATA 

Training Length ‐0.0002497 0.0000297 ‐8.41 0.000

National Emergency Grant 0.0667152 0.0619613 1.08 0.282

Trade Adjustment Assistance (TAA) 0.0176829 0.0789964 0.22 0.823

Wagner-Peyser Act 0.0018309 0.0198566 0.09 0.927

SERVICES AND OTHER RELATED ASSISTANCE DATA 

Received Supportive Services (except needs-related 

payments) 0.0378822 0.019315 1.96 0.050

Needs-Related payments 0.0215168 0.0812317 0.26 0.791

Received Core Self-Services and Informational Activities 0.0029197 0.0222041 0.13 0.895

Received Workforce Information Services 0.0283687 0.0209903 1.35 0.177

Established Individual Training Account (ITA) 0.0116964 0.0208823 0.56 0.575

Pell Grant Recipient ‐0.0264372 0.0555906 ‐0.48 0.634
25 

Received Pre-Vocational Activities ‐0.0132815 0.0259433 ‐0.51 0.609

 Type of Training Service 

On-the-Job Training 0.117898 0.0292538 4.03 0.000

Skill Upgrading & Retraining 0.0088351 0.0336603 0.26 0.793

Entrepreneurial Training ‐0.0410963 0.1769088 ‐0.23 0.816

ABE or ESL in Combination with Training 0.1049368 0.1004306 1.04 0.296

Customized Training ‐0.1438236 0.0717727 ‐2 0.045

 Occupational Category of Training 

Managerial, administrative, professional or technical 0.1437004 0.0567912 2.53 0.011

Sales, clerical and administrative support 0.1043988 0.0575076 1.82 0.069

Service workers 0.160866 0.0586988 2.74 0.006

Mechanics, installers, repairers, precision workers, 

machine setters, set-up operators, operators, tenders, 

assemblers, hand workers, transportation and related 

workers, and military 

0.1314017 0.0551502 2.38 0.017

constant 1.115842 0.143045 7.8 0.000

sigma_u 0.04576398

sigma_e 0.38470506

rho 0.01395369 (fraction of variance due to u_i)

Number of Observations: 2380

Group Variable: WIB

Number of Groups: 342

Obs per Group: min= 1

 avg= 7

 max= 91

R-sq: within= 0.0796

 between= 0.1527

 overall= 0.1062

corr(u_i, Xb): 0 (assumed)

Wald chi2(41) = 12.23

 Prob > chi(2) =  0.0000

Discussion

Although the two regression models are based on the same model, the different data and the very 

different nature of older dislocated workers and older adults result in some different findings. The regression 

diagnostic statistics also show differences. For example, the model for older dislocated workers has limited 

explanation power due to a relatively low R-squared values, but the model for older adults has higher R-squared 

values. The higher financial and labor market homogeneity among adults could be related to the better model 

fit and therefore a better explanation power with higher R-squared values. On the other hand, the current model 

specification could be further enhanced. Considering the data limitations, a few robustness check and 

sensitivity analysis dropping certain variables and observations could be the first extension of this research. 

Adding a few variables, such as industry details, considering a microdata level test, and incorporating some 
26 

non-linear formalities could enhance the model specification. This study has tested for serial autocorrelations, 

but sometimes spatial autocorrelations could distract model estimates as well. Future study can also consider 

spatial autocorrelations and spatial spillover effects in the empirical models. 

Conclusion 

 This paper relies on the WIASRD 2007 data and BLS unemployment data and adopts a random effect 

model and a fixed effect panel data model to identify factors associated with older workers’ EER after exiting 

WIA programs. The statistical test is conducted separately for older dislocated workers and older adults. 

Summary of Findings

Several common factors are identified that affect older workers’ EER. The common factors that are 

associated with a higher EER include having a college degree or finished full-time technical or vocational 

school education, attained education with occupational skills and credentials, receiving Supportive Services 

(except needs-related payments), receiving On-the-Job Training, and training for Service Workers as 

occupations. For example, when a Workforce Investment Area’s share of older workers who received 

Supportive Services (except needs-related payments) increases by 1 percentage point, the area’s older workers’ 

EER increases by 0.04 of a percentage point, controlling for other variables. This effect is consistent for both 

older dislocated workers and older adult workers. Compared to Other Occupational Skills Training and 

controlling for other variables, when a Workforce Investment Area’s shares of older adult workers who received 

On-the-Job Training increase by 1 percentage point, the area’s older adult workers’ EER becomes 0.12 of a 

percentage points higher; when a Workforce Investment Area’s share of older dislocated workers who received 

On-the-Job Training increases by 1 percentage point, the area’s older dislocated workers’ EER becomes 0.06 of 

a percentage point higher. 

The common factors that are associated with a lower EER include an older age and a longer training 

length. Holding other variables constant, when the mean age of a Workforce Investment Area’s older 

dislocated workers increases by 1 year, these older dislocated workers’ EER decreases by 1.4 percentage points; 

when the mean age of a Workforce Investment Area’s older adult workers increases by 1 year, those older adult 

workers’ EER drops by 0.93 of a percentage point. Compared to other factors observed in the model testing, 

the age effects are relatively strong. Controlling for other variables, when the average training duration of a 

Workforce Investment Area’s older dislocated workers increase by 1 day, those older dislocated workers’ EER 

drops by 0.03 of a percentage point; when the training length of a Workforce Investment Area’s older adults 

increase by 1 day, those older adult workers’ EER drops by 0.02 of a percentage point. This training length 

effect is highly significant (p=0.001). Although this training length effect appears not to be strong with 

relatively small regression coefficients (0.0003 or 0.0002), the 0.03 or 0.02 percentage points’ decrease in EER 

is relative to each additional day of training. If changing the training length scale to weeks, months, or even 

years, this training length effect will jump to a much larger scale. 

 The model for older dislocated workers and the model for older adults also identify several different 

factor effects on EER. Some are related to data distributions. For example, disability is not a significant 

(p=0.1) factor affecting older dislocated workers’ EER, but this factor has a significant effect on older adults’ 

EER. Controlling for other variables, when a Workforce Investment Area’s share of disabled older adults 

increases by 1 percentage point, the area’s older adults’ EER drops by 0.07 of a percentage point. It is not 

surprising that disability brings down older workers’ EER because disability often results in greater difficulty 

for older workers in finding a job. The fact that a higher share of older adults than older dislocated workers 

reported disabilities helps contribute to statistical significance in the case of older adults. According to 

Appendix B and Appendix C, on average 3.9 percent of dislocated workers reported disability across all units of 

analysis, but 7.7 percent of older adults reported disability on average. Compared to older dislocated workers, 
27 

older adults’ lower income level is another contributing factor that could deepen the negative impact of 

disability. Also, different from older dislocated workers, receiving services or benefits financially assisted by 

Trade Adjustment Assistance (TAA) programs do not show statistically significant (p=0.1) impact on older 

adult workers’ EER. The relatively small number of older adults who were assisted by TAA could have 

contributed to reduce the statistical significance. Among older dislocated workers, 20 percent of them were 

assisted by TAA, but for older adults, this percentage dropped to only 1.1 percent, according to Appendix B and 

Appendix C. 

Other different or even opposite variable effects for older dislocated workers versus older adults are 

related to the different natures of dislocated workers and adults. For example, low income is not a significant 

factor for older dislocated workers’ EER, but it is a significant factor for older adults’ EER. Controlling for 

other variables, when a Workforce Area’s share of low income older adults increase by 1 percentage point, the 

area’s older adults’ EER drops by 0.04 of a percentage point. The prevalence of low income among adults is a 

direct cause for the significant low income effect on older adults. Adults tend to have very low income and 

more than half of older adults in this study are low-income residents; however, only 1.7 percent of older 

dislocated workers are low-income residents. While Core Self-Services and Information Activities, Skill 

Upgrading & Retraining and Customized Training have significant and positive effect for older dislocated 

workers’ EER, those services and types of training do not show statistical significance for older adults’ EER. 

Older adults’ limited prior attachment to the labor market makes it more difficult to customize training or 

choose specific skill upgrading and retraining that cater to their needs. Similarly, older adults’ limited prior 

attachment to the labor market makes the minimal level of training, such as self-services and informational 

activities, not sufficiently helpful. 

 WIA program success with older dislocated workers is sensitive to cyclical changes in labor market 

conditions. The innovative two-vector unemployment rate measure shows its advantage in measuring economic 

cycles. For older dislocated workers, the unemployment rate level does not have significant impact, but it is the 

unemployment rate trend that captures the economic cyclical effect on EER. The unemployment rate trend

shows an advantage in measuring unemployment rate, not just because it captures the direction of economic 

change, but because it captures the timing of unemployment rate level. This solves the potential dubious role of 

unemployment rates in employment growth. When the unemployment rate increase of a state where a 

Workforce Investment Area situates goes up by 1 percentage point, this Workforce Investment Area’s older 

dislocated workers’ EER drops by 0.03 of a percentage point, holding other variables constant. For older 

adults, although unemployment rate measures do not seem significant, it could be related to adults’ limited prior 

attachment to the labor market that makes them less sensitive to cyclical economic conditions. 

Policy Implications 

The empirical tests also identify some policy implications. In terms of policy equity perspective, the 

findings suggest a need to draw policy attentions in several specific directions. The older cohorts among older 

workers deserve more policy attention in workforce programs and training, as older ages among older workers 

are associated with a lower EER, and this age effect is strong, as noted earlier. 

Low income older workers, particularly low income older adults, deserve more policy attention to 

enhance their EER. Over half of observed older adults are low income residents, and their low income status 

results in a lower EER. Low income older adult workers are among those who need the most assistance. 

 Similarly vulnerable are the older adult workers with disabilities. They deserve more funds to be 

allocated to empower them in the labor market. Additional social support would also be helpful to them. 

For older adult workers, special focus can also be placed on certain minority groups, including nonHispanic Blacks, non-Hispanic Asians, Native Hawaiian or Pacific Islander (not Hispanic), American Indian or 

Alaska Native, and those with non-Hispanic mixed races. 

The findings also suggest some implications for policy efficiency and effectiveness. Among the services 

and related assistance, keeping and expanding Supportive Services (except needs-related payments) would help 
28 

to stabilize and strengthening EER for older workers. For the training types, more funding focusing on On-theJob Training would help to enhance EER for both older dislocated workers and older adults. 

Better monitoring training length would be helpful to enhance the cost effectiveness of WIA training 

programs. For both older dislocated workers and older adults, the tests reveal that longer training is associated 

with a lower EER. However, please note that it is necessary to allow relatively longer training time for older 

workers to digest the training and learning. The standard deviation of training length shown in the WIASRD 

2007 data is as high as about a year with the longest training duration spanning almost ten years. What this 

paper wants to call for policy makers’ monitoring is not the reasonably longer learning time necessary to 

trainees, but the unreasonably long training duration spanning for over several years22. Based on preliminary 

data analysis, the long training length was not related to older ages. Instead, many occurrence of long training 

lengths were from the youngest cohorts among senior WIA participants. The number of cases of long training 

length was not extremely few. Further investigation on the impact of training length from one specific service 

or a few services would be needed. 

 It is also necessary to target funds on needs and prioritize the funding directions. Emphasizing Core 

Self-Service and Information Activities would be helpful for older dislocated workers who are better prepared 

with skills and labor market information, but not necessarily for older adults. For older dislocated workers, 

policy attention can continue focusing on Skill Upgrading and Retraining and Customized Training, but not 

necessarily for older adults. For older dislocated workers, not all occupational categories of training should be 

priorities, but focusing on Service Worker training could be effective. For older adults, policy could be oriented 

to allocate extra funds to On-the-Job Training and could be diversified to various occupational categories of 

training. 

Future Research Directions

Further exploration on other potential factors, such as industry details and spatial effects, could be 

helpful to add more nuances. Due to the data limitations, a few robustness check and sensitivity analysis can be 

conducted as an extension of this current research. Future research could also consider incorporating non-linear 

modeling, micro level data, and spatial autocorrelation and spillover effects to enhance the model specification. 

 

22 Please note that the training length does not mean a participant’s total participation of a specific WIA training 

program. Instead, it is calculated as the time span from the participation of the first service to the exit from the 

last service. This would therefore result in some long training length because of returning participants for the 

same or different services over the years. Based on preliminary analysis on training length, it was associated 

with the nature of service/training and other personal factors like the participants’ education attainment. 
29 

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32 

Appendix 

Appendix A 

Seniors’ Population, Labor Force, and Employment by Age 

Data Source: Current Population Survey, Employment Status of the Civilian Noninstitutional Population by 

Age, Sex, and Race, 2009 Annual Average of Household data. Retrievable from the World Wide Web 

ftp://ftp.bls.gov/pub/special.requests/lf/aat3.txt. 

Appendix B 

Description of Variables for Older WIA Program Participants Who are Dislocated Workers 

Variable Obs Mean Std. Dev. Min Max

Older Dislocated Workers' EER 6084 0.7939 0.3912 0 1

Number of Older Dislocated Workers 6084 1.2346 0.9595 1 28

Exit Date 6084 Nov 20,

2006 186.0058 Jan 01,

2006

Sep 30,

2007

Unemployment Rate Level 6084 4.7687 0.9766 1.8 12

Unemployment Rate Trend 5839 ‐0.0342 0.3802 ‐1.299999 1.2

Age at Registration 6084 58.3149 3.2705 55 97

Male 6084 0.4920 0.4846 0 1

Hispanic 6023 0.0976 0.2908 0 1

Asian (not Hispanic) 6023 0.0313 0.1675 0 1

Black (not Hispanic) 6023 0.1177 0.3118 0 1

Native Hawaiian or Pacific Islander (not Hispanic) 6023 0.0018 0.0407 0 1
33 

American Indian or Alaska Native 6023 0.0060 0.0753 0 1

White(not Hispanic) 6023 0.7361 0.4294 0 1

Mixed not Hispanic 6023 0.0095 0.0945 0 1

Disabled 6084 0.0386 0.1845 0 1

Veteran 6084 0.1939 0.3819 0 1

Employed at Participation 6084 0.0783 0.2644 0 1

Limited English 6084 0.0796 0.2671 0 1

Low Income 6084 0.0169 0.1260 0 1

High School Diploma /GED 6084 0.0040 0.0603 0 1

AA or AS Diploma/Degree 6084 0.0341 0.1769 0 1

BA or BS Diploma/Degree 6084 0.0028 0.0517 0 1

Occupational Skills Licensure/Certificate/Credential 6084 0.6478 0.4673 0 1

Temporary Assistance to Needy Families (TANF) 6084 0.0003 0.0181 0 1

Other Public Assistance Recipient 6084 0.0026 0.0502 0 1

Training Length 6084 417.6150 341.2550 0 3548

National Emergency Grant 6084 0.1023 0.3003 0 1

Trade Adjustment Assistance (TAA) 6084 0.1982 0.3939 0 1

Wagner-Peyser Act 6084 0.7277 0.4412 0 1

Received Supportive Services (except needs-related 

payments) 6084 0.2928 0.4463 0 1

Needs-Related payments 6084 0.0090 0.0916 0 1

Received Core Self-Services and Informational Activities 6084 0.3206 0.4620 0 1

Received Workforce Information Services 6084 0.4297 0.4902 0 1

Established Individual Training Account (ITA) 6084 0.6645 0.4663 0 1

Pell Grant Recipient 6084 0.0255 0.1548 0 1

Received Pre-Vocational Activities 6084 0.1885 0.3875 0 1

On-the-Job Training 6084 0.0722 0.2551 0 1

Skill Upgrading & Retraining 6084 0.1237 0.3271 0 1

Entrepreneurial Training 6084 0.0082 0.0894 0 1

ABE or ESL in Combination with Training 6084 0.0083 0.0890 0 1

Customized Training 6084 0.0167 0.1247 0 1

Managerial, administrative, professional or technical 6084 0.3206 0.4530 0 1

Sales, clerical and administrative support 6084 0.2827 0.4369 0 1

Service workers 6084 0.1080 0.3005 0 1

Agricultural, forestry, fishing and related workers, construction 

and extractive workers 6084 0.0312 0.1691 0 1

Mechanics, installers, repairers, precision workers, machine 

setters, set-up operators, operators, tenders, assemblers, hand 

workers, transportation and related workers, and military 

6084 0.2575 0.4264 0 1
34 

Appendix C 

Description of Variables for Older WIA Program Participants Who are Adults 

Variable Obs Mean Std. Dev. Min Max

Older Adults' EER 2489 0.7736 0.4088 0 1

Number of Older Adults 2489 1.1535 0.7210 1 16

Exit Date 2489 Dec 01,

2006 186.3938 Jan 01,

2006

Sep 30,

2007

Unemployment Rate Level 2489 4.7356 1.2251 2.2 12.1

Unemployment Rate Trend 2410 ‐0.0137 0.3794 ‐1.1 1.2

Age at Registration 2489 58.5982 3.8906 55 99

Male 2489 0.4993 0.4887 0 1

Hispanic 2457 0.1232 0.3220 0 1

Asian (not Hispanic) 2457 0.0243 0.1500 0 1

Black (not Hispanic) 2457 0.2029 0.3950 0 1

Native Hawaiian or Pacific Islander (not Hispanic) 2457 0.0032 0.0547 0 1

American Indian or Alaska Native 2457 0.0074 0.0810 0 1

White(not Hispanic) 2457 0.6287 0.4754 0 1

Mixed not Hispanic 2457 0.0103 0.0982 0 1

Disabled 2489 0.0767 0.2597 0 1

Veteran 2489 0.1948 0.3877 0 1

Employed at Participation 2489 0.0085 0.0892 0 1

Limited English 2489 0.0434 0.1996 0 1

Low Income 2489 0.5613 0.4891 0 1

High School Diploma /GED 2489 0.0028 0.0530 0 1

AA or AS Diploma/Degree 2489 0.0123 0.1086 0 1

BA or BS Diploma/Degree 2489 0.0034 0.0575 0 1

Occupational Skills Licensure/Certificate/Credential 2489 0.6647 0.4650 0 1

Temporary Assistance to Needy Families (TANF) 2489 0.0113 0.1032 0 1

Other Public Assistance Recipient 2489 0.0997 0.2945 0 1

Training Length 2489 331.6957 309.4293 0 3013

National Emergency Grant 2489 0.0185 0.1340 0 1

Trade Adjustment Assistance (TAA) 2489 0.0111 0.1042 0 1

Wagner-Peyser Act 2489 0.7008 0.4531 0 1

Received Supportive Services (except needs-related payments) 2489 0.2896 0.4471 0 1

Needs-Related payments 2489 0.0111 0.1029 0 1

Received Core Self-Services and Informational Activities 2489 0.3146 0.4607 0 1

Received Workforce Information Services 2489 0.3752 0.4816 0 1

Established Individual Training Account (ITA) 2489 0.6550 0.4714 0 1

Pell Grant Recipient 2489 0.0234 0.1493 0 1

Received Pre-Vocational Activities 2489 0.1499 0.3549 0 1

On-the-Job Training 2489 0.1270 0.3299 0 1

Skill Upgrading & Retraining 2489 0.0970 0.2952 0 1

Entrepreneurial Training 2489 0.0020 0.0448 0 1
35 

ABE or ESL in Combination with Training 2489 0.0080 0.0879 0 1

Customized Training 2489 0.0158 0.1210 0 1

Managerial, administrative, professional or technical 2489 0.2339 0.4155 0 1

Sales, clerical and administrative support 2489 0.2482 0.4236 0 1

Service workers 2489 0.1548 0.3554 0 1

Agricultural, forestry, fishing and related workers, construction 

and extractive workers 2489 0.0239 0.1502 0 1

Mechanics, installers, repairers, precision workers, machine 

setters, set-up operators, operators, tenders, assemblers, hand 

workers, transportation and related workers, and military 

2489 0.3393 0.4663 0 1