Regression Models

The dependent variables used to assess the impacts of job training services were wage level differences upon re-employment and the duration of unemployment

contingent upon the control and job training variables. It was recognized that other factors affect GDOL job training effectiveness but from a worker’s point of view, prompt re-employment at a comparable wage is an important measure of the efficacy of job training services. The wage and re-employment histories of workers completing job training programs were statistically compared to workers not receiving training. The research methodology controlled for systematic variations between the treatment (training after job loss) and non-treatment (no training after job loss) groups by

estimating the regression-adjusted differences in the means of the wage and time for re- employment variables. Both the trained and non-trained groups had employment and demographic and training data. Based on these controls, differences in outcomes for program participants to those of comparison group members were attributed to the program(s) of instruction.

Following Hollenbeck (2003) and Barnow (1987), the net program impact PI (Equation 3.4) on the ith structurally unemployed worker receiving job services from the jth program was estimated with conditional difference in means:

PI= E(TrainEffecti|Xi; i=training) - E(NoTrainEffectj|Xj; j=no training) (3.4)

Econometrically, this conditional dependence was parametrically estimated through a general linear regression (Equation 3.5):

EFFECT = α + βX + γTRAIN + ηGEO + e (3.5)

where:

EFFECT = TrainEffect if in trained worker population and NoTrainEffect if in untrained worker population;

X = vector of individual characteristics including age, gender, and race; β = individual characteristic impact coefficients;

TRAIN = vector of job training program dummy variables; 1 for workers receiving job training; 0 for workers not receiving job training; γ = net impacts of job training;

GEO = vector of geographic dummy variables; η = net impact of geography;

e = error term.

The dependent variable EFFECT was either wage differences pre- and post- transition or duration of unemployment between periods of stable employment from the GDOL employment histories. The control vector X contained demographic and

employment data unique to each worker: individual worker characteristics including age, gender, and race. TRAIN was a vector of job training service dummy variables, each representing specific types of job training services. The parameter estimate γ was the net program impact of participation in a particular type of job training service. GEO was a vector of geographic dummy variables, each representing one of Georgia’s twenty Workforce Investment Areas (WIAs) or one of nine areas on the Urban-Rural (UR)

Continuum from the U.S. Department of Agriculture or, in the case of the state as a whole, the variable GEO does not appear. The parameter estimate η is the net impact of participation in a GDOL job training service in a specific geography.

A statistically significant difference between the training and non-training populations in terms of mean wages before and after transition to new stable employment, or the time to find new employment, was taken as indicative of the

usefulness of the GDOL’s job training to long-term and structurally unemployed workers. The job training service dummy variables (sign and magnitude of significant impact coefficient) estimated the impacts of job training services on post-training wages or the reduction in the time to regain stable work for the structurally unemployed.

When all relevant variables are observed, the error term and the unobservable factors are not correlated with the treatment, here job training. When unobservable factors influence the results, regression analysis can be challenging. With a true experimental research design, randomization guarantees that the observable and the unobservable factors that affect outcomes are unrelated to treatment. When

randomization is not possible as with this non-experimental research design, alternate means must be found to generate unbiased results. In a regression, the unobservable variables are unmeasured or even immeasurable and are implicitly included in the error term. Thus, the error term may be correlated with the treatment variable, violating one of the assumptions of OLS regression analysis: the unbiasedness of estimators (Wooldridge 1999).

For a simple difference in means calculation to be valid, either enrollment into job training services would have to be totally random or the outcome would have to be

independent of characteristics that are systematically different between the treatment and comparison group, an unrealistic assumption in this case. One basic assumption

underlying longitudinal methods is that many differences between training participants and non-participants are constant over time. If they are constant they can be differenced given two or more periods of data. This type of estimator is the “difference-in-

differences” estimator. With a difference-in-differences comparison of means estimator, before and after changes in outcomes for participants are compared to before and after changes for non-participants. Two periods of data, one before and one after the period in which individuals decide whether or not to participate, is sufficient to produce a valid estimate.

Because the rich GDOL wage datasets contained wage data both before and after program participation, it was possible to use a difference-in-differences approach as the method for estimating the net program impact. This method effectively allows the use of preprogram levels of wages to control for the net impact effect of receiving GDOL job training services. To control for systematic differences between groups, the method used in this research was to regression-adjust the difference-in-differences; both the pre- and post- training wages for both the treatment and control groups. In other words, the net training impact estimator became the difference-in-differences in conditional means (Hollenbeck 2003, Hollenbeck 2004).

It was recognized that all factors cannot be controlled with these variables, among them: systematic variations in the quality of GDOL-sponsored training despite the

federally-mandated uniform program requirements, relative under-funding of specific job training programs in some areas, personal factors not accounted for in the GDOL

demographic data, and other factors. Results are qualified to the extent that, as with any regression analysis, some of the potentially influential variables remain unknown. The influence of these unquantified factors is considered acceptable compared to the direct effects of job training on wage income and the duration of unemployment.

3.4.2.1. Statewide Regression Analysis Model

These general variables can be operationalized for statewide, WIA, and UR geographies in terms of specific variables for worker characteristics and training services (Equation 3.6):

EFFECTij = α + βAAGEij + βGFEMALEij + βRWHITEij (3.6) + γCCoreij + γIIntensiveij + γOOccupSkillsij

+ γRRemedialij + γOOJTij + γMMentorij + γJJobSearchij + γSSkillsUpgradeij + Interaction Variables + e;

where:

EFFECT = dependent variables to be modeled are wage and job search time; AGE = age variable;

FEMALE = gender variable; WHITE = race variable;

Core = Core Services dummy variable; 1 for workers receiving Core Services; 0 for workers not receiving Core Services;

Intensive = Intensive Services dummy variable;

Remedial = Remedial educational services dummy variable; OJT = On-the-job training services dummy variable; Mentor = Mentoring services dummy variable;

JobSearch = Extended Job Search services dummy variable; SkillsUpgrade = Skills Upgrade training services dummy variable; γ = net impacts of job training;

e = error term including unobservables.

3.4.2.2. Workforce Investment Area (WIA) and Urban-Rural (UR) Continuum Regression Model

Long-term and structural unemployment are not geographically uniform

phenomena. Geography can play an important role in this job training program impact analysis because the size and diversity of labor markets constrain worker behavior and the fact that workforce development programs are implemented across many regional and local jurisdictions. Important differences in unemployment can arise from the diverse populations between urban and rural areas, suburbs and the central city, and growing and declining areas. Additionally, plant closings and large-scale layoffs in certain areas can sharply escalate local unemployment rates. Considering previous research findings, it was expected that geography would emerge from this analysis as an important

explanatory factor behind differences in the effectiveness of the GDOL job training programs. To explore this possibility this research utilized two geographies in addition to the statewide analysis. Equation 3 was modified to allow analysis for specific sub-state geographic areas (Equation 3.7):

EFFECTij = α + βAAGEij + βGFEMALEij + βRWHITEij (3.7) + γCCoreij + γIIntensiveij + γOOccupSkillsij

+ γRRemedialij + γOOJTij + γMMentorij + γJJobSearchij + γSSkillsUpgradeij + η2GEO2ij + η3GEO3ij + … + η20GEO20ij + eij;

where:

GEO = vector of geographic dummy variables; and η = net impact of geography.

This regression model was run separately for the nine UR areas and the twenty WIAs using one area as a reference in each case. In this way, differences in job training services across these geographies were ascertained.

The U.S. Department of Agriculture Rural-Urban Continuum Codes (see Figure 3.5) form a classification scheme that distinguishes metropolitan counties by population size from non-metropolitan counties by degree of urbanization and adjacency to a metro area or areas. These two categories have been subdivided into metropolitan (3) and non- metropolitan (6) groupings, resulting in a nine-part county classification system.

In compliance with U.S. Workforce Investment Act (WIA), the state of Georgia has established 20 WIA service areas in Georgia (Figure 3.6). Each workforce area has at least one full-service One-Stop Center from which a range of workforce services, including job training, are made available to job seekers and employers. Georgia’s WIAs are a diverse group of economic regions ranging from the county-size urbanized WIAs of

Atlanta and Macon to the expansive rural WIAs which, in the case of the Heart of

Georgia, can be comprised of as many as seventeen counties. The GDOL not only serves as a conduit of federal funds to the DTAE and other education service providers but also enforces the uniform quality-of-service requirements that flow down from the USDOL to state and local agencies and their contractors. It is recognized that even with uniform program requirements, important differences in job training program function and effectiveness may exist due to administrative and other local factors beyond the scope of this research.

CHAPTER 4