literature review on impact of unemployment

Unemployment, Social Vulnerability, and Health in Europe pp 167–183 Cite as

The Effects of Youth Unemployment: A Review of the Literature

• V. L. Damstrup  

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Young adults and teenagers are engaged in work on a much smaller scale than older workers. Young people are engaged less in work because they are still in school, or they are involved in leisure activities. Some, on the other hand, would like to work, but find it difficult obtaining employment. The transition from school to employment is a process that involves searching and changing jobs before deciding on a more or less permanent employment. Today, more than ever, youths have a lower rate of employment, hence there has been much concern about the youth labor market.

• Young People
• Labor Force
• Vocational Training
• Young Person
• School Dropout

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Damstrup, V.L. (1987). The Effects of Youth Unemployment: A Review of the Literature. In: Schwefel, D., Svensson, PG., Zöllner, H. (eds) Unemployment, Social Vulnerability, and Health in Europe. Health Systems Research. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-83112-6_12

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Related links, south african journal of economic and management sciences, on-line version  issn 2222-3436 print version  issn 1015-8812, s. afr. j. econ. manag. sci. vol.23 n.1 pretoria  2020, http://dx.doi.org/10.4102/sajems.v23i1.3049 .

ORIGINAL RESEARCH

A systematic literature review of the implementation and evaluation of the JOBS programme: A suggested framework for South Africa

Rachéle Paver I , II ; Hans De Witte I , II ; Sebastiaan Rothmann II ; Anja Van den Broeck II , III ; Roland Blonk II , IV , V

I Research Group Work, Organisational and Personnel Psychology, KU Leuven, Belgium II Optentia Research Focus Area, North-West University, Vanderbijlpark, South Africa III Work and Organization Studies, KU Leuven, Brussels, Belgium IV Healthy Living, Toegepast Natuurwetenschappelijk Onderzoek (TNO), Leiden, the Netherlands V Department of Human Resource Studies, Tilburg University, Tilburg, the Netherlands

Correspondence

BACKGROUND : South Africa is challenged with high levels of unemployment, comprising many people with low levels of education and also individuals who have never held a job before. Despite having many vulnerable participants, interventions aimed at the unemployed generally exclude psychosocial training and are methodologically weak. AIM : The JOBS programme, a scientifically sound intervention, has been developed specifically to help people affected by unemployment to cope with the psychological effects. As a means of applying such a programme in South Africa, this study aimed to develop a framework based on the insights gained on the implementation and evaluation of the JOBS programme. METHODS : The study comprised a systematic review of literature regarding the JOBS intervention and its derivatives ( n = 34). RESULTS : The results revealed that populations similar to the unemployed in South Africa had benefitted significantly regarding re-employment and mental health outcomes. CONCLUSION : Suggestions derived from the literature included aiming the programme at the most vulnerable unemployed in South Africa: the youth and long-term unemployed. Furthermore, expanding the programme by adding an entrepreneurial component may yield positive results, considering the lack of employment opportunities in South Africa.

Keywords : JOBS programme; employment interventions; systematic literature review; unemployment; South Africa.

Introduction

South Africa is facing an unemployment crisis: currently 29.1% of people in South Africa are jobless (Stats SA 2019). While statistics may indicate the magnitude of the problem at hand, they fail to depict the nature and severe impact of unemployment. Unemployment is not only associated with societal and economic ramifications; it also has serious psychological consequences for those who are unemployed (see Strandh et al. 2014; Wanberg 2012).

Numerous interventions have been implemented to alleviate unemployment (Independent Evaluation Group 2013; McCarthy 2008). Despite the benefits of evidence-based practices (see Heckman, Lalonde & Smith 1999; Ravallion 2008), it is startling to see that most vocational interventions are consensus-based - implying that the interventions are based on what the stakeholders think is necessary, without the supporting evidence to prove what is really needed (Marais & Matebesi 2013). Considering the urgency of unemployment, the shortage of evidence-based practices in South Africa is a concern.

One profound example of a scientifically sound employment initiative is the JOBS programme (Caplan et al. 1989). The JOBS intervention seeks to enhance the employability of jobseekers by equipping them with the necessary job search, social and problem-solving skills to support them in their job search efforts. Several factors contribute to the achieved outcomes. Two of the strengths of the programme are its strong theoretical foundation and empirically tested evidence (Vinokur & Price 2015). Likewise, the comprehensive protocol guiding the programme contributes significantly to successful dissemination undertakings (Curran, Wishart & Gingrich 1999). Extensive evidence of the effectiveness of the JOBS programme has previously been reported (Price & Vinokur 2014).

Due to the encouraging results, several JOBS derivatives have been implemented in other countries. These programmes include the Työhön Job Search Programme in Finland (Vuori et al. 2002), the Jobs in China programme (Price & Fang 2002), the Job-Search programme in Israel (Shirom Vinokur & Price 2008), the Winning New Jobs (WNJ) programme in Ireland (Barry et al. 2006) and the JOBS intervention in the Netherlands (Brenninkmeijer & Blonk 2011). Although these programmes have proven to be reliable in different economic contexts (Vinokur et al. 1995a), they have been implemented mainly in developed countries, except for China. The lack of evidence-based practices in South Africa, together with the successful replication of the JOBS programme, creates an opportunity to explore whether such an intervention can be tailored to suit the South African context.

Much effort has been devoted to developing materials that explicitly explain the procedures and dynamics of the JOBS programme (Curran et al. 1999). Yet investigating literature pertaining to the execution and subsequent outcomes of the JOBS programme may assist further dissemination. A greater understanding of typical components of an intervention - implementation and evaluation - can be used to serve as guiding principles for application and assessment in the South African context. Based on the above statement of the research problem, the objectives of this research were:

To review literature regarding the implementation (context and process aspects) and evaluation (promoting and impeding effects) of the JOBS programme and variations of it.

Based on the previous findings, to develop a framework to assist with the implementation and evaluation of the JOBS programme in South Africa.

To make recommendations for future research and practice.

Research design

Research approach

A systematic literature review was done to achieve the objectives of this study. A systematic review identifies the main scientific contributions relevant to a specific topic by conducting extensive literature searches of published and unpublished studies (Tranfield, Denyer & Smart 2003). This review aimed to identify literature containing information about the JOBS programme and variations of it. Transparent and reproducible procedures were used to enhance the quality and outcomes of the review process.

Research method

Targeted body of literature

Before starting with the review, the founders of the JOBS programme and the web page of the Michigan Prevention Research Center (MPRC) were consulted to obtain information regarding the JOBS programme and its dissemination. Electronic searches were undertaken to allocate articles identified on the web page. Next, a search was conducted to ascertain whether possible articles were excluded. Databases such as Google Scholar and EBSCOhost (Academic Search Premier, Africa-Wide Information, American Doctoral Dissertations, PsycARTICLES and PsycINFO) were utilised to find the relevant articles.

Search terms and selected criteria

It was anticipated that the articles worth including in the review would refer to the JOBS programme in the articles themselves. Therefore, numerous searches were conducted by including the authors involved (as obtained from the dissemination page) with the term Job Opportunities and Basic Skills programme (e.g. AUTHOR: Vuori; IN-TEXT: 'JOBS program*'). Because another programme, called the JOBS Program, yielded additional results when searching for 'Jobs Program*', it was necessary to include the various authors. The search string consisted of two search concepts joined by the Boolean operator AND; the second string contained a list of authors joined by the Boolean operator OR. The following search string was entered in the databases: [1] IN-TEXT: 'jobs program*' [2] AUTHOR: 'Barry', 'Caplan'; 'Choi'; 'Kessler' 'Price'; 'Schul'; 'Van Ryn' 'Vinokur' and 'Vuori'.

To prevent the omission of essential articles, a complementary search was performed. Reference lists of the selected articles were reviewed for more relevant publications. During the process, it became evident that there were indeed articles that did not refer to the JOBS programme in their content. Therefore, an additional search was conducted using the authors involved from the different variations of the JOBS programme and each of the different JOBS variations (e.g. AUTHOR: 'Barry'; IN-TEXT: 'Winning New Jobs'). The second search string again consisted of two search concepts joined by the Boolean operator AND; the first string contained a list of names for variations of the JOBS programme and the second a list of authors, with both strings joined by the Boolean operator OR. The following search string was entered in the databases: (1) IN-TEXT: Jobs in China Project, Job-search Intervention, Työhön Job Search Program, or Winning New Jobs (2) AUTHOR: 'Blonk', 'Brenninkmeijer', 'Choi', 'Donaldson', 'Fang', 'Friedland', 'Ling', 'Shirom', and 'Turner'.

Criteria for including articles were as follows:

Articles and chapters had to be peer-reviewed.

Articles and chapters had to be written in English.

Articles had to be about the JOBS programme or variations of it.

The study population had to be unemployed people.

Gathering the data

Conference proceedings and papers to which access was limited or where no full-text papers were available were excluded. Price and Vinokur (2014) mention that the JOBS programme has previously been executed in Sweden and South Korea; however, the literature seemed limited and unavailable. Furthermore, the JOBS programme has also been implemented in organisational and school contexts. Considering that the circumstances of the participants are not the same, these studies were omitted. The inclusion and exclusion criteria narrowed the scope of this review. Finally, 34 articles met all the inclusion criteria ( Figure 1 ).

Analysis and presentation of the data

Implementation and evaluation aspects were studied to gain a better understanding of the JOBS programme and its derivatives.

Implementation is described as the process of putting a plan into action to achieve objectives (Miller, Wilson & Hickson 2004). To ensure sufficient programme fidelity and to effectively replicate the JOBS, it seemed necessary to study the various components involved in executing such a programme. Evaluation can be described as the determination of the merit, worth and significance of an area of interest using criteria directed by specific standards for purposes of decision-making (Richards & Schmidt 2002). Evaluation practices are a crucial component in the success of evidence-based programmes (Jané-Llopis et al. 2005). To develop a framework aimed at guiding the implementation and evaluation of the JOBS programme in South Africa, the following aspects of the papers included in the systematic literature review were studied ( Table 1 ).

Assessment of methodological quality

An additional reviewer - a researcher involved in the broader project - was consulted to ensure methodological quality. After the duplicates had been removed, both the researcher and reviewer were involved in selecting studies to remain in the systematic review based on their abstracts and full content.

Ethical considerations

Ethical clearance was obtained from the Humanities and Health Research Ethics Committee (HHREC), North-West University, with ethical clearance number: NWU-HS-2018-0006.

The literature review comprised 34 studies. The predetermined criteria, as per Table 1 , guided the systematic review. The main aspects regarding the implementation and evaluation of the JOBS programme and variations of it were tabulated. The summarised findings are reported below.

Implementation

This section contains information about the context in which the various programmes were implemented as well as participant and programme specifics.

The JOBS programme has been implemented in numerous states and countries, in all of which the context has differed to some degree (see Appendix 1 ). The unemployment rates, as reported in the studies included, varied from 4% to 20% (Brenninkmeijer & Blonk 2011; Vuori et al. 2002). Additionally, the unemployed seemed to be supported by welfare benefits, also safeguarding them against severe financial hardship. Unemployment grants differed, depending on the social policies of the countries involved (Vinokur & Price 2015).

Participants

Biographical variables

According to Vinokur and Price (2015), benefits of the JOBS programmes did not seem to be distributed equally to all the participants. Some findings are reported below; yet it should be noted that only a few of the studies included mentioned the impact of demographic variables on the intended outcomes. Findings regarding participants' demographic variables were not always consistent and sufficient to substantiate these relationships. Compared to other demographic variables, education had the strongest impact on the outcomes of the job search interventions.

Gender: Female participants seemed to suffer greater economic losses and experience more difficulties regaining employment (Vinokur et al. 2000). Yet women generally benefitted more from the programme than their male counterparts (MPRC 2003). They were more likely to (1) obtain employment six months after the intervention (Shirom et al. 2008), (2) score higher on participant engagement (Caplan et al. 1989), (3) experience positive group participation (Vuori et al. 2005) and (4) participate voluntarily (Vuori et al. 2002).

Age: Unemployed people between the ages of 16 and 65 were generally the targeted population. The mean age of participants in the studies included was 36 (Standard deviation [SD] = 9). Vuori et al. (2005) state that younger participants were usually more positive and found employment more easily than older participants, but showed a higher tendency of non-participation (Van Ryn & Vinokur 1992; Vinokur et al. 2000; Vinokur, Price & Caplan 1991a). The training programme seemed to also have a positive impact on older participants regarding improved job search skills and increased self-confidence (Price & Choi 2001).

Level of education: The majority of participants in the programmes involved had a secondary level of education (equivalent to 12 years of schooling; mean of all the studies: 40.65%). Participants with higher levels of education more often gained in terms of obtaining re-employment (MPRC 2003), increases in job search self-efficacy (Choi, Price & Vinokur 2003), a lower likelihood of major depressive episode diagnosis (Vinokur et al. 2000) and non-participation (Caplan et al. 1989; Van Ryn & Vinokur 1992; Vinokur et al. 2000), and higher levels of voluntary participation (Vuori et al. 2002). Despite these positive findings, in some other studies, it was evident that the programme also clearly yielded mental health benefits and economic benefits for those less educated and most disadvantaged in terms of the job market (Price & Choi 2001; Vinokur, Price & Schul 1995b).

Duration of unemployment: The JOBS programme was originally designed to prevent further deterioration in mental health among the unemployed and was not specifically intended to deal with potential problems associated with long-term unemployment (Caplan et al. 1989). Therefore, the majority of the initial studies included only those who had been unemployed for less than 13 weeks (Caplan et al. 1989; Price et al. 1992; Van Ryn & Vinokur 1992; Vinokur et al. 1991a, 1991b, 1995a, 2000; Vinokur, Price & Caplan 1996; Vinokur & Schul 1997). However, because the long-term unemployed were reported as the most vulnerable, more recent developments included participants who had been unemployed for longer periods (Brenninkmeijer & Blonk 2011; Malmberg-Heimonen & Vuori 2005; Price & Choi 2001; Reynolds, Barry & Gabhainn 2010; Vuori et al. 2005).

Population, sample size and recruitment

Population: Eligibility criteria required individuals to be aged between 16 and 65. Prospective screening questionnaires were used in some studies to determine participants' risk score for poor mental health (Vinokur et al. 1995b). However, those who showed major signs of mental illness, serious psychosocial problems or behavioural problems or who scored extremely high on depression symptoms were omitted from the study (Brenninkmeijer & Blonk 2011; Vinokur et al. 1995a).

Sample size: Most of the studies included were conducted as a part of large-scale field experiments, ranging from 1087 to 3402 participants. Smaller-scale studies ranged from 125 to 672 participants. Sample sizes did not seem to influence the results achieved or the sustainability of the programmes. It was rather the use (or lack) of effective methods that seemed to have an impact on the outcomes (Price & Vinokur 2014).

Recruitment: The primary method used to recruit participants was through recruiters who approached individuals eligible for employment benefits while collecting grants at employment offices. In studies including an experimental and control group, participants were told about the two programmes on job-seeking methods. One programme was described as a workshop consisting of five half-day sessions (the experimental condition); the other was described as a self-guided programme, in which participants received a booklet with job search information (the control condition). To prevent biases, participants had to show no preference for a type of intervention (experimental or control condition; Caplan et al. 1989; Vinokur et al. 1995a).

Voluntary or enforced participation: Participants from some programmes had to participate in the job search workshop to qualify for welfare benefits (Brenninkmeijer & Blonk 2011; Lee & Vinokur 2007). When studying the impact of voluntary or enforced participation, a Finnish study showed that enforced participation did not increase re-employment; however, it impaired the positive mental health impacts of the programme. Further analysis demonstrated that enforced participation in job search training decreased re-employment among the longer-term unemployed workers (Malmberg-Heimonen & Vuori 2005).

Dropout rates: In the US programmes, on average, 59% (varying by 5%) of participants failed to show up for the intervention (Caplan et al. 1989; Vinokur et al. 1995a). Consequently, dropout rates could be anticipated and, therefore, twice as many participants were recruited and allocated to intervention groups in the Israeli study (Shirom et al. 2008). In the Malmberg-Heimonen and Vuori (2005) study, it was surprising to find that response rates did not differ much among the enforced, voluntary and control groups (94%, 92%, and 91%).

The information presented below was derived from the JOBS training manual (Curran et al. 1999).

The JOBS programme entails two main processes. On the one hand, job search skills (the actual content of the programme) are taught to participants, while, on the other hand, empowerment of the participants, by applying the programme's underlying principles in the method of delivery, is the true underlying mission of the workshop. The following aspects guided the method of delivery used by the trainers:

Referent power: Moderate self-disclosures shared by facilitators create an environment in which participants feel safe to reveal their concerns and experiences. These also contribute to creating an atmosphere of unconditional acceptance and to enhancing feelings of being normal and valued.

Guiding behaviour: Specific positive feedback is given to participants to reinforce positive behaviour. Strategies used to generate positive feedback include active listening, observation and reflecting on what participants have shared as a means of showing participants that they are valued.

Inoculation against setbacks: The group is encouraged to identify potential setbacks and difficulties in the job search process. Strategies are developed to overcome the identified challenges and, consequently, participants realise that their problems can be solved. Participants are asked to commit themselves to action by selecting and vowing to undertake a solution most appealing to them.

Social support: Social support forms an integral part of the underlying processes, as exercises are specifically designed to create opportunities for facilitators and participants to support each other. An environment where participants are unconditionally accepted is created. Such a safe environment contributes to participants feeling comfortable to express their opinions and reveal their feelings.

Active leaning: The learning process relies greatly on participants' knowledge and skills. Participants acquire job search skills by using active learning methods, elicited using group discussions and brainstorming sessions.

In contrast to traditional top-down, trainer-focused training methods, the JOBS programme relies heavily on its individual-focused approach. The delivery principles mentioned above contribute to the strong individual-focused approach. Principles are continuously applied and integrated and form the basis on which the content is delivered.

The programme consists of five sessions. During the first session, participants discover their job skills; the second session focuses on dealing with obstacles related to employment; the third session is used to introduce participants to some job search techniques; the fourth session covers topics such as curriculum vitae writing and preparing participants for job interviews; during the fifth session participants rehearse skills acquired throughout the week. The workshop concludes with a certificate ceremony, during which facilitators boost participants' confidence by highlighting their strengths and skills and providing each participant with a sincere and inspiring message.

The JOBS protocol describes the programme processes meticulously. Yet these processes are flexible and can be altered, depending on the needs of the groups, without losing the intended effects of the programme. The majority of the disseminated versions of the JOBS programme were implemented strictly according to the protocol. The content differed in terms of minor language, cultural, procedural and scheduling changes to suit different contexts. To maintain the standard of the JOBS programme, all materials were piloted and approved. It is worth mentioning that, when the protocol was somewhat neglected, it was reported that the programme was less successful in achieving the intended outcomes (Shirom et al. 2008).

Participants were rewarded monetarily for participation or each returned questionnaire (varying between $5.00 and $15.00, depending on the currency of the country). In cases where questionnaires were not returned, an additional amount was issued on the completion of their questionnaires. This incentive was reported to result in a substantial increase in response rates (about 20%; Shirom et al. 2008). Participants in the JOBS programme and Netherlands JOBS programme also received a certificate of participation for completing the programme (Brenninkmeijer & Blonk 2011; Caplan et al. 1989).

Researchers obtained higher response rates when offering incentives: in cases with relatively high dropout rates, no mention of rewards or incentives was evident (Barry et al. 2006; Reynolds et al. 2010; Shirom et al. 2008). The same finding was, however, not true in the WNJ California studies, which managed to retain approximately 70% of their participants, seemingly without the use of incentives (Choi et al. 2003).

Facilitators

Pairing: Teams consisting of one male and one female trainer are prescribed by Curran et al. (1999) to complement each other well. An untested assumption existed that a pair of trainers reduced deviation from the principles of the JOBS programme. However, the assumed benefits of having male-female pair facilitators have not yet been tested. Benefit-cost research could determine whether the cost of using two trainers, rather than one, is outweighed by the benefits that are generated (Price et al. 1998).

Prerequisites: Facilitators were generally social workers, labour advisors, educational counsellors or high school teachers. It was suggested that facilitators ought to be skillful in working with people (public speaking and communications backgrounds). Because trained individuals (that is, mental health professionals, such as counsellors or clinicians) might execute strongly embedded techniques not necessarily consistent with unemployment-related counselling methods, professional training was not a prerequisite (Caplan et al. 1989).

Programme-related training: Facilitators had to undergo extensive formal training. The content of the training covered understanding of group processes, theoretical foundations of the programme and extensive rehearsal in the form of pilot studies. The duration of training varied from 6 to 30 days (48 h-240 h). The reason for the extensive training was that facilitators were not only conveyors of information, but also experts in navigating the group processes, with the ability to connect emotionally with the participants and facilitate interactions in a group setting. To promote conformity, trainers' performance was evaluated by trained supervisors.

Duration of the programme

Some of the findings yielded by the original JOBS trial encouraged the revision of the programme, which consequently led to the development of the JOBS II intervention (Vinokur et al. 1995a). The first version (the JOBS I programme) spanned eight 3 h sessions, over a two-week period (four mornings per week; Caplan et al. 1989). To increase programme efficiency and the attendance of participants, meeting hours were reduced by 30%, delivered over five 4 h sessions in a one-week period in the JOBS II (Vinokur et al. 1995a). The majority of disseminated versions of the JOBS programme continued to apply the programme following the JOBS II protocol. In some groups, the Finnish programme was delivered over four days, as the first day was used to deal with recently laid-off workers' negative emotions (Vuori et al. 2002) - an illustration of how the programme can be altered to meet the needs of the group, without affecting the outcomes.

Group sizes

Guidelines of the JOBS programme suggest groups consisting of 12-20 participants (Curran et al. 1999). There were exceptions, where the groups ranged from three to 110 participants per group (median = 11; Malmberg-Heimonen & Vuori 2005). Although only a few studies reported on the impact of group sizes, larger groups seemed to have more negative experiences than smaller groups (Vuori et al. 2005).

Venue of training

Venues such as community centres, school classrooms, churches and union halls, easily accessible to participants, were mostly used. Venues had to be large enough to accommodate 25 people and furnished with movable chairs, arranged in a semicircular layout. Such a layout was reported to be most effective in delivering the group intervention (Curran et al. 1999).

Stakeholder involvement

Crucial to the success of the WNJ programme in Ireland was that the developers of the original JOBS programme were involved from the outset and contributed to obtaining buy-in from strategic stakeholder agencies. Despite a substantial initial investment of resources for demonstration, neither the WNJ in California nor the JOBS in China project continued beyond their initial stages, as commitment of resources for continuation was not offered by the government or other stakeholders. Therefore, the success of programme dissemination depended considerably on the involvement of, and support received from, stakeholders (Vinokur & Price 2015).

This section is comprised of information regarding evaluation of the processes and the impact of the JOBS programme.

Methodology

Data collection method

Self-administered questionnaires were used to assess participants' attitudes, intentions, various behavioural components and experience of the workshop (Van Ryn & Vinokur 1992). In cases of unreturned questionnaires or where participants failed to show up for the workshop, telephonic interviews were conducted (Barry et al. 2006).

Research design and data collection intervals

Randomised field study designs were used to investigate the intervention effect between experimental and control conditions (Caplan et al. 1989; Vinokur et al. 1995a; Vuori et al. 2002). Programmes that made use of a randomised field study design had three to four interval times, namely pre-intervention (two weeks before the programme), post-intervention (directly after the programme), post-post-intervention (between two and six months after the programme) and long-term follow-ups, varying from 12 to 32 months after the intervention (Barry et al. 2006; Brenninkmeijer & Blonk 2011; Vinokur et al. 1991a). Other programmes only tested pre-intervention and post-intervention to determine the impact of the programme (Lee & Vinokur 2007; Shirom et al. 2008).

Process evaluation

To determine the internal validity and the strength and integrity of the JOBS programmes, two types of analysis were generally conducted. These process measures consisted of testing the integrity of randomisation and strength of the programme (Vinokur et al. 1995a).

Effectiveness of randomisation

The first check to determine the validity of the programme was to determine whether the statistical analyses were conducted on a randomised (true) experimental design. This was established by comparing the demographic and other tested variables of the experimental and control conditions at baseline to identify possible differences. In cases where differences were found, these variables were controlled for in further analyses (Vinokur et al. 1995a).

Manipulation checks, integrity and strength of the intervention

The second test was to test the strength and integrity of the intervention through self-reported questionnaires at the end of each session. Participants were asked to evaluate their experience of facilitators and the programme. These evaluations were used to determine whether various intervention elements had been implemented and had operated as designed (Vinokur et al. 1995a). Participants who scored high on these measures also reported higher levels of internal control and job-seeking self-efficacy (Choi et al. 2003), decreases in depression and anger, and increases in self-esteem and quality of life (Caplan et al. 1989). Also, trainer skills (one of the evaluated variables) exhibited during group interactions contributed to increased re-employment, even at the 12-month follow-up (Reynolds et al. 2010).

Two additional methods were used to ensure the quality of the programme and a high level of trainer adherence to the protocol. Firstly, members of the research team frequently observed programme trainers: after each session, constructive feedback was given to trainers. Secondly, the facilitators met weekly to discuss skill-related topics they encountered during their sessions (Vinokur et al. 1995b).

Impact evaluation

The positive outcomes of the JOBS programme were documented amply. Below are some of the most prominent findings related to the two core objectives of the JOBS programmes: prevention of poor mental health and promotion of re-employment, and other post-hoc outcomes.

Prevention of poor mental health: Participation in the intervention resulted in increased self-esteem, self-efficacy and social assertiveness among participants; consequently, participants also showed improved psychological and mental health and well-being (Lee & Vinokur 2007; Reynolds et al. 2010). Furthermore, long-term effects of the programme revealed that participants experienced lower symptoms of depression (Price et al. 1992; Vuori & Silvonen 2005), improved self-esteem (Reynolds et al. 2010) and an enhanced ability to deal with setbacks. A noteworthy finding is that participants screened for showing higher risk for depression seemed to benefit the most in terms of mental health and re-employment outcomes (Vinokur et al. 1995b).

Promotion of re-employment: Several programmes demonstrated increased rates of re-employment, with an average of 46% after the intervention, compared to the control group, with an average of 18% (Brenninkmeijer & Blonk 2011; Caplan et al. 1989; Donaldson 2012; Shirom et al. 2008; Vuori et al. 2005). Programme participants also showed higher motivation to persist in job search efforts (Caplan et al. 1989), were employed in better jobs (in terms of earnings and job satisfaction) (Vinokur et al. 1991b), were employed faster, had less recurring episodes of unemployment (Vinokur & Price 2015) and experienced reduced economic hardship after being employed (Barry et al. 2006). Results remained over time, as long-term effects of the programme revealed that participants, compared to their counterparts, experienced higher re-employment (Brenninkmeijer & Blonk 2011). Another crucial finding is that both the Työhön and the Netherland's JOBS programmes confirmed the effectiveness of the intervention to help even the more vulnerable long-term unemployed gain employment (Brenninkmeijer & Blonk 2011; Vuori et al. 2002).

Consequential outcomes: Finally, the JOBS programme demonstrated substantial cost-benefit effectiveness because the higher earnings led, on average, to higher tax revenues and decreased welfare grants for governments (Vinokur et al. 1991b).

The purpose of this study was to review literature regarding the JOBS programme and variations of it, with the intention of developing a framework that could guide the successful implementation and evaluation of the JOBS programme within the South African context. To gain a better understanding of the components related to the implementation of the JOBS programme, the contexts in which the programme have previously been implemented and the targeted population, as well as aspects regarding the programme, were studied. Based on the findings of the systematic review, as well as context-specific matters, a framework is proposed for the implementation and evaluation of the JOBS programme within the South African context (see Appendix 2 ).

In terms of contextual differences between developed countries (where the JOBS programme has previously been implemented), and developmental countries (e.g. South Africa), some differences are crucial to consider when implementing an employment programme, such as the JOBS intervention. While the unemployment rates of the involved developed countries averaged 12%, more than 27% (37.3% when including those who have stopped looking for employment; Stats SA 2018) of South Africans are currently unemployed. Moreover, it has been reported that 69% of these individuals have been unemployed for longer than a year (Stats SA 2018). In South Africa, unlike the other countries, unemployment grants safeguarding people from financial hardship are not available. Also, the unemployed are generally situated in rural areas isolated from major economic activity. With limited job opportunities, jobseekers feel discouraged, and deprived of a chance to compete in the labour market (Du Toit et al. 2018). Fortunately, the JOBS programme is specifically designed to deal with such conditions, yet it remains important to be cognisant of the impact of contextual factors on potential participants' state of mind.

With regard to participant-related matters, the reviewed literature showed that young and old, educated and less educated participants had previously benefitted from the JOBS interventions. However, it is important to note that South Africa has a youth unemployment rate of 52% (aged between 15 and 24; Stats SA 2018); 62% of the unemployed population have never even held a job before (Stats SA 2017); and 57% of South Africans have an education of less than matric (Grade 12). Therefore, although the unemployed in general could benefit from the programme, it is suggested to target vulnerable populations, such as younger, less educated and long-term unemployed individuals, as it may yield promising results.

Furthermore, participants from previous studies were reached at employment services offices. Because unemployment grants are not available in South Africa, participants cannot be reached on a large scale in a similar way. Therefore, different strategies of reaching the intended population should be considered. Suggestions include making use of newspaper and radio advertisements, and government agencies working with jobseekers, or working with youth and community leaders. One programme in particular tested the effectiveness of forced versus voluntary participation. Findings revealed that enforced participation did not increase re-employment and impaired the positive mental health impact of the programme (Malmberg-Heimonen & Vuori 2005). Giving participants the autonomy to participate voluntarily in the programme seems to yield more positive benefits. This may be an important finding for policymakers, as a precondition for receiving unemployment grants is often enforced attendance of a job search programme. Yet responsibility also rests with workshop trainers to be particularly devoted in creating an environment to which participants choose to return.

Considering the possibility that participants showing a preference to partake in employment programmes may be somewhat more intrinsically motivated, at the same time, it is those who show a higher risk of depressive symptoms that may benefit more (Vinokur et al. 1995a). Thus, careful attention should be paid to recruitment measures, ensuring that both the motivated and those who may be at risk of depressive symptoms are reached through recruitment methods, as they are equally important in achieving intended programme outcomes. Similarly, some programmes made use of screening questionnaires to identify participants at risk of poor mental health (Vinokur et al. 1995b); those who scored exceptionally high on depression symptoms or showed major signs of mental illness were omitted from the programme (Brenninkmeijer & Blonk 2011). As previously mentioned, given that many of the unemployed in South Africa may be severely discouraged, it is recommended to refrain from screening participants to identify high-risk cases, as it may result in the exclusion of participants who may benefit from the programme.

The next implementation aspect investigated related to programme-related matters. In line with previous adaptations of the JOBS programme, it is suggested to tailor the content of the manuals and activities to better suit the context and to increase cultural acceptability (Barry et al. 2006; Brenninkmeijer & Blonk 2011). Due to slow economic growth and the lack of skills in specific disadvantaged populations, changes in conditions of obtaining a job may be difficult (Vinokur & Price 2015). A solution that may fill both of these voids could be to consider fostering an entrepreneurial mindset among programme participants. People working in the informal sector often have a lower education level (although not lower wages) compared to those employed in the formal sector (Kim 2002), which may be a suitable solution within the South African context.

Furthermore, the ability to facilitate and understand group processes, build feelings of competence and create an environment of unconditional acceptance were essential requirements for facilitators. Yet education levels of the facilitators were not reported as particularly important. Due to the great demand for social work in South Africa, a shortfall of qualified social workers exists, which often results in employing people at social services offices who are less skilled and experienced (Collin 2017). Failure to grasp the importance of, and means of executing, the principal components of the programme may be problematic for the successful execution of the programme. Consequently, involving trainers knowledgeable and experienced in this area, while at the same time having the ability to relate to participants, should be considered. These may typically include individuals with higher degrees, coming from a similar background, who can also serve as role models for participants. Additionally, trainers should have the ability to adopt an individual-focused training approach, aimed at the enhancement of active learning among participants, instead of taking on the traditional role of teacher.

A noteworthy lesson was that the success of the programme lies greatly in the adherence to the designed protocols, as fewer of the anticipated outcomes were achieved when the protocol was neglected (Shirom et al. 2008). The majority of workshops included between 12 and 18 participants per group, as it was effective and economical. Ideally, delivery was guided by two training facilitators, as two were more capable of monitoring the behaviour and reactions of participants (Vinokur & Price 2015). Also, five half-day sessions, compared to longer two-week sessions, seemed to be more effective in keeping participants engaged. A vital lesson could be learned from the Finnish study that allowed for a debriefing day. During this session former appointed employees had an opportunity to deal with negative emotions caused by their dismissals (Vuori et al. 2005). Providing participants with such a venting opportunity may have made them more receptive to the programme.

Lastly, the founders of the JOBS programme strongly advised involving an effective champion, advocating for the programme at the policy level from the outset. It was also suggested that service delivery agencies be included that were open to applying innovative initiatives. Furthermore, a continuous flow of resources and funding seemed fundamental to the success and sustainability of large-scale programmes (Price & Vinokur 2014). In the South African context, economic development departments in local governments, supported by training providers, could act as champions of the JOBS programme.

This study also explored three elements (methodology, process and impact) related to the evaluation of the JOBS programme. The investigated studies were either conducted with a randomised field or quasi-experiment design as the chosen methodology, with self-reported questionnaires as the main data collection method. Considering the effectiveness of these designs in reporting the effectiveness and changes over time, it is suggested to use a similar approach. Furthermore, attrition was a pervasive problem experienced by most of the studies. However, offering incentives and recruiting more participants due to anticipated dropouts yielded higher attendance rates (Caplan et al. 1989; Shirom et al. 2008).

Aspects contributing to the process evaluation of the intervention included randomisation and manipulation checks of the studies included. To ensure internal validity, comparisons between the control and experimental groups' demographic and other variables were tested for possible biases. Furthermore, the strength and integrity of the various interventions were assessed by means of self-reported questionnaires at the end of each workshop. Several benefits can be gained from delivering a programme that is valid and reliable. Firstly, as mentioned earlier, adhering to programme protocols is strongly recommended, as the intended outcomes are achieved through reliable practices. Secondly, ensuring that participants experience the programme positively has previously been shown to increase engagement and, consequently, has led to other outcomes, such as decreased depression and anger, increased internal control, job-seeking self-efficacy and self-esteem (Vinokur et al. 1995a; Vinokur & Schul 1997).

With regard to the JOBS programme's impact, one of the most significant findings was the beneficial re-employment outcomes for those who had been unemployed for a moderate length of time (longer than a year; Brenninkmeijer & Blonk 2011; Vuori et al. 2002). Likewise, findings from examined literature also showed beneficial mental health and re-employment outcomes, particularly for high-risk participants (Vinokur & Schul 1997). These findings are valuable as it was found in a South African study that approximately 70% of the unemployed population was categorised as desperate or discouraged (Van der Vaart et al. 2018). The unemployed in both clusters generally came from poor socio-economic backgrounds, had relatively low levels of education, had limited opportunities for odd jobs or temporary employment, and were quite pessimistic. Given the capability of the JOBS programme to produce significant outcomes for high-risk participants, it appears that it could hold valuable outcomes, also for those who have been unemployed for long periods and may be truly discouraged.

Limitations and recommendations

Some limitations of this study need to be considered. Firstly, only peer-reviewed articles and book chapters that were written in English were included in the current study. Since the JOBS programme has been implemented in the Netherlands, Israel, Finland and China, where other official languages occur, the possibility of excluding potential articles exists. Secondly, access to some articles (Jobs in China project and Työhön trainers' manual) was limited, or they could not be found, resulting in their omission from the review (i.e. Fang & Ling 2001; Mäkitalo, Tervahartiala & Saarinen 1997; Price 2001). In the third place, due to the nature of intervention studies, it is possible that only studies yielding significant results were published. Although all versions of the JOBS programme known to the developers were reported, it is possible that there may be unpublished efforts. Consequently, meaningful lessons that could have been learnt from these papers were not available. However, much effort was invested in systematically searching for and including all possible studies. Lastly, the study did not include articles where the JOBS programme had been applied in work-to-school and organisational contexts. Although these programmes may have yielded valuable findings, these studies were omitted, as the aim of this study was to specifically focus on the most effective methods to assist the unemployed.

This study reviewed literature about the JOBS programme as a means of extending our knowledge of applying such a job search intervention in a South African context. Therefore, core aspects regarding the implementation and evaluation of the JOBS programme and variations of it were investigated. Specifically, implementation features such as contextual factors, participant characteristics and programme aspects were studied, while evaluation features included impact and process evaluation components.

Evidently, the success of the JOBS programme largely depended on following the protocol. Thus, studying the previously performed methods and outcomes of the JOBS programme, in various contexts, may serve as a valuable guideline to prescribe possible best practices. The integration of included literature and important aspects regarding the South African context produced a framework that could be valuable in the implementation and evaluation of the JOBS programme in South Africa.

Acknowledgements

This work was supported by the Experiences of Unemployment Research Project funded by the Flemish Interuniversity Council - University Development Cooperation (VLIR-UOS). We are truly grateful for this opportunity.

Competing interests

The authors have declared that no competing interest exists.

Authors' contributions

This publication was based on the PhD thesis of R.P. H.D.W., S.R. and A.V.d.B. were co-authors as well as supervisors of the project. R.B. made conceptual contributions to the manuscript.

Funding information

Flemish Interuniversity Council - University Development Cooperation (VLIR-UOS), ZEIN2013PR397.

Data availability statement

The main aspects regarding the implementation and evaluation of the JOBS programme and variations of it were tabulated. A summary table can be requested from the first author. The literature review comprised 34 studies; these articles can be requested from the first author.

The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policy or position of any affiliated agency of the authors.

Curran, J., Wishart, P. & Gingrich, J., 1999, JOBS: A manual for teaching people successful job search strategies , University of Michigan, Institute for Social Research, Michigan Prevention Research Center, Ann Arbor, MI.         [  Links  ]

Donaldson, S.I., 2012, 'Evaluation of the winning new jobs program', in S.I. Donaldson (ed.), Program theory-driven evaluation science: Strategies and applications , pp. 93-112, Routledge, New York.         [  Links  ]

Donaldson, S.I., Gooler, L.E. & Weiss, R., 1998, 'Promoting health and well-being through work: Science and practice', in X.B. Arriaga & S. Oskamp (eds.), Addressing community problems: Psychological research and interventions , pp. 160-194, Sage, Thousand Oaks, CA.         [  Links  ]

Donaldson, S.I. & Gooler, L.E., 2002, 'Theory-driven evaluation of the Work and Health initiative: A focus on winning new jobs', American Journal of Evaluation , 23(3), 341-346.         [  Links  ]

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Mäkitalo, M., Tervahartiala, T. & Saarinen, M., 1997, Työhön Työhöohjelma Ohjaajan käsikirja [Työhön program trainers' manual] , Finnish Institute of Occupational Health, Helsinki.         [  Links  ]

McCarthy, P., 2008, South Africa's 'door knockers': Young people and unemployment in metropolitan South Africa , Centre for Development and Enterprise, Johannesburg.         [  Links  ]

Michigan Prevention Research Center (MPRC), 2013, The JOBS project for the unemployed: Update , Michigan Prevention Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI.         [  Links  ]

Price, R.H., 2001, 'Winning new jobs in China. Preface' in L. Fang & W. Ling (eds.), Jobs in China: A seven city project , Institute of Psychology, Chinese Academy of Sciences, Beijing, Republic of China.         [  Links  ]

Price, R.H., Choi, J.N., & Lim, S., 2006, 'Beyond the iron rice bowl: Life stage and family dynamics in unemployed Chinese workers', in M. Warner & G. Lee (eds.), Unemployment in China , pp.123-142, Routledge, London, UK.         [  Links  ]

Price, R.H., Friedland, D.S., Choi, J. & Caplan, R.D., 1998, 'Job loss and work transitions in a time of global economic change', in X. Arriaga & S. Oskamp (eds.), Addressing community problems , pp. 195-222, Sage, Thousand Oaks, CA.         [  Links  ]

Price, R.H., Van Ryn, M. & Vinokur, A.D., 1992, 'Impact of preventive job search intervention on the likelihood of depression among the unemployed', Journal of Health and Social Behavior 33(2), 158-167.         [  Links  ]

Price, R.H. & Vinokur, A.D., 2014, 'The JOBS program: Impact on job seeker motivation reemployment, and mental health', in U. Klehe & E.A.J. van Hooft (eds.), Oxford handbook of job loss and job search , pp. 575-590, Oxford University Press, Oxford, UK.         [  Links  ]

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Received: 28 Feb. 2019 Accepted: 06 Nov. 2019 Published: 25 Feb. 2020

The context of the countries and states in which the JOBS programme have been implemented.

The overall findings regarding the implementation and evaluation of the JOBS programme.

Unemployment among young people and mental health: A systematic review

Affiliations.

• 1 Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Sweden.
• 2 Department of Public health Sciences, Karolinska Institutet, Public Health Agency of Sweden, Sweden.
• PMID: 31291827
• DOI: 10.1177/1403494819852847

Aim: The aim of this systematic review is to obtain a better understanding of the association between unemployment among young people and mental health. Methods : After screening the title and abstract of 794 articles drawn from four electronic databases, 52 articles remained for full-text reading. Of these, 20 studies met the inclusion criteria and were assessed on methodological quality. All steps were performed independently by two reviewers. Finally, a total of 17 articles were included in the systematic review. Results : Analysis of cross-sectional studies ( N = 5) showed an association between unemployment among young people and mental health. An effect of unemployment on mental health was found when considering cohort studies ( N = 12) that did not control for confounders (7/7). When controlling for confounders except mental health at baseline, this effect decreased in most studies leading to mixed results, although the majority (6/8) still found an effect. However, when taking mental health at baseline into account as one of the confounders, only a minority of studies (3/8) found a significant effect of unemployment on mental health. Conclusions : This systematic review showed an association between unemployment among young people and mental health. However, whether there is a causal relationship is less clear. More evidence from, for example, natural experiments and longitudinal studies that control for confounding variables, especially mental health at baseline, is required to better understand the association and potential causation between unemployment among young people and mental health.

Keywords: Unemployment; anxiety; depression; mental health; systematic review; young people; youth.

Publication types

• Systematic Review
• Cross-Sectional Studies
• Mental Health / statistics & numerical data*
• Unemployment / statistics & numerical data*
• Young Adult

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Analysis of the COVID-19 impacts on employment and unemployment across the multi-dimensional social disadvantaged areas

This is the study of economic impacts in the context of social disadvantage. It specifically considers economic conditions in regions with pre-existing inequalities and examines labor market outcomes in already socially vulnerable areas. The economic outcomes remain relatively unexplored by the studies on the COVID-19 impacts. To fill the gap, we study the relationship between the pandemic-caused economic recession and vulnerable communities in the unprecedented times. More marginalized regions may have broader economic damages related to the pandemic. First, based on a literature review, we delineate areas with high social disadvantage. These areas have multiple factors associated with various dimensions of vulnerability which existed pre-COVID-19. We term these places “ multi-dimensional social disadvantaged areas ”. Second, we compare employment and unemployment rates between areas with high and low disadvantage. We integrate geospatial science with the exploration of social factors associated with disadvantage across counties in Tennessee which is part of coronavirus “red zone” states of the US southern Sunbelt region. We disagree with a misleading label of COVID-19 as the “great equalizer”. During COVID-19, marginalized regions experience disproportionate economic impacts. The negative effect of social disadvantage on pandemic-caused economic outcomes is supported by several lines of evidence. We find that both urban and rural areas may be vulnerable to the broad social and economic damages. The study contributes to current research on economic impacts of the COVID-19 outbreak and social distributions of economic vulnerability. The results can help inform post-COVID recovery interventions strategies to reduce COVID-19-related economic vulnerability burdens.

1. Introduction: social disadvantage

Pandemics create severe disruptions to a functioning society. The economic and social disruptions intersect in complex ways and affect physical and mental health and illness ( Wu et al, 2020 ). Additionally, loss of jobs, wages, housing, or health insurance, as well as disruption to health care, hospital avoidance, postponement of planned medical treatment increase mortality, e.g., premature deaths ( Kiang et al., 2020 ; Petterson et al., 2020 ). The COVID-19, misleadingly labelled the “great equalizer” implies everyone is equally vulnerable to the virus, and that the economic activity of almost everyone is similarly impacted regardless of social status ( Jones & Jones, 2020 ). We set out to answer whether economic vulnerability is equally distributed during the COVID-19-caused economic recession or whether is it based on structural disadvantages? Is the social distribution of economic vulnerability magnified in regions with pre-existing social disparities, thus, creating new forms of inequalities? Knowledge of what areas experience the greater economic burden will help identify the most economically vulnerable communities relevant to post-COVID recovery interventions ( Qian and Fan, 2020 ).

Current studies on the impacts of COVID-19 largely focus on medical aspects including the COVID diagnosis and treatment ( Cai et al., 2020 ; Kass et al., 2020 ; O’Hearn et al., 2021 ; Price-Haywood et al., 2020 ). Non-medical urban research primarily concentrates on the impact of COVID on cities by studying factors related to environmental quality including meteorological parameters, and air and water quality ( Sharifi and Khavarian-Garmsir, 2020 ). COVID-related socio-economic impacts on cities are relatively less well studied, especially during the later stages of the recession.

Many pre-pandemic disparities unfold during COVID-19. To illustrate, residents of Black and Latino communities are suffering disproportionately higher unemployment rates, greater mortality due to the COVID-19 ( Thebault, Tran, & Williams, 2020 ; Wade, 2020 ), higher hospitalizations ( O’Hearn et al., 2021 ) and financial troubles. In contrast, some attributes make persons and communities more resilient. In China’s context, these include higher worker education and family economic status, membership in Communist Party, state-sector employment, and other traditional markers. These factors protect people from the pandemic-related financial stress and diminish its adverse economic effects ( Qian and Fan, 2020 ). Building on these recent studies on economic impacts, this social justice research focuses on areas with pre-existing social disadvantages. We study the role of social disadvantage and its impact on labor market during the COVID.

The distribution of economic vulnerability may potentially be related to COVID-19 conditions including those of economic burdens for people living in the pandemic epicenters ( Creţan and Light, 2020 ). Similarly, socio-economic disruptions create “a characteristic mosaic pattern in the region” ( Krzysztofik et al., 2020 , p. 583). The disruptions are strongly correlated with the spatial distribution of the COVID-19-related health effects. This study is set in Tennessee which is part of coronavirus “red zone” states of the US southern Sunbelt region. It is among the U.S. states with the highest rates of cases per capita, with 137,829 cases per 1 million people, or the 6th highest as of August 13, 2021 ( Worldometers, 2020 ; https://www.worldometers.info/coronavirus/country/us/ ). The study seeks to explore the impacts of social disadvantage on economy. The impact is measured by employment and unemployment in unprecedented times in the US context of prolonged disruptions to the health system, society, and economy intersecting in complex ways ( Kiang et al., 2020 ). We answer the following questions: (1) Do communities with high social disadvantage already burdened pre-COVID-19 by the lack of income, healthcare access, lacking resources, have less jobs available during the COVID-19 pandemic? (2) Do these areas simultaneously experience higher unemployment compared with other areas in the context of the pandemic?

The paper is organized as follows: Section 1 introduces the topic, provides the background information on social disadvantage and a brief description of the study implementation. It further discusses the links between employment and unemployment, and coronavirus, respectively, and introduces the study area. Section 2 describes in detail materials and methods used in the study. Section 3 provides the theory and calculations. Section 4 reports the results, and Section 5 offers a discussion. Finally, the paper concludes with conclusions found in Section 6 .

1.1. Background

Certain socio-economic and demographic conditions burden some communities more than others including racial and ethnic minorities, lower-income groups, and rural residents. The conditions include lacking economic opportunities and other inequalities ( Petterson et al., 2020 ) caused by social environment. Prior to the pandemic, it was challenging to live in areas with high social disadvantage where residents already have increased vulnerability to poor health due to greater psychosocial stress such as discrimination, unhealthy behaviors, and poorer health status ( Hajat et al., 2015 ). This is true for poor, marginalized communities elsewhere as spatial segregation of disadvantaged and marginalized communities decreases life opportunities for their members who have limited relationships with broader communities ( Méreiné-Berki et al., 2021 ). Within the context of studying disadvantaged urban communities, a recent work by Creţan et al. (2020) focused on the everyday manifestations of contemporary stigmatization of the urban poor using the case study of the Roma people who have been historically subject to state discrimination, ghettoization, inadequate access to education, housing, and the labor market for many decades in the past in multicultural urban societies of Central and Eastern Europe. The inequalities may persist and even increase if left unaddressed during pandemics ( Wade, 2020 ) leading to stark COVID-19-related health and economic disparities. Indeed, during the COVID-19, economic impacts of the pandemic disproportionately affect marginalized groups. The impact of coronavirus was harsh for those people as many of the already existing disparities unfold during COVID-19: black communities in the United States are disproportionately affected by higher death rates due to the COVID-19 virus ( Thebault et al., 2020 ), unemployment, and financial stress. Other growing COVID-19 research similarly suggests that elsewhere outside of the United States, areas that were disadvantaged prior to the pandemic with high rates of poverty and unemployment tended to be affected the strongest by the COVID-19 with the largest concentration of cases, while other spatially segregated ethnicity-based communities (e.g., the Roma) that have been vulnerable decades prior to COVID-19, saw an increase in the existing discrimination and stigmatization experiencing greater marginalization even during the current COVID-19 pandemic period ( Crețan & Light, 2020 ).

To achieve greater economic stability, and secure a dynamic labor market, countries in the global north and south for several decades have been increasing service employment much of which is low wage. The recent book Corona and Work around the Globe ( Eckert and Hentschke, 2020 ) describes the tremendous impact of the pandemic on human life and livelihoods as it sheds light on various experiences of workers during COVID-19 in various countries. Among the dramatically different cases worldwide, Germany which for decades has been promoting the low-wage sector to combat unemployment, provides a good example. The official approach to handling a disease differed substantially depending on whether the infected individuals were working people from the low- or upper-wage sector of the economy: applying a strict lockdown to the entire high-rise building where ethnic workers lived and preventing them from going to work in the former case and granting permission to work from home in the latter ( Mayer-Ahuja, 2020 ). The plight of the agricultural migrant workers who come to Germany from Eastern and Southeastern Europe, subjected during the pandemic to low wages or no payments and poor working and living conditions, however, is shared among the workers of low-wage sector across all countries who are more likely to get infected due to higher exposure and direct contact, but often experience unfair treatment based on ethnicity, migration and class status.

In yet another case set in the U.K., disadvantaged households have experienced intensified disadvantage during the COVID-19 as they could not access vital necessities, already stretched for resources pre-COVID-19. As provision of services or employment was discontinued due to their closure, disadvantaged households had significant impacts on their income level, mental health and wellbeing, education, nutrition, and domestic violence. In the absence of the key support of public institutions including schools, community centers, and social services, care for the most vulnerable members such as elderly, children, the disabled, have been absorbed by households ( Bear et al., 2020 ).

Another aspect experienced by workers during the pandemic is the total loss of earnings which is especially harsh in places with precarious employment even under normal circumstances. Informal workers in India who represent the vast majority of working population (over 93%), with no social security benefits and absent job security, experienced prolonged periods of time of no work due to lockdown and suspended transport services preventing them from getting to their workplaces, many on the verge of starvation ( Banerjee, 2020 ). This study looks into this aspect of COVID-19 economic impacts and confirms the findings of the growing COVID-19 research.

However, not only the poorest and marginalized people, but also marginalized regions are more likely to suffer from broader social and economic damages related to the pandemic compared with more privileged areas ( Creţan and Light, 2020 ; Krzysztofik et al., 2020 ). When disadvantages combine, it may lead to environment-driven COVID-19-related disparities in health. Besides a direct health effect, disadvantaged communities are disproportionally experiencing other side effects of COVID-19 such as negative labor market outcomes including forced unemployment, loss of income and social isolation. Studies found the extreme vulnerability of cities and urban areas exposed during the global pandemic ( Batty, 2020 ; Gössling et al., 2020 ). We argue that rural areas may be equally vulnerable to the broad range of social and economic damages if there is a spatial concentration of factors related to various dimensions of vulnerability.

This study is situated in the context of social disadvantage. Prior studies developed the methodology of the delineation of disadvantaged residential communities proxied by low-income workers ( Antipova, 2020 ). Disadvantaged low-income workers can be defined as those with inadequate access to material and social resources in the study area. However, this is a narrow approach which uses only a single dimension of a disadvantage, that of worker low earnings and misses other social inequality indicators. Accordingly, an approach adopted in this study identifies areas where socio-economic and demographic attributes each associated with multiple dimensions of social disadvantage are spatially co-locating. Spatial segregation of disadvantaged and marginalized communities decreases life opportunities for their members who have limited relationships with wider communities ( Méreiné-Berki et al., 2021 ). We identify these attributes based on a thorough literature review. Thus, we simultaneously consider multiple factors associated with disadvantage capturing a multi-dimensional social disadvantage. To meet the objective, we integrate geospatial science with the exploration of predictive geographic and social factors associated with disadvantage across counties in TN. The geospatial analysis includes point interpolation within the Geographic Information System (GIS) environment for the generation of a surface from a sample of social disadvantage values. This allowed us to visualize the spatial extent of disadvantaged communities. The focus is on labor market outcomes which are important indicators of society well-being. We study the association between pre-existing inequalities and COVID-19-related employment and unemployment rates. Thus, we identify the role of social disadvantage on labor market conditions in the context of the ongoing pandemic-caused economic recession.

Prior research determined the key metrics of social disadvantage. Conditions contributing to various aspects of disadvantage include poverty, occupations with low earnings, low rent, segregation and discrimination-related residential concentrations of minorities, and exposure to poor air quality ( Bullard, 2000 ). The recent COVID-19-related literature focuses on the separate effect of minorities, Hispanics, crowded households, dense areas, obesity, poverty, air pollution exposure and identifies those as important COVID-19 health risk factors ( Finch & Hernández Finch, 2020 ; Golestaneh et al., 2020 ; Han et al., 2020 ; Millett et al., 2020 ). These community-level variables result in neighborhood disadvantage comprising sub-standard housing quality, crowded conditions, poverty- and violence-caused stress which combined increase the risk of disease and other negative outcomes in life among socially disadvantaged groups ( Malhotra et al., 2014 ). The demographic and socio-economic attributes selected to represent the various aspects of social disadvantage in this research include minorities and ethnicities, poverty, housing crowdedness, educational attainment, underlying population health conditions, and pre-COVID-19 unemployment which may collectively drive a greater vulnerability to the COVID-19 infection and mortality as well as loss in employment and higher unemployment. It is challenging to isolate the separate effects of the multiple risk factors. By “critically analyzing the theoretically intended meaning of a concept” ( Song et al., 2013 ), a composite variable can be created to logically represent a multi-dimensional social disadvantage .

The following subsection briefly describes study implementation. First, we locate areas of disadvantage where multiple factors associated with various aspects of disadvantage co-locate spatially and term these places “multi-dimensional social disadvantaged areas”. Then, we examine how employment and unemployment were impacted in these already socially vulnerable areas. We map geographical inequalities in employment and unemployment rates during the period of COVID-19-related economic recession. For the first objective, we identify socially disadvantaged counties within TN which is part of coronavirus “red zone” states of the US southern Sunbelt region applying consistent criteria. For the second objective, we compare employment and unemployment outcomes between areas with high and low disadvantage.

1.1.1. Employment and coronavirus

This subsection discusses the role of employment and how it was impacted by the COVID-19-caused economic recession. The literature recognizes the complex interrelationship between employment and overall health and well-being. Negative COVID-19 impacts on urban economy include loss of citizens' income, while movement restrictions and ‘stay home’ measures adversely impacted tourism and hospitality and small- and medium sized businesses due to the closure of markets, food outlets and social spaces ( Wilkinson et al., 2020 ).

Millions of essential or blue-collar workers are still doing their jobs out of necessity and because they cannot telecommute and work jobs that cannot be done from home and have higher exposure to the virus. Some racial groups disproportionally have jobs that do not allow them to work from home and where social distancing is a challenge. Prior studies find that workplaces of low-income individuals tend to be close to their residential spaces, and disproportionately concentrated in lower-wage industries such as hospitality and retail services ( Antipova, 2020 ). These industries commonly represent essential services experiencing higher exposure to the COVID virus through workplaces. At the same time, minorities and lower-income groups often live in inner-ring suburbs with older housing and aging infrastructure ( Antipova, 2020 ) in multiunit structures and in multigenerational households which inhibit the ability to practice social distancing increasing the risks of disease occurrence and deaths ( Qualls et al., 2017 ). In addition, minorities and lower-income groups have fewer options for protecting both their health and economic well-being ( Gould and Wilson, 2020 ). Nearly two-thirds of Hispanic people (64.5%) considered at high risk for coronavirus live with at least one person who is unable to work from home, compared to 56.5% of black and less than half (47%) of white Americans, according to a recent study ( Selden and Berdahl, 2020 ).

Despite the pandemic-induced layoffs, job hires have occurred by major retailers such as Walmart and e-commerce giant Amazon, and takeout and delivery-based services such as Domino’s Pizza and Papa John’s which may become permanent positions. These workplaces may match the job skill sets of low-income residents of vulnerable communities. However, oftentimes many low-income workers benefitted less, even when jobs were created during the COVID-19. To illustrate, big technology companies (i.e., communication services: Netflix, Tencent, Facebook, T-Mobile; information technology: Microsoft, Nvidia, Apple, Zoom Video, PayPal, Shopify; consumer discretionary: Amazon, Tesla, Alibaba, etc.) prospered in the pandemic with the financial success measured by equity value added ( Financial Times, 2020 ). Workers who lost jobs in low-income segment such as hospitality sector may be hired by retailers such as Kroger or CVS. However, many others from the communities with high social disadvantage may not have a skill set needed at technology firms that benefit from the working from home trend and hire skilled workers including software engineers and product designers. Cross-industry employment shifts plays a minor role in total job creation, while employer-specific factors primarily account for job reallocation ( Barrero et al., 2020 ).

1.1.2. Unemployment and coronavirus

This subsection discusses how unemployment was impacted by the COVID-19-caused economic recession. An economic recession occurs when there is a substantial drop in overall economic activity diffused throughout the economy for longer than a few months. While past recessions were driven by an inherently economic or financial shock, the current recession is caused by a public health crisis ( Weinstock, 2020 ). COVID-19 caused a drop in consumer demand across all industrial sectors resulting in economic recession and massive unemployment where not only hourly workers but salaried professionals lost their jobs ( Petterson et al., 2020 ). A range of factors contributed to the spatial variation in economic damage including the share of jobs in industries delivering non-essential services to in-person customers ( Dey and Loewenstein, 2020 ), declines in personal consumption caused by individual fears of contracting COVID-19 ( Goolsbee and Syverson, 2020 ), and the implementation of social policies including stay-at-home orders and business shutdowns ( Gupta et al., 2020 ).

Unemployment rate is defined as a percentage of unemployed workers in the total labor force. The rate is published monthly by the Bureau of Labor Statistics (BLS) which uses both the establishment data (captured by the Current Employment Statistics program) and household surveys (Current Population Survey) to generate the labor market data ( Bureau of Labor Statistics (BLS), 2020b ). A person is unemployed if they were not employed during the survey’s reference week and who had actively searched for a job in the 4-week period ending with the reference week, and were presently available for work ( BLS, 2020b ).

Caused by the COVID-19, the unemployment rate reached a peak in April 2020 at 14.7% nationwide, an unprecedented joblessness amount since employment data collection started in 1948. It exceeded the previous peaks during the Great Recession and after ( Falk et al., 2020 ). The official unemployment rate may have been over 20%, since the actual level of joblessness could have been understated due to local unemployment rate measurement errors ( Coibion et al., 2020 ). In addition, the unemployment rate was understated due to a geographically widespread misclassification of those who was not at work but considered employed and non-inclusion of labor force non-participants who still counted as employed ( Bureau of Labor Statistics (BLS), 2020a ). Further, the COVID-19 caused the rapid rate of change in unemployment at the national level challenging accurate forecast of the monthly unemployment rate ( Weinstock, 2020 ).

Overall, current unemployment (using the most recently available county-level data at the time of writing for December 2020) is still elevated and is almost twice as high as it was back in February 2020 which represented the business cycle peak with the peak of payroll employment. March 2020 was the first month of the subsequent current economic recession as declared by The National Bureau of Economic Research (NBER, 2020) caused by the COVID-19 pandemic which turned out the worst downturn after the Great Recession. As Fig. 1 shows using the Current Population Survey data (Series ID: LNS14000000) from the BLS, during the prior recessions the unemployment rate rose gradually reaching its peak, and in the pandemic-caused recession it increased unprecedentedly to its peak over one month, from March 2020 to April 2020 by 10.3% (from 3.5% in February 2020 to 4.4% in March 2020 to 14.7% in April). After that, the rate declined as workers continued to return to work to 6.3% in December 2020.

U.S. Historical unemployment rate for workers 16 years and over, January 1948 to December 2020, % (seasonally adjusted).

Some communities can absorb the impact of economic downturns due to more favorable economic and social factors protecting residents from adversity. Yet other communities are witnessing the effect of rising unemployment in the time of COVID-19. Loss of income and livelihood has further effects: as wages drop, more people are forced into poverty while simultaneously people's health is impacted. Unemployment impacts all-cause mortality. Fig. 2 presents the dynamics of unemployment distribution across counties in TN for the selected months. Shown are pre-COVID-19 unemployment rates as of August 2019 ( Fig. 2 a), followed by May 2020 ( Fig. 2 b) where even the lowest levels of unemployment exceed the highest rates of the pre-pandemic period even in wealthy counties around Nashville (seen in the legend entries), August 2020 ( Fig. 2 c), and September 2020 ( Fig. 2 d). The overall unemployment abates somewhat during the later stage, and the general spatial pattern resembles that of the pre-COVID-19 period with higher unemployment concentrated in the southwestern corner of the state around Memphis.

Dynamics of unemployment rate across counties in TN for selected months: (a) August 2019, (b) May 2020; (c) August 2020; (d) September 2020.

1.1.3. Study area

Tennessee is home to large cities including Nashville (the county seat), Memphis, Knoxville and Chattanooga. Despite urban diversified economy, there was a steep decline in the number of international and domestic tourists impacting urban economy. Among cities listed above, Memphis, located in Shelby County, is a shrinking city with a declining population base. Urban shrinkage makes cities more vulnerable due to very negative impacts on urban economy. Shrinking cities are characterized by higher unemployment rates, depopulation (as people with higher economic and social status leave elsewhere), and a higher share of older people (increasing a share of individuals with underlying health conditions) ( Haase et al., 2014 ; Hartt 2019 ; Hoekveld 2012 ; Krzysztofik et al., 2020 ). The shrinking cities have higher exposure to extreme socioeconomic phenomena, including financial stress due to the decreases in the city’s budget. Decreasing budget in its turn has further urban development implications since implementation of some plans deemed of lesser priority such as environmental and cultural may be delayed and cancelled altogether ( Kunzmann, 2020 ; Sharifi and Khavarian-Garmsir, 2020 ).

Tennessee is one of the US southern Sunbelt states which had infection surges since summer 2020 due to the aggressive push for economy opening by then-President Trump administration. The pandemic has affected unemployment for every state in the United States ( Falk et al., 2020 ). Fig. 3 portrays selected industries impacted by the economic recession in Tennessee using seasonally adjusted data on employees on nonfarm payrolls for November 2019 (as a base period), September–November 2020. Unemployment rates concentrate disproportionately in sectors providing in-person non-essential services where some demographic groups are overrepresented. This results in substantially higher unemployment rates for those workers ( Cortes and Forsythe, 2020 ; Fairlie, 2020 ). Accordingly, it can be seen in Fig. 3 that in Tennessee, among the reported industries, leisure and hospitality has suffered the most, followed by jobs in government, education and health services, professional and business services, and trade, transportation, utilities. There was a slight increase in jobs in financial activities from 2019 to 2020 ( Bureau of Labor Statistics (BLS), 2020a ). The hardest hit industries tend to employ demographic groups such as women, minorities, low-income workers, and younger workers who have experienced greater job losses ( Murray and Olivares, 2020 ).

Employees on nonfarm payrolls by selected industry sector, seasonally adjusted, in TN.

2. Materials and methods

In the absence of fine-scale monthly data on employment and unemployment, we sourced county-level data from the Bureau of Labor Statistics (BLS) to track monthly changes in employment and unemployment in Tennessee (retrieved from https://www.bls.gov/lau/ ). Labor force data were extracted from this official primary source.

We used a comparative assessment approach to analyze the COVID-19-based labor market outcomes including the rates of COVID-19-related employment and unemployment attributable to social disadvantage conditions. For this, we stratify data based on community disadvantage status, and combine data in a comparative assessment framework. We proceed and identify disadvantaged communities using the methodology described below. Next, we test the hypothesis that in areas with high social disadvantage where more essential workers are more likely to reside, the unemployment is higher while employment opportunities are lower by comparing unemployment and employment rates within these communities to those of more privileged communities.

3. Theory/calculation

We focus on the areas where the multiple risk factors identified in the recent literature co-locate spatially and term these places “ multi-dimensional social disadvantaged areas ”. We carried out a rigorous literature review of the variables to stand in for social disadvantage in this research. The following demographic and socio-economic factors have been selected to represent community’s vulnerability: (1) Minorities and ethnicity; (2) Crowded households; (3) Poverty; (4) Education; (5) Underlying medical conditions (obesity); and (6) Unemployment. For the 1st variable, minorities and ethnicity , we used percent minority population and Hispanic ethnicity as studies commonly use race and ethnicity as vulnerability metrics (as explained in Section 2 Background information). For the 2nd variable, crowded households , we used percent households that are multigenerational as an indicator of crowdedness, and thus, indicating area’s disadvantage with a high share of such households. For the 3rd variable, poverty , we chose percent of households below 100% of federal poverty level which is also known as the poverty line. It is an economic measure of income. The poverty guidelines are updated annually by the US Department of Health and Human Services to indicate the minimum income needed by a family for housing, food, clothing, transportation, and other basic necessities and to determine eligibility for certain welfare benefits. This measure was used because less affluent and less privileged households have fewer means and less access to various resources to cope with the effects of financial crises ( Pfeffer et al., 2013 ). Low-income households may be especially vulnerable to wage losses during the outbreak ( Qian and Fan, 2020 ). For the 4th variable, education , we used percent of population with less than high school diploma since lower educational attainment is an indicator of poverty and thus captures social disadvantage, while workers with better education have higher economic resilience when challenged with a large-scaled social shock ( Cutler et al., 2015 ; Kalleberg, 2011 ). For the 5th variable, underlying medical conditions , we used percent population with obesity as the top risk for COVID-19-related hospitalization. Supported by several lines of evidence, both domestically and internationally, obesity may predispose to more severe COVID-19 outcomes ( O’Hearn et al., 2021 ). Finally, for the 6th variable, unemployment , unemployment rate (averaged from August 2019 to January 2020 to adjust for seasonality) was used as a marker of overall vulnerability as it is linked to overall mortality. Further, regions with higher unemployment are more susceptible to business-cycle fluctuations, and thus, are more socially and economically vulnerable.

These socio-economic and demographic attributes (minority population, Hispanic ethnicity, federal poverty level, crowded households, adult obesity, lower educational attainment, and unemployment) have been used in this research to create a composite variable to represent a multi-dimensional social disadvantage (also referred to as vulnerability). Due to different variances in the original variables, we standardized them to prevent a disproportionate impact which may be caused by any one original variable with a large variance. The z-score transformation was applied by averaging the original variables and computing z scores with a mean of 0 and values ranging from negative to positive numbers ( Song et al., 2013 ).

Thus, the original variables were converted to z-scores to preserve the distribution of the raw scores and to ensure the equal contributions of the original variables. Next, we created a composite variable capturing a multi-dimensional social disadvantage. It was calculated by summing standardized z-scores of the original risk factors. The higher value can be interpreted as higher disadvantage while the lower value means more privileged communities. Based on the frequency distribution of values of the composite variable, we established a cut-off value for the composite variable to designate communities with high or low exposure to social disadvantage. We used the following method to determine the cut-off value of the composite variable. The values greater than 3.38 correspond to 1 standard deviation above the mean (or, the 88th percentile in the value distribution) indicating communities in the top 12 percent of social disadvantage and therefore, a higher share of factors contributing to disadvantage. This value was used to differentiate communities according to their disadvantage status. We identified twelve counties with high social disadvantage (N high  = 12), and other counties represent more privileged communities (N low  = 83). To test whether the taken approach correctly identifies disadvantaged communities, we conducted a Wilcoxon two-sample test for the variables of interest ( Table 1 ). We report the results of the estimates in the following section. The above socio-economic and demographic population characteristics come from the 2018 American Community Survey (ACS) 5-year data, an annual nationwide survey conducted by the US Census Bureau, available for various geographic units and applied for areal units within the study area ( U. S. Census Bureau, 2020 ).

Descriptive statistics.

The basic descriptive demographic and socio-economic characteristics of the TN population are shown in Table 1 . It includes the summaries for communities with high and low social disadvantage allowing to compare the variables of interest between these communities. The following variables are reported: percent African American, percent Hispanic, median income, percent of people over 25 years who are less than high school graduates, estimated percent of obese adults, percent households below 100% of federal poverty level, and percent of multi-generation households. The factors comprising social disadvantage were statistically significantly different than those extant in more privileged counties. Compared with the general TN population, the disadvantaged cohort was generally more likely to be of non-Hispanic Black race; more impoverished; with less educational attainment, more obese, and had more households with crowded conditions.

To visualize social disadvantage and show how it varies across the space, we used our sample of social disadvantage measurements and created a surface of social disadvantage within the study area using the Geographic Information System (GIS). The interpolated surface was derived from an Inverse Distance Weighted technique ( Watson and Philip, 1985 ). Fig. 4 presents the surface illustrating that both urban and rural counties in Tennessee are subject to social disadvantage.

Social disadvantage within the study area.

We examined how unemployment changed from August 2019 to December 2020. Currently, all counties have substantially higher unemployment compared with that prior to COVID. Fig. 5 presents the results of the Nonparametric One-Way ANOVA test showing the distribution of Wilcoxon scores for unemployment rate for all counties in Tennessee combined, regardless of social disadvantage status, for 17 months. A statistically significant difference is found for unemployment rates between the pre-COVID period and the period since April 2020, with current unemployment rates although decreased but still significantly higher compared with those prior to the recession.

Nonparametric One-Way ANOVA and distribution of Wilcoxon scores for unemployment rate for all counties combined for 17 months (August 2019–October 2020), regardless of social disadvantage status.

We compared employment and unemployment rates for Tennessee counties stratified by the type of social disadvantage separately for each month. Fig. 6 presents the average employment and unemployment rates by community disadvantage from August 2019 to December 2020 in a graphical form. The results of the non-parametric Wilcoxon test for employment and unemployment rates are presented in Table 2 . Pre-COVID and before the unemployment peak in April 2020, communities with high social disadvantage consistently had less jobs and greater unemployment, which we tested statistically and found a significant difference for both outcomes of the labor market between communities by their disadvantage status ( Table 2 ). Shown in Table 2 , in April and May 2020, during the peak of unemployment and immediately after, unemployment rates observed in both types of communities were high with no statistical difference. In June, the differences again became prominent, when there were more jobs available in more advantaged areas and employment rate remained consistently greater in areas with less disadvantage. Also in June, unemployment rate remained consistently greater in areas with higher disadvantage. This month saw the greater difference in both outcomes since the COVID-19 than pre-pandemic (supported by higher p-values). Compared with all TN population, residents of disadvantaged counties had less jobs available and were more likely to be unemployed during all periods except for April and May.

Mean employment and unemployment stratified by community disadvantage status.

Wilcoxon Two-Sample Test: Distribution of Wilcoxon scores in employment and unemployment rates by community disadvantage status by month (August 2019–December 2020).

We examined the percent change in both labor market outcomes. Fig. 7 presents the percent change in mean employment ( Fig. 7 a), and mean unemployment by community disadvantage ( Fig. 7 b). The percent change in employment and unemployment was relatively small in both types of community during the pre-COVID period. However, the overall fluctuations in both conditions were greater in communities with high social disadvantage (evidenced by a greater range between ups and downs for disadvantaged communities shown with the black-colored symbols). On the other hand, employment and unemployment were more stable in more privileged communities (shown with the grey-colored symbols in the Fig. 7 ). During the unemployment peak in April 2020, the change in percent employment was −11.5 points from the previous month even in more advantaged counties, while the unemployment in April increased by 10.42 percentage points in disadvantaged counties.

Percent change in (a) mean employment; (b) mean unemployment by community disadvantage.

We show how various factors of social disadvantage intersect and combined impact economic vulnerability measured by unemployment rate. Fig. 8 reports the link between unemployment and social disadvantage pre-COVID (unemployment rate was averaged over August 2019–January 2020 in Fig. 8 a), and during COVID (unemployment rate for November 2020 is shown in Fig. 8 b). During the COVID pandemic, its impact is even stronger as evidenced by a greater slope of the line of fit, larger coefficients, and a greater R-squared value ( Fig. 8 b). The strong relationship between these factors of social disadvantage and economic outcomes in COVID-19 might inform post-COVID recovery intervention strategies to reduce COVID-19-related economic vulnerability burdens. For example, in the light of findings on socio-economic and demographic subpopulations at a higher risk for economic damages, prioritization of economic relief distribution might be based on community disadvantage status targeting individuals from areas with existing inequalities to increase economic resilience of marginalized communities.

Unemployment and Social disadvantage: (a) pre-COVID (averaged August 2019–January 2020); (b) during COVID (November 2020).

5. Discussion

Current studies on the impacts of COVID-19 tend to focus on medical aspects while non-medical urban research mostly analyzes the role of environmental quality. To better understand the full effects of pandemics on communities and minimize the various impacts as well as to improved response, other aspects need to be examined. This includes studying less researched themes including socio-economic impacts consisting of both social impacts and social factors making individuals and communities less resilient and more vulnerable to the effects of the COVID. Additionally, economic impacts of the pandemic-caused recession so far remain relatively underexplored and need to be investigated ( Sharifi and Khavarian-Garmsir, 2020 ).

Communities are often severely segregated along wealth and social lines in developing and developed world ( Wilkinson et al., 2020 ). We study the role of social factors and the impact of the COVID on labor market conditions in Tennessee. Specifically, we studied the impacts of social environment on employment and unemployment through the concept of a multi-dimensional social disadvantage by using geospatial science.

A recent study identified factors which can make a community more vulnerable to the pandemic’s effects using as a case study the province of Silesia in Poland, one of the largest industrial and mining regions in Europe. Specialized functions such as mining-oriented industries, large care centers, polycentricity, and urban shrinkage make communities most at risk due to very negative impacts on urban economy ( Krzysztofik et al., 2020 ). Since vulnerability is always very context-specific, we found a combination of different causal factors of social disadvantage captured by a composite variable making communities most at risk during the COVID reflected in broader social and economic outcomes. In creating a composite variable to capture social disadvantage logically and meaningfully, the following variables were used: % African American, % Hispanic, % below 100% federal poverty level, % population with less than high school diploma (an indicator of poverty), % multi-generation households (an indicator of crowdedness), % estimated obese adults reporting to be obese with the BMI 30 or greater, % unemployed. The proposed method can be generalized beyond the study area and used as a tool by policy makers using consistent criteria for the delineation of areas carrying a greater risk for the more severe impact by the pandemic due to co-existence and co-location of the multi-dimensional social disadvantage factors which are more likely to experience further socio-economic disruptions.

Current urban research on COVID economic impacts found that some cities are more vulnerable than others and are most at risk. Cities with an undiversified economic structure with industries where a large number of workers are shoulder-to-shoulder share cramped spaces for a prolonged time and where social distancing is challenging (e.g., meat-packing and poultry processing plants), cities relying on tourism as well as cities that have large care centers, polycentric cities, and shrinking cities are the most vulnerable to negative impacts on urban economy. The urban hotel market, city tax revenues, citizens' income, tourism and hospitality, small- and medium sized firms, urban food supply chain, and migrant workers are all impacted ( Krzysztofik et al., 2020 ). Other recent studies similarly concluded that the COVID has revealed the extreme vulnerability of cities and urban areas disrupting tourism and affecting supply chains in cities ( Batty, 2020 ; Gössling et al., 2020 ). We support this statement but also find that rural areas can experience a broad range of social and economic damages related to COVID.

Before and during the COVID-19 period, money laundering, limitations of economic development, environmental pollution and uncontrolled deforestation, population displacement, institutional incompetence, and corruption of political elites have been debated including corruption and conflagration in Bucharest before the pandemic ( Creţan & O’Brien, 2020 ), as well as other contestations on selling masks and different medical products highlighted in different countries during the pandemic period. Following catalytic events, the affected community may respond to long-held concerns with demands to address these problems bringing about important changes to the systems. Marginalized stigmatized minorities may effectively overcome discriminatory laws, higher poverty and other constraints and influence public opinion and politics in their favor through collective action via various strategies including protests against corruption and the inaction of the political leaders in Romania in 2015 forcing the resignation of the Government, and protests in the US in the aftermath of police violence against black people have been documented ( Creţan & O’Brien, 2020 ; Fryer, 2019 ). During the COVID-19, the non-payment of wages and poor working and living conditions caused seasonal workers in Germany to protest against this unfair treatment, however, generating low coverage in the national press ( Mayer-Ahuja, 2020 ).

6. Conclusions

Some socio-economic and demographic conditions consistently and significantly impact some communities more often than others, particularly based on ethnic minority status, low income, and rural location. The conditions include systemic issues such as fragmented health care system (within which some individuals do not get health care in a timely fashion), racism and structural disparities in education, income, wealth, a consistent lack of economic opportunity, environmental factors, transportation and housing ( Petterson et al., 2020 ). These factors interact in complex ways resulting in persisting social environment-driven health and other inequalities which if left unaddressed will only increase.

Respectively, among policies goals across the Global North enhancing wellbeing and social mobility for disadvantaged and marginalized families, creating socially mixed, heterogeneous neighborhoods (that is, desegregation) is promoted to avoid spatial segregation based on racial and ethnic membership and class while supporting social cohesion ( Méreiné-Berki et al., 2021 ). Importantly, a marginalized community is not a homogeneous group as the lived experience of disadvantage within the communities is variegated: respectively, policies to improve socio-spatial integration and addressing the various causes of extreme poverty including social, economic, and cultural that improve social equity have been suggested since desegregation on its own is insufficient (( Méreiné-Berki et al., 2021 ). Sustainable planning may mitigate consequences of urban sprawl noted in the urban studies literature including urban blight which is the greatest in poorest areas entrapping the low-income residents in the inner city where they have only limited regional mobility and access to job opportunities at the urban edge. Understanding the links between a development of a metropolitan-wide blight remediation strategy toward a sustainable urban form and welfare enhancing among the disadvantaged populations needs to be further investigated.

During public health crises, the importance of the central role of the community has been highlighted especially when some state-based social services may be less available due to lockdown. Rather than inventing new solutions, voluntary informal social networks that have been generated by communities utilize local assets and resources ( Bear et al., 2020 ). Community-based initiatives may rely on the voluntary sector, faith- and charities-based organizations, and social enterprises for various services including help with visiting housebound people, or using them as a distribution hub for food distribution to families in need.

In conclusion, in this study, we situated the research on economic impacts of the COVID in the broader context of social disadvantage with findings both domestically and from other countries in line with those in our study. The earlier misleading view of the global epidemic representing a systematic disadvantage that may affect and limit everyone’s economic activity, with any socioeconomic status or from any geographic location, was rejected. Our finding indicates that certain factors may increase people's vulnerability to the financial stress related to COVID-19. We find support that the social distribution of economic vulnerability is magnified in regions with pre-existing social disparities, creating new forms of disparity ( Qian and Fan, 2020 ).

This work was supported by the UTHSC/UofM SARS-CoV-2/COVID-19 Research CORNET (Collaboration Research Network) Award.

CRediT authorship contribution statement

Anzhelika Antipova: Conceptualization, Formal analysis, Methodology, Visualization, Writing – original draft, Writing – review & editing.

Declaration of competing interest

The author declares no conflict of interest.

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The independent source for health policy research, polling, and news.

The Relationship Between Work and Health: Findings from a Literature Review

Larisa Antonisse and Rachel Garfield Published: Aug 07, 2018

• Issue Brief

A central question in the current debate over work requirements in Medicaid is whether such policies promote health and are therefore within the goals of the Medicaid program. Work requirements in welfare programs in the past have had different goals of strengthening self-esteem and providing a ladder to economic progress, versus improving health. This brief examines literature on the relationship between work and health and analyzes the implications of this research in the context of Medicaid work requirements. We review literature cited in policy documents, as well as additional studies identified through a search of academic papers and policy evaluation reports, focusing primarily on systematic reviews and meta-analyses. Key findings include the following:

KFF review: Research about the relationship between work and health finds only limited evidence that employment improves health, with some studies showing a positive impact and others showing no relationship or only limited effects.

• Job availability and quality are important modifiers in how work affects health; transition from unemployment to poor quality or unstable employment options can be detrimental to health.
• Selection bias in the research (e.g., healthy people being more likely to work) and other methodological limitations restrict the ability to determine a causal work-health relationship.
• The work-health relationship may differ for the Medicaid population compared to the broader populations studied in the literature, as Medicaid enrollees report worse health than the general population and face significant challenges related to social determinants of health.
• Limited job availability or poor job quality may moderate or reverse any positive effects of work.
• Work or volunteering to fulfill a requirement may produce different health effects than work or volunteer activities studied in existing literature.
• Loss of Medicaid coverage under work requirements could negatively impact health care access and outcomes, as well as exacerbate health disparities.

Introduction

On January 11, 2018, CMS issued a State Medicaid Director Letter  providing new guidance for Section 1115 waiver proposals that would impose work requirements (referred to as community engagement) in Medicaid as a condition of eligibility. On January 12, 2018, CMS approved the first work requirement waiver in  Kentucky , and three additional work requirement waiver approvals followed in  Indiana  (February 1, 2018), Arkansas (March 5, 2018), and New Hampshire (May 7, 2018). The new guidance and work requirement approvals reverse previous positions of both Democratic and Republican Administrations, which had not approved work requirement waiver requests on the basis that such provisions would not further the Medicaid program’s purposes of promoting health coverage and access. However, in both the new guidance and work requirement waiver approvals, CMS explains its policy reversal by maintaining that employment leads to improved health outcomes, and policies that condition Medicaid eligibility on meeting a work requirement will further this objective. Though the structure of work requirements is similar to those used in other programs, the administration’s stated goal of  improving health through Medicaid work requirements is different from the goals of welfare reform work requirements in the past, which were to strengthen self-esteem and provide a ladder to economic progress.

On June 29, 2018, the DC federal district court vacated HHS’s approval of the Kentucky Section 1115 waiver program. The court held that consideration of whether the waiver would promote beneficiary health in general is not a substitute for considering whether the waiver promotes Medicaid’s primary purpose of providing affordable health coverage and remanded to HHS to consider how the waiver would help furnish medical assistance consistent with Medicaid program objectives. However, the court also noted that plaintiffs and their amici assert that proclaimed health benefits of employment are unsupported by substantial evidence. Thus, there is likely to be ongoing debate and policy discussion over whether work requirements will further the aims of Medicaid.

To address whether work will further the aims of Medicaid, we examine the literature on the relationship between work and health and analyze the implications of this research in the context of Medicaid work requirements. Due to the large number of studies in this field spanning decades, this literature review focuses primarily (although not exclusively) on findings from other literature or systematic reviews rather than individual studies on these topics. We drew on studies cited in policy documents on work requirements in Medicaid, results of keyword searches of PubMed and other academic health/social policy search engines, and snowballing through searches of reference lists in previously pulled papers. In total, we reviewed more than 50 sources, the vast majority of which were published academic studies or program evaluations and most of which are reviews of multiple studies themselves. A more detailed description of the methods underlying this analysis is provided in the Methods box at the end of this brief.

What effect do health and health coverage have on work?

Not surprisingly, research has demonstrated that being in poor health is associated with an increased risk of job loss or unemployment. 1 , 2 , 3 , 4 , 5 A meta-analysis of longitudinal studies on the relationship between health measures and exit from paid employment found that poor health, particularly self-perceived health, is associated with increased risk of exit from paid employment. 6 Another study that simultaneously examined and contrasted the relative effects of unemployment on mental health and mental health on employment status in a single general population sample found mental health to be both a consequence of and a risk factor for unemployment. However, the evidence for men in particular suggested that mental health was a stronger predictor of subsequent unemployment than unemployment was a predictor of subsequent mental health. 7 Additional research suggests that, in some cases, individual characteristics such as income, race, sex, or education level may mediate the relationship between poor health and unemployment. 8 , 9 10 Research also demonstrates that an unmet need for mental health or substance use disorder treatment results in greater difficulty with obtaining and maintaining employment. 11 , 12 , 13 , 14 , 15

Additional research suggests that, in addition, access to affordable health insurance and care, which may help people maintain or manage their health, promotes individuals’ ability to obtain and maintain employment. For example, in an analysis of Medicaid expansion in Ohio, most expansion enrollees who were unemployed but looking for work reported that Medicaid enrollment made it easier to seek employment, and over half of employed expansion enrollees reported that Medicaid enrollment made it easier to continue working. 16 Similarly, a study on Medicaid expansion in Michigan found that 69% of enrollees who were working said they performed better at work once they got coverage, and 55% of enrollees who were out of work said the coverage made them better able to look for a job. 17 A study on Montana’s Medicaid expansion found a substantial increase of 6 percentage points in labor force participation among low-income, non-disabled Montanans ages 18-64 following expansion, compared to a decline in labor force participation among higher-income Montanans. 18 National research found increases in the share of individuals with disabilities reporting employment and decreases in the share reporting not working due to a disability in Medicaid expansion states following expansion implementation, with no corresponding trends observed in non-expansion states. 19 Additional literature suggests that access to health insurance and care promotes volunteerism, finding that the expansion of Medicaid under the ACA was significantly associated with increased volunteerism among low-income adults. 20 , 21

What effect does work have on health and health coverage?

Overall, the body of literature examining whether work affects health shows mixed results, with some studies showing a positive effect of work on health yet others showing no relationship or isolated effects . A 2006 literature review found that, while “there is limited amount of high quality scientific evidence that directly addresses the question [of whether work is good for your health]… there is a strong body of indirect evidence that work is generally good for health and well-being.” 22 That assessment was based on comprehensive review of the literature, including other systematic reviews as well as narrative and opinion pieces. A more focused 2014 systematic review about the health effects of employment, which included 33 longitudinal studies, 23 found strong evidence that employment reduces the risk of depression and improves general mental health, yet it found insufficient evidence for an effect on other health outcomes due to a lack of studies or inconsistent findings of the studies. 24 A 2015 review of 22 longitudinal studies found an association between employment and re-employment with better physical health. 25

In contrast, research shows a strong association between unemployment and poor health outcomes, though researchers caution that these findings do not necessarily mean the reverse is true (e.g. employment causes improved health). The effect of unemployment on health has long been an area of research focus, and a substantial body of research from the U.S. and abroad consistently demonstrates a strong association between unemployment and poorer health outcomes, 26 , 27 , 28 , 29 30 , 31 , 32  with some evidence suggesting a causal relationship in which unemployment leads to poor health. 33 , 34 , 35 The bulk of the research in the unemployment and health field focuses on mental health outcomes. 36   Examples of negative health outcomes associated with unemployment include increases in depression, anxiety, mixed symptoms of distress, and low self-esteem. 37 , 38 A more limited body of research suggests an association of unemployment with poorer physical health (including increases in cardiovascular risk factors such as hypertension and serum cholesterol as well as increased susceptibility to respiratory infections), and mortality. 39 , 40 A 2006 literature review noted that there is continuing debate about the relative importance of possible mechanisms involved in this relationship, and adverse effects of unemployment may vary in nature and degree for different individuals in different social contexts. 41 Some evidence also indicates that cumulative length of unemployment is correlated with deteriorated health and health behavior. 42 However, despite the evidence of a relationship between unemployment and health, researchers caution against using findings to infer that an opposite relationship (employment causing improved health) exists. 43 , 44   In addition, researchers note that the literature on unemployment tends to study more negative than positive health outcome variables, 45 which may skew our understanding of the health effects of unemployment. 46

Another related area of research is studies examining the relationship between re-employment (i.e., returning to work) and health, which find some association between re-employment and mental health . A 2012 systematic review on this topic found support for a beneficial health effect of returning to work, with most of the 18 studies included in this review focusing on mental health-related outcomes. 47 The review also tried to assess to what extent the relationship was causal (i.e., reemployment caused health improvements) versus due to selection (e.g., people with poor health were more likely to remain unemployed) and concluded that both were at play. The review did not reach a definitive conclusion about mechanisms linking re-employment to improved health (due to lack of evidence), and it noted that it is still unclear whether health effects of reemployment are moderated by factors such as socioeconomic status, reason for unemployment, and the nature of employment. 48 The 2006 literature review described above also analyzed research findings on re-employment and found strong evidence that re-employment leads to improved psychological health and measures of general well-being, with a dearth of information on physical health and some but not all studies showing that re-employment/health relationship is at least partly due to health selection. However, these authors also cite evidence from numerous studies suggesting that “the beneficial effects of re-employment depend mainly on the security of the new job, and also on the individual’s motivation, desires, and satisfaction” 49

Research review: Low-quality, unstable and poorly paid jobs lead to or are associated with adverse health effects, suggesting that all jobs should not be expected to have similar effects on workers’ health.</p> <p>

Studies on work and health have found that the quality and stability of work is a key factor in the work-health relationship: research finds that low-quality, unstable, or poorly-paid jobs lead to or are associated with adverse effects on health. 50 , 51 , 52 , 53 , 54 , 55 , 56   For example, a 2014 meta-analysis of studies published after 2004 found that job insecurity can pose a comparable (and even modestly increased) risk of subsequent depressive symptoms compared to unemployment. 57 A 2011 longitudinal analysis found that while unemployed respondents had poorer mental health than those who were employed, the mental health of those who were unemployed was comparable or more often superior to those in jobs of poor psychosocial quality (based on measures of job control, perceived job security, and job demands and complexity) and the mental health of those in poor quality jobs declined more over time than the mental health of those who were unemployed. Moreover, while moving from unemployment into a high quality job led to improvement in mental health, the transitioning from unemployment to a poor quality job was more detrimental to mental health than remaining unemployed. 58 Additionally, a 2003 study that examined the association of different employment categories with physical health and depression found a consistent association between less than optimal jobs (based on economic, non-income, and psychological aspects of the jobs) and poorer physical and mental health among adults. 59

It is possible that the work-health association reflects people in good health being more likely to work, versus work causing good health. Some researchers caution against the possibility that selection bias has occurred in many of the studies on work and health. The existence of a “healthy worker effect”—in which relatively healthy individuals are more likely to enter the workforce whereas those with health problems are at increased risk to withdraw from and remain outside of the workforce—has been documented in multiple studies. 60 , 61 , 62 , 63 64 , 65   Authors of both individual studies and literature reviews on this topic explain that the healthy worker effect is difficult to control for even in studies that attempt to do so, and thus this effect may cause an overestimation of the findings in the literature on health effects of work. 66 , 67 As authors of a 2014 systematic review of studies on health effects of employment point out, there are no randomized controlled trials on this topic available in the literature because performing such trials would be unethical, 68 yet randomized controlled trials are the gold standard for determining a causal relationship.

Most study authors specifically note additional caveats to drawing broad conclusions about work and health. The 2006 review concluding a general positive effect of work on health emphasized three major provisos to this conclusion: (1) findings are about average or group affects, and a minority of people may experience contrary health effects from work, (2) the beneficial health effects of work depend on the nature and quality of work (described above), and (3) the social context must be taken into account, particularly social gradients in health (i.e. inequalities in population health status related to inequalities in social status) and regional deprivation. 69 These caveats could explain the seemingly contradictory findings about employment and unemployment: While unemployment is almost universally a negative experience and thus linked to poor outcomes, especially poor mental health outcomes, employment may be positive or negative, depending on the nature of the job (e.g., stability, stress, hours, pay, etc.). As discussed below, these provisos have implications for the applicability of research to Medicaid work requirements.

While work can help people access employer-sponsored health coverage, many jobs—especially low-wage jobs—do not come with an affordable offer of employer coverage. In 2017, just over half (53%) of firms offered health coverage to their employees, 70 and workers in low-wage firms are less likely than those in higher wage firms to be eligible for coverage through their employer. 71 In 2017, less than a third of workers who worked at or below their state’s minimum wage had an offer of health coverage through their employer. 72 Though most employees take up employer-sponsored coverage when offered, workers in low-wage firms are less likely to be covered by their employer even if coverage is offered, likely reflecting the fact that workers in such firms pay a larger share of the premium than workers in higher-wage firms. 73 The fact that work does not always lead to health coverage is further demonstrated by the large majority of uninsured people who are in a family with either a full-time (74%) or part-time (11%) worker. 74

What is the effect of volunteerism on health?

In the January 2018 guidance, CMS includes volunteering as a “community engagement” activity that may improve health outcomes, 75 and the Medicaid work requirement waivers approved to date all permit volunteer activities to count towards the required weekly/monthly hours of work activity.

However, there is limited existing evidence that volunteer activities benefit health outcomes. One literature review on the health effects of volunteering “did not find any consistent, significant health benefits arising through volunteering” based on experimental studies available at the time of the literature review. 76 The authors’ analysis of cohort studies revealed limited benefits of volunteering on depression, life satisfaction, and well-being (with no significant benefits on physical health). In addition, the cohort studies focused primarily on volunteers ages 50 and over, with some of the studies suggesting that the association between volunteerism and improved health outcomes may be limited to older volunteers and that that the health benefits of volunteering may diminish as hours of volunteering increase. 77 Another study (published in 2018) examined the health benefits of “other-oriented volunteering” (other-regarding, altruistic, and humanitarian-concerned volunteering) compared to “self-oriented volunteering” (volunteering focused on seeking benefits and enhancing the volunteers themselves in return). While the authors found beneficial effects of both forms of volunteer activity on health and well-being, other-oriented volunteering had significantly stronger effects on the health outcomes of mental and physical health, life satisfaction, and social well-being than did self-oriented volunteering. 78 As discussed below, this finding may indicate that health benefits of volunteering are likely to be weaker when individuals are compelled to engage in volunteering.

What does this research mean for Medicaid work requirements?

The body of literature summarized above includes several notable caveats and conclusions to consider in applying findings to a work requirement in Medicaid. Limitations and implications that are particularly relevant include:

Effects found for the general population may not apply to Medicaid, as the link between work and health is not universal across populations or social contexts. In general, the studies examined above analyze the relationship between work and health among broad populations of all income levels. However, several authors suggest that population differences may modify the relationship between work and health.  A 2003 study found that nationally, older adults, women, blacks, and individuals with low education levels were more likely to be employed in jobs viewed as “barely adequate” or “inadequate” (the types of jobs that the study found to be independently associated with poorer physical health and higher rates of depression) compared to other populations. 79 Authors of a 2006 literature review qualify their broad findings on the work/health relationship with the proviso that the social context must be taken into account (particularly social inequities in health and regional deprivation), and also cite evidence that the strong association between socioeconomic status and physical and mental health and mortality likely outweighs (and is confounded with) all other work characteristics that influence health. 80 Authors of a 2005 review on unemployment and health found a strong association between deprived areas, poor health, poverty and unemployment (although the exact relationship is not clear), and highlight the need for more research on the geographical dimension on unemployment and health. 81 These findings imply that the work/health relationship may differ significantly for the low-income Medicaid population, who report worse health status compared to the total US population and often face more significant challenges related to housing, food security, and other social determinants of health. 82 , 83 , 84 In addition, some volunteerism research suggests that the association between volunteerism and improved health outcomes may be limited to older volunteers, yet approved and pending Section 1115 Medicaid work requirement waiver requests all include exemptions for individuals above a certain age (which varies by state but ranges from 50 to 65 years). 85

Work or volunteering undertaken to fulfill a requirement may produce different health effects than work and volunteer activities studied in existing literature. For example, research on health effects of work requirements in Temporary Assistance for Needy Families (TANF) suggests that they did not benefit and sometimes negatively affected health among enrollees and their dependents. 86 Another study found that welfare reform was associated with increases in self-reported poor health and self-reported disability among white single mothers without a high school diploma or GED. 87 These adverse effects could reflect different relationships between work and health for low-income populations, as described above, or different effects of work undertaken voluntarily versus as a requirement. Authors of a 2006 literature review on work and health found that forcing claimants off benefits and into work without adequate supports would more likely harm than improve their health and well-being. 88 Similarly, most studies on volunteerism and health define volunteerism as an act of free-will (essentially, a voluntary act), a definition that may not be applicable to volunteer activity undertaken for the purpose of meeting work/community engagement requirements in order to maintain eligibility for Medicaid. Volunteer activities undertaken to retain Medicaid appear more closely aligned with the self-oriented form of volunteerism (volunteering focused on seeking benefits and enhancing the volunteers themselves in return), which research shows has weaker health effects than the other-oriented form (other-regarding, altruistic, and humanitarian-concerned volunteering).

Limited job availability, low demand for labor, or poor job quality may moderate any positive health effects of employment. Authors of a 2014 systematic review of prospective studies on health effects of employment commented that most studies in this field do not adjust for quality of employment and include all kinds of jobs in their analysis (e.g. part- and full-time employment, self-employment, and both blue- and white-collared jobs) despite the possibility that different forms of employment have different health effects. 89 Under Medicaid work requirement programs, the population subject to Medicaid work requirements may have access to only low-wage, unstable, or low-quality jobs to meet the weekly/monthly hours requirement, as these are the types of positions adults with Medicaid who currently work hold. 90 In discussing the policy implications of their findings, multiple researchers have concluded that such policies could be detrimental to health, with authors of one study asserting that, “Policies that promote job growth without giving attention to the overall adequacy of the jobs may undermine health and well-being.” 91

Long-term effects of work on health are unclear. Much of the evidence on the work/health relationship is about short-term effects after about one year, which, as authors of one literature review point out, is a short period when assessing health impacts. 92 There is less evidence on longer-term effects over a lifetime perspective. 93 In addition, research on work requirements in other public programs shows little evidence of long-term impacts on employment or income. Studies on welfare recipients subject to work requirements generally have found that any initial increase in employment after an imposition of a work requirement faded over time. 94 , 95 , 96 After five years, one study showed those who were not required to work were just as likely or more likely to be working compared to those who were subject to a work requirement, suggesting that these work requirements had little impact on increasing employment over the long-term. 97 Other research has found that employment among people who left welfare was unsteady and did not lift them out of poverty. 98 Thus, even short-term effects are likely to disappear as short-term boosts in employment fade over time.

Loss of health insurance coverage due to not meeting reporting or work requirements under waivers could affect access to health care and health. Low-wage workers typically work in small firms and industries that often have limited employer-based coverage options, and very few have an offer of coverage through their employer. Work requirements in Medicaid could lead to large Medicaid coverage losses, especially among people who would remain eligible for the program but lose coverage due to new administrative burdens or red tape versus those who would lose eligibility due to not working. 99 Several studies on individuals leaving TANF following welfare reform show reductions in insurance coverage across this “welfare leaver” population, with significant decreases in Medicaid coverage that were not fully offset by the smaller increases in private coverage. 100 , 101 , 102 , 103 , 104 A study evaluating welfare-to-work interventions found that some programs led to a reduction in health insurance coverage for both children and parents. 105   Given the evidence of Medicaid’s positive impact on access to care and health outcomes, 106 as well as data demonstrating that uninsured individuals go without needed care due to cost at much higher rates than those with Medicaid coverage, 107 widespread coverage losses as a result of Medicaid work requirements are likely to result in adverse effects on health outcomes. In TANF evaluations, for example, studies found that children of TANF enrollees who lose benefits for failure to comply with a work requirement experience adverse health effects such as behavioral health problems 108 or hospitalization. 109

Policies that have disproportionate effects on certain Medicaid enrollees could widen health disparities. Data demonstrate the persistence of clear disparities in health insurance coverage, access to care, and health outcomes for certain vulnerable populations in the US, including people with disabilities (compared to their non-disabled counterparts) 110 and people of color (compared to whites). 111 Research shows that people with disabilities and people of color are face disproportionate challenges in meeting and are disproportionately sanctioned under existing work requirement programs. 112 , 113 If racial minority groups, people with disabilities, or other vulnerable populations face similarly disproportionate challenges in meeting work requirements when they are attached to the Medicaid program, these policies could result in wider disparities in health insurance coverage and health outcomes.

Looking Ahead

Taken as a whole, the large body of research on the link between work and health indicates that proposed policies requiring work as a condition of Medicaid eligibility may not necessarily benefit health among Medicaid enrollees and their dependents, and some literature also suggests that such policies could negatively affect health. While it is difficult to determine a causal relationship between employment and health status (largely due to challenges controlling for health selection bias and the inability to conduct randomized controlled trials on this topic), there is strong evidence of an association between employment and good health. However, research suggests that factors like job availability and quality, as well as the social context of workers, mediate the effect of work or work requirements on health. Given the characteristics of the Medicaid population, research indicates that policies could lead to emotional strain, loss of health coverage, or widening of health disparities for vulnerable populations. As debate considers the question of whether policies to promote health—versus health coverage—are the aim of the Medicaid program, the question of whether work requirements will promote health also will remain key to the ongoing debate over the legality of work requirements in Medicaid.

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• Does Employment Lead to Improved Health? New Research Review Finds Mixed Evidence with Caveats that Could Impact Applicability to Medicaid Work Requirements

Also of Interest

• Implications of a Medicaid Work Requirement: National Estimates of Potential Coverage Losses
• Implications of Work Requirements in Medicaid: What Does the Data Say?
• Explaining Stewart v. Azar: Implications of the Court’s Decision on Kentucky’s Medicaid Waiver

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Jessica Grose

Screens are everywhere in schools. do they actually help kids learn.

By Jessica Grose

Opinion Writer

A few weeks ago, a parent who lives in Texas asked me how much my kids were using screens to do schoolwork in their classrooms. She wasn’t talking about personal devices. (Smartwatches and smartphones are banned in my children’s schools during the school day, which I’m very happy about; I find any argument for allowing these devices in the classroom to be risible.) No, this parent was talking about screens that are school sanctioned, like iPads and Chromebooks issued to children individually for educational activities.

I’m embarrassed to say that I couldn’t answer her question because I had never asked or even thought about asking. Partly because the Covid-19 era made screens imperative in an instant — as one ed-tech executive told my colleague Natasha Singer in 2021, the pandemic “sped the adoption of technology in education by easily five to 10 years.” In the early Covid years, when my older daughter started using a Chromebook to do assignments for second and third grade, I was mostly just relieved that she had great teachers and seemed to be learning what she needed to know. By the time she was in fifth grade and the world was mostly back to normal, I knew she took her laptop to school for in-class assignments, but I never asked for specifics about how devices were being used. I trusted her teachers and her school implicitly.

In New York State, ed tech is often discussed as an equity problem — with good reason: At home, less privileged children might not have access to personal devices and high-speed internet that would allow them to complete digital assignments. But in our learn-to-code society, in which computer skills are seen as a meal ticket and the humanities as a ticket to the unemployment line, there seems to be less chatter about whether there are too many screens in our kids’ day-to-day educational environment beyond the classes that are specifically tech focused. I rarely heard details about what these screens are adding to our children’s literacy, math, science or history skills.

And screens truly are everywhere. For example, according to 2022 data from the National Assessment of Educational Progress, only about 8 percent of eighth graders in public schools said their math teachers “never or hardly ever” used computers or digital devices to teach math, 37 percent said their math teachers used this technology half or more than half the time, and 44 percent said their math teachers used this technology all or most of the time.

As is often the case with rapid change, “the speed at which new technologies and intervention models are reaching the market has far outpaced the ability of policy researchers to keep up with evaluating them,” according to a dazzlingly thorough review of the research on education technology by Maya Escueta, Andre Joshua Nickow, Philip Oreopoulos and Vincent Quan published in The Journal of Economic Literature in 2020.

Despite the relative paucity of research, particularly on in-class use of tech, Escueta and her co-authors put together “a comprehensive list of all publicly available studies on technology-based education interventions that report findings from studies following either of two research designs, randomized controlled trials or regression discontinuity designs.”

They found that increasing access to devices didn’t always lead to positive academic outcomes. In a couple of cases, it just increased the amount of time kids were spending on devices playing games. They wrote, “We found that simply providing students with access to technology yields largely mixed results. At the K-12 level, much of the experimental evidence suggests that giving a child a computer may have limited impacts on learning outcomes but generally improves computer proficiency and other cognitive outcomes.”

Some of the most promising research is around computer-assisted learning, which the researchers defined as “computer programs and other software applications designed to improve academic skills.” They cited a 2016 randomized study of 2,850 seventh-grade math students in Maine who used an online homework tool. The authors of that study “found that the program improved math scores for treatment students by 0.18 standard deviations. This impact is particularly noteworthy, given that treatment students used the program, on average, for less than 10 minutes per night, three to four nights per week,” according to Escueta and her co-authors.

They also explained that in the classroom, computer programs may help teachers meet the needs of students who are at different levels, since “when confronted with a wide range of student ability, teachers often end up teaching the core curriculum and tailoring instruction to the middle of the class.” A good program, they found, could help provide individual attention and skill building for kids at the bottom and the top, as well. There are computer programs for reading comprehension that have shown similar positive results in the research. Anecdotally: My older daughter practices her Spanish language skills using an app, and she hand-writes Spanish vocabulary words on index cards. The combination seems to be working well for her.

Though their review was published in 2020, before the data was out on our grand remote-learning experiment, Escueta and her co-authors found that fully online remote learning did not work as well as hybrid or in-person school. I called Thomas Dee, a professor at Stanford’s Graduate School of Education, who said that in light of earlier studies “and what we’re coming to understand about the long-lived effects of the pandemic on learning, it underscores for me that there’s a social dimension to learning that we ignore at our peril. And I think technology can often strip that away.”

Still, Dee summarized the entire topic of ed tech to me this way: “I don’t want to be black and white about this. I think there are really positive things coming from technology.” But he said that they are “meaningful supports on the margins, not fundamental changes in the modality of how people learn.”

I’d add that the implementation of any technology also matters a great deal; any educational tool can be great or awful, depending on how it’s used.

I’m neither a tech evangelist nor a Luddite. (Though I haven’t even touched on the potential implications of classroom teaching with artificial intelligence, a technology that, in other contexts, has so much destructive potential .) What I do want is the most effective educational experience for all kids.

Because there’s such a lag in the data and a lack of granularity to the information we do have, I want to hear from my readers: If you’re a teacher or a parent of a current K-12 student, I want to know how you and they are using technology — the good and the bad. Please complete the questionnaire below and let me know. I may reach out to you for further conversation.

Do your children or your students use technology in the classroom?

If you’re a parent, an educator or both, I want to hear from you.

Jessica Grose is an Opinion writer for The Times, covering family, religion, education, culture and the way we live now.