Is There Any Relationship between Unemployment in Young Graduates and Psychological Resources? An Empirical Research from the Conservation of Resources Theory

Introduction

As a consequence of the economic recession initiated in 2007 in the United States and Europe, a great number of people have been left unemployed and, among these, the younger sector of the population had to face more difficulties in accessing the labor market (Wanberg, 2012). One of the European countries most affected by unemployment is Spain. Regarding the data from the Centre for Sociological Research in July 2018 (www.cis.es), unemployment is the main existing problem for 64.3% of the Spanish population, and has remained as the main concern since the economic crisis started. The situation is especially worrying for the young population. The unemployment rate for the second trimester of 2018 is 34.7% for individuals under 25 years of age and 20.57% for people aged between 25 and 29 (National Institute of Statistics, www.ine.es). In addition, young people are polarized into two segments, depending on their educational level. On one side, a group with a very low educational level, who completed primary and/or secondary education, and on the other side, there is a highly-qualified group with university certification (García, 2011, 2014). Although unemployment affects the most qualified group less, 14.9% of young university graduates between 25 and 29 years of age is unemployed, (www.ine.es), slightly less than half of the university students access jobs in accordance with their academic training a year after graduating (48.5%) (www.mecd.gob.es). Therefore, and albeit with a difficult scenario, a number of young graduates find jobs related to their training soon after graduating. Independently of variables such as macroeconomic situation, labor market, type of degree, networking, or family support, which obviously play an important role in youth unemployment (Álvaro & Garrido, 2005; Felgueroso, 2012; Garrido, 2012; Moreno, 2012, 2015), we believe that individual differences, such as psychological resources of those recently graduated, could play a determining role in allowing these young graduates to find a job. Following the approach of the Conservation of Resources theory (COR theory) proposed by Hobfoll (1989), this type of resources would be positively influential in job search and appraisal.

Conservation of Resources Theory

The COR theory proposed by Hobfoll (1989, 2002, 2010) considers resources as either entities with value in themselves or as a means to reach a valued goal. They can be: 1) objects, like owning a house; 2) personal characteristics, either physical (e.g., health) or psychological (e.g., optimism, self-esteem, etc.); 3) conditions, such as being married or employed; and 4) energies, such as time, money, or knowledge. Furthermore, resources contribute to our own identity and, consequently, differentiate us from others.

The key principle of this theory is that people fight to maintain, protect, and build resources, as well-being (eustress) will depend on their gain and stress (distress) will be subject to their loss. Distress, commonly known as stress, refers to the negative response to stressors that is translated into negative affect and a detriment to mental health. On the other hand, eustress is defined as the positive response to adversity and is portrayed in the presence of positive affect and well-being (McGowan, Gardner, & Fletcher, 2006; Nelson & Simmon, 2003; Watson, Clark, & Tellegen, 1988; Watson & Pennebaker, 1989). Psychological stress occurs as a response to an environment in which there is: 1) a threat of resource loss, such as suspicions about your company being restructured, this meaning that one’s job might be at risk; 2) real loss of resources, for example, losing one’s job; 3) absence of return of resources when other resources have been invested for that purpose, for instance, studying a university degree, which implies a great energy investment in terms of time, money, effort, etc., to achieve the goal of finding a job related to one’s training, but not finding any after graduating.

According to Hobfoll (1989, 2002, 2010), people with a good amount of resources – psychological or else – will better face environmental stressors (e.g., unemployment) and will also have more probabilities of coping with the situation and successfully getting out of it. In this sense, Schaufeli (1997) finds that employment among university graduates is predicted by an active and positive attitude when it comes to facing unemployment. This result is consistent with the one found in another research carried out with other groups of unemployed people, where reemployment was found more likely to happen to those individuals who were more optimistic at the beginning of the unemployment stage (Leana & Feldman, 1995; Wanberg, 1995), the reason being that more optimistic people tend to experience well-being in a more favourable way and better resist stress (Carver & Scheier, 1998). In this respect, a positive relationship between reemployment and psychological well-being has been found (d = 0.89) in a meta-analysis by McKee-Ryan, Song, Wanberg, and Kinicki (2005) with an N = 1,911, and more recently between reemployment, positive affect, and life satisfaction (Ferreira et al., 2015).

Considering the previous research, we could ask ourselves:

Which psychological resources make it more likely for young graduates to find a job that resembles their academic training immediately after finishing their studies?

We understand that this is a relevant question because: 1) there are barely any studies that have addressed this question; 2) according to the University-Society Barometer (Mayor et al., 2016), 80% of students choose a degree for preferential or vocational reasons, and so the idea that they prioritize the finding of a meaningful job for them is worth considering. In fact, what they value the most when it comes to finding a job is that it fits their training; 3) their lack of experience, networking, and knowledge about the labor market makes it harder for them to find a job (Morsy, 2012); and 4) research points out that being unemployed shortly after graduating has consequences not only in the short run but also in the long run, because these graduates have more probabilities of losing their job years later and tend to earn less than those who became employed more easily (Kahn, 2010).

Moreover, we must consider that, although the percentage of university graduates who find a job in accordance with their training increases over time, it does not do so in a significant manner, thus we find that four years after graduating only 55.5% of the graduates in 2012 work in jobs for which they have received training (www.mecd.gob.es). As stated by Hobfoll (1989, 2002, 2010), one of the causes of distress is the lack of resources payoff after having invested in other resources for that purpose, and young graduates are a population who has invested numerous resources during their study years (e.g., money, effort, time, etc.), seeking future access to a job related to their training level. Therefore, the COR theory predicts loss of resources and, consequently, distress for unemployed graduates. Following the previous statements, it is worth asking:

• - Which psychological resources differentiate young unemployed graduates from employed ones? And,
• - Does unemployment affect this group’s distress and eustress?

In relation to the first question, young graduates most often live with their parents – the average emancipation age being 28.9, as reported by Eurostat (https://ec.europa.eu/eurostat). This group possesses social support for job searching and bears no economic pressure – as opposed to older unemployed individuals (Álvaro & Garrido, 2005). Therefore, a decrement in these resources would not be expected to take place (Hobfoll, 2002), although a loss of other resources (i.e., psychological resources) might be experienced. For instance, some research has shown that unemployment brings about lack of both self-efficacy and environmental mastery (Eden & Aviram, 1993; McKee-Ryan & Kinicky, 2002; Vinokur, Schul, Vuori, & Price, 2000). With respect to the second question, Jahoda (1982) states that employment provides both overt (e.g., income, regulated activity, organization, social relationships) and covert (e.g., psychological) benefits, while unemployment undermines well-being, increases stress, and affects mental health. Consistent with this view, Paul and Moser (2009) – in a meta-analysis with N = 40,985 – found poorer mental health among unemployed people (d = 0.56), a result similar to that obtained in another meta-analysis (d = 0.57) with N = 21,735 (McKee-Ryan et al, 2005), and in other studies with unemployed individuals (Creed & Bartrum, 2008; Wiener, Oei, & Creed, 1999). These results can be modulated by the cultural and socioeconomic conditions of the countries studied. For example, the impact of macroeconomic conditions on the relationship between unemployment and health has been analyzed, finding lower mental health levels in Spain among unemployed people during the recession (Urbanos-Garrido & Lopez-Valcarcel, 2015), while the opposite was found in Sweden (Brydsten, Hammarström, & San Sebastian, 2016). These discrepancies might be explained by the socio-cultural differences between both countries. Accordingly, Schaufeli (1997) found that unemployed graduates in The Netherlands did not experience either a worsening of mental health nor higher distress, putting these results down to the social and cultural conditions of the country, as the unemployed group was especially protected, and a proactive attitude towards job search was inoculated in them.

Furthermore, regarding the COR theory, people strive to make the loss of resources as small as possible when they face an adverse environment – and hence to be in a better position to overcome stress. When they are not facing any stressors, they fight to gain resources, thus allowing these reserves to compensate future adverse situations that might put their resources at risk. Therefore, individuals possessing a good amount of resources will better cope with environmental stressors and, consequently, their mental health will be less affected. Conversely, those with poorer reserves will become more vulnerable and susceptible to distress (Hobfoll, 2002). As Huffman, Culbertson, Wayment, and Irving (2015) pose, although loss of resources leads to stress, people can make up for that loss through other resources. Thus, for instance, after WWII research showed that resources such as sense of mastery and social support had been key factors to preserve mental health among combatants (Caplan, 1964). In the event of catastrophes such as the terrorist attack on the Twin Towers in New York, resilience to the trauma and positive affect softened the stress in those involved in the tragedy (Fredrickson, Tugade, Waugh, & Larkin, 2003). In a similar way, a relationship between unemployment and distress has been found. However, it is not yet clear what mechanisms mediate between the two, nor which role do psychological resources play in that mediation (Huffman et al., 2015; Wanberg, 2012). Huffman et al. (2015) found that family support has a positive effect on the well-being of unemployed people, and this relationship is partially modulated by latent benefits – collective purpose, social contact, status, time structure, and enforced activity – collected by Jahoda (1982). Creed and Bartum (2008) indicate that unemployed individuals perceive low sense of control over their lives, which leads them to distress – low levels of control are associated with high psychological distress – while high levels of control improve appraisal and well-being. Therefore, we could ask ourselves:

Which psychological resources predict distress among young unemployed graduates, and which ones predict well-being?

We believe that this is a matter of great relevance when it comes to being able of establishing programs for resources improvement among unemployed individuals that will allow them to achieve a better quality of life, mental health, stress-coping strategies and, consequently, to be in a better position for finding a job. In this regard, Matt, Bellardita, Fischer, and Silverman (2006) applied a program to unemployed people over three weeks to modify dysfunctional attributional styles and self-efficacy, which resulted in a decrement in psychological stress and an increase in psychological resources concerning job search.

Objectives

The present research has three goals aiming at answering the three main questions about the role psychological resources play in unemployment among young graduates, all of them within the context of the COR theory developed by Hobfoll (1989, 2002, 2010). First, understanding which psychological resources make it more likely for young graduates to find a job related to the training they have received. For this purpose, we will try to figure out which psychological resources predict the success of a young graduate (having finished the degree within a maximum of four months) on becoming employed, in accordance with the training received. Second, learning which psychological resources distinguish young unemployed graduates from those employed, and analyzing whether unemployment affects distress and eustress. To that end, existing differences in psychological resources between employed and unemployed individuals will be studied, considering both recent graduates and those who graduated more than four months earlier. We will also test if these differences between young employed and unemployed graduates are influenced by the type of university degree they have. Third, studying the loss of which resources predicts distress among young unemployed graduates and the gain of which resources predicts eustress.

Method

Participants

The sample was composed of 542 young graduates with a mean age of 25.04 years (SD = 2.31 years), individuals ranging from 21 to 30 years old. Women made up 54.4% of the sample. Of the total sample, 29.2% had graduated in Social Sciences, 31.2% in Health Sciences, 19.6% in Humanities-Arts, and 20.0% in Technical Studies. Most of them (87%) had graduated between 2010 and 2016 and the rest between 2002 and 2009. They all had graduated between 2002 and 2016. Unemployed individuals (48.3%) had spent an average of 11.81 months (SD = 16.21 months) in that situation, with a maximum of 94 months of unemployment. Employed graduates (51.75%) were involved in an activity related to the degree they had studied within an average of 11.44 months (SD = 10.84 months), ranging from 1 to 55 months. Most of these young individuals were living with their families (55.5%), with friends (17.6%), or with their partners (13.8%), only an 8.2% reporting to live alone.

Procedure

Data collection took place between 2014 and 2016. Researchers trained several evaluators in the administration of measurement instruments used in the study, as well as in data collection. All participants read the instructions and filled in the measures in the presence of the evaluators and in one session. Only participants with a maximum of 30 years of age were evaluated, and considerations were taken to make the percentage of the employed and unemployed groups similar, as well as to contemplate people who had been enrolled in different degrees, to obtain a more representative sample.

Measures

Psychological resources. The scale of Positive Psychological Functioning (PPF) by Merino and Privado (2015), composed of 33 items, in a Likert format with 5 alternative responses, was used. The scale’s internal structure was analysed by confirmatory factor analysis in a representative sample of Spanish population with 3,000 participants. This analysis showed a hierarchical factorial structure at two levels, with good fit to the data (RMSEA = .04, CFI = .918, NFI = .908, PNFI = .0784). More specifically, the 33 items were grouped into 11 psychological resources: autonomy, resilience, self-esteem, purpose in life, enjoyment, optimism, curiosity, creativity, humour, environmental mastery, and vitality. These resources were grouped into a second-order factor called Positive Psychological Functioning, a measure for eudaimonic well-being. Moreover, the scale’s convergent validity was studied using other similar measurements and obtaining good results. Subsequently, the scale was validated in other populations like Mexicans (Merino, Privado, & Gracia, 2015).

As an additional measure of environmental mastery, we used the subscale included in the Psychological Well-being Scale (Ryff, 1989) adapted to the Spanish population by Díaz et al. (2006), since the equivalent PPF is not entirely adequate for for a young population.

Self-efficacy. The Baessler and Schwarzer’s scale from 1996 adapted by Sanjuán Pérez and Bermúdez (2000), consisting of 10 items, was used and applied in a Likert type scale with 5 responses.

Subjective well-being. The Satisfaction with Life Scale (Diener, Emmonds, Larsen, & Griffin, 1985), which emphasizes how happy or unhappy people perceive themselves to be – instead of objective circumstances surrounding their lives – was used. It contains 5 items, and follows a Likert type scale with 5 alternative responses. The SWLS Spanish version was used (Garrido, Fernández, Villalba, Pérez, & Fernández, 2010).

Distress and eustress. The Positive and Negative Affect PANAS by Watson et al. (1988) was used. This scale is composed of 20 items, half of them measuring positive affect (eustress) and the other half measuring negative affect (distress). The response format is a Likert type scale with 5 alternatives. For the purpose of this research, PANAS Spanish version was used (Sandín et al., 1999).

Data Analysis

First, the descriptive statistics of measures were calculated, as well as Cronbach’s reliability index. Then, to achieve the first goal of the study, a binary logistic regression was performed employing the different resources as predictors. The condition of being employed or unemployed at the time of evaluation, acted as the criterion variable. This analysis was done only with those who had graduated in the same year they were evaluated, and each student was assessed within four months of graduation. To provide an answer for the second goal, an ANOVA of two independent factors for measures on psychological resources, distress, eustress, satisfaction with life, and PPF was performed to study possible differences between the employed and unemployed groups, and the type of studies completed (Social Sciences, Health Sciences, Humanities-Arts, and Technical Studies). Finally, to meet the third aim of the study, linear regressions for distress and eustress were carried out, using psychological resources among young unemployed graduates as predictors. Age and sex were also used as predictors in order to analyze their possible influence on the results. All the analyses were carried out with the statistical package SPSS V. 18.

Results

Descriptives

In Table 1, the mean, standard deviation, and reliability of each of the measures considered in this study are displayed. All the measures present reliability values around .70 – acceptable values in research (Abad, Olea, Ponsoda, & García, 2011) – except for environmental mastery in the PPF scale. Thus, this scale was removed from the rest of the analysis. In any case, as explained in the Measures section, this variable is measured through the environmental mastery subscale included in the Psychological Well-Being Scale (Ryff, 1989) adapted to the Spanish population by Díaz et al. (2006). This subscale is more adequate for young population.

Binary Logistic Regression

With the purpose of knowing which predictors forecast the fact that a person finds a job shortly after graduating, a binary logistic regression was conducted using the different resources as predictors. The forward inclusion procedure of variables was used. There were only 168 participants who met the inclusion criterion – having graduated the same year of the evaluation. The mean age was 23.27 years old (SD = 1.55 years), of which 54.2% were women. The model obtained could only include PPF’s optimism as a predictor. This variable gets to explain a 5.8% of the individual differences between those who find a job right after graduating and those who do not (Nagelkerke’s R² = .058). The model presents an adequate adjustment to the data, χ²(6) = 7.73, p = .258. The regression equation from the model was the following: Logit (Employed = 1) = -2.662 + 0.208 * Optimism.

Both regression coefficients were statistically significant, β₀ = - 2.662, Wald(1) = 7.82, p = .005; β₁ = 0.208, Wald(1) = 6.87, p = .009. With the aim of interpreting the predictive variable in the model, we calculate eβ¹ = e^0.208 = 1.231, which indicates that employed individuals have an advantage of 1.231 in optimism when compared to the unemployed ones. In terms of probability: 1.231/(1.231 + 1) = 0.5518, the probability of having more optimism is 55.18% among the employed individuals.

ANOVA of Independent Measures

We studied – within the entire sample – what the differences were in resources, subjective well-being, PPF, stress, and eustress between the employed and unemployed individuals, considering those who had just graduated and those who did so more than four months before the study. An ANOVA of independent measures was conducted using as factors being or not employed and the type of studies completed (Social Sciences, Health Sciences, Humanities-Arts, and Technical Studies). Table 2 shows the results obtained.

Statistically significant differences between employed and unemployed lie only in resilience, optimism, autonomy, environmental mastery, self-efficacy, distress, and subjective well-being – all with higher scores for the employed group, except for negative affect. Moreover, the size effect (η²_parcial) is small according to Cohen’s (1992) criteria (.01 is low, .06 is medium, and .14 is large). Thus, employed people would present higher values in resilience, optimism, autonomy, environmental mastery, and subjective well-being, and lower values in distress. According to the type of degree completed, statistically significant differences appear in self-esteem, purpose in life, creativity, self-efficacy, and eustress, in all cases with large effect sizes. In self-esteem, there are differences between those who studied Social Sciences and Technical Studies in favor of the first ones (Bonferroni = 0.79, p = .026) and Technical Studies and Health Sciences in favor of the first (Bonferroni = 0.73, p = .048). In purpose in life, there are differences between Social Sciences and Humanities-Art with higher scores in Social Sciences (Bonferroni = 0.77, p = .018). In creativity, there are differences between Technical Studies and Social Sciences with higher scores in Technical Studies (Bonferroni = 0.70, p = .044). In self-efficacy, there are differences between Technical Studies and Humanities-Art in favor of the first (Bonferroni = 3.17, p = .007). Finally, in eustress, there are differences between Social Sciences and Humanities-Art with higher scores in the first (Bonferroni = 2.59, p = .002). In relation to the interaction between the two factors used (employed vs. unemployed and type of studies completed) and the criteria variables, no effect was found.

Multiple Linear Regression

As a means of finding out which psychological resources predicted distress and eustress among the unemployed graduates at the time of the evaluation, two multiple linear regressions were performed – one for distress and the other for eustress as the criterion. Age and sex were also used as predictors in order to analyze their possible influence. The stepwise method was used. The sample evaluated consisted of 175 participants with a mean age of 24.41 years (SD = 2.10 years), of which 52% were women. The predictors found for eustress were environmental mastery, vitality, resilience, optimism, curiosity, and humour (see Table 3). The model composed of the six predictors can explain a 59.1% of the total variance, adjusted R² = .591, F_{(6, 168)} = 42.16, p < .001, although the most explanatory predictors are the first three ones. The regression model attained in standard scores was the following: Z_Eustress = 0.254 * Z_{Environmental Mastery} + 0.170 * Z_Vitality + 0.197 * Z_Resilience + 0.176 * Z_Optimism + 0.119 * Z_Curiosity + 0.127 * Z_Humour. Every regression coefficient resulted to be statistically significant (see Table 3). Since neither age nor sex were included as predictors in the final model, it can be considered that these two variables did not influence the criterion considered.

As for distress, only two predictors were found: environmental mastery and optimism (see Table 3). This model explains a 21.3% of the total variance, adjusted R² = .213, F_{(2, 172)} = 28.66, p < .001. The regression model obtained in standard scores was the following: Z_Distress = - 0.357 * Z_{Environmental Mastery} - 0.210 * Z_Optimism, hence environmental mastery and optimism negatively predict negative affect. The regression coefficients were statistically significant (see Table 3). In this case, neither age nor sex managed to predict the criterion.

Therefore, in the case of eustress, as in environmental mastery, vitality, and resilience, it seems that it fundamentally predicted individual differences among unemployed graduated in a positive manner, while distress is negatively predicted primarily by environmental mastery and optimism.

Discussion

For this study, more than 500 young graduates from different branches (Social and Health Sciences, Humanities, Technical Studies and Arts) have been evaluated, of which 51.75% were employed in an activity related to their training and the rest being unemployed. The aim was to observe the role psychological resources, well-being, and affect play in the latter group.

First, we aimed at finding out the extent to which psychological resources could predict the employment of young graduates as far as four months after their graduation. The results suggest that more optimistic graduates are most likely to find a job shortly after the completion of their degree compared to less optimistic individuals. More concretely, employed graduates are 55.18% more optimistic than the employed ones. These results are concordant with other previous studies carried out with university graduates (Schaufeli, 1997), as well as with other unemployed individuals (Carver & Scheier, 1998; Leana & Feldman, 1995; Wanberg, 1995). Due to the kind of methodology used, less optimistic individuals could be thought to be so because of the lack of employment. However, in Spain, where structural problems play an important role in unemployment (Álvaro & Garrido, 2005; Felgueroso, 2012; Garrido, 2012; Moreno, 2012, 2015), a four-month period for finding a job is not enough to modify a relatively stable variable like optimism. Although optimism could be dispositional or state, in this study we refer to it as the first, thus, as the positive expectation about life and events, and the generalized tendency to expect good things to happen (Scheier & Carver,1987). In the same line, as Chico (2002) points out, research indicates that optimism is positively related to positive coping strategies such as planning, positive reinterpretation and personal growth, problem-focused coping, and adaptive coping. On the contrary, optimism is negatively related to negative coping strategies such as focusing on emotions, denial, behavioural and mental detachment, and non-adaptive coping. Thus, following the Hobfoll’s COR theory (1989, 2002), optimistic people would more easily display an array of resources than place them in a better position to move in a labour market as complex as the current one. Positive strategies activate you, give a sense of control over what you do, while negative strategies block you and are associated with poor control over the situation. That being said, we want to make it clear that it cannot be inferred from this result that optimism has a causal effect over employment, since the methodology used does not allow such an inference. Nevertheless, we can assert that there is an association between both variables, optimism and employment-unemployment. In addition, the literature about this topic and the characteristics of the design used allow us to hypothesize that optimism acts as a predictor, while future research will have to use longitudinal designs in order to test this matter. Moreover, the relationship between optimism and employment-unemployment, although modest, is quite interesting, because it reflects the presence of individual differences and the possibility of being able to intervene on them, and this is something impossible to do with structural variables. In this regard, it could be of interest that universities provide students in the last grade year with labour market coping workshops with a specific module for strengthening psychological resources. This could consist of analysing and evaluating a student’s resources and working on the development and strengthening of those resources in which they presented the greatest deficiencies, paying special attention to optimism.

The second goal of this research is to detect differences in resources, subjective well-being, PPF, distress, and eustress, between employed and unemployed individuals. We find that unemployed graduates score significantly lower in resilience, optimism, autonomy, environmental mastery, self-efficacy, and global life satisfaction (subjective well-being), and show higher values of distress compared to those holding a job in accordance with their training. These results match those obtained in previous studies (Creed & Bartrum, 2008; Eden & Aviram, 1993; McKee-Ryan et al., 2005; McKee-Ryan & Kinicki, 2002; Paul & Moser, 2009; Vinokur et al., 2000; Wiener et al., 1999). The result is consistent with the COR theory, which states that the lack of resources gained after the investment of resources for this precise purpose brings about more loss of resources and distress (Hobfoll, 1989).

Considerations must be taken with effect sizes, which are from medium to low, but not irrelevant (Wanberg, 2012). This is positive data that we believe is conditioned by some characteristics of the current Spanish society. The first one is the value attributed to the family and the constant support source it provides. As Álvaro and Garrido (2005) remark, one of the characteristics that distinguish the context of unemployment for young people in Spain is the social and economic welfare provided by family. Considering this, family support would alleviate the loss of resources under Hobfoll’s (2002) COR theory. According to Parke (2004), family constitutes the most important support system for the well-being and adjustment of the members that integrate it. In line with the data obtained from the CIS Barometer in June 2014 about “opinions and attitudes towards family”, 86.3% of the young population between 25 and 34 years old value the family as something very important in their lives, apart from the 97.3% who feel satisfied or very satisfied with their family relations (www.cis.es). In our study, most of them live with their families (55.5%), and it is conceivable that these individuals not only receive economic support, but also moral and emotional support in the face of coping with unemployment. In addition, by living with their parents they have no family burdens nor economic load, which are the two most relevant sources of distress among unemployed people (McKee-Ryan et al., 2005). The second factor is that the unemployment rate among the young population in Spain is so high that it is possible that a normalizing effect has occurred. As indicated by Schaufeli (1997), social acceptance of unemployment is higher as unemployment rate increases dramatically. This normalization of unemployment allows unemployed individuals to attribute the cause of unemployment to external factors, away from their control (McKee-Ryan et al., 2005; Paul & Moser, 2009). These two variables – family support and normalization of unemployment – would justify not only the smallest effect sizes found, but also the apparent absence of differences in a resource as essential to the self as self-esteem. In any case, the results provided by the literature regarding self-esteem are contradictory, perhaps because this is a reasonably stable variable that requires time to change (McKee-Ryan & Kinicki, 2002). In conclusion, on the one hand, the effect on psychological resources is relevant and it would be interesting to provide to the unemployed youth specific modules for the development and strengthening of psychological resources in the context of job search workshops. On the other hand, in the comparison between type of studies completed and criterion variables, we see that some cases, specifically Technical Studies and Social Sciences, have more resources in comparison with Humanities-Art and Health Sciences. Finally, we do not find differences in the interaction between employment- unemployment and type of studies completed with respect to the criterion variables, which means that gain or loss of resources in the employment-unemployment setting is independent from the kind of studies completed. This is a result of interest and it is worth considering.

As for the third goal, we wanted to understand the gain of which resources promoted eustress among young unemployed graduates, and the loss of which led to distress. Our results give great relevance to environmental mastery, which holds a very important role as its loss predicts distress (21.8% of the variance), and its gain predicts eustress (39.7% of the variance). This outcome goes in line with the COR theory. Not only does it predict that loss of resources leads to distress, but also suggests that conservation of certain resources can protect us from environmental stressors such as unemployment (Hobfoll, 2002; Huffman et al., 2015). Several research studies are consistent with our results, and point to resources of control (e.g., environmental mastery) as being beneficial for general stress coping, specifically with unemployment (Creed & Bartrum, 2008; Hannan, O’Riain, & Whelan, 1997). In fact, low levels of environmental mastery would lead to helplessness, which would prove to be pernicious for unemployed individuals, 1) because of the negative consequences on mental health and 2) because they would desist from job search (Goldsmith, Veum, & Darity, 1997). In the same line, McKee-Ryan and Kinicki (2002) and McKee-Ryan et al. (2005) pose that the perception of environmental control is positive for unemployed people and positively associated with the probability of full-time permanent employment, while it is negatively related to depression and anxiety. Moreover, we have also found that loss of optimism predicts – while in a much smaller degree – distress (3.2% of variance) and eustress (2.3% of variance). Additionally, we observe that vitality predicts 11.6% of eustress among unemployed people. Although literature says nothing about this resource or its role in unemployment, to us it is very relevant because it brings energy and enthusiasm to unemployed individuals, aspects that are necessary to mobilize a person for job search. Finally, resilience predicts 4.6% of eustress, a result that coincides with studies of previous disasters, such as the terrorist attack on the Twin Towers, where this resource was found to alleviate distress (Fredrickson et al., 2003).

In conclusion, the results from this third aim indicate that unemployment is not experienced in the same way among young graduates. Knowledge the main environmental mastery plays in the face of both distress and eustress is key when it comes to implementing training programs that help improve well-being and quality of life in unemployed graduates, which would consequently allow them to hold a better position for finding a job. Literature about this issue finds that these types of programs significantly improve the probability of acquiring a job (Matt et al., 2006). Therefore, future research should work on this matter.

Limitations

One of the limitations of this study is the use of a cross-sectional methodology to examine how unemployment affects loss of resources. It would be convenient to carry out a longitudinal design to see if loss of resources is maintained over time – as some authors have suggested (Kahn, 2010) – or if it gets to a point where it stops being relevant. The problem with a cross-sectional methodology can arise from the assumption that unemployment causes both loss of psychological resources and discomfort, when one could argue the opposite scenario – lack of psychological resources and the presence of discomfort lead to unemployment. However, as Wanberg (2012) points out, unemployment tends to be the cause of discomfort and not the other way around. Moreover, in the meta-analysis carried out by Paul and Moser (2009), 27 studies corresponding to unemployed workers because of closed factories were identified. In this type of situation, unemployment is obviously not caused by the poor mental health of a worker. It was observed that, in situations as the one described, unemployed worsened an unemployed individual’s mental health, hence it could be concluded that it was unemployment which caused distress and not conversely. In any case, it would be convenient that future research addresses this question by using a longitudinal methodology; specifically, as Garrido (1999) points out, the most adequate design would be the following:

A quasi-experimental design could also be used, with two groups of unemployed individuals: an experimental and a control group. The experimental group would receive training in the psychological resources we have seen could play a role when it comes to finding a job, while unemployed people in the control group, with similar characteristics to the former (training, social class, age, sex, etc.), would not receive any training. Pre- and posttreatment psychological resources measures would be taken, as well as time needed for both groups to find a job.

In addition, it could be interesting to study what happens to the role resources play in people older than 30. We have focused on young graduates, but it is worth wondering if the results could be replicated in unemployed individuals at an older age. It would also be pertinent to study the differences between graduates whose jobs do not match the academic training received and what has been addressed in the present study. Ultimately, we did not analyzed non-graduated people, for whom resources probably differ from graduated individuals.