Introduction
Workforce development programs are defined as “a combination of education and training services to prepare individuals for work and to help them improve their prospects in the labor market” (Bradley, 2015, p. 1). It goes beyond the narrow definition of employment and training to include “substantial employer engagement, deep community connections, career advancement, integrative human service supports, contextual and industry-driven education and training, and the connective tissue of networks” (Giloth, 2000, p.340). Jacobs and Hawley (2009) define workforce development as “the coordination of public and private sector policies and programs that provides individuals with the opportunity for a sustainable livelihood and helps organizations achieve exemplary goals, consistent with the societal context” (p. 12).
In the United States, the Workforce Investment Act of 1998 (WIA; P.L. 105–220) and Title III of Workforce Innovation and Opportunity Act of 2014 (WIOA; P.L. 113–128) have been enacted to support job seekers with significant barriers to employment to access education, training, and support services that lead to good jobs and to provide employers with quality workers to be competitive in the global economy. WIA and WIOA moved the public employment service that was originally created by the Wagner–Peyser Act of 1933 toward a more demand-driven One-Stop system to provide training and employment support. WIOA’s Title II on Adult Education and Literacy authorized education services to be provided toward improving adult basic skills and attaining secondary and post-secondary education.
Similarly in the United Kingdom, particularly in England, a demand-driven paradigm is used with greater attempts to match the needs of employers—the labor demand—with the expressed and identified skill competencies needed that are requisite for occupations in diverse industry sectors (Payne, 2008). This has been problematic with education providers having to play the role of employer engagement and skills supply to the taste of employers (Hodgson, Spours, Smith, & Jeanes, 2019). To be sure, there are positive roles for employers to play in the public employment services (Bartik, 2001; Clymer, 2003; Green & Galetto, 2005). But the overreaching dominance of the employer’s voice as the driver of the public workforce system has the potential danger of neglecting the counterweight of jobseekers (Hong, Sheriff, & Naeger, 2009)—the labor supply—that the dual customer approach by which WIOA and its legislative predecessor, WIA, are premised (Holland, 2016; National Association of Workforce Boards, 2014).
As such, the reliance on education to serve the employers creates “dislocation between knowledge and knower as a consequence of the preeminence of the market principle” (Bernstein, 2000, p. 86). Industry dominance would do a disservice to articulate what authoritative professional knowledge base as “truth” entails in terms of educational and training actors maintaining sociality and organization within the workforce development regions or systems (Hordern, 2016). Industry-School Partnerships (ISPs) in Australia is another form of employer-led workforce development model that has been found to be problematic when there is a broad and vague framing of partnership goals to track the effectiveness of working with schools, families, community agencies, and businesses (Flynn, Pillay, & Watters, 2016). Due to the profit focus of the industry, there is also lack of genuine commitment for cultural adjustment and cohesion necessary to work with schools and students.
There has been a growing convergence in the interest in economic self-sufficiency (ESS) as a goal of anti-poverty policies by industrialized countries around the world but divergence in the ways to conceptualize and use the term (Tosun, Pauknerová, & Kittel, 2021). This neoliberal concept has found roots pervasively in all aspects of social and labor market policies in the United States to keep the reluctant welfare state on the sidelines. A number of European countries have seen the effect of welfare state reforms affecting employment protection, unemployment benefits, and active labor market policies (Eichhorst & Konle-Seidl, 2006). The United Kingdom has experimented with the welfare-to-work program under “The Work Programme (WP)”and had to rely on employer involvement to ensure success in its active labor market policy (ALMP) (Ingold & Stuart, 2015). The United States, the United Kingdom, and Australia have active labor market programs to move persons with disabilities from the workfare to independence and self-sufficiency (SS) (Parker Harris, Owen, & Gould, 2012).
In fact, this overexpectation of the demand side participation rests on the false assumption that employers will have incentives to compete for and invest in the low-wage, low-skilled workers who participate in workforce development programs. Instead of putting pressure on the employers by way of demand side reform or regulation for demand side activation, ALMP has mostly relied mostly on the supply side activation (Frøyland, Andreassen, & Innvær, 2019). This leads to fragmentation of the public workforce system, resulting in the broken links between jobseekers’ respective capacity to move toward becoming job ready and the workforce development system that opens opportunity to access the labor market at the point of entry (Hong, 2013). This is particularly a challenge for jobseekers who have multiple direct and indirect barriers to employment, whether skill-based or noncognitive in nature (Dworsky & Courtney, 2007; Hong & Wernet, 2007). While there may be “processes of downscaling labor regulation, displacing and downloading socioeconomic risks, and dismantling welfare-state institutions” that leaves the labor powerless against the capital in a neoliberal, regressive institutional environment, renewed collective solidarity using a local level, individualized identities can reconstruct labor–capital relations (Peck & Theodore, 2012, p. 757).
Based on a local definition of success in workforce development generated by low-income jobseekers, SS—typically thought of as ESS—was reconceptualized as an empowerment-based and inclusive practice called psychological self-sufficiency (PSS; Hong, 2013; Hong et al., 2009). In this regard, the purpose of this paper was to examine a holistic relationship between the process and outcome elements of SS in the context of a sector-based, demand-driven career pathway model that includes a partnership between post-secondary education and training institutions and health care employers in high growth, high demand areas (King & Prince, 2015, 2019). Using data from a health care career pathway program called Health Profession Opportunity Grant (HPOG), it examines the human-centric developmental aspect of SS that has been found to generate program success (Hong, O’Brien et al., 2019). HPOG grantees provide sector-based education and training, coupled with industry-specific credentialing and employer partnership for job placement and retention for low-income individuals (King & Hong, 2019). The research question guiding this paper is: How does psychological self-sufficiency contribute to economic self-sufficiency among participants in a health care career pathways workforce development program?
Literature Review
SS has been largely understood in the workforce development literature as an economic outcome that one achieves by way of finding work and leaving public assistance. In transitioning welfare leavers into the labor market, social policies have relied on workforce development to decrease welfare dependency and to help low-income jobseekers to achieve SS by finding employment (Hong & Crawley, 2015). It is also been taken to be a neoliberal concept that blames the victim whose fate has been constrained by the structural inequities and declining socioeconomic protection in our society. Despite ideological contention and questioning of its relevance and application, SS has been deeply rooted as the main social policy goal in the United States. It has been shared as a goal by various public workforce development programs that differ in populations, services, implementation processes, and performance indicators used to evaluate program effectiveness (Brown, Kirby, & Conroy, 2019).
In this regard, SS is commonly accepted by the policymakers, program evaluators, and service providers as a success measure (Dworsky, 2005; Hall, Graefe, & De Jong, 2010; Hong et al., 2009; Lehrer, Crittenden, & Norr, 2002). Dworsky (2005) measured SS as employment, earning, and receiving welfare services among the Wisconsin’s former foster youths. Hall et al. (2010) measured SS using employment, employment stability, and wages. Lehrer et al. (2002) studied inner-city minority mothers’ ESS. They measured SS as to whether they received welfare services or not, worked full- or part-time, and considered activities the respondents involved outside of home. These studies consider SS as ESS by measuring economic-related outcomes with the assumption that work will bring financial security and stability.
However, there have been a growing number of researchers in the recent decades who have found the process element of workforce development to be critical to individual preparation for the job market. A study in the context of the U.K. government’s effort in connecting higher education and creative industry found that personal development as students to being industry-ready by attaining the necessary skills take the form of identify transformations in the process of “becoming” (Ashton, 2009). In making transitions from higher education to the professional world, Millenials who possess higher self-efficacy differed on how they search for jobs, expectations they had, and the characteristics they most valued in the companies and managers compared to their counterparts who demonstrated lower self-efficacy (Achenreiner, Kleckner, Knight, & Lilly, 2019).
Similarly when focusing on low-income jobseekers, a few researchers have studied SS from a more process-based approach (Gowdy & Pearlmutter, 1993; Hawkins, 2005; Hong et al., 2009). Hawkins (2005) considered it to be related to maximization of human potential and viewed it as having multiple dimensions than simply focusing on the economic aspect. Hong et al. (2009) also found SS to not be unidimensional as an economic concept but rather complemented by an empowerment-based process called PSS that was originally represented as a positive motivational trait of employment hope (EH). PSS, as identified through a series of additional focus groups with service providers and program participants, had two components—EH and perceived employment barriers (PEBs) (Hong, 2013).
As a workforce development theory of change, PSS was presented as the process dimension of SS that leads to the ESS outcome (Hong, Choi, & Key, 2018). The two components of PSS are negatively related to each other in the path model to ESS with EH serving as the mediator. Once individuals become aware of and assess the degree to which their barriers exist, the negative effects of barriers can be redirected by EH that positively contributes to ESS (Hong et al., 2018; Hong, Polanin, Key, & Choi, 2014). Mental contrasting is a self-regulatory strategy in psychology that supports the PSS process. It suggests that mentally contrasting a desired future with obstacles of reality activates goal-directed behaviors given high expectations of successfully achieving the goal (Kappes, Singmann, & Oettingen, 2012; Oettingen, 2000). To reach feasible goals, positive and negative realities have to work simultaneously in which people would picture a positive future outlook first and then imagine a negative obstacle filled reality (Oettingen, Stephens, Mayer, & Brinkmann, 2010). PSS similarly consists of taking negative obstacles (PEBs) and switching to positive goal-directed actions (EH). However, PSS is different from mental contrasting by the order in which the positive and negative elements are contrasted. In mental contrasting literature, it is referred to as reverse contrasting—starting with thinking about the obstacles and then turning to the desired future (Kappes et al., 2012). The PSS process has EH mediating the effect of PEBs on ESS (Hong et al., 2018).
PSS has further developed as a workforce development theory that guides the process of facilitating transformation in education and training (Hong, Hong, Lewis, & Williams, 2019; Hong, Kim, Hong, Park, & Lewis, 2020; Hong, Northcut, Spira, & Hong, 2019; Hong, O’Brien et al., 2019). The theory suggests that a goal-directed hope action is triggered from self-awareness of barriers as one begins to perceive them as such vis-à-vis the goal that they set their mind to. As the perceived assessment of barriers—both structural and individual—decrease with relationship-based support, one’s view of the goals become more attainable and hope actions become more rigorous and stronger. And, PSS theory was further translated into developing an intervention model called Transforming Impossible into Possible (TIP; Hong, 2016), the content of has been found to have neurobiologically strengthening core elements (Hong & Hong, 2019). Its effects have been found to increase PSS and other psychological capital (Hong, Choi, & Hong, 2020).
While PSS may simply seem to be an individual process theory, applying Anthony Giddens’s (1984) structuration theory provides the agency-structure relationship context to posit a transformative process for a new workforce development system to emerge. Structuration theory stresses the duality of structure with the rules and resources being “both the medium and outcome of social interaction” and allows for “the centrality of agency in the reproduction of society” (Shilling, 1992). Social actions may be enabled or constrained by structures; structures as self-organizing social systems may be the outcomes generated by knowledgeable and creative human actors (Fuchs, 2003). Structuration theory can provide the inclusive culture for employers to develop rules and resources aligned with those based on employees’ perception of work–life balance (Hoffman & Cowan, 2010). Therefore, focusing on PSS in the short-term as the human agency is significant to yield system reaction in the long term by signaling PSS as a new organizing principle toward which new rules and resources could be deployed.
Studies have validated the extent to which each component of PSS—EH and PEB—separately affect ESS. The Employment Hope Scale (EHS) was validated by Hong, Polanin, and Pigott (2012), and it has been found to positively contribute to ESS among low-income jobseekers (Hong, Hodge, & Choi, 2015). EH was found to mediate the relationship between spirituality and ESS. Specifically, the agency factor—psychological empowerment or “goal-directed determination”—mediated the relationship between spirituality and ESS (Hong et al., 2015). Hong, Lewis et al. (2014) examined the effects of EH on ESS among ex-offenders. They found that EH had a positive impact on ESS and played a mediator role in the paths from self-esteem and self-efficacy to ESS. EHS has also been validated among summer youth employment program participants (Hong, Hong, & Choi, 2020).
Research consistently suggests that barriers faced by workforce development program participants negatively affect their success outcomes (Danziger, Kalil, & Anderson, 2000; Dworsky & Courtney, 2007; Holzer, Stoll, & Wissoker, 2004; Hong et al., 2014). Hong, Polanin, Key, and Choi (2014) validated the Perceived Employment Barrier Scale (PEBS) and found it to negatively contribute to EH. Some studies were able to identify the barriers that workforce development program participants face and how these barriers directly impact ESS. Participants’ health barriers were found to negatively impact their income (Danziger et al., 2000). Their family-related barriers, such as domestic violence and child care, were correlated with lower earnings (Dworsky & Courtney, 2007; Holzer et al., 2004; Taylor & Barusch, 2004). Nam (2005) found that human capital barriers (e.g., education and work-related experience) were predictors of participants’ success. In particular, the working poor were having more personal and structural barriers than the nonworking poor (Hong & Wernet, 2007). The working poor with less education, less job training experience, and more health problems, greatly increased their chances of becoming the working poor.
Other demographic variables are found to affect ESS. A number of researchers examined the effects of low-income jobseekers’ demographic and personal characteristic variables on ESS (Cancian, Meyer, & Wu, 2005; Caputo, 1997; Cheng, 2007; Danziger et al., 2000; Dworsky & Courtney, 2007; Hall et al., 2010; Henly, Danziger, & Offer, 2005; Hong & Pandey, 2008; Kim, 2000; Kramer, Myhra, Zuiker, & Bauer, 2015; Lee, Slack, & Lewis, 2004; Lehrer et al., 2002; Nam, 2005; Seefeldt & Orzol, 2005; Wu, 2011).
Age is an important variable that affects labor market outcomes. Older participants were less likely to be employed (Lee et al., 2004). Cheng (2007) also found from the sample of welfare mothers that younger mothers were more likely to become employed. On the other hand, Hong and Wernet (2007) found that program participants who are young, non-White, male, unmarried, and with children have higher odds of becoming the working poor. Caputo (1997) and Danziger et al. (2000) further found that there was no significant relationship between age and ESS—escaping poverty, employed status, and becoming self-sufficient.
Race has been explored with regard to its impact on ESS. In workforce development programs, race had to be controlled because of its significant correlation with economic outcomes. Cancian et al. (2005) studied females who participated in Temporary Assistance for Needy Families (TANF) and found African American women tended to receive cash benefits more than their White counterparts. Racial minority—primarily African Americans—tended to stay on TANF longer (Seefeldt & Orzol, 2005). However, Wu (2011) did not find the relationship between race and economic success among low-income mothers with children.
Marital status and gender have also been studied as potential barriers to economic success. Kramer et al. (2015) studied gender disparity between single parents across income and poverty. Single mothers generally had less income than single fathers, controlling other variables—human capital and other demographic variables. In addition, they found that single mothers are generally and economically more disadvantaged than single fathers (Kramer et al., 2015). Kim (2000)’s study supported that mothers on welfare have less probability of employment than fathers. However, Hong and Wernet (2007) found that females are less likely to become the working poor than their male counterparts, controlling for human capital, employment barriers, and labor market variables. Dworsky and Courtney (2007) did not find any gender differences in employment among the welfare recipients.
Marital status alone can impact ESS. Seefeldt and Orzol (2005) found being married or living with a partner was related to decreased stay on welfare. Hong and Pandey (2008) found that being married was positively related to living above poverty status. Caputo (1997) also found the relationship between married respondents and escaping poverty in the national representative data set. However, Henly et al. (2005) found that cohabiting couples were negatively associated with monthly earnings and poverty status. Some researchers found that no statistically significant relationship existed between welfare recipients’ marital status and economic-related outcomes (Danziger et al., 2000; Kim, 2000; Nam, 2005).
Having another household earner(s) lowered the possibility of becoming the working poor (Hong & Wernet, 2007). Hong and Pandey (2008) found that having an extra earner(s) made it more likely that they would live above the poverty line.
As one of the human capital variables, the level of education is an important predictor of employment among the welfare recipients. Dworsky and Courtney (2007) found that welfare recipients with lower levels of education had lower odds of becoming employed. Kim (2000) found that welfare recipients who have more than 13 years of education were more likely to be employed. The level of less than high school education was related to stay in the welfare system longer (Seefeldt & Orzol, 2005). Wu (2011) also found that low-income mothers with more than high school education had more long-term success than those with less than high school education. Caputo (1997) found that the length in years of receiving welfare benefits had a negative impact on becoming self-sufficient.
The gap in the literature is that limited studies focus on the process-related change that participants experience during their engagement with workforce development programs. Since most research investigated participants’ status at a specific time using a cross-sectional data set, the investigation of the effect of PSS on ESS using a longitudinal data set provides a rigorous approach to further testing this hypothesized relationship. Hence, the present study uses a lagged multivariate regression model to test the hypothesis that increase in PSS positively affects ESS, controlling for other demographic and economic-related variables.
Methods
Data Collection and Sample
The data were derived from a convenience sample of 350 low-income jobseekers participating in the HPOG programs in two major metropolitan areas in the United States from which surveys were collected—Site A (196 cases) and Site B (154 cases). Data were collected between September 2013 and January 2014. The participants were asked to participate in the agency survey during the initial orientation, at two-midpoints (in the mid of the program and when participants exit the program) and a follow-up survey at 6 month post exit. To answer the research question, we selected only those participants who answered surveys T1 and T2—the orientation and first two midpoints. Each participant’s ID number was used to merge data.
This study was approved by the ethics committee or institutional review board (IRB) of the lead author’s institution. Each survey was administered by agency staff in collaboration with the research team. The survey participants were notified at the beginning of survey that the data obtained would remain anonymous and their personal information would be kept confidential. The participants voluntarily completed the self-report surveys. The completed surveys were deidentified and kept in a locked cabinet until the data were entered into SPSS for analysis. After the data cleaning process, all surveys were discarded.
The 350 participants were between the age of 17 and 59 years (M = 31.06, SD = 9.72). Half of the respondents were African American (50.9%) and most were female (93.1%). About a quarter of the respondents were high school graduates or obtained General Educational Diploma (GED; 24.6%), approximately a third had some college education but no degrees (35.8%), and the rest had college education (39.6%). Approximately two-thirds of the sample were never married (64.2%). The participants had an average program participation of 145.62 days (SD = 115.77). Almost two-thirds were employed while participating in the program (65.9%). This sample had an average of 1.31 (SD = 0.76) income earners, excluding the participant, in the participant’s family. Table 1 displays the demographic and background characteristics of the participants.
Description of demographic variables of participants in the HPOG program (N = 350)
| % | N | Mean | SD | |
|---|---|---|---|---|
| Age | 31.06 | 9.72 | ||
| Race | ||||
| African American | 50.9 | 175 | ||
| Others (Alaska Native, White, Hispanic, Multi-racial, and others) | 49.1 | 169 | ||
| Gender | ||||
| Male | 6.9 | 24 | ||
| Female | 93.1 | 323 | ||
| Education level | ||||
| High-school | 24.6 | 85 | ||
| Some college but no degree | 35.8 | 124 | ||
| Above | 39.6 | 137 | ||
| Marital status | ||||
| Married (spouse present) | 19.1 | 66 | ||
| Spouse absent (spouse absent, divorced, separated, or widowed) | 16.8 | 58 | ||
| Never married | 64.2 | 222 | ||
| Days of participation | 145.62 | 115.77 | ||
| Employed | ||||
| Yes | 65.9 | 222 | ||
| No | 34.1 | 115 | ||
| Number of earners in household | 1.31 | 0.76 |
Measures
Based on focus group findings of low-income jobseekers on the barriers they face, Hong et al. (2014) developed the PEBS that comprises five factors—physical and mental health, labor market exclusion, child care, human capital, and soft skills. The focus group also informed the development of the EHS that included four factors—psychological empowerment, futuristic self-motivation, utilization of skills and resources, and goal-orientation (Hong, Choi et al., 2014).
PSS was measured by using PEBS (α = 0.942, M = 1.88, SD = 0.88, Hong et al., 2014) and EHS (α = 0.914, M = 9.18, SD = 1.14, Hong, Polanin et al., 2012). Hong et al. (2014) revalidated the EHS using a multi-sample confirmatory factor analysis (CFA). The 14 total items constitute a self-reporting Likert scale ranging from 0 (strongly disagree, not hopeful) to 10 (strongly agree, very hopeful). We used the mean of the 14 items, a higher score indicating higher EH. Hong et al. (2014) validated the PEBS using a CFA. The total 20 items constitute a self-reporting Likert scale ranging from 1 (not a barrier) to 5 (strong barrier). In this study, we used the mean of the 20 items, a higher mean value on the scale indicating more PEBs.
As there are no studies conducted yet on how to measure PSS, we made the PSS variables based on the difference of scores between EH and PEBS. This value was calculated by subtracting PEBS from EH. Finally, considering the purpose of this study was to investigate the improved rates of PSS, we calculated improved PSS values by subtracting PSS at T1 from PSS at T2 (M = 0.3718, SD = 1.42).
The dependent variable of this study, ESS (↑ = 0.922, M = 2.83, SD = 0.97) was measured by using the Women’s Empowerment Network (WEN; Gowdy & Pearlmutter, 1993). This 15-item measure is a self-reporting scale ranging from 1 (not at all) to 5 (all of the time). In this study, we used the mean of the scale at T2. A higher score on the scale indicates increased ESS.
Lastly, several demographic factors at T1, such as age, race, gender, education, marital status, days of participation in the program, employment status, and the number of earners in their family, were included in this study. Age, days of participation in the program, and earners in their family were used as continuous variables. Gender was a dichotomous variable. Race was regrouped into two dichotomous groups (African-American = 1 or others = 0). Education level was divided into three groups (high school = 0, some college but no degree = 1, college and above = 2) and the “graduated high school or GED” group was used as a reference group. Marital status was divided into three groups (married = 0, spouse absent = 1, and never married = 2) using the married group as the reference group. We converted these variables into dummy variables using the reference groups, the high school or GED, and the married groups, and assigned them as control variables.
Findings
The model was tested by a multiple regression using SPSS 18.0. The goal of this study was to find out whether the change of PSS, which consists of EH and PEB from T1 to T2, impacts SS at T2. We used demographic variables as control variables along with the EH and PEB at T1 only.
According to the analysis, all the independent variables explained the 18.6% of the variance of SS. The change in PSS is positively related with SS (↓ = 0.13, ⇧ < 0.01). The improved rate of PSS is related to increasing SS. EH at T1 positively (↓ = 0.19, ⇧ < 0.01) and PEB at T1 negatively (↓ = –0.26, ⇧ < 0.001) impacted SS. In sum, as important components of PSS, EH and PEB were also important variables that impacted SS.
Among the demographic variables, never married status (↓ = –0.43, ⇧ < 0.01), a number of earners (↓ = 0.25, ⇧ < 0.01), college but no degree (↓ = –0.35, ⇧ < 0.01), and days of participation (↓ = 0.00, ⇧ < 0.05) were found to have a statistically significant impact on the participants’ SS. Program participants who were married with high school level of education had more chance of being SS. Having additional earners and individuals who experienced the program longer had a higher possibility of reaching SS.
Discussion and Conclusion
This study sought to examine the relationship between the rate of change in PSS and ESS among the low-income jobseekers after controlling for other socioeconomic variables. As expected, the improved rate of PSS positively impacts ESS. Therefore, we conclude that PSS is a condition that positively contributes to achieving ESS. Given this finding, PSS as a human-centered developmental process would play an important role in evaluating the effectiveness of workforce development programs.
Multiple regression results
| Unstandardized coefficients | Standardized coefficients | t | ||
|---|---|---|---|---|
| B | Std. error | |||
| (Constant) | 1.680 | 0.644 | 2.606 | |
| Age | −0.007 | 0.007 | −0.075 | −1.122 |
| Race (Ref.: African American) | −0.197 | 0.108 | −0.103 | −1.834 |
| Gender | 0.094 | 0.230 | 0.022 | 0.409 |
| Marital status (Ref.: Married) | ||||
| Spouse absent | −0.042 | 0.172 | −0.017 | −0.244 |
| Never married | −0.429 | 0.148 | −0.216 | −2.886** |
| Additional earner | 0.252 | 0.075 | 0.199 | 3.342** |
| Employed | 0.099 | 0.116 | 0.049 | 0.848 |
| Educational level | ||||
| Some college | −0.348 | 0.133 | −0.174 | −2.618** |
| Above college | −0.258 | 0.134 | −0.132 | −1.918 |
| Days of participation | 0.001 | 0.000 | 0.135 | 2.527* |
| PSS t2-t1 | 0.127 | 0.046 | 0.193 | 2.728** |
| EHS t1 | 0.188 | 0.056 | 0.233 | 3.376** |
| PEBS t1 | −0.255 | 0.064 | −0.224 | −4.003*** |
| Model fit |
N = 303 / F = 6.303*** R2 = 0.221 / Adj. R2 = 0.186 |
|||
* p < 0.05;
** p < 0.01;
*** p < 0.001.
Note: Dependent variable: Economic self-sufficiency (ESS) at t2.
Each component of PSS variables at T1 affected ESS at T2. EH was found to be positively associated with ESS at T2 and PEBs negatively with ESS at T2. These findings were consistent with previous research (Danziger et al., 2000; Dworsky & Courtney, 2007; Hong et al., 2014, 2015; Nam, 2005).
The control variables—marital status, additional earners in household, high school level of education, and days of participation—were found to affect ESS. Within marital status, the married individuals having a higher possibility of reaching ESS was supported by previous research findings (Caputo, 1997; Hong & Pandey, 2008). Having an additional earner in the household which was positively related to achieving ESS also corroborated previous study results (Hong & Pandey, 2008; Hong & Wernet, 2007). Having a high school level of education also showed to have increased the level of ESS, which was not related to previous research studies. Most of the studies found that a higher level of education led to higher ESS (Dworsky & Courtney, 2007; Kim, 2000; Seefeldt & Orzol, 2005; Wu, 2011).
Clearly, the study findings contributed to our understanding, especially in terms of the positive effects of PSS in workforce development programs. Nevertheless, the results should be interpreted in light of several limitations. This study bears the limitations of generalizability, research design, and subjective measure of the dependent variable. First, since this study used geographically limited convenience sampling—two Midwestern metropolitan areas—findings would not be generalizable to all low-income jobseekers in other HPOG programs or other job training programs. Second, a more structured longitudinal research design is needed to strengthen the results. To evaluate the improved rate of PSS, we selected only T1 and T2, ruling out the missing data set. Further study is thus required in order to examine a more precise rate of change in PSS, including more time points and carefully considering the missing cases. Lastly, considering that the dependent variable in this study, ESS, is a self-reported variable that asks how individuals assess the degree to which they agree or disagree on items that relate to ESS, our research findings could be further supported by incorporating other objective outcomes (i.e., employment status and income).
In spite of aforementioned limitations, the current study certainly contributes to the existing literature in a variety of ways and provides practical implications for the policymakers and service providers. Since the importance of PSS has been supported on the pathways toward achieving ESS, it is suggested that policymakers consider PSS when they evaluate workforce development programs, i.e., policymakers could place more value on the transformative process during program participation as a precursor to reaching their economic outcomes. For service providers, practical programs must be developed to increase participants’ PSS. The findings warrant our attention to psychological readiness by increasing hope and decreasing barriers in workforce development programs. Thus, these findings highlight the importance for policymakers and service providers to invest more time and resources in the “process” element of SS.
Workforce development is commonly characterized as a set of training and employment activities. This shorthand definition is premised on the intuitive economic logic that suggests that skills acquisition is directly correlated with increases in employment opportunities within the labor market or other financial gains for the individual jobseeker. Furthermore, as the workforce development field has evolved in the last several years, there is increased discussion about the need for jobseekers to obtain credentials, or stackable credentials (starting with basic skills to more advanced technical certification, or post-secondary education attainment), in order for these persons to be more economically competitive. These two premises suggest a corollary: a greater or higher level of skills leads to an increased level of employment opportunities. Thus, positive outcomes, or pejoratively the success of the public workforce system, are measured by the increased job prospects of the jobseekers.
Taken with a broader perspective, workforce development is actually “a set of processes that govern the identification, recruitment, assessment, and training of jobseekers into employment as well as the maintenance and advancement of these persons in their careers that enhance self-sufficiency and revitalize the communities in which there individuals live” (Holland, 2015, p. 55). These processes, as Holland (2015, p. 61) further notes, fall into three continuous phases:
Phase I—the identification, recruitment, and assessment of jobseekers for potential training and employment opportunities.
Phase II—the training of jobseekers into occupations.
Phase II—the placement of these jobseekers into positions following training.
Earlier criticisms of the public workforce system (Lafer, 2002; LaLonde, 1995) on the supply side approach note the “drive to meet numbers” where jobseekers are referred on for job postings by case managers who have paid little or no attention to the job description and sent on candidates without screening for the “fit” more carefully. Moreover, the assessment process is not well understood or articulated in a convincing way to bolster the confidence of employers to use the public workforce system as a recruitment pipeline, as there is conflation of employment with the negative perceptions and misunderstood purposes of social policy (Bloom et al., 1997; Giloth, 2000; Osterman, 2007).
In applying the structuration theory, the jobseekers must be understood as the principal inputs of the workforce policies. As such, the point of entry into One-Stop Centers must articulate the need and criteria for assessment to determine readiness for work, and case management is necessary to move jobseekers forward to become closer to enter, maintain, and progress in the labor market. This case management approach system is not and cannot be exclusive of identifying and addressing cognitive skill needs and deficiencies. Yet, a work-first directive to the workforce system belies the fact that some jobseekers face barriers to employment. It is the barriers that lead skeptics of the workforce system to label some persons, often long-term unemployed and more often drawn from traditionally underrepresented populations, as unemployable. Stated differently, these barriers are not solely proximate to job readiness, in terms of not knowing how to identify job opportunities and successfully navigate through a recruitment process. Rather, there are indirect barriers to employment faced by jobseekers which are external to the workplace or to one’s performance in a given set of job duties. Effective and comprehensive assessment can identify three categories of indirect barriers:
Economic impediments
Gaps in comprehensive supportive services
Lack of PSS
Holland (2016), in his analysis of WIOA, notes, “the public workforce system’s integration of a demand-side approach suggests an idealization that the educational system is producing graduates (the labor supply) who are able to take on the multitude of occupations that the labor market generates” (p. 434). In part, the shift toward demand-driven workforce development strategy is the natural consequence of adopting an evolutionary set of ALMPs. The implementation of ALMPs brings three key components to forefront of the employment debate in the United States:
the delivery of public employment services (to include, but not be limited to, disseminating information about job vacancies and provide writing assistance),
investment of public resources to train workers—regardless of employment status—through classes and/or apprenticeships, and
introduction of employment subsidies, such as unemployment insurance or individual training accounts, to provide cash and/or in-kind support to facilitate access or progress in employment (Auer, Efendioglu, & Leschke, 2008; Martin, 2000).
The links between the labor demand and supply may seem broken when focusing mainly on “training and employment” outcomes and not the process toward being job ready (Hong, 2013). PSS as the process-oriented concept can serve as the new humanistic link to realize a holistic definition of workforce development on the supply side and human resource development on the demand side. The public workforce training programs on the supply side focus on the outcome of job placements and retention, and the employers invest in screening and talent acquisition, reduction in the turnover rates, and increase in the motivation level of their human capital. The common thread between the two parties that can serve as the win-win solution to fasten the broken links is PSS as the human core at the most intrinsic level—as jobseekers moving toward the goal of obtaining a job on the supply side and as workers pursuing career advancement aspiration on the demand side.
PSS can be the innovative model toward building that linkage back that ties labor supply and demand while recognizing the virtues of integrating “vertical” workforce development activities that jobseekers need to undertake to become employable and “horizontal” workforce development coordination among government, employer, and educational stakeholders (Holland, 2018; 2019). PSS can provide the organizing link for a new demand side system to emerge from, connect with, adapt to the knowledgeable and creative human agents—e.g., the vulnerable jobseekers and low-skilled workers. As a politicization strategy, PSS can represent a bottom-up resistance including “strikes against employment casualization, living-wage campaigns, organizing” and community-labor coalition efforts to reconstruct regulatory responsibilities—e.g., legislative action and labor law enforcement (Peck & Theodore, 2012, p. 758). As a market strategy, PSS can fit the needs of the demand side that search for the most capable of bringing the greatest productivity to the firm. Instead of being subject to the inactive demand side and remaining in a powerless position as victims to the structure, empowered jobseekers with high PSS can pull the employers into a competition system to incentivize not only hiring but also investing in the improvement of the working condition and environment.
Funding
This work was supported by the U.S. Department of Health and Human Services/Administration for Children and Families under Grant [90HG1003]; Korea Foundation; and Lloyd A. Fry Foundation.
Conflict of Interest
Authors declare that they have no potential conflict of interest that could have a direct influence or impart bias on the work and that they maintain the integrity and transparency in data analysis and research process.
Ethical Approval
Ethical approval of the human subjects study was obtained from Loyola University Chicago’s Institutional Review Board (Project #1460).
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Philip Young P. Hong, Ph.D., is a Dean and Professor, School of Social Work, University of Georgia, 279 Williams Street, Athens, GA 30602, USA. He can be contacted at pyphong@uga.edu.
Brian Holland, MS, MPA, BLH Technologies & School of Public Affairs, Department of Public Administration and Policy, American University, Washington, DC, USA. He can be contacted at bholland@american.edu.
Jang Ho Park, Ph.D., School of Social Work, Loyola University Chicago, Chicago, IL 60611, USA. He can be contacted at jpark38@luc.edu.
Caleb K. Kim, Ph.D., School of Social Work, Loyola University Chicago, Chicago, IL 60611, USA. He can be contacted at ckim4@luc.edu.