“Human oppression and social development serve antithetical cross-purposes. The unfortunate outcome is global ill fare and continuing dehumanization. Social development seeks to enhance human freedom through social reconstruction which is thwarted by the forces of oppression.” (Mohan & Sharma, 1985, p. 12)
While social development, as we know it, should ideally promote human freedom and justice, it has been associated with “the White Man’s Burden” in that the West’s economic policies for the world’s poor have been ineffective in mitigating global poverty (Easterly, 2006; Easterly & Chamberlain, 2017). Mohan (2007a, p. 80) argues that social developmentalism is fraught with fallacies of growth by ignoring “symbiotic hybridity of human and social development processes.” Its outdated constructs, as Mohan (2007b, p. 69) contends, “sustain and perpetuate oppressive structures of social exclusion.” Cultural, ideological, and institutional moral dissonance that Mohan (2011) calls out as the poverty of culture (PoC) sustains toxic politics and impedes social development. The failure of politico-ideological systems of belief that prioritized economy and economic systems to shape the values of human behavior and wellbeing requires a social transformation by restructuring the civil society toward global democracy (Mohan, 2020).
Social development in the United States falls short of its ideals by the structural dependence of the political system on the economic system and limiting the problem definition of poverty to remain individualistic (Hong, 2013a). In the wake of welfare reform, poverty was politically argued in a sequential causal function of the weak-minded individuals’ employment barriers leading to welfare dependency, subsequently affecting the culture of intergenerational and long spells of poverty (Hong & Crawley, 2014). The policy solutions conveniently targeted welfare dependency that was seen to reside in the public domain and ignored addressing employment barriers by moving this causal link into the private domain. The source of the problem is not “welfare dependency” of the most vulnerable citizens but “labor market dependency” of the welfare state system and the “demand-side dependency” of the workforce development and labor policy system (Hong, 2013a).
However, the continued doubling down on the government commitment to welfare-to-work as the solution to poverty has found evidence of the US poverty as a form of social exclusion (Hong & Pandey, 2008). The neoliberal policy priority of self-sufficiency becomes ever more pronounced as the policy targets reducing welfare dependency to exacerbate poverty (Hong & Crawley, 2014). Overlooking employment barriers is exacerbated by the
performance-based contracting relationships for social service organizations and their “funding dependency” and employer-centered job development and placement practices that often lead to creaming for the best candidates and their “labor market dependency.” (Hong, Song, Choi, & Park, 2018, p. 35)
Employment barriers are often assessed by the caseworkers for triaging referrals, services, and job training by grouping clients into “harder-to-serve” or “employable” categories—with the latter group being favored to receive employment services for desired success outcomes to report to funders.
In this regard, this paper examines employment barriers from a bottom-up perspective with the purpose of contributing to advancing human–social development in the United States (Mohan, 2011). The bottom-up approach does not condone the “blame the victim” rhetoric prevalent in the individualistic problem definition of poverty with employment barriers being kept in the private domain. In fact, anchoring on barriers—both structural and individual—as perceived by individuals by low-income jobseekers puts the responsibility back on society to restructure the foundation of development to be human-centered for an upward transformative system-change movement (Hong, 2016; Mohan, 2020). Perceived employment barriers is the core construct in the psychological self-sufficiency (PSS) theory that espouses switching from barriers to goal-directed hope as an individualized empowerment-based process leading to success outcomes (Hong, 2013b). Organizing around this human development process as a foundation, social development becomes not a tool for the paternalistic growth-centered government initiative but a process that truly enhances human freedom and justice (Mohan, 2020).
Literature Review
Low-income jobseekers in the United States often face a myriad of external and internal employment barriers that challenge the goal of achieving economic self-sufficiency (ESS) (Hong & Wernet, 2007). Research has identified both structural contexts and behavioral attributes of barriers that can be categorized into the following five core perpetuating domains of barriers: Health and mental health, human capital, child care, labor market exclusion, and personal balance or soft skills (Hong, Polanin, Key, & Choi, 2014; Hong, Stokar, & Choi, 2016; Hong et al., 2018). While Hong et al. (2014) have divided employment barriers into two dimensions—structural and individual barriers—with labor market exclusion and human capital barriers regarded as structural barriers, and health and mental health, child care, and personal balance, or soft skill barriers regarded as individual barriers—Hong et al. (2016) categorized child care under structural barriers.
Structural Barriers
Structural barriers refer to the condition that no matter how good the individual’s qualifications may be, elements within the social and economic structures make it difficult for the individual to obtain employment. These elements include secondary labor market; racial discrimination; immigrant status; gender discrimination; lack of jobs; transportation; neighborhood/location; and general structural factors.
Labor market exclusion barriers
Dual labor market theory posits that the labor market is segmented into primary and secondary submarkets (Reich, Gordon, & Edwards, 1973). The former consists of jobs that provide higher wages, stability, promotion opportunities, and greater returns on education and work experiences (Doeringer & Piore, 1971; Rebitzer & Robinson, 1991). The exclusion of capable workers to the secondary sector—defined by characteristics that are opposite to those of the primary sector—takes place in the labor market by residence, inadequate skills, poor work histories, and discrimination (Doeringer & Piore, 1971). The experience of being confined to “bad” jobs can reinforce disadvantaged positions for low-wage workers to create a “vicious circle” or “self-fulfilling prophecy” (Cain, 1976, p. 1223).
Hacker (2006) suggests that there has been a significant growth of precarious work in recent years, with the shift in risk from employers to employees. Precarious work is defined as “employment that is uncertain, unpredictable, and risky from the point of view of the worker” (Kalleberg, 2009, p. 2). Standing (2014, p. 10) refers to this group of excluded workers as “precariat” characterized by “flexible labor contracts; temporary jobs; labor as casuals, part timers, or intermittently for labor brokers or employment agencies” with “no occupational identity” and high dependency on money wages without non-wage benefits. Hong and Wernet (2007) focused on the structural context of working poverty and found that employment barriers and labor market positions significantly contributed to the effects of human capital and demographic variables.
The family wage stressor offers an interesting perspective because employment is frequently touted as a primary eliminator of welfare dependency. However, it has to be a job at a livable wage within a labor market sector that allows for stable employment. As Crew and Eyerman (2001) and Reese (2007) demonstrate from their research that, quite simply, welfare-dependent families’ earnings are very low and many remain in poverty when they leave welfare. Former welfare recipients face “unstable employment conditions, coupled with persistent employment barriers, and shortages in child care and other supportive services,” which often lead them back to welfare (Reese, 2007, p. 57).
Coulton (2003) argues that place-based disparities in opportunity structures and social and institutional resources affect labor market success, especially for a large number of welfare recipients who live in urban areas. Cities and neighborhoods can either help or hinder achievement of employment goals for welfare recipients. Recipients who attempt to move from welfare to work can be trapped in urban labor markets that are limited by locale. Community- or location-based measures are generally called into the welfare dependency context when considering the underclass (Mincy, Sawhill, & Wolf, 1990; Niskanen, 1996). Location-based measures include factors such as chronic incidences of poverty, welfare dependency, single parenthood, high dropout rates, and male joblessness. Such geographical areas are disproportionately occupied by minorities such as African Americans and Hispanics/Latinos (Mincy et al., 1990).
Lack of employment is related to “disappearance of work” and “racist oppression” in inner cities and they lead to manifest behaviors emerging from blocked opportunities that embody structural and cultural constraints for residents (Wilson, 2011, p. xix).
As more people become employed, crime, including violent crime, and drug use will subside; families will be strengthened and welfare receipt will decline significantly; ghetto-related culture and behavior, no longer sustained and nourished by persistent joblessness, will gradually fade. (Wilson, 1996, p. 238)
Child care barriers
Child care is discussed by several authors in terms of structural employment barriers that cannot be discussed in isolation of the labor market structure. For example, Dutton, Warhurst, Nickson, and Lockyer (2005) discussed a program in Scotland, whereby single parents were offered employment opportunities but still faced significant barriers because of child care difficulty. Siegel and Abbott (2007) and Siegel, Green, Abbott, and Mogul (2007) compared persons on welfare and those who have left welfare, and found that a range of factors were barriers to employment, including gender, race, single-parent status, and neighborhood environment. These authors emphasized that child wellbeing is correlated with adequacy of child care, and the unavailability of child care is an important factor in the return of some persons to welfare.
Human capital barriers
Structural employment barriers include human capital barriers such as “limited education, fewer marketable skills, and inadequate training opportunities” that are connected to precarious positions to embody the most desired work culture in the global market with institutional and employer misunderstandings (Vick & Lightman, 2010, p. 75). Hong and Pandey (2007, p. 19) argue that the human capital theory has often been interpreted in a limited fashion that “failure of individuals to invest in certain personal qualities (i.e. higher education) results in poor economic outcomes later in life.” Decades of acceptance of this interpretation of human capital theory essentially leads one to the same or similar responses found in emphasizing changing the individual as a primary policy response—that is, improving the training and educational levels of the person.
Rank’s (2004) structural vulnerability thesis posits that poverty “explained by the structural vulnerability of individuals, whose human capital and labor market attributes of poverty are structurally conditioned by their vulnerable positions in the economic system” (Hong & Pandey, 2007, p. 19). Human capital is more of a structural barrier than an individualistic phenomenon, as education, health, and training are work-enhancing ingredients invested in for individuals but conditioned by one’s socioeconomic positions. Using the theory of (job) screening, the theory of employer search, discrimination, and dual labor market theory, human capital was found to co-occur with a structurally vulnerable attribute—underemployment—to significantly increase the likelihood of one being in poverty (Hong & Pandey, 2007). Such intermix requires a more sophisticated policy responses with structural reinterpretations of poverty (Hong & Wernet, 2007).
Individual Barriers
Commonly referred to as personal or attitudinal barriers, individual barriers include personal characteristics that make it difficult for jobseekers to obtain employment regardless of how favorable the social structure may be. These elements include physical health; mental health; substance abuse/dependency; lack of child care; lack of motivation; lack of social network; and general individual risk factors. Also referred to as personal or attitudinal barriers, Vick and Lightman (2010, p. 75) included precarious identity and emotional struggles such as “feelings of inadequacy, shame, incompetence, poor self-worth, and rejection.”
Physical and mental health barriers
Research focusing on people with chronic illness and employment suggests that physical health is one of the primary barriers to employment. For instance, Dyer, Twllman, and Sequeira (2006) conducted a focus group study among participants who were suffering from AIDS and classified two clusters of barriers—one, having a strong motivation to work but suffering from a debilitating illness; and two, having a relatively functional body system but a strong reluctance to work because of low self-efficacy. Romero, Chavkin, Wise, and Smith (2003) studied 504 low-income mothers who suffered from chronic illness and revealed that women who experienced domestic violence suffered from debilitating health, which, in turn, lead to more barriers to employment compared to women who had poor health but had never been involved in abusive relationships.
Mental health status and substance abuse receive significant attention in the studies of barriers to employment. Both of these factors were listed under the top ten employment barriers among welfare recipients (Theriault, 2002). Studies comparing female recipients and nonrecipients of Temporary Assistance for Needy Families (TANF) indicated that TANF recipients who suffered from severe mental illness had more difficulties getting jobs compared to nonrecipients (Stromwall, 2001, 2002). There is also significant prevalence of substance abuse that serves as barriers to employment among welfare recipients (Schoppelrey, Martinez, & Jang, 2005). Often mental health and substance abuse factors interact with each other. Braitman et al. (1995), for example, compared the barriers to employment between unemployed and employed patients of a public community mental health services. The perceived barriers among unemployed patients included lack of desire to work, negative side effects from medication, poor physical health, and substance abuse.
Soft skills or personal balance / capacity barriers
In a society that values market place and labor market as the means by which to meet one’s own and one’s dependent needs, the individualistic view permits that psychological, attitudinal, behavioral, and cultural elements of a person’s life as the sole or primary determinants of life’s outcome (Hong, 2013a, 2013b). The culture of poverty cite helplessness, hopelessness, impulsiveness, apathy, low self-esteem, limited coping skills, indifference, being poorly motivated, “present-mindedness,” and the intergenerational transmission of values and attitudes as things that lead to (welfare) dependency (Mincy et al., 1990). Proponents of the cultural view of poverty, not infrequently link their cultural explanations, which contain psychological, attitudinal as well as behavioral components, to the receipt of welfare and to not being able to hold a stable employment.
Method
This study investigates the relationship between perceived employment barriers, employment status, and ESS (Sample 1). We examine the difference in ways that structural and individual barriers affect employment and ESS outcomes. Using a supplemental larger sample (Sample 2), the study examines the types of groupings of perceived barriers in ways they are distributed across the five domains and by structural and individual barriers.
Sample and Data Collection
Sample 1. The study participants were 388 low-income jobseekers (146 males, 242 females) from a social service agency in Chicago, IL, USA. Surveys were collected from adult clients who were receiving services in job readiness, financial literacy, life skills, public benefits, and other family-support services. As reported in Table 1, the 388 respondents were on average aged 40.5 years (SD = 13.7), and females were more (62.4%) than males. The vast majority of participants were African Americans (97.9%), with White/Caucasians (0.3%) and other races (1.9%) accounting for much smaller proportions. About a quarter of respondents had not finished high school (24.9%). Slightly less than half had completed high school or General Educational Development (GED) test (44.3%) and had received job training in the past 10 years (41.7%). A large portion of sample was not employed (79.7%) and earned less than $5,000 for the previous year (57.7%).
| Frequency | Percentage | |
|---|---|---|
| Gender | ||
| Male | 146 | 37.6 |
| Female | 242 | 62.4 |
| Race | ||
| African American | 377 | 97.9 |
| Other races | 8 | 2.1 |
| Age group (years) | Mean (SD) | 40.5 (13.7) |
| 18–29 | 105 | 27.2 |
| 30–39 | 66 | 17.1 |
| 40–49 | 106 | 27.5 |
| 50–59 | 85 | 22.0 |
| over 60 | 24 | 6.2 |
| Education years | Mean (SD) | 10.12 (3.74) |
| Less than 12 years | 175 | 52.2 |
| 12 years or more | 160 | 47.8 |
| Employment status | ||
| Employed | 76 | 20.3 |
| Unemployed | 298 | 79.7 |
| Job training experience | ||
| Yes | 160 | 41.7 |
| No | 224 | 58.3 |
| Residence Type | ||
| Rental | 232 | 63.4 |
| Own home/condo | 11 | 3.0 |
| No home | 40 | 10.9 |
| Assisted housing | 58 | 15.8 |
| Other | 25 | 6.8 |
| Household income ($) | Mean (SD) | 13,332.1 (63,830.6) |
| None–999 | 91 | 46.9 |
| 1,000–4,999 | 21 | 10.8 |
| 5,000–9,999 | 26 | 13.4 |
| 10,000–29,000 | 35 | 18.1 |
| Above 30,000 | 21 | 10.8 |
Sample 2. The second sample of 835 low-income jobseekers is a combined sample of participants of three community-based workforce development programs from three cities—Chicago, IL; Chicago Heights, IL; and Kenosha, WI. Respondents comprised more of females (73.5%), and the majority of participants were African Americans (66.86%) followed by White/Caucasians (16.36%) and other races (17.79%). About 29.73% had lower than a high school degree and 60.25% were employed.
Measures
A recently validated Perceived Employment Barrier Scale (PEBS; Hong et al., 2014) was used to measure the employment barriers. The measure had a high internal consistency and high validity scores across samples. This 20-item scale has the following five factors: (1) physical and mental health; (2) labor market exclusion; (3) child care; (4) human capital; and (5) personal balance or soft skills. PEBS is a Likert-type scale ranging from 1 to 5, 1 indicating “not a barrier” and 5 indicating “strong barrier.” Each item reflects respondents’ perception on the degree to which it is an employment barrier—that is, lack of adequate job skills.
We used two different scales to measure ESS. One is a self-assessed scale, and another is an objective indicator. To measure self-assessed ESS, the WEN ESS Scale was used (Gowdy & Pearlmutter, 1993). This continuous measure includes 15 questions that load under the following four factors: (1) autonomy and self-determination; (2) financial security and responsibility; (3) family and self-wellbeing; and (4) basic assets for community living. Each question reflects respondents’ assessment of how their financial situation in the past 3 months allowed them to do certain things that represent ESS—that is, pay myself in own way without borrowing from family or friends. Respondents rated each statement on a Likert-type scale ranging from 1 to 5, 1 indicating “not at all” and 5 indicating “all the time.”
The objective ESS measure included the following three indicators: (1) the employment status, (2) ability to pay all bills with paid income, and (3) welfare receipt status. These three variables are surveyed as categorical and coded as 1 for “yes” and 0 for “no.” These variables were summed generating scores from 0 to 3, where 3 indicates “economically self-sufficient” and 0 indicates “not economically self-sufficient.”
Employment status, used as a moderator of multi-group analyses, is a discrete variable classified into the employed and the unemployed.
Analyses
T-test. In order to study difference in employment barriers by employment status, an independent samples t-test was performed.
Structural equation modeling (SEM). The path models from perceived barriers to ESS were investigated using SEM. Following a two-step approach recommended by Anderson and Gerbing (1988), we first conducted a confirmatory factor analysis (CFA) to evaluate the factor structure, followed by SEM analysis to examine hypothesized relationships.
Test of multi-group invariance. A multi-group invariance test was conducted to test difference in the path from perceived barriers to ESS by employment status. The prerequisite for multi-group analyses is to consider a baseline model without constraint. Given the baseline model is equivalent across groups, we can test factorial invariance by comparing the baseline model and the equal factor loading model. Barring factorial invariance, we proceed to test structural invariance. Sets of parameters are put to the test in a logically ordered and an increasingly or decreasingly restrictive fashion, depending on the model and hypotheses to be tested. Because we hypothesize that the paths from perceived barriers to ESS are different across groups, we start to constrain all parameters to be equal, and free one by one in the order of greater regression weight difference between groups. The significant Chi-square difference between the full constraint model (equal factor loadings and equal regression weights) and the equal factor loading model indicates significant parameter difference across groups.
Model evaluation criteria. In CFA and SEM, several goodness-of-fit indices could be used to quantify the degree of correspondence between any particular model and the data. We evaluated models using the comparative fit index (CFI) (Bentler, 1990), the non-normed fit index (NNFI) (Bentler & Bonnett, 1980), and the root mean square error of approximation (RMSEA) (Steiger & Lind, 1980) because they are relatively independent of sample size (Hong, Malik, & Lee, 2003). CFI and NNFI values greater than 0.90 and RMSEA values up to 0.08 indicate acceptable fit (e.g., Kline, 2009).
Latent profile analysis. A latent profile analysis (LPA) was conducted to divide participants into sub-groups according to their responses on the global PEBS measure and by each sub-domain of PEBS. The sub-domains are: (1) physical and mental health barriers; (2) labor market exclusion barriers; (3) child care barriers; (4) human capital barriers; and (5) personal balance or soft skills barriers. Mplus was used to conduct the LPA by which program participants were classified based on their level of PEBS. Among the model fit indices, Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), the Sample-Size-Adjusted BIC (SSA-BIC), and Lo–Mendell–Rubin (LMR) test were considered to best identify model fit.
Results
Descriptive Analyses
Table 2 shows the descriptive statistics and correlations of the study variables. The correlations between PEBS and ESS showed conflicting results. Whereas perceived barriers are generally positively correlated with each other and self-assessed ESS, they are negatively associated with objective ESS. This result shows that people who perceive having more employment barriers would assess themselves more economically self-sufficient, even though they are objectively less economically self-sufficient.
| Variables | Mean (SD) | Range | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
|---|---|---|---|---|---|---|---|---|---|
| Employment barriers | 2.06 (1.00) | 1.00–5.00 | |||||||
| Physical & mental health | 1.81 (1.19) | 1.00–5.00 | 0.818** | ||||||
| Labor market exclusion | 2.67 (1.29) | 1.00–5.00 | 0.748** | 0.523** | |||||
| Child care | 2.14 (1.29) | 1.00–5.00 | 0.764** | 0.577** | 0.518** | ||||
| Human capital | 2.54 (1.20) | 1.00–5.00 | 0.828** | 0.549** | 0.518** | 0.475** | |||
| Soft skills | 2.02 (1.23) | 1.00–5.00 | 0.882** | 0.707** | 0.597** | 0.627** | 0.606** | ||
| Self-assessed ESS | 2.98 (1.00) | 1.00–5.00 | 0.073 | 0.158* | –0.112 | 0.142* | 0.106 | 0.039 | |
| Objective ESS | 1.12 (0.85) | 0.00–3.00 | –0.192** | –0.101 | –0.319** | –0.129* | –0.131* | –0.112 | 0.305** |
* p < 0.05,
** p < 0.01.
Notes: 1. 1 = employment barriers, 2 = physical & mental health, 3 = labor market exclusion (community), 4 = child care, 5 = human capital, 6 = soft skills (personal balance/capacity), 7 = self-assessed ESS.
2. Employment hope measured on a Likert-type scale ranges from 0 (strongly disagree) to 10 (strongly agree). Self-assessed ESS measured on a Likert-type scale ranges from 1 (not at all) to 5 (all the time). Objective ESS rescored ranges from 0 (not self-sufficient at all) to 3 (self-sufficient) by summing three indicators of employment status (employed = 1, unemployed = 0), affordability to pay bills (affordable = 1, not affordable), and welfare receipt (receiving = 0, not receiving = 1).
T-test
The independent samples t-test was performed using SPSS 15.0 in order to investigate the difference of employment barriers between the employed and the unemployed groups. In all dimensions and items, the unemployed group appeared to have greater perceived barriers than the employed. Because of the problem of p-value strongly influenced by sample size, Cohen’s d is considered (Browne, 2010). For Cohen’s d, an effect size of 0.2–0.3 might be a small effect, around 0.5 a medium effect, and 0.8 to infinity a large effect (Cohen, 1988, p. 25). As shaded in Table 3, differences between groups were significant in all dimensions of perceived barriers except child care (EB3), and 13 out of 20 items had Cohen’s d values above 0.4.
Results of t-test on PEBS scores between employed and unemployed
Measurement Model
Confirmatory factor analysis was performed using AMOS 7.0 to assess the measurement model. A maximum likelihood estimation method was used based on a covariance matrix. The measurement model fits are acceptable in the model with the second-order PEBS (x2(p) = 6.357 (0.273), df =5, NNFI = 0.995, CFI = 0.998, RMSEA = 0.026 (0.000–0.079)) and model with the sub-dimensions of PEBS (x2(p) = 600.955 (0.000), df =160, NNFI = 0.875, CFI = 0.960, RMSEA = 0.084 (0.077–0.091)).
Structural Model
Structural equation modeling analyses were conducted to test the hypothesized paths from PEBS to ESS. All fit indices demonstrated an acceptable fit in Model 1 (x2 = 62.309, df= 26, NNFI = 0.963, CFI = 0.979, RMSEA = 0.060 (0.041–0.079)) and Model 3 (x2 = 27.680, df= 9, NNFI = 0.943, CFI = 0.976, RMSEA = 0.073 (0.043–0.105)) with second-order PEBS. Although somewhat less than the recommended cutoff criterion of 0.90 for NNFI, Model 2 (x2 = 704.087, df= 237, NNFI = 0.895, CFI = 0.917, RMSEA = 0.071 (0.065–0.077)) and Model 4 (x2 = 614.364, df= 175, NNFI = 0.876, CFI = 0.906, RMSEA = 0.080 (0.073–0.087)) represented a relatively good fit. The SEM results are presented in Table 4.
| Standardized path coefficients | Model fit | ||||||
|---|---|---|---|---|---|---|---|
| x2(p) | df | NNFI | CFI | RMSEA | |||
| Model 1 | PEBS→ESS1 | 0.124** | 62.309 | 26 | 0.963 | 0.979 | 0.060 (0.041–0.079) |
| Model 2 | EB1→ESS1 | 0.232* | 704.087 | 237 | 0.895 | 0.917 | 0.071 (0.065–0.077) |
| EB2→ESS1 | –0.377** | ||||||
| EB3→ESS1 | 0.272* | ||||||
| EB4→ESS1 | 0.065 | ||||||
| EB5→ESS1 | –0.078 | ||||||
| Model 3 | PEBS→ESS2 | –0.191*** | 27.680 | 9 | 0.943 | 0.976 | .073 (.043–.105) |
| Model 4 | EB1→ESS2 | 0.089 | 614.364 | 175 | 0.876 | 0.906 | 0.080 (0.073–0.087) |
| EB2→ESS2 | –0.506** | ||||||
| EB3→ESS2 | 0.048 | ||||||
| EB4→ESS2 | –0.015 | ||||||
| EB5→ESS2 | 0.149 | ||||||
* p < 0.05,
** p < 0.01,
*** p < 0.001.
Notes: 1. EB1 = physical and mental health, EB2 = labor market exclusion (community), EB3 = child care, EB4 = human capital, EB5 = soft skills (personal balance/capacity), ESS1 = self-assessed ESS, ESS2 = objective ESS.
2. Models are divided according to the type of ESS and the order of PEBS: Model 1 with the second-order PEBS and self-assessed ESS; Model 2 with the sub-dimensions of PEBS and self-assessed ESS; Model 3 with second-order PEBS and objective ESS; Model 4 with sub-dimensions of PEBS and objective ESS.
As noted in Model 1, PEBS appeared to have a positive effect on self-assessed ESS, indicating that higher the level of perceived barriers, greater the self-assessed economic self-sufficiency. According to sub-dimensions, while the labor market exclusion barrier (EB2) has a negative effect on self-assessed ESS, health and mental health (EB1) and child care (EB3) positively affected self-perceived ESS (see Model 2 in Table 4). In Model 3, PEBS appeared to have a negative effect on objective ESS. Labor market exclusion (EB2) continued to show negative association with objective ESS in Model 4.
Multi-Group Analysis
In order to investigate the moderating effect of employment status on the path from PEBS to self-assessed ESS, a multi-group analysis was conducted using Amos 7.0. The multi-group analysis was performed only with self-assessed ESS, because employment status used as a moderating variable is embedded in objective ESS and could influence the results. The proposed models (Model 1 with the second-order PEBS and Model 2 with the sub-dimensions of PEBS) were tested across the two groups in order to determine a significant difference in structural weights. Table 5 displays the results of multi-group analyses.
| Models | χ2 (df) | △χ2 (△df) | Compared models | NNFI | CFI | RMSEA | |
|---|---|---|---|---|---|---|---|
| Model 1 | A | 80.531 (52) | 0.969 | 0.982 | 0.038 (0.020–0.054) | ||
| B | 87.590 (59) | 7.059 (7) | A | 0.973 | 0.982 | 0.036 (0.018–0.051) | |
| C | 92.394 (60) | 4.804 (1)* | B | 0.970 | 0.980 | 0.038 (0.021–0.053) | |
| Model 2 | D | 1119.625 (474) | 0.850 | 0.881 | 0.061 (0.056–0.065) | ||
| E | 1139.341 (492) | 19.716 (18) | D | 0.855 | 0.881 | 0.059 (0.055–0.064) | |
| F | 1145.564 (497) | 6.223 (5) | E | 0.856 | 0.881 | 0.059 (0.055–0.064) | |
| G | 1140.488 (496) | 4.076 (1)* | F | 0.857 | 0.882 | 0.059 (0.055–0.064) | |
| H | 1139.432 (495) | 1.056 (1) | G | 0.856 | 0.881 | 0.059 (0.055–0.064) |
* p < 0.05.
Note: △χ2 = Chi-square difference; △df = degrees of freedom difference; A = the base line model of Model 1 (all parameters free); B = equal factor loadings; C = equal factor loadings and equal regression weights; D = the base line model of Model 2 (all parameters free); E = equal factor loadings; F = equal factor loadings and equal regression weights; G = equal factor loadings and partial regression weights (unconstrained path from EB4 to ESS); H = equal factor loadings and partial regression weights (unconstrained path from EB3 and EB4 to ESS).
As the first step, the multi-group base line models were estimated in Model 1 (Model A; x2(df) = 80.531(52), NNFI = 0.969, CFI = 0.982, RMSEA = 0.038 (0.020–0.054)) and Model 2 (Model D; x2(df) =1119.625(474), NNFI = 0.850, CFI = 0.881, RMSEA = 0.061 (0.056–0.065)). Even though Model 2 has somewhat less values than the recommended cutoff criteria of 0.90 for NNFI and CFI, Models 1 and 2 indicated a relatively acceptable fit across groups. Accordingly, we proceeded to test invariance of these models.
The factorial invariance was tested by comparing the baseline models with the equal factor-loading models (Model 1; Model A vs. Model B, Model 2; Model D vs. Model E) in order to ensure that different groups respond to the items in the same way, and thus to compare meaningfully the ratings obtained from different groups (Hong et al., 2003; Steenkamp & Baumgartner, 1998). A Chi-square difference test was conducted because the equal factor-loading models (Models B and E) were nested within the baseline models (Models A and D). The Chi-square increase indicated insignificant difference in both Model 1 (Ä÷2 = 7.059 (Ädf= 7), p > 0.05) and Model 2 (Ä÷2 = 19.716 (Ädf= 18), p > 0.05). Even though the Chi-square difference test is widely used, studies have contended that the Chi-square difference test should not be used exclusively (Anderson & Gerbing, 1988; Hong et al., 2003; Marsh & Grayson, 1990; Steenkamp & Baumgartner, 1998). Thus, we additionally considered changes in NNFI and RMSEA and found that neither Model 1 (DNNFI = 0.004, DRMSEA = −0.002) nor Model 2 (DNNFI = 0.005, DRMSEA = −0.002) demonstrated a significant difference in factor loadings across groups.
Given that the factorial invariance was supported, we continue to test the structural invariance. To estimate the path difference across groups, all parameter estimates were constrained to be equal across groups. This fully constrained model was compared with the equal factor loading model (Model 1; Model B vs. Model C, Model 2; Model E vs. Model F). Because the Chi-square difference was statistically significant at á = 0.05 (Ä÷2 (Ädf) = 4.804 (1)) in Model 1, structural invariance was not supported (see Table 5), indicating that the parameter from second-order PEBS to self-assessed ESS is significantly different across groups. By constraining the parameters to be equal, the RMSEA and NNFI also deteriorated (DNNFI = −0.002, DRMSEA = 0.002).
For Model 2 to see the effects of sub-dimensions of PEBS on ESS, the structural invariance was rejected because the Chi-square difference was statistically insignificant at á = 0.05 (Ä÷2 (Ädf) = 6.223(5)). This result implies that the parameters do not differ significantly between employed and unemployed groups. In spite of insignificant Chi-square difference, there was a potential of paths with significant difference because the Chi-square difference (6.223) was greater than the threshold value of Chi-square (÷2 = 3.84 with df= 1), and the RMSEA and NNFI in Model 2 increased (ÄNNFI = 0.001, ÄRMSEA = 0.000). Therefore, we proceeded to investigate whether there was a path not invariant across groups.
To identify which parameter was not invariant, we freed regression weight constraints one by one from the full equal regression weight model (Model F). Because the path from EB4 to ESS had the biggest regression weight difference between groups (see Figure 1), the constraint on this path was freed (Model G) and compared with the full structural invariance model (Model F). The Chi-square difference was statistically significant at á = 0.05 (Ä÷2 (Ädf) = 4.076(1)), revealing that the path from EB4 to ESS was significantly different between two groups. In sequence, we repeated the same process for the path from EB3 to ESS with the second biggest regression weight difference, but no significant difference was found (Ä÷2 (Ädf) = 1.056 (1), p > 0.05) and we stopped further analysis. In sum, the path from PEBS to ESS was found to be significantly different between employed and unemployed groups, and this result was mostly due to the difference of the path from the barrier in the job skills (EB4) to ESS.
Results of multi-group analyses across employment status
Note: Parameter estimates are unstandardized (standardized) values in the order of employed and unemployed; Chi-square difference between equal factor loadings (Model B) and equal regression weights (Model C) models is shown in bold italics in Model 1; Chi-square difference between equal regression weights (Model F) and partial regression weights (Model G) models is shown in bold italics in Model 2. *p < 0.05 **p < 0.01 ***p< 0.001.
Latent Profile Analysis
As can be seen in Table 6, a latent profile analysis was conducted using Sample 2. Although the values of AIC, BIC, and SSA-BIC were lower for the four-, five-, and six-class solutions, the three-class model was retained due to the significant LMRT value (p= 0.006) and the higher Entropy value compared with the four-class solution. The three-class model best represents the co-occurrence pattern of barriers among low-income jobseekers.
| 3 Classes | 4 Classes | 5 Classes | 6 Classes | |
|---|---|---|---|---|
| AIC | 10,314.126 | 9,970.506 | 9,527.940 | 9,246.310 |
| BIC | 10,422.446 | 10,108.367 | 9,695.344 | 9,443.255 |
| SSA-BIC | 10,352.572 | 10,019.437 | 9,587.357 | 9,316.211 |
| Entropy | 0.908 | 0.907 | 0.922 | 0.914 |
| LMRT | 522.485 | 347.261 | 435.041 | 425.975 |
| p-value | 0.006 | 0.0349 | 0.088 | 0.513 |
As illustrated in Figure 2, sub-group one (S1) comprised 626 participants (75% of Sample 2) characterized by low levels of perceived barriers in all five domains. Sub-group two (S2) comprised 117 participants (14% of Sample 2) exhibiting a moderate level of perceived barriers. Sub-group three (S3) included 92 participants (11% of Sample 2) characterized by high levels of perceived barriers, particularly led by high levels of health and mental health barriers. S1 and S3 were more likely than S2 to be employed and to have greater employment hope, self-assessed and objective ESS, and other psychological capital variables—for example, mastery, self-esteem, resilience, and self-efficacy (see Table 7).
Latent profile analysis of perceived employment barriers
Notes: 1. PM (EB1) = physical and mental health, LM (EB2) = labor market exclusion (community), CC (EB3) = child care, HC (EB4) = human capital, PB (EB5) = soft skills (personal balance/capacity).
| 3 Classes | S1 | S2 | S3 | Statistics | |
|---|---|---|---|---|---|
| Employment hope | Psychological empowerment | 9.440 | 8.958 | 9.268 | 7.257** |
| Goal-oriented pathway | 9.122 | 8.470 | 8.976 | 13.551*** | |
| Mastery | 3.358 | 2.897 | 3.308 | 27.604*** | |
| Goal-orientation | 3.518 | 3.534 | 3.436 | 1.173 | |
| Self-esteem | 2.143 | 1.930 | 2.106 | 17.856*** | |
| Resilience | 2.549 | 3.255 | 3.533 | 8.695*** | |
| Self-efficacy | 4.439 | 4.233 | 4.394 | 8.903*** | |
| Self-assessed ESS | 2.753 | 2.438 | 2.509 | 4.004* | |
| Objective ESS | 1.387 | 0.933 | 1.352 | 13.272*** | |
Discussion and Conclusion
This paper examined structural and individual barriers as they relate to employment and ESS outcomes. PEBS—observed both globally and by each domain—was found to be higher for the unemployed group compared to its employed counterpart. While the group difference was consistent, it is interesting to note that structural barriers (labor market exclusion and human capital) were greater for both employed and unemployed compared to other individual barriers (see Table 3). One conflicting finding was the positive relationship found between PEBS and self-assessed ESS, while the labor market exclusion was having a strong negative relationship with self-assessed ESS.
Perceived employment barrier scale had a negative association with objective ESS with the labor market exclusion having a strong negative effect. When examining the findings from the multi-group analysis, PEBS had a differential effect on self-assessed ESS by employment status, whereas the employed group had a negative effect and the unemployed group had a positive effect. As a structural barrier, labor market exclusion consistently had negative effects on self-assessed ESS for both employed and unemployed groups, and human capital had a negative effect for employed group and a positive one for unemployed group. Interestingly, other individual barriers (health and mental health and child care) had positive relationships.
The positive relationship of individual barriers and self-assessed ESS needs further investigation, but it is consistent with the findings from previous studies done by Hong et al. (2016) and Hong, Hong, Choi, and Hodge (2021). Against the hypothesized directionality, PEBS was found to affect positively self-assessed ESS similar to the findings from these studies in the context of physical disabilities and mental health barriers. While this study did not include employment hope as a key component of the psychological self-sufficiency theory in workforce development (Hong, 2013b), this theoretical framework brings to light “an ongoing reflective process for self-awareness and acceptance of the existing barriers could become an activating agent for instilling hope” (Hong, Northcut, Spira, & Hong, 2019, p. 184).
Providing a pattern of perceived barriers distributed across the five domains of PEBS in a three-class solution, a latent profile analysis revealed evidence of intersectional overlap of both structural and individual barriers in S3. This class was high on both structural and individual barriers, which may indicate that individual barriers were structurally vulnerable qualities manifested at individual level. Perhaps the concentration of high barriers in S3 had a similar pattern found with “individuals with the disability barrier” who “tend to score significantly higher on all other 26 barrier items in PEBS … [and] has 19 total employment barriers on average, compared to only 4 for its counterpart” (Hong et al., 2016, p. 70). It is significant to find that this group was equally empowered on all measures of strength-based psychological capital variables as S1 that had low levels on all five domains of perceived barriers. Both S1 and S3 had significantly higher scores on these variables compared to S2 (see Table 7). This would explain the positive directionality of PEBS in its relationship with self-assessed ESS.
Structural and individual barriers tend to co-occur. Many studies suggest that barriers to employment are primarily due to neither structural nor individual barriers, but are a combination of both. A longitudinal study of 1,075 Milwaukee’s TANF applicants identified potential structural and individual barriers to employment, which included disability, lack of child care, poor physical health and mental health, substance abuse, lack of work skills and education, and domestic violence (Dworsky and Courtney, 2007). Another example of structural and individual barriers to employment is a study conducted by Webster et al. (2007), who examined mental illness as a barrier to employment among drug court participants. The study revealed that women who had suffered from severe mental health issues faced significantly more employment barriers than the male counterpart did. In other words, besides the individual barriers to mental health and substance abuse, the structural barrier of gender discrimination added difficulty to this population in gaining employment.
The study of mismatch in jobseekers’ job skills with required job skills by employers often coexist with other barriers, such as education, physical health, and mental health. Nam (2005) analyzed data from the Women’s Employment Study and found that there were multiple barriers to employment associated with those who leave welfare. The most frequently mentioned barriers were low education, lack of work skills, drug abuse, physical health issues, domestic violence, and lack of transportation. Tonkin, Dickie, Alemagno, and Grove (2004) examined the level of employability skills among 52 female jail inmates who presented criminal and substance abuse issues. It was found that women demonstrated deficits in three skill domains: basic skills, interpersonal skills, and personal characteristics and attitudes.
Elements of race and gender discrimination are given significant attention as co-occurring structural barriers. Racial and gender discrimination have been studied in a range of countries and among people of different social classes, such as executives, professors, shopkeepers, skilled, and unskilled workers. Collins (1989) studied top-ranking Black executives in Chicago and found that although Blacks had succeeded within White management hierarchies, their upward mobility was delineated racially. Essentially, the corporate response to civil rights protests created a visible but economically vulnerable Black elite. Lamanna, Miller, and Moore (1987) studied the status of academic women sociologists in a secondary labor market of part-time employment and found that women were overrepresented in the secondary labor market. Racial and ethnic discrimination is negatively associated with various indicators of physical and mental health (Perry, Harp, & Oser, 2013; Williams, Neighbors, & Jackson, 2003).
At lower socioeconomic levels, persons from minority ethnic groups faced significant barriers that resulted from their position in the secondary labor market. Holder (1998) studied West Indian Blacks from Anglophone Caribbean who migrated to New York City in 1900–1952. The author found that this group of skilled workers was involved in unskilled and menial positions, and the author suggested that racial discrimination was a significant barrier to skilled employment. Sorensen and Zibman (2001) compared the participation in work-support programs between poor noncustodial fathers and poor custodial mothers and found that poor fathers are less likely to be involved in work-support program, which in turn, may partially explain why poor noncustodial fathers are less likely to fulfill financial responsibility to their children.
Lack of transportation to the workplace is a frequently discussed structural barrier to employment in literature. Brooks, Nackerud, and Risler (2001) studied 40 TANF recipients who joined 2-week job-finding clubs. Fifteen of the 40 recipients did not find employment during the sessions and they were more than twice as likely to view that lack of transportation as compared to other factors was their biggest barrier to employment. An interview study with Indochinese refugees found that English language fluency was the biggest factor in refugee employment. Health and transportation were deemed as the secondary barriers to employment depending on the ethnic groups the refugees identified with (Strand, 1984). Among the low-income elderly, the three most frequently mentioned barriers were situational, including personal health, lack of transportation, and lack of relevant qualifications (Brady, Palermino, Scott, Fernandez, & Norland, 1987).
Transportation issues occurred in both inner-city and rural areas. Rasheed (1999) examined African-American low-income, noncustodial fathers who live in inner-city areas and found that they faced occurring barriers to employment related to transportation, skills, and racial discrimination. In rural areas, Pandey, Brown, Scheuler-Whitaker, and Collier-Tenison (2002) revealed that welfare recipients living on reservations in Arizona faced additional barriers to employment because of difficulty in transportation, lack of accessibility to basic needs, and racial discrimination.
As such, individual barriers cannot stand alone without considering the structural conditions that exacerbate vulnerability (Rank, 2004). The neoliberal rhetoric may continue to target social welfare programs as “… both a consequence and a cause of several conditions best described as social pathologies” (Niskanen, 1996, p. 1). This position blames the victim—the welfare recipients—who should be responsible for the[ir] conditions of: poverty, out-of-wedlock births, unemployment, abortion, and violent crime (Niskanen, 1996). Because the cultural explanation attributes primary or sole responsibility for remedy of the above conditions to the individual, proponents cite the need to interrupt the family (parental) transmission of values and attitudes as critical to prevent further generations from becoming dependent (Bartholomae, Fox, & McKenry, 2004; McLanahan, 1988). The politics of viewing individual barriers separate from the structural conditions itself is a systemic barrier to social–human development (Mohan, 2020).
Vilifying individual barriers and disconnecting them from structural barriers allow keeping the solution in the private domain and the market place by dismantling the social contract for protecting the basic human rights of all citizens. For instance, the impact of parental welfare history, family structure (usually related to female-headed households), barriers to family success, family wage, and numerous other aspects of families are explored in the welfare dependency/poverty literature. Family stressors of various types are suggested to be contributors to welfare dependency: physical health, educational deficits, substance abuse, learning limitations, child behavior problems, divorce, insufficient family wage, and DSM-IV-diagnosed anxiety disorders such as posttraumatic stress disorder and clinical depression, to name a few (Altman, 2007; McLanahan 1988; Schmidt, Dohan, Wiley & Zabkiewicz, 2002; Taylor & Barusch, 2004).
Dehumanizing welfare recipients by targeting individual attributes as their choice could not avoid the eventual meeting of their connections with structural barriers. Antel (1992) found that a mother’s welfare dependency in the home seemed to increase the daughter’s welfare dependency. Bartholomae et al. (2004, p. 784) also investigated the relationship between parental welfare history and the status of being a current recipient, which was attempted to substantiate the rhetoric of the mid-1990s welfare reform that was designed “… to prevent the transfer of welfare use from generation to generation.” They initially found some evidence that those with a parental history of welfare utilized welfare to a greater degree than those who had no generational history. However, their overall finding was that economic background factors were more predictive of current welfare use relative to cultural factors. Respondents’ human capital factors related to education, employment, and work-limiting disability provided more explanatory power than cultural factors.
In conclusion, we suggest rebuilding of social contract by centering on structural and individual barriers toward social and human development—freedom, justice, diversity, equity, and inclusion (Mohan, 2020). System transformation could be built on the process-based strengthening and maintaining of psychological self-sufficiency by targeting specific structural and individual barriers and contextualizing the magnitude of barriers as experienced by low-income jobseekers (Hong & Choi, 2017). Such individualized processes can be facilitated using the Transforming Impossible into Possible (TIP) intervention for human and social development (Hong, 2016; Hong et al., 2020, 2021). Matching of these varying barrier levels with strategies to increase and sustain the human-centered process to reach employment and ESS outcomes should be supported structurally in the labor market. Centering on structural and individual barriers as co-occurring and co-defining aspects of the human–social development dynamics could help build a renewed commitment to a bottom-up process of social transformation—reconnecting the social fabric and cohesion in order to enhance human freedom and justice (Mohan, 2020).
Conflict of Interest
The authors have no conflicts of interest to disclose.
This work was supported by the U.S. Department of Health and Human Services/Administration for Children and Families under Grant [90PH0018; 90HG1003]; Korea Foundation [2016-RF-021; 2019-RF-020]; and Lloyd A. Fry Foundation.
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