literature review on unemployed youth

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Youth unemployment: a review of the literature

1985, Journal of Adolescence

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Youth unemployment and mental health: prevalence and associated factors of depression among unemployed young adults in Gedeo zone, Southern Ethiopia

Hirbaye mokona.

1 Department of Psychiatry, College of Medicine and Health Sciences, Dilla University, P.O. Box 419, Dilla, Ethiopia

2 Department of Psychiatry, School of Medicine, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia

Kalkidan Yohannes

Getinet ayano.

3 Reserach and training department, Amanuel Mental Specialized Hospital, Addis Ababa, Ethiopia

4 School of Public Health, Curtin University, Perth, Australia

Associated Data

Due to ethical issues and protection of confidentiality of the study participants, raw data cannot be provided. But, the summary data are available in the main document. When needed they are available from the corresponding author on reasonable request.

The high rate of unemployment among young adults in Ethiopia, which was 25.3% in 2018, is a major social, and public health concern. The risk of mental health problems like depression is higher among the unemployed than among the employed. However, there was no study conducted on the prevalence and associated factors of depression among unemployed young adults in Ethiopia. Hence, this study was aimed to assess the prevalence and associated factors of depression among unemployed young adults in Gedeo zone, Southern Ethiopia.

Community based cross sectional study design was employed among 1452 unemployed young adults in Gedeo zone, Southern Ethiopia from May to July, 2019. In order to select the study participants, systematic random sampling technique was used. The presence of depression was assessed by using Patient Health Questionnaire-9 (PHQ-9), and data about socio-demographic characteristics of study participants were collected by using structured questionnaire. Data were coded and entered into Epi-Data version 3.1, and analyzed by SPSS version 20. A multivariable logistic regression analysis was carried out to identify factors associated with depression, and variables with p values < 0.05 were considered as statistically significant. The strength of the association was presented by adjusted odds ratio with 95% confidence interval.

The overall prevalence of depression among unemployed young adults in the present study was 30.9% (95% CI: 28.4%, 33.1%). Of the total study participants with depression, 56.7% had mild depression, 36% had moderate depression, and 7.3% had severe depression. Being male (AOR = 1.40, 95% CI: 1.10, 1.80), long duration of unemployment (≥ 1 years) (AOR = 1.56, 95% CI: 1.21, 1.99), low self-esteem (AOR = 1.32, 95% CI: 1.03, 1.68), poor social support (AOR = 1.98, 95% CI: 1.34, 2.93), and current alcohol use (AOR = 1.86, 95% CI: 1.33, 2.59) were significantly associated with depression.

The results of our study indicated that depression is an important public health problem among unemployed young adults in Ethiopia. Therefore, our study suggested that policy makers and program planners should establish appropriate strategy for prevention, early detection and management of depression among this population. Besides, addressing the need of unemployed young people, improving access to care for depression is an important next step. Furthermore, we recommend further studies to understand the nature of depression among unemployed young people, and to strengthen the current results.

The life period of young adulthood (emerging adulthood) is not only the period of transition from adolescence to adulthood, but also the period of transition from education to employment, which is characterized by high instability [ 1 ] and several major life changes such as leaving the parental home, starting a partner relationship, and finding a stable employment [ 2 , 3 ].

Depressive disorders, as the most common mental problems [ 4 ] and leading cause of disability [ 5 ], are related to reduced quality of life and increased risk for physical health problems [ 6 ]. In 2015, the Global Burden of Diseases study (GBD) estimated that seven of the top 25 causes of Years Lived with Disability (YLD) globally were mental disorders, with major depressive disorder ranked second [ 7 ]. Depression among young adults, the period of transition from adolescence to adulthood [ 8 ], influences long-term consequences through recurrent depressive episodes [ 9 ] and worse socioeconomic outcomes [ 10 ] even though it has substantial consequences throughout the lifespan.

According to International Labour Organization (ILO), unemployment is measured using the following 3 criteria; without work, available for work, and seeking work [ 11 ]. However, this definition varies in the context of developed and developing countries. In the developed countries where the labour market is largely organized and labour absorption is adequate, unemployment is measured based on the standard definition of the seeking work criteria that is having taken active steps to search for work during specified reference period (i.e. during last 1 week).

On the other hand, in developing countries like Ethiopia, where there is no strong labour market information, labour absorption is inadequate and where the labour force is predominantly self-employed, the standard definition with its emphasis on seeking work criteria is somewhat restrictive and might not fully capture the prevailing employment situation. The relaxed definition which measures unemployment in relation to” without work” and “availability for work” criterion is found to be more plausible in most developing countries.

Employment is a source of financial security, provides people the opportunity to fulfill a social and family role, which is a key prerequisite for both physical and mental health [ 12 ]. However, unemployment is a major social problem that determines loss of income, increases the risk of poverty and affect overall health [ 13 , 14 ]. In addition, unemployment is regarded as a change in social position, particularly a change in family role, and is usually perceived as a very stressful life event [ 15 – 17 ].

The number of unemployed people, in both developed and developing countries, is currently increasing than ever before. Globally, according to International Labour Organization (ILO) report, the number of unemployed people was 192.7 million in 2017, 192.3 million in 2018 and 193.6 million in 2019 [ 18 ]. In Africa, based on this report, the number of unemployed people was 37.8 million in 2017, 37.9 million in 2018 and 40.1 million in 2019 [ 18 ].

In Ethiopia, according to Ethiopian Central Statistical Agency (CSA), the rate of unemployed people was 16.9% in 2016 and 19.1% in 2018 [ 19 ]. The rate of unemployment among young people in Ethiopia was 22% in 2016 and 25.3% in 2018 [ 19 ], and these indicated that young people are more affected by unemployment than adults.

Unemployment related elements such as economic or financial distress frequently cause feelings of failure which in turn leads to depression. And also the family and societal pressures associated with job seeking activities and higher expectations from college or university graduates to be employed act as potential mediators of depression among unemployed young adults.

The estimated prevalence of depression among unemployed young adults varies across the studies due to different methods, tools and sample size. A systematic literature review and meta-analysis study found prevalence of depression among unemployed individuals with range from 13 to 14% [ 20 ].

Based on the cross-sectional study conducted among 426 unemployed people in United State of America by using the Center for Epidemiological Study Depression Scale (CES-D), the reported prevalence of depression was 29% [ 21 ]. According to recent cross-sectional study from Greece conducted among 1064 unemployed young adults by using Depression Anxiety Stress Scale (DASS-21), the reported prevalence of depression was 32.2% [ 22 ]. Another cross-sectional study conducted in Spain among 244 unemployed young adults by using Zung’s self-rating depression scale (SDS) showed the prevalence of depression with its severity: 41.8% slight depression, 42.2% moderate depression and 9.3% severe depression [ 23 ]. Similar study done in Korea among 124 unemployed young adults by using Beck Depression Inventory-II (BDI-II) found prevalence of depression 39.5% [ 24 ]. Another cross-sectional study done in Bangladesh among 304 unemployed young adults by using Depression Anxiety Stress Scale (DASS-21) showed prevalence of depression 49.3% [ 25 ].

Several studies have revealed that being male [ 26 , 27 ], long duration of unemployment [ 28 , 29 ], low self-esteem [ 30 , 31 ], poor social support [ 32 – 34 ] and substance use [ 35 , 36 ] were associated with depression among unemployed young people.

Unemployment among young people has been described as having serious consequences for future lives of young adults and for society at large. Previous studies have suggested that unemployed young people are more likely to have poor physical health [ 37 , 38 ], engage more frequently in criminal behaviors [ 39 ], increased risk of smoking [ 40 ], increased risk of alcohol consumption and substance abuse [ 39 , 41 ]. Moreover, unemployment among young people has been associated with higher mortality rates due to suicide [ 39 , 42 ] and alcohol-related mortality [ 43 ]. Furthermore, unemployment among young adults may increases the risk of psychological crises such as low self esteem, depression, and loss of confidence [ 44 ].

Despite World Health Organization (WHO) in 2013 considered unemployed young adults as newly emerged vulnerable groups for mental disorders [ 45 ], still there is lack of attention to assess the magnitude of mental health problems among this vulnerable population in African countries, particularly in Ethiopia. To the best of our knowledge no study had been conducted to assess prevalence and associated factors of depression among unemployed young adults in Ethiopia as well as in the study area. Therefore, the present study assessed prevalence and associated factors of depression among unemployed young adults in Gedeo zone, Southern Ethiopia. The findings of this study help health programmers and policy makers at large to design preventive strategies and intervention programs of mental health problems for unemployed young people.

Study design and period

Community based cross sectional study was employed to assess prevalence and associated factors of depression among unemployed young adults in Gedeo zone, Southern Ethiopia from May to July, 2019.

The study was conducted in the Gedeo zone, Southern Nations, Nationalities and Peoples Region (SNNPR) of Ethiopia. It is located about 375 km south of the capital city, Addis Ababa. The total population of the zone is 1,129,051 persons (565,145 men; 563,906 women) living in 6 districts and 2 town administrations. According to the report from zonal office of women, children, and youth, the unemployment rate among young adults in 2019 was 24.9% (34,724 persons) [ 46 ].

Study populations

All unemployed young adults aged 18–30 years old who were graduated from college or university and living in the study area (in the selected districts and town administration of the zone) for at least 6 months prior to the study were study population. Unemployed young adults who were severely ill and unable to communicate during study period were excluded. In addition, young adults who did not finish schools, or dropped out of college/university were excluded from the study because it is unlikely to be available for work (i.e. being ready for a paid employment) without having educational certificate (i.e. diploma or degree certificate).

Sample size determination and sampling procedures

In this study, we have tried to calculate sample size for both specific objective 1 (i.e. prevalence of depression) and specific objective 2 (i.e. associated factors of depression), and took the largest sample size. Sample size for specific objective 1 of our study (i.e. prevalence of depression) was calculated by using single proportion formula taking assumptions of: 95% confidence interval, 5% margin of error, and the prevalence of depression among unemployed young adults in Ethiopia is considered to be 50% because per our search we did not find published and even unpublished studies in our country, Ethiopia. Then, we added 10% of non-response rate to the sample size, giving the final sample size of 423. Sample size for specific objective 2 of our study (i.e. associated factors of depression) was calculated using EPI-Info version 7 statistical software (Epi-info/StatCalc) by taking the following assumptions: 80% power, 95% confidence interval, 9.6% prevalence of depression among unemployed males, 15% prevalence of depression among unemployed females and a ratio of 1.66:1 of non-exposed (male gender) to exposed (female gender) which was taken from previous study [ 47 ]. We added 10% of non-response rate to the sample size, giving the final sample size of 1452. Therefore, we took the larger sample size for this study (i.e. 1452).

Out of the 6 districts and 2 town administrations of Gedeo zone, 2 districts (Bule district and Gedeb district) and 2 town administrations (Dilla town and Yirgacheffe town) were randomly selected. Then from each selected town administration and town of selected district 3 kebeles (the smallest administrative unit in Ethiopia) were randomly selected again. The lists of unemployed young adults were obtained from Office of Job opportunity creation and food security of each selected district and town administration. According to updated registration from the zonal office of Job opportunity creation and food security, the number of unemployed young adults in the Dilla town administration, Yirgacheffe town administration, Gedeb district, and Bule district were 5483, 3008, 4543, and 4103 respectively. To fix a sampling frame, we conducted census of households with unemployed young adults prior to actual data collection for 1 week by 8 data collectors and numbering of households was done in the selected kebeles. Next, population proportion allocation was done to identify representative study participants from each district and town administration based on the number of the unemployed young adults they have. Then, systematic sampling technique with an interval (K) was used to select study participants. Then after, the first study participant was randomly selected. Finally, every 3 households was interviewed for Dilla town, every four household was interviewed for Gedeb district, every 4 household was interviewed for Bule district and every 6 household was interviewed for Yirgacheffe town. In situations where households had 2 or more eligible study participants, only 1 was randomly selected.

Data collection tools

A self-constructed structured questionnaire was used to collect data about socio-demographic characteristics of the study participants such as age, sex, marital status, ethnicity, religion, educational level and duration of unemployment.

Patient health questionnaire-9 (PHQ-9)

Patient health questionnaire-9 (PHQ-9) based on the DSM-IV criteria was used to assess the presence of depression symptoms with recall period of 2 weeks [ 48 ]. The PHQ-9 is a multipurpose instrument for screening, diagnosing, monitoring and measuring the severity of depression. The scale consists of 9-items representing symptoms of depression and each symptom will be rated on a 4-point scale indicating the occurrence and the severity of symptoms: 0 (not at all), 1 (several days), 2 (more than half the days) and 3 (nearly every day). The PHQ-9 items are added up together to give scores ranging from zero to 27. A score of 10 and above indicate presence of depression. A score of 10–14 indicates ‘mild depression’, 15–19 indicates ‘moderate depression, and 20–27 indicates ‘severe depression. The PHQ-9 items showed good internal consistency with Cronbach alpha of 0.799 in the present study.

Alcohol, Smoking and Substance Involvement Screening Test (ASSIST)

The presence of substance use was measured by using WHO Alcohol, Smoking and Substance Involvement Screening Test (ASSIST) tool, an 8 item questionnaire developed to assess substance use [ 49 ]. The purpose of ASSIST is to detect psychoactive substance use and related problems among primary care patients. It provides information about: the substances people have ever used in their lifetime; the substances they have used in the past 3 months; problems related to substance use; risk of current or future harm; level of dependence; and injecting use. Lifetime substance use is defined as consuming any substances at least once in lifetime and current substance use is defined as use of at least 1 of specified substances for non-medical purpose in the last 3 months [ 49 ]. The ASSIST was developed for the World Health Organization (WHO) by an international group of researchers and clinicians as a technical tool to assist with early identification of substance use related health risks and substance use disorders in primary health care, general medical care and other settings [ 49 ]. Each question on the ASSIST has a set of responses to choose from, and each response has a numerical score. The Specific Substance Involvement score is calculated by adding together the responses to Questions 2–7 for each of the following locally available substances: tobacco, alcohol, khat (amphetamine type stimulants), and cannabis (marihuana, hashish, ganja). The ASSIST specific substance involvement scores of ≥ 10 for alcohol and ≥ 4 for any substance are an indication of problematic substance use. The ASSIST items showed high internal consistency with Cronbach alpha of 0.946 in the current study.

Rosenberg Self-esteem Scale (RSES)

The self-esteem was measured with Rosenberg Self-esteem Scale (RSES) [ 50 ]. The RSES determines individual self-worth by measuring both positive and negative feelings about the self. The scale is a 10-item self-report scale designed to measure global self-esteem, the individual’s positive and negative attitude toward the self as a totality. Responses are provided on a 4-point Likert scale ranging from “Strongly Agree” with 3 marks, “Agree” with 2 marks, “Disagree” with 1 mark and “Strongly Disagree” with 0 marks. Items 3, 5, 8, 9 and 10 are reverse scored in which a “Strongly Agree” response attracts 0 mark, “Agree” with 1 mark, “Disagree” with 2 marks and “Strongly Disagree” with 3 marks. The RSES items are added up together to give scores ranging from 0 to 30. A score greater than 15 suggest high self-esteem and scores less than 15 suggest low self-esteem [ 50 ]. The Cronbach alpha of RSES in the present study was 0.604.

Oslo Social Support Scale-3 (OSS-3)

Social support was measured by using 3 items Oslo Social Support Scale (OSS-3) [ 51 ]. The OSS-3 provides a brief measure of social support and functioning, and it is considered to be one of the best predictors of mental health. It covers different fields of social support by measuring the number of people the respondent feels close to, the interest and concern shown by others, and the ease of obtaining practical help from others. In order to score OSS-3, total scores are calculated by adding up the raw scores for each item. The sum of the raw scores has a range from 3–14. The scores of”3–8” indicate poor social support, “9–11″ indicate moderate social support, and “12–14″ indicate strong social support. The Cronbach alpha of OSS-3 in the present study was 0.64.

Data collection procedures

The questionnaire was first prepared in English; translated to Amharic (local working language) by language experts; and again translated back to English by another person to ensure consistency and accuracy. Next, the data collectors and supervisors were recruited based on previous experience on data collection and supervision. Then, training was given for 3 consecutive days for data collectors and supervisors by the researchers on how to interview, handle ethical issues, supervise and maintain confidentiality and privacy of study subjects. Then after, the data collection instrument was pre-tested on 5% of the actual sample size in similar setting, and amendments were made accordingly. After that, data collection was performed by 8 trained BSc Psychiatry nurses in face-to-face technique (interviewer administered) at the home of study participants, minimizing the risk of misunderstanding questionnaires that may occur in self-administered technique. Data collection was supervised by four MSc Mental health professionals and the principal investigator. Finally, after checked completeness of the required type of data by principal investigator and supervisors, the completed data was coded.

Data analysis

The supervisors and principal investigator checked the data for completeness, coded and entered into Epi-Data version 3.1 and exported to statistical package for social sciences (SPSS) version 20 for analysis. Means, frequencies, and percentages were used to summarize data and figures, tables and text to present data. Besides, Bivariate analysis was done to describe the associations of each independent variable with depression among unemployed young adults. Variables which had p-value less than 0.2 were considered for the multivariable logistic regression to control the effects of confounding variables. The Hosmer–Lemeshow goodness of fit test was checked for the model. Finally, Variables which had P-values less than 0.05 on multivariable logistic regression were considered as statistically significant and were identified on the basis of odds ratio (OR) with 95% confidence intervals (CI).

Socio-demographic characteristics of unemployed young adults

Of 1452 proposed study participants, a total number of 1419 unemployed young adults were included in the present study with the response rate of 97.7% which means 33 (2.3%) refused to participate. The mean age of the study participants was 23.7 (SD ± 3.35) years, and 59% of the participants were in the age range of 18–24 years. Among unemployed young adults participated in the current study, 57.8% were males and 42.2% were females. Of the 1419 respondents, 69.8% were single; 75.1% were Gedeo in ethnicity; and 42.8% were Christian Orthodox in religion. On the other hand, 674 (47.5%) of participants had diploma educational level; and 48.5% of participants reported poor social support. Regarding duration of unemployment, 67% of study participants had duration of unemployment less than 1 year and followed by those who had ≥ 1 year duration of unemployment, 33%. Of the total study subjects participated, 55.5% reported high self-esteem, while 44.5% reported low self-esteem, (Table ​ (Table1 1 ).

Socio demographic characteristics of unemployed young adults in Gedeo zone, Southern, Ethiopia, 2019 (N = 1419)

a Oromo, Amhara, Gurage, Wolaita

b Catholic, Adventist

Prevalence of depression among unemployed young adults

As indicated on the Fig.  1 , the overall prevalence of depression among unemployed young adults in the present study was 30.9 (95% CI: 28.4, 33.1%). Of the total unemployed young adults with depression, 56.7% had mild depression, 36% had moderate depression, and 7.3% had severe depression (Fig.  2 ).

Prevalence of depression among unemployed young adults living in Gedeo zone, Southern Ethiopia, 2019

Depression severity level among unemployed young adults living in Gedeo zone, Southern Ethiopia, 2019

Substance use among unemployed young adults

Both lifetime and current substance use was measured in our study by using ASSIST WHO tool. The overall prevalence of lifetime substance in our study was 44.7%. Of the total lifetime substance users; 34.5% were alcohol users, 30.9% were khat users (Khat-Amphetamine type stimulant), 18.2% were cigarette smokers, and 6.5% were illicit drug users (e.g. marijuana, cannabis) (Fig.  3 ).

Prevalence of lifetime substance use by type of substance among unemployed young adults living in Gedeo zone, Southern Ethiopia, 2019

On the other hand, the overall prevalence of current substance use in the present study was 38.8%. Of the total current substance users, 26.3% were alcohol users, 30.6% were khat users, 20% were cigarette smokers, and 12.3% were illicit drug users (e.g. marijuana, cannabis) (Fig.  4 ).

Prevalence of current substance use by type of substance among unemployed young adults living in Gedeo zone, Southern Ethiopia, 2019

Factors associated with depression among unemployed young adults

During the bivariate logistic regression analysis, variables such as age (25–30 years), sex (being male), long duration of unemployment, low self-esteem, poor social support, current cigarette smoking, current alcohol use, current khat use, current illicit drug use (e.g. marijuana, cannabis) were associated with depression (had p-value less than 0.2) and entered into multivariate logistic regression analysis for further analysis. On the other hand, variables such as educational level and marital status were not associated with depression and therefore excluded from further analyses.

In the multivariable logistic regression analysis, variables such as sex (being male), long duration of unemployment, low self-esteem, poor social support, and current alcohol use were statistically significant with depression among unemployed young adults, while there was no statistical difference between unemployed young adults with depression and those without depression, with respect to age, current cigarette smoking, current khat use, and current illicit drug (e.g. marijuana, cannabis) use.

Hence, unemployed young men were at higher risk for depression as compared to unemployed young women (AOR = 1.40, 95% CI: 1.10, 1.80). The likelihood of depression among unemployed young adults with long duration of unemployment (≥ 1 year) was found to be 1.56 times as compared to those with short duration of unemployment (< 1 year). Unemployed young adults with low self-esteem were at higher risk for depression (AOR = 1.32, 95% CI: 1.03, 1.68) as compared to those with high self-esteem. Unemployed young adults with poor social support were 1.98 times at risk for depression as compared to those with strong social support. The likelihood of depression among unemployed young adults with current alcohol use was found to be 1.86 times higher as compared to those without current alcohol use (Table ​ (Table2 2 ).

Factors associated with depression among unemployed young adults living in Gedeo zone, Southern Ethiopia, 2019 (N = 1419)

*Statistically significant (p-value < 0.05)

1 = Reference variable

Our study revealed a high prevalence of depression in sample of unemployed young adults residing in Gedeo zone, Southern Ethiopia. To the best of our knowledge, this is the first community-based cross-sectional study that has investigated the prevalence and associated factors of depression among unemployed young people aged 18–30 years in Ethiopia. The prevalence of depression among unemployed young adults in the current study was 30.9%. Our finding was consistent with the findings of previous studies conducted in Greece 32.2% [ 22 ] and in USA 29% [ 21 ]. However, the prevalence of depression among unemployed young adults in the present study is significantly higher than the finding of systematic literature review and meta-analysis study conducted by Paul and Moser [ 20 ] that found the prevalence range of depression from 13 to 14% among unemployed individuals.

On the other hand, the finding of our study is lower than the finding of study done in Germany among 365 long-term unemployed individuals by using Hospital Anxiety and Depression Scale (HADS) which was 37% [ 52 ]. Another study conducted in Germany also reported the higher prevalence of depression (34.4%) among long-term unemployed people measured by using Patient Health Questionnaire-9 (PHQ-9) as compared to the result of our study (30.9%). Also the results of studies conducted in Spain (51.5%) [ 23 ], Korea (39.5%) [ 24 ] and Bangladesh (49.3%) [ 25 ] were higher than the finding of our study.

The reason for variation might be due to difference in sample size. The assessment instrument might also be the possible reason for the differences in the prevalence of depression among unemployed people. For instance, the previous studies conducted in Germany, Greece, and Bangladesh used Depression Anxiety Stress Scale (DASS-21) to assess depression, whereas our study used the PHQ-9. The other explanation for the difference might be due to the type of data collection procedure that researchers used (interviewer administered versus self-administered) and the study settings (community based versus institutional based).

The present study identified factors associated with depression among unemployed young adults. Sex was identified as significant variable, as unemployed young men were at higher risk for depression as compared to women. Our study finding is consistent with findings of previous studies that found unemployed men were more likely to be affected by depression [ 26 , 27 ], but the finding of our study is inconsistent with those of previous studies that reported unemployed women exhibited higher rate of depression [ 28 , 47 , 52 ]. The reasons why unemployed men are more affected by depression than women might be explained by: first, unemployed men experience stronger distress as traditional values and social expectations make employment more important for men- unemployment might be experienced by men as a personal failure. Second, masculine identity is intricately linked to having a job in most developing countries including Ethiopia and is severely threatened by unemployment related loss of income that threatens other areas of goal attainment, including capacities to provide security for self and family, which leads to depression. Third, for women, on the other hand, work is seen as only 1 of several roles (e.g. the role of being wife and mother are assumed to be as important as work in women’s lives). For example, Dew and Bromet [ 26 ] found that unemployment has a lesser impact on women which might be due to different gender roles with women valuing their jobs less and gaining more self-esteem from their family. Additionally, 2 main arguments based on the study conducted by Shamir [ 53 ] to explain the reason why depression is more common among unemployed men than women were; First, men are assumed to have a higher commitment to the work role than women, resulting in stronger distress when deprived of this role. Second, women are assumed to have an alternative role that can serve as a substitute to employment.

In our study, we found that the likelihood of depression among unemployed young adults with long duration of unemployment (≥ 1 year) was 1.56 times more likely as compared to those with short duration of unemployment (< 1 year). Our finding is consistent with findings of previous studies [ 28 , 29 ] that support the link between unemployment duration and poor mental health: the longer a person is unemployed, the worse mental health outcomes (e.g. depression). The possible explanation could be due the fact that unemployed young adults experience continued and more and more discouraging failures in job seeking and financial pressures that become stronger as time passes.

In the present study, we found that unemployed young adults with low self-esteem were at higher risk for depression as compared to those with high self-esteem. This finding is supported by the findings of previous longitudinal studies conducted in United State of America [ 30 , 31 ]. Several pathways have been proposed that explain why people with lower self-esteem might be at higher risk for depression. For example, according to Beck’s cognitive theory of depression, negative beliefs about the self, which are central to low self-esteem, would contribute to the development of depressive disorders [ 54 ].

Unemployed young adults with poor social support were 1.98 times at risk for depression as compared to those with strong social support, which is in agreement with the findings of previous studies that found poor social support was strongly correlated with depression among unemployed young people [ 32 – 34 ]. There has been previous study that indicates social support has an important influence on the mental health of unemployed people. For instance, a population based case–control study on young people conducted in Sweden found that mental health was generally poor among unemployed persons with low social support from family and friends than among unemployed persons with higher social support [ 55 ].

Our study revealed that the likelihood of depression among unemployed young adults who reported current alcohol use was found to be 1.86 times higher as compared to those who did not report current alcohol use. The finding of our study is supported by the evidences from 2 cohort studies conducted by Fergusson et.al [ 35 , 36 ] who found alcohol abuse /dependence was most likely to lead to depression. The possible reason is that alcohol might be used as a self-medication strategy against distress or unemployment-related struggles- a way to deal with financial hardship, which in turn increases likelihood of depression in this population.

Several strengths of this study need to be highlighted. First, our study is the first study to assess prevalence of depression and associated factors among unemployed young adults in Ethiopia, which can be considered as strength. Second, our large sample which was recruited from Community sample with an excellent response rate can also be considered as strength. Third, we used standard tool that takes a non-judgmental and more acceptable approach to measure depression and other variables. On the other hand, this study has several limitations. First, our study used a cross-sectional study design that makes it difficult to determine the causality of the observed associations between depression and its associated factors. Second, due to the sensitive nature of the study in terms of social stigma, the study participants may have underreported depression and some other variables such as substance use. Third, the exclusion of young adults who did not finish school, or dropped out of college/university may have resulted in an underrepresentation of severe cases of depression. Fourth, the study did not include detailed risk factors (i.e. income or financial distress, coping strategy, and emotional intelligence) which might contribute for depression among this vulnerable population.

Conclusions

The results of our study indicated that depression is an important public health problem among unemployed young adults in Gedeo zone, southern Ethiopia. Being male, long duration of unemployment, low self-esteem, poor social support, and current alcohol use were statistically significant with depression. Therefore, our study suggested that Policy makers and program planners should establish appropriate strategy for prevention, early detection and management of depression among unemployed young adults. Besides, it is important to design and implement effective community based depression prevention programs for unemployed young adults in Ethiopia. Furthermore, addressing the need of unemployed young people, improving access to care for mental health problems, particularly for depression, is an important next step. Moreover, we recommend further studies to understand the nature of depression among unemployed young people, investigating other contributing factors by using different study designs to strengthen the current results.

Acknowledgements

The authors would like to thank Dilla University for funding this research project. The authors also would like to thank data collectors and supervisors for their commitment work. Finally, authors would like to express their heartfelt thanks to the study participants for their willingness to participate in the study, without whom this research would be impossible.

Abbreviations

Authors’ contributions

HM wrote the proposal, involved in study design, participated in data collection process, analyzed the data, and drafted the manuscript. KY and GA were involved in designing of the study and drafted the manuscript. All authors read and approved the final manuscript.

Dilla University funds the research project for data collection and analysis but not for publication.

Availability of data and materials

Ethics approval and consent to participate.

Ethical approval was obtained from Institutional Review Board (IRB) of Dilla University, College of Medicine and Health Sciences. Letter of permission was obtained from each selected district and town administration of Gedeo zone, southern Ethiopia. Each unemployed young adult participated in the study was informed about the purpose, method, expected benefit, and risk of the study. Participants were also informed about their right not to participate or stop the interview at any time. Hence, informed verbal consent was obtained from the unemployed young adults who were involved in the study and participant involvement was on voluntary basis. Moreover, confidentiality of study participants was maintained by using codes rather than names.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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• Original Article
• Open access
• Published: 08 March 2018

Unemployment among younger and older individuals: does conventional data about unemployment tell us the whole story?

• Hila Axelrad 1 , 2 ,
• Miki Malul 3 &
• Israel Luski 4  

Journal for Labour Market Research volume  52 , Article number:  3 ( 2018 ) Cite this article

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In this research we show that workers aged 30–44 were significantly more likely than those aged 45–59 to find a job a year after being unemployed. The main contribution is demonstrating empirically that since older workers’ difficulties are related to their age, while for younger individuals the difficulties are more related to the business cycle, policy makers must devise different programs to address unemployment among young and older individuals. The solution to youth unemployment is the creation of more jobs, and combining differential minimum wage levels and earned income tax credits might improve the rate of employment for older individuals.

1 Introduction

Literature about unemployment references both the unemployment of older workers (ages 45 or 50 and over) and youth unemployment (15–24). These two phenomena differ from one another in their characteristics, scope and solutions.

Unemployment among young people begins when they are eligible to work. According to the International Labor Office (ILO), young people are increasingly having trouble when looking for their first job (ILO 2011 ). The sharp increase in youth unemployment and underemployment is rooted in long-standing structural obstacles that prevent many youngsters in both OECD countries and emerging economies from making a successful transition from school to work. Not all young people face the same difficulties in gaining access to productive and rewarding jobs, and the extent of these difficulties varies across countries. Nevertheless, in all countries, there is a core group of young people facing various combinations of high and persistent unemployment, poor quality jobs when they do find work and a high risk of social exclusion (Keese et al. 2013 ). The rate of youth unemployment is much higher than that of adults in most countries of the world (ILO 2011 ; Keese et al. 2013 ; O’Higgins 1997 ; Morsy 2012 ). Official youth unemployment rates in the early decade of the 2010s ranged from under 10% in Germany to around 50% in Spain ( http://www.indexmundi.com/g/r.aspx?v=2229 ; Pasquali 2012 ). The youngest employees, typically the newest, are more likely to be let go compared to older employees who have been in their jobs for a long time and have more job experience and job security (Furlong et al. 2012 ). However, although unemployment rates among young workers are relatively higher than those of older people, the period of time they spend unemployed is generally shorter than that of older adults (O’Higgins 2001 ).

We would like to argue that one of the most important determinants of youth unemployment is the economy’s rate of growth. When the aggregate level of economic activity and the level of adult employment are high, youth employment is also high. Footnote 1 Quantitatively, the employment of young people appears to be one of the most sensitive variables in the labor market, rising substantially during boom periods and falling substantially during less active periods (Freeman and Wise 1982 ; Bell and Blanchflower 2011 ; Dietrich and Möller 2016 ). Several explanations have been offered for this phenomenon. First, youth unemployment might be caused by insufficient skills of young workers. Another reason is a fall in aggregate demand, which leads to a decline in the demand for labor in general. Young workers are affected more strongly than older workers by such changes in aggregate demand (O’Higgins 2001 ). Thus, our first research question is whether young adults are more vulnerable to economic shocks compared to their older counterparts.

Older workers’ unemployment is mainly characterized by difficulties in finding a new job for those who have lost their jobs (Axelrad et al. et al. 2013 ). This fact seems counter-intuitive because older workers have the experience and accumulated knowledge that the younger working population lacks. The losses to society and the individuals are substantial because life expectancy is increasing, the retirement age is rising in many countries, and people are generally in good health (Axelrad et al. 2013 ; Vodopivec and Dolenc 2008 ).

The difficulty that adults have in reintegrating into the labor market after losing their jobs is more severe than that of the younger unemployed. Studies show that as workers get older, the duration of their unemployment lengthens and the chances of finding a job decline (Böheim et al. 2011 ; De Coen et al. 2010 ). Therefore, our second research question is whether older workers’ unemployment stems from their age.

In this paper, we argue that the unemployment rates of young people and older workers are often misinterpreted. Even if the data show that unemployment rates are higher among young people, such statistics do not necessarily imply that it is harder for them to find a job compared to older individuals. We maintain that youth unemployment stems mainly from the characteristics of the labor market, not from specific attributes of young people. In contrast, the unemployment of older individuals is more related to their specific characteristics, such as higher salary expectations, higher labor costs and stereotypes about being less productive (Henkens and Schippers 2008 ; Keese et al. 2006 ). To test these hypotheses, we conduct an empirical analysis using statistics from the Israeli labor market and data published by the OECD. We also discuss some policy implications stemming from our results, specifically, a differential policy of minimum wages and earned income tax credits depending on the worker’s age.

Following the introduction and literary review, the next part of our paper presents the existing data about the unemployment rates of young people and adults in the OECD countries in general and Israel in particular. Than we present the research hypotheses and theoretical model, we describe the data, variables and methods used to test our hypotheses. The regression results are presented in Sect.  4 , the model of Business Cycle is presented in Sect.  5 , and the paper concludes with some policy implications, a summary and conclusions in Sect.  6 .

2 Literature review

Over the past 30 years, unemployment in general and youth unemployment in particular has been a major problem in many industrial societies (Isengard 2003 ). The transition from school to work is a rather complex and turbulent period. The risk of unemployment is greater for young people than for adults, and first jobs are often unstable and rather short-lived (Jacob 2008 ). Many young people have short spells of unemployment during their transition from school to work; however, some often get trapped in unemployment and risk becoming unemployed in the long term (Kelly et al. 2012 ).

Youth unemployment leads to social problems such as a lack of orientation and hostility towards foreigners, which in turn lead to increased social expenditures. At the societal level, high youth unemployment endangers the functioning of social security systems, which depend on a sufficient number of compulsory payments from workers in order to operate (Isengard 2003 ).

Workers 45 and older who have lost their jobs often encounter difficulties in finding a new job (Axelrad et al. 2013 ; Marmora and Ritter 2015 ) although today they are more able to work longer than in years past (Johnson 2004 ). In addition to the monetary rewards, work also offers mental and psychological benefits (Axelrad et al. 2016 ; Jahoda 1982 ; Winkelmann and Winkelmann 1998 ). Working at an older age may contribute to an individual’s mental acuity and provide a sense of usefulness.

On average, throughout the OECD, the hiring rate of workers aged 50 and over is less than half the rate for workers aged 25–49. The low re-employment rates among older job seekers reflect, among other things, the reluctance of employers to hire older workers. Lahey ( 2005 ) found evidence of age discrimination against older workers in labor markets. Older job applicants (aged 50 or older), are treated differently than younger applicants. A younger worker is more than 40% more likely to be called back for an interview compared to an older worker. Age discrimination is also reflected in the time it takes for older adults to find a job. Many workers aged 45 or 50 and older who have lost their jobs often encounter difficulties in finding a new job, even if they are physically and intellectually fit (Hendels 2008 ; Malul 2009 ). Despite the fact that older workers are considered to be more reliable (McGregor and Gray 2002 ) and to have better business ethics, they are perceived as less flexible or adaptable, less productive and having higher salary expectations (Henkens and Schippers 2008 ). Employers who hesitated in hiring older workers also mentioned factors such as wages and non-wage labor costs that rise more steeply with age and the difficulties firms may face in adjusting working conditions to meet the requirements of employment protection rules (Keese et al. 2006 ).

Thus, we have a paradox. On one hand, people live longer, the retirement age is rising, and older people in good health want or need to keep working. At the same time, employers seek more and more young workers all the time. This phenomenon might marginalize skilled and experience workers, and take away their ability to make a living and accrue pension rights. Thus, employers’ reluctance to hire older workers creates a cycle of poverty and distress, burdening the already overcrowded social institutions and negatively affecting the economy’s productivity and GDP (Axelrad et al. 2013 ).

2.1 OECD countries during the post 2008 crisis

The recent global economic crisis took an outsized toll on young workers across the globe, especially in advanced economies, which were hit harder and recovered more slowly than emerging markets and developing economies. Does this fact imply that the labor market in Spain and Portugal (with relatively high youth unemployment rates) is less “friendly” toward younger individuals than the labor market in Israel and Germany (with a relatively low youth unemployment rate)? Has the market in Spain and Portugal become less “friendly” toward young people during the last 4 years? We argue that the main factor causing the increasing youth unemployment rates in Spain and Portugal is the poor state of the economy in the last 4 years in these countries rather than a change in attitudes toward hiring young people.

OECD data indicate that adult unemployment is significantly lower than youth unemployment. The global economic crisis has hit young people very hard. In 2010, there were nearly 15 million unemployed youngsters in the OECD area, about four million more than at the end of 2007 (Scarpetta et al. 2010 ).

From an international perspective, and unlike other developed countries, Israel has a young age structure, with a high birthrate and a small fraction of elderly population. Israel has a mandatory retirement age, which differs for men (67) and women (62), and the labor force participation of older workers is relatively high (Stier and Endeweld 2015 ), therefore, we believe that Israel is an interesting case for studying.

The Israeli labor market is extremely flexible (e.g. hiring and firing are relatively easy), and mobile (workers can easily move between jobs) (Peretz 2016 ). Focusing on Israel’s labor market, we want to check whether this is true for older Israeli workers as well, and whether there is a difference between young and older workers.

The problem of unemployment among young people in Israel is less severe than in most other developed countries. This low unemployment rate is a result of long-term processes that have enabled the labor market to respond relatively quickly to changes in the economic environment and have reduced structural unemployment. Footnote 2 Furthermore, responsible fiscal and monetary policies, and strong integration into the global market have also promoted employment at all ages. With regard to the differences between younger and older workers in Israel, Stier and Endeweld ( 2015 ) determined that older workers, men and women alike, are indeed less likely to leave their jobs. This finding is similar to other studies showing that older workers are less likely to move from one employer to another. According to the U.S. Bureau of Labor Statistics, the median employee tenure is generally higher among older workers than younger ones (BLS 2014 ). Movement in and out of the labor market is highest among the youngest workers. However, these young people are re-employed quickly, while older workers have the hardest time finding jobs once they become unemployed. The Bank of Israel calculated the chances of unemployed people finding work between two consecutive quarters using a panel of the Labor Force Survey for the years 1996–2011. Their calculations show that since the middle of the last decade the chances of unemployed people finding a job between two consecutive quarters increased. Footnote 3 However, as noted earlier, as workers age, the duration of their unemployment lengthens. Prolonged unemployment erodes the human capital of the unemployed (Addison et al. 2004 ), which has a particularly deleterious effect on older workers. Thus, the longer the period of unemployment of older workers, the less likely they will find a job (Axelrad and Luski 2017 ). Nevertheless, as Fig.  1 shows, the rates of youth unemployment in Israel are higher than those of older workers.

(Source: Calculated by the authors by using data from the Labor Force survey of the Israeli CBS, 2011)

Unemployed persons and discouraged workers as percentages of the civilian labor force, by age group (Bank of Israel 2011 ). We excluded those living outside settled communities or in institutions. The percentages of discouraged workers are calculated from the civilian labor force after including them in it

We argue that the main reason for this situation is the status quo in the labor market, which is general and not specific to Israel. It applies both to older workers and young workers who have a job. The status quo is evident in the situation in which adults (and young people) already in the labor market manage to keep their jobs, making the entrance of new young people into the labor market more difficult. What we are witnessing is not evidence of a preference for the old over the young, but the maintaining of the status quo.

The rate of employed Israelis covered by collective bargaining agreements increases with age: up to age 35, the rate is less than one-quarter, and between 50 and 64 the rate reaches about one-half. In effect, in each age group between 25 and 60, there are about 100,000 covered employees, and the lower coverage rate among the younger ages derives from the natural growth in the cohorts over time (Bank of Israel 2013 ). The wave of unionization in recent years is likely to change only the age profile of the unionization rate and the decline in the share of covered people over the years, to the extent that it strengthens and includes tens of thousands more employees from the younger age groups. Footnote 4

The fact that the percentage of employees covered by collective agreement increases with age implies that there is a status quo effect. Older workers are protected by collective agreements, and it is hard to dismiss them (Culpepper 2002 ; Palier and Thelen 2010 ). However, young workers enter the workforce with individual contracts and are not protected, making it is easier to change their working conditions and dismiss them.

To complete the picture, Fig.  2 shows that the number of layoffs among adults is lower, possibly due to their protection under collective bargaining agreements.

(Source: Israeli Central Bureau of Statistics, 2008, data processed by the authors)

Dismissal of employees in Israel, by age. Percentage of total employed persons ages 20–75 and over including those dismissed

In order to determine the real difference between the difficulties of older versus younger individuals in finding work, we have to eliminate the effect of the status quo in the labor market. For example, if we removed all of the workers from the labor market, what would be the difference between the difficulties of older people versus younger individuals in finding work? In the next section we will analyze the probability of younger and older individuals moving from unemployment to employment when we control for the status quo. We will do so by considering only individuals who have not been employed at least part of the previous year.

3 Estimating the chances of finding a job and research hypotheses

Based on the literature and the classic premise that young workers are more vulnerable to economic shocks (ILO 2011 ), we posit that:

H 1 : The unemployment rate of young people stems mainly from the characteristics of the labor market and less from their personal attributes.

Based on the low hiring rate of older workers (OECD 2006 ) and the literature about age discrimination against older workers in labor markets (Axelrad et al. 2013 ; Lahey 2005 ), we hypothesis that:

H 2 : The difficulty face by unemployed older workers searching for a job stems mainly from their age and less from the characteristics of the labor market.

To assess the chances of younger and older workers finding a job, we used a logit regression model that has been validated in previous studies (Brander et al. 2002 ; Flug and Kassir 2001 ). Being employed was the dependent variable, and the characteristics of the respondents (age, gender, ethnicity and education) were the independent variables. The dependent variable was nominal and dichotomous with two categories: 0 or 1. We defined the unemployed as those who did not work at all during the last year or worked less than 9 months last year. The dependent variable was a dummy variable of the current employment situation, which received the value of 1 if the individual worked last week and 0 otherwise.

3.1 The model

i—individual i, P i —the chances that individual i will have a full or part time job (at the time of the survey). \(\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{\text{X}}_{\text{i}}\) —vector of explanatory variables of individual i. Each of the variables in vector \(\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{X}_{i}\) was defined as a dummy variable with the value of 1 or 0. β—vector of marginal addition to the log of the odds ratio. For example, if the explanatory variable was the log of 13 years or more of schooling, then the log odds ratio refers to the marginal addition of 13 years of education to the chances of being employed, compared with 12 years of education or less.

The regression allowed us to predict the probability of an individual finding a job. The dependent variable was the natural base log of the probability ratio P divided by (1 − P) that a particular individual would find a job. The odds ratio from the regression answers the question of how much more likely it is that an individual will find a job if he or she has certain characteristics. The importance of the probability analysis is the consideration of the marginal contribution of each feature to the probability of finding a job.

3.2 The sample

We used data gathered from the 2011 Labor Force Survey Footnote 5 of the Israeli Central Bureau of Statistics (CBS), Footnote 6 which is a major survey conducted annually among households. The survey follows the development of the labor force in Israel, its size and characteristics, as well as the extent of unemployment and other trends. Given our focus on working age individuals, we excluded all of the respondents under the age of 18 or over the age of 59. The data sample includes only the Jewish population, because structural problems in the non-Jewish sector made it difficult to estimate this sector using the existing data only. The sample does not include the ultra-Orthodox population because of their special characteristics, particularly the limited involvement of men in this population in the labor market.

The base population is individuals who did not work at all during the past year or worked less than 9 months last year (meaning that they worked but were unemployed at least part of last year). To determine whether they managed to find work after 1 year of unemployment, we used the question on the ICBS questionnaire, “Did you work last week?” We used the answer to this question to distinguish between those who had succeeded in finding a job and those who did not. The data include individuals who were out of the labor force Footnote 7 at the time of the survey, but exclude those who were not working for medical reasons (illness, disability or other medical restrictions) or due to their mandatory military service. Footnote 8

3.3 Data and variables

The survey contains 104,055 respondents, but after omitting all of the respondents under the age of 18 or above 59, those who were outside the labor force for medical reasons or due to mandatory military service, non-Jews, the ultra-Orthodox, and those who worked more than 9 months last year, the sample includes 13,494 individuals (the base population). Of these, 9409 are individuals who had not managed to find work, and 4085 are individuals who were employed when the survey was conducted.

The participants’ ages range between 18 and 59, with the average age being 33.07 (SD 12.88) and the median age being 29. 40.8% are males; 43.5% have an academic education; 52.5% are single, and 53.5% of the respondents have no children under 17.

3.4 Dependent and independent variables

While previous studies have assessed the probability of being unemployed in the general population, our study examines a more specific case: the probability of unemployed individuals finding a job. Therefore, we use the same explanatory variables that have been used in similar studies conducted in Israel (Brander et al. 2002 ; Flug and Kassir 2001 ), which were also based on an income survey and the Labor Force Survey of the Central Bureau of Statistics.

3.5 The dependent variable—being employed

According to the definition of the CBS, employed persons are those who worked at least 1 h during a given week for pay, profit or other compensation.

3.6 Independent variables

We divided the population into sub-groups of age intervals: 18–24, 25–29, 30–44, 45–54 and 55–59, according to the sub-groups provided by the CBS. We then assigned a special dummy variable to each group—except the 30–44 sub-group, which is considered as the base group. Age is measured as a dummy variable, and is codded as 1 if the individual belongs to the age group, and 0 otherwise. Age appears in the regression results as a variable in and of itself. Its significance is the marginal contribution of each age group to the probability of finding work relative to the base group (ages 30–44), and also as an interaction variable.

3.6.2 Gender

This variable is codded as 1 if the individual is female and 0 otherwise. Gender also appears in the interaction with age.

3.6.3 Marital status

Two dummy variables are used: one for married respondents and one for those who are divorced or widowed. In accordance with the practice of the CBS, we combined the divorced and the widowed into one variable. This variable is a dummy variable that is codded as 1 if the individual belongs to the appropriate group (divorced/widowed or married) and 0 otherwise. The base group is those who are single.

3.6.4 Education

This variable is codded as 1 if the individual has 13 or more years of schooling, and 0 otherwise. The variable also appears in interactions between it and the age variable.

3.6.5 Vocational education

This variable is codded as 1 if the individual has a secondary school diploma that is not an academic degree or another diploma, and 0 otherwise.

3.6.6 Academic education

This variable is codded as 1 if the individual has any university degree (bachelors, masters or Ph.D.) and 0 otherwise.

3.6.7 Children

In accordance with similar studies that examined the probability of employment in Israel (Brander et al. 2002 ), we define children as those up to age 17. This variable is a dummy variable that is codded as 1 if the respondents have children under the age of 17, and 0 otherwise.

3.6.8 Ethnicity

This variable is codded as 1 if the individual was born in an Arabic-speaking country, in an African country other than South Africa, or in an Asian country, or was born in Israel but had a father who was born in one of these countries. Israel generally refers to such individuals as Mizrahim. Respondents who were not Mizrahim received a value of 0. The base group in our study are men aged 30–44 who are not Mizrahim.

We also assessed the interactions between the variables. For example, the interaction between age and the number of years of schooling is the contribution of education (i.e., 13 years of schooling) to the probability of finding a job for every age group separately relative to the situation of having less education (i.e., 12 years of education). The interaction between age and gender is the contribution of gender (i.e., being a female respondent) to the probability of finding a job for each age group separately relative to being a man.

To demonstrate the differences between old and young individuals in their chances of finding a job, we computed the rates of those who managed to find a job relative to all of the respondents in the sample. Table  1 shows that the rate of those who found a job declines with age. For example, 36% of the men age 30–44 found a job, but those rates drop to 29% at the age of 45–54 and decline again to 17% at the age of 55–59. As for women, 31% of them aged 30–44 found a job, but those rates drop to 20% at the age of 45–54 and decline again to 9% at the age of 55–59.

In an attempt to determine the role of education in finding employment, we created Model 1 and Model 2, which differ only in terms of how we defined education. In Model 1 the sample is divided into two groups: those with up to 12 years of schooling (the base group) and those with 13 or more years of schooling. In Model 2 there are three sub-groups: those with a university degree, those who have a vocational education, and the base group that has only a high school degree.

Table  2 shows that the probability of a young person (age 18–24) getting a job is larger than that of an individual aged 30–44 who belongs to the base group (the coefficient of the dummy variable “age 18–24” is significant and positive). Similarly, individuals who are older than 45 are less likely than those in the base group to find work.

Women aged 30–44 are less likely to be employed than men in the same age group. Additionally, when we compare women aged 18–24 to women aged 30–44, we see that the chances of the latter being employed are lower. Older women (45+) are much less likely than men of the same age group to find work. Additionally, having children under the age of 17 at home reduces the probability of finding a job.

A university education increases the probability of being employed for both men and women aged 30–44. Furthermore, for older people (55+) an academic education reduces the negative effect of age on the probability of being employed. While a vocational education increases the likelihood of finding a job for those aged 30–44, such a qualification has no significant impact on the prospects of older people.

Interestingly, being a Mizrahi Jew increases the probability of being employed.

In addition, we estimated the models separately twice—for the male and for the female population. For male and female, the probability of an unemployed individual finding a job declines with age.

Analyzing the male population (Table  3 ) reveals that those aged 18–24 are more likely than the base group (ages 30–44) to find a job. However, the significance level is relatively low, and in Model 2, this variable is not significant at all. Those 45 and older are less likely than the base group (ages 30–44) to find a job. Married men are more likely than single men to be employed. However, divorced and widowed men are less likely than single men to find a job. For men, the presence in their household of children under the age of 17 further reduces the probability of their being employed. Mizrahi men aged 18–24 are more likely to be employed than men of the same age who are from other regions.

Table  3 illustrates that educated men are more likely to find work than those who are not. However, in Model 1, at the ages 18–29 and 45–54, the probability of finding a job for educated men is less than that of uneducated males. Among younger workers, this might be due to excess supply—the share of academic degree owners has risen, in contrast to almost no change in the overall share of individuals receiving some other post-secondary certificate (Fuchs 2015 ). Among older job seeking men, this might be due to the fact that the increase in employment among men during 2002–2010 occurred mainly in part-time jobs (Bank of Israel 2011 ). In Model 2, men with an academic or vocational education have a better chance of finding a job, but at the group age of 18–24, those with a vocational education are less likely to find a job compared to those without a vocational education. The reason might be the lack of experience of young workers (18–24), experience that is particularly needed in jobs that require vocational education (Salvisberg and Sacchi 2014 ).

Analyzing the female population (Table  3 ) reveals that women between 18 and 24 are more likely to be employed than those who are 30–44, and those who are 45–59 are less likely to be employed than those who are 30–44. The probability of finding a job for women at the age of 25 to 29 is not significantly different from the probability of the base group (women ages 30–44).

Married women are less likely than single women to be employed. Women who have children under the age of 17 are less likely to be employed than women who do not have dependents that age. According to Model 2, Mizrahi women are more likely to be employed compared to women from other regions. According to both models, women originally from Asia or Africa ages 25–29 have a better chance of being employed than women the same age from other regions. Future research should examine this finding in depth to understand it.

With regard to education, in Model 1 (Table  3 ), where we divided the respondents simply on the question of whether they had a post-high school education, women who were educated were more likely to find work than those who were not. However, in the 18–29 age categories, educated women were less likely to find a job compared to uneducated women, probably due to the same reason cited above for men in the same age group—the inflation of academic degrees (Fuchs 2015 ). These findings become more nuanced when we consider the results of Model 2. There, women with an academic or vocational education have a better chance of finding a job, but at the ages of 18–24 those with an academic education are less likely to find a job than those without an academic education. Finally, at the ages of 25–29, those with a vocational education have a better chance of finding a job than those without a vocational education, due to the stagnation in the overall share of individuals receiving post-secondary certificate (Fuchs 2015 ).

Thus, based on the results in Table  3 , we can draw several conclusions. First, the effect of aging on women is more severe than the impact on men. In addition, the “marriage premium” is positive for men and negative for women. Divorced or widowed men lose their “marriage premium”. Finally, having children at home has a negative effect on both men and women—almost at the same magnitude.

5 Unemployment as a function of the business cycle

To determine whether unemployment of young workers is caused by the business cycle, we examined the unemployment figures in 34 OECD countries in 2007–2009, years of economic crisis, and in 2009–2011, years of recovery and economic growth. For each country, we considered the data on unemployment among young workers (15–24) and older adults (55–64) and calculated the difference between 2009 and 2007 and between 2011 and 2009 for both groups. The data were taken from OECD publications and included information about the growth rates from 2007 to 2011. Our assessment of unemployment rates in 34 OECD countries reveals that the average rate of youth unemployment in 2007 was 13.4%, compared to 18.9% in 2011, so the delta of youth unemployment before and after the economic crisis was 5.55. The average rate of adult unemployment in 2007 was 4% compared to 5.8% in 2011, so the delta for adults was 1.88. Both of the differences are significantly different from zero, and the delta for young people is significantly larger than the delta for adults. These results indicate that among young people (15–24), the increase in unemployment due to the crisis was very large.

An OLS model of the reduced form was estimated to determine whether unemployment is a function of the business cycle, which is represented by the growth rate. The variables GR2007, GR2009 and GR2011 are the rate of GDP growth in 2007, 2009 and 2011 respectively ( Appendix ). The explanatory variable is either GR2009 minus GR2007 or GR2011 minus GR2009. In both periods, 2007–2009 and 2009–2011, the coefficient of the change in growth rates is negative and significant for young people, but insignificant for adults. Thus, it seems that the unemployment rates of young people are affected by the business cycle, but those of older workers are not. In a time of recession (2007–2009), unemployment among young individuals increases whereas for older individuals the increase in unemployment is not significant. In recovery periods (2009–2011), unemployment among young individuals declines, whereas the drop in unemployment among older individuals is not significant (Table  4 ).

6 Summary and conclusions

The purpose of this paper was to show that while the unemployment rates of young workers are higher than those of older workers, the data alone do not necessarily tell the whole story. Our findings confirm our first hypothesis, that the high unemployment rate of young people stems mainly from the characteristics of the labor market and less from their personal attributes. Using data from Israel and 34 OECD countries, we demonstrated that a country’s growth rate is the main factor that determines youth unemployment. However, the GDP rate of growth cannot explain adult unemployment. Our results also support our second hypothesis, that the difficulties faced by unemployed older workers when searching for a job are more a function of their age than the overall business environment.

Indeed, one limitation of the study is the fact that we could not follow individuals over time and capture individual changes. We analyze a sample of those who have been unemployed in the previous year and then analyze the probability of being employed in the subsequent year but cannot take into account people could have found a job in between which they already lost again. Yet, in this sample we could isolate and analyze those who did not work last year and look at their employment status in the present. By doing so, we found out that the rate of those who found a job declines with age, and that the difficulties faced by unemployed older workers stems mainly from their age.

To solve both of these problems, youth unemployment and older workers unemployment, countries need to adopt different methods. Creating more jobs will help young people enter the labor market. Creating differential levels for the minimum wage and supplementing the income of older workers with earned income tax credits will help older people re-enter the job market.

Further research may explore the effect of structural and institutional differences which can also determine individual unemployment vs. employment among different age groups.

In addition to presenting a theory about the factors that affect the differences in employment opportunities for young people and those over 45, the main contribution of this paper is demonstrating the validity of our contention that it is age specifically that works to keep older people out of the job market, whereas it is the business cycle that has a deleterious effect on the job prospects of younger people. Given these differences, these two sectors of unemployment require different approaches for solving their employment problems. The common wisdom maintains that the high level of youth unemployment requires policy makers to focus on programs targeting younger unemployed individuals. However, we argue that given the results of our study, policy makers must adopt two different strategies to dealing with unemployment in these two groups.

6.1 Policy implications

In order to cope with the problem of youth unemployment, we must create more jobs. When the recession ends in Portugal and Spain, the problem of youth unemployment should be alleviated. Since there is no discrimination against young people—evidenced by the fact that when the aggregate level of economic activity and the level of adult employment are high, youth employment is also high—creating more jobs in general by enhancing economic growth should improve the employment rates of young workers.

In contrast, the issue of adult unemployment requires a different solution due to the fact that their chances of finding a job are related specifically to their age. One solution might be a differential minimum wage for older and younger individuals and earned income tax credits (EITC) Footnote 9 for older individuals, as Malul and Luski ( 2009 ) suggested.

According to this solution, the government should reduce the minimum wage for older individuals. As a complementary policy and in order to avoid differences in wages between older and younger individuals, the former would receive an earned income tax credit so that their minimum wage together with their EITC would be equal to the minimum wage of younger individuals. Earned income tax credits could increase employment among older workers while increasing their income. For older workers, EITCs are more effective than a minimum wage both in terms of employment and income. Such policies of a differential minimum wage plus an EITC can help older adults and constitute a kind of social safety net for them. Imposing a higher minimum wage exclusively for younger individuals may be beneficial in encouraging them to seek more education.

Young workers who face layoffs as a result of their high minimum wage (Kalenkoski and Lacombe 2008 ) may choose to increase their investment in their human capital (Nawakitphaitoon 2014 ). The ability of young workers to improve their professional level protects them against the unemployment that might result from a higher minimum wage (Malul and Luski 2009 ). For older workers, if the minimum wage is higher than their productivity, they will be unemployed. This will be true even if their productivity is higher than the value of their leisure. Such a situation might result in an inefficient allocation between work and leisure for this group. One way to fix this inefficient allocation without reducing the wages of older individuals is to use the EITC, which is actually a subsidy for this group. This social policy might prompt employers to substitute older workers with a lower minimum wage for more expensive younger workers, making it possible for traditional factories to continue their domestic production. However, a necessary condition for this suggestion to work is the availability of efficient systems of training and learning. Axelrad et al. ( 2013 ) provided another justification for subsidizing the work of older individuals. They found that stereotypes about older workers might lead to a distorted allocation of the labor force. Subsidizing the work of older workers might correct this distortion. Ultimately, however, policy makers must understand that they must implement two different approaches to dealing with the problems of unemployment among young people and in the older population.

For example, in the US, the UK and Portugal, we witnessed higher rates of growth during late 1990 s and lower rates of youth unemployment compared to 2011.

Bank of Israel Annual Report—2013, http://www.boi.org.il/en/NewsAndPublications/RegularPublications/Research%20Department%20Publications/BankIsraelAnnualReport/Annual%20Report-2013/p5-2013e.pdf .

http://www.boi.org.il/en/NewsAndPublications/RegularPublications/Research%20Department%20Publications/RecentEconomicDevelopments/develop136e.pdf .

The Labor Force Survey is a major survey conducted by the Israeli Central Bureau of Statistics among households nationwide. The survey follows the development of the labor force in Israel, its size and characteristics, as well as the extent of unemployment and other trends. The publication contains detailed data on labor force characteristics such as their age, years of schooling, type of school last attended, and immigration status. It is also a source of information on living conditions, mobility in employment, and many other topics.

The survey population is the permanent (de jure) population of Israel aged 15 and over. For more details see: http://www.cbs.gov.il/publications13/1504/pdf/intro04_e.pdf .

When we looked at those who had not managed to find a job at the time of the survey, we included all individuals who were not working, regardless of whether they were discouraged workers, volunteers or had other reasons. As long as they are not out of the labor force due to medical reasons or their mandatory military service, we classified them as "did not manage to find a job."

Until 2012, active soldiers were considered outside the labor force in the samples of the CBS.

EITC is a refundable tax credit for low to moderate income working individuals and couples.

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HA, MM and IL conceptualized and designed the study. HA collected and managed study data, HA and IL carried out statistical analyses. HA drafted the initial manuscript. MM and IL reviewed and revised the manuscript. All authors read and approved the final manuscript.

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Axelrad, H., Malul, M. & Luski, I. Unemployment among younger and older individuals: does conventional data about unemployment tell us the whole story?. J Labour Market Res 52 , 3 (2018). https://doi.org/10.1186/s12651-018-0237-9

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Youth unemployment: a review of the literature

• PMID: 4019874
• DOI: 10.1016/s0140-1971(85)80041-5

This paper sets out to review the studies on youth unemployment conducted in a range of English speaking countries: America, Australia and Great Britain. The studies have been divided into six sections: psychological adjustment, attributions and expectations, education about unemployment, job choice and work experience, values, and job interview training. The paucity of good studies in this area partly explains the lack of clear replicated findings or coherent theories for the causes, correlates and consequences of unemployment among young people, though this is an area of relevance to social policy. Furthermore, it was concluded that various factors such as individual differences, salient demographic variables and previous work experience have been neglected. Nevertheless, many of the studies seem to indicate the presence of a destructive vicious circle which young people experience when failing to get a job: stress and disappointment, leading to lowered self-esteem, a change in expectations, and minor psychiatric illnesses which handicap the job search and application process so making unemployment all the more likely.

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Emotion Regulation and Academic Burnout Among Youth: a Quantitative Meta-analysis

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• Ioana Alexandra Iuga   ORCID: orcid.org/0000-0001-9152-2004 1 , 2 &
• Oana Alexandra David   ORCID: orcid.org/0000-0001-8706-1778 2 , 3  

Emotion regulation (ER) represents an important factor in youth’s academic wellbeing even in contexts that are not characterized by outstanding levels of academic stress. Effective ER not only enhances learning and, consequentially, improves youths’ academic achievement, but can also serve as a protective factor against academic burnout. The relationship between ER and academic burnout is complex and varies across studies. This meta-analysis examines the connection between ER strategies and student burnout, considering a series of influencing factors. Data analysis involved a random effects meta-analytic approach, assessing heterogeneity and employing multiple methods to address publication bias, along with meta-regression for continuous moderating variables (quality, female percentage and mean age) and subgroup analyses for categorical moderating variables (sample grade level). According to our findings, adaptive ER strategies are negatively associated with overall burnout scores, whereas ER difficulties are positively associated with burnout and its dimensions, comprising emotional exhaustion, cynicism, and lack of efficacy. These results suggest the nuanced role of ER in psychopathology and well-being. We also identified moderating factors such as mean age, grade level and gender composition of the sample in shaping these associations. This study highlights the need for the expansion of the body of literature concerning ER and academic burnout, that would allow for particularized analyses, along with context-specific ER research and consistent measurement approaches in understanding academic burnout. Despite methodological limitations, our findings contribute to a deeper understanding of ER's intricate relationship with student burnout, guiding future research in this field.

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Introduction

The transitional stages of late adolescence and early adulthood are characterized by significant physiological and psychological changes, including increased stress (Matud et al., 2020 ). Academic stress among students has long been studied in various samples, most of them focusing on university students (Bedewy & Gabriel, 2015 ; Córdova Olivera et al., 2023 ; Hystad et al., 2009 ) and, more recently, high school (Deb et al., 2015 ) and middle school students (Luo et al., 2020 ). Further, studies report an exacerbation of academic stress and mental health difficulties in response to the COVID-19 pandemic (Guessoum et al., 2020 ), with children facing additional challenges that affect their academic well-being, such as increasing workloads, influences from the family, and the issue of decreasing financial income (Ibda et al., 2023 ; Yang et al., 2021 ). For youth to maintain their well-being in stressful academic settings, emotion regulation (ER) has been identified as an important factor (Santos Alves Peixoto et al., 2022 ; Yildiz, 2017 ; Zahniser & Conley, 2018 ).

Emotion regulation, referring to”the process by which individuals influence which emotions they have, when they have them, and how they experience and express their emotions” (Gross, 1998b ), represents an important factor in youth’s academic well-being even in contexts that are not characterized by outstanding levels of stress. Emotion regulation strategies promote more efficient learning and, consequentially, improve youth’s academic achievement and motivation (Asareh et al., 2022 ; Davis & Levine, 2013 ), discourage academic procrastination (Mohammadi Bytamar et al., 2020 ), and decrease the chances of developing emotional problems such as burnout (Narimanj et al., 2021 ) and anxiety (Shahidi et al., 2017 ).

Approaches to Emotion Regulation

Numerous theories have been proposed to elucidate the process underlying the emergence and progression of emotional regulation (Gross, 1998a , 1998b ; Koole, 2009 ; Larsen, 2000 ; Parkinson & Totterdell, 1999 ). One prominent approach, developed by Gross ( 2015 ), refers to the process model of emotion regulation, which lays out the sequential actions people take to regulate their emotions during the emotion-generative process. These steps involve situation selection, situation modification, attentional deployment, cognitive change, and response modulation. The kind and timing of the emotion regulation strategies people use, according to this paradigm, influence the specific emotions people experience and express.

Recent theories of emotion regulation propose two separate, yet interconnected approaches: ER abilities and ER strategies. ER abilities are considered a higher-order process that guides the type of ER strategy an individual uses in the context of an emotion-generative circumstance. Further, ER strategies are considered factors that can also influence ER abilities, forming a bidirectional relationship (Tull & Aldao, 2015 ). Researchers use many definitions and classifications of emotion regulation, however, upon closer inspection, it becomes clear that there are notable similarities across these concepts. While there are many models of emotion regulation, it's important to keep from seeing them as competing or incompatible since each one represents a unique and important aspect of the multifaceted concept of emotion regulation.

Emotion Regulation and Emotional Problems

The connection between ER strategies and psychopathology is intricate and multifaceted. While some researchers propose that ER’s effectiveness is context-dependent (Kobylińska & Kusev, 2019 ; Troy et al., 2013 ), several ER strategies have long been attested as adaptive or maladaptive. This body of work suggests that certain emotion regulation strategies (such as avoidance and expressive suppression) demonstrate, based on findings from experimental studies, inefficacy in altering affect and appear to be linked to higher levels of psychological symptoms. These strategies have been categorized as ER difficulties. In contrast, alternative emotion regulation strategies (such as reappraisal and acceptance) have demonstrated effectiveness in modifying affect within controlled laboratory environments, exhibiting a negative association with clinical symptoms. As a result, these strategies have been characterized as potentially adaptive (Aldao & Nolen-Hoeksema, 2012a , 2012b ; Aldao et al., 2010 ; Gross, 2013 ; Webb et al., 2012 ).

A long line of research highlights the divergent impact of putatively maladaptive and adaptive ER strategies on psychopathology and overall well-being (Gross & Levenson, 1993 ; Gross, 1998a ). Increased negative affect, increased physiological reactivity, memory problems (Richards et al., 2003 ), a decline in functional behavior (Dixon-Gordon et al., 2011 ), and a decline in social support (Séguin & MacDonald, 2018 ) are just a few of the negative effects that have consistently been linked to emotional regulation difficulties, which include but are not limited to the use of avoidance, suppression, rumination, and self-blame strategies. Additionally, a wide range of mental problems, such as depression (Nolen-Hoeksema et al., 2008 ), anxiety disorders (Campbell-Sills et al., 2006a , 2006b ; Mennin et al., 2007 ), eating disorders (Prefit et al., 2019 ), and borderline personality disorder (Lynch et al., 2007 ; Neacsiu et al., 2010 ) are connected to self-reports of using these strategies.

Conversely, putatively adaptive strategies, including acceptance, problem-solving, and cognitive reappraisal, have consistently yielded beneficial outcomes in experimental studies. These outcomes encompass reductions in negative emotional responses, enhancements in interpersonal relationships, increased pain tolerance, reductions in physiological reactivity, and lower levels of psychopathological symptoms (Aldao et al., 2010 ; Goldin et al., 2008 ; Hayes et al., 1999 ; Richards & Gross, 2000 ).

Notably, despite the fact that therapeutic techniquest for enhancing the use of adaptive ER strategies are core elements of many therapeutic approaches, from traditional Cognitive Behavioral Therapy (CBT) to more recent third-wave interventions (Beck, 1976 ; Hofmann & Asmundson, 2008 ; Linehan, 1993 ; Roemer et al., 2008 ; Segal et al., 2002 ), the association between ER difficulties and psychopathology frequently show a stronger positive correlation compared to the inverse negative association with adaptive ER strategies, as highlighted by Aldao and Nolen-Hoeksema ( 2012a ).

Pines & Aronson ( 1988 ) characterize burnout that arises in the workplace context as a state wherein individuals encounter emotional challenges, such as experiencing fatigue and physical exhaustion due to heightened task demands. Recently, driven by the rationale that schools are the environments where students engage in significant work, the concept of burnout has been extended to educational contexts (Salmela-Aro, 2017 ; Salmela-Aro & Tynkkynen, 2012 ; Walburg, 2014 ). Academic burnout is defined as a syndrome comprising three dimensions: exhaustion stemming from school demands, a cynical and detached attitude toward one's academic environment, and feelings of inadequacy as a student (Salmela-Aro et al., 2004 ; Schaufeli et al., 2002 ).

School burnout has quickly garnered international attention, despite its relatively recent emergence, underscoring its relevance across multiple nations (Herrmann et al., 2019 ; May et al., 2015 ; Meylan et al., 2015 ; Yang & Chen, 2016 ). Similar to other emotional difficulties, it has been observed among students from various educational systems and academic policies, suggesting that this phenomenon transcends cultural and geographical boundaries (Walburg, 2014 ).

The link between ER and school burnout can be understood through Gross's ( 1998a ) process model of emotion regulation. This model suggests that an individual's emotional responses are influenced by their ER strategies, which are adaptive or maladaptive reactions to stressors like academic pressure. Given that academic stress greatly influences school burnout (Jiang et al., 2021 ; Nikdel et al., 2019 ), the ER strategies students use to manage this stress may impact their likelihood of experiencing burnout. In essence, whether a student employs efficient ER strategies or encounters ER difficulties could influence their susceptibility to school burnout.

The exploration of ER in relation to student burnout has garnered attention through various studies. However, the existing body of research is not yet robust enough, and its outcomes are not universally congruent. Suppression, defined as efforts to inhibit ongoing emotional expression (Balzarotti et al., 2010 ), has demonstrated a positive and significant correlation with both general and specific burnout dimensions (Chacón-Cuberos et al., 2019 ; Seibert et al., 2017 ), with the exception of the study conducted by Yu et al., ( 2022 ), where there is a negative, but not significant association between suppression and reduced accomplishment. Notably, research by Muchacka-Cymerman and Tomaszek ( 2018 ) indicates that ER strategies, encompassing both dispositional and situational approaches, exhibit a negative relationship with overall burnout. Situational ER, however, displays a negative impact on dimensions like inadequacy and declining interest, particularly concerning the school environment.

Cognitive ER strategies such as reappraisal, positive refocusing, and planning are, generally, negatively associated with burnout, while self-blame, other-blame, rumination, and catastrophizing present a positive association with burnout (Dominguez-Lara, 2018 ; Vinter et al., 2021 ). It's important to note that these relationships have not been consistently replicated across all investigations. Inconsistencies in the findings highlight the complexity of the interactions and the potential influence of various contextual factors. Consequently, there remains a critical need for further research to thoroughly examine these associations and identify the factors contributing to the variability in results.

Existing Research

Although we were unable to identify any reviews or meta-analyses that synthesize the literature concerning emotion regulation strategies and student burnout, recent meta-analyses have identified the role of emotion regulation across pathologies. A recent network meta-analysis identified the role of rumination and non-acceptance of emotions to be closely related to eating disorders (Leppanen et al., 2022 ). Further, compared to healthy controls, people presenting bipolar disorder symptoms reported significantly higher difficulties in emotion regulation (Miola et al., 2022 ). Weiss et al. ( 2022 ) identified a small to medium association between emotion regulation and substance use, and a subsequent meta-analysis conducted by Stellern et al. ( 2023 ) confirmed that individuals with substance use disorders have significantly higher emotion regulation difficulties compared to controls. The study of Dawel et al. ( 2021 ) represents the many research papers asking the question”Cause or symptom” in the context of emotion regulation. The longitudinal study brings forward the bidirectional relationship between ER and depression and anxiety, particularly in the case of suppression, suggesting that suppressing emotions is indicative of and can predict psychological distress.

Despite the increasing research attention to academic burnout in recent years, the current body of literature primarily concentrates on specific groups such as medical students (Almutairi et al., 2022 ; Frajerman et al., 2019 ), educators (Aloe et al., 2014 ; Park & Shin, 2020 ), and students at the secondary and tertiary education levels (Madigan & Curran, 2021 ) in the context of meta-analyses and reviews. A limited number of recent reviews have expanded their focus to include a more diverse range of participants, encompassing middle school, graduate, and university students (Kim et al., 2018 , 2021 ), with a particular emphasis on investigating social support and exploring the demand-control-support model in relation to student burnout.

The significance of managing burnout in educational settings is becoming more widely acknowledged, as seen by the rise in interventions designed to reduce the symptoms of burnout in students. Specific interventions for alleviating burnout symptoms among students continue to proliferate (Madigan et al., 2023 ), with a focus on stress reduction through mindfulness-based strategies (Lo et al., 2021 ; Modrego-Alarcón et al., 2021 ) and rational-emotive behavioral techniques (Ogbuanya et al., 2019 ) to enhance emotion-regulation skills (Charbonnier et al., 2022 ) and foster rational thinking (Bresó et al., 2011 ; Ezeudu et al., 2020 ). This underscores the significance of emotion regulation in addressing burnout.

Despite several randomized clinical trials addressing student burnout and an emerging body of research relating emotion regulation and academic burnout, there's a lack of a systematic examination of how emotion regulation strategies relate to various dimensions of student burnout. This highlights the necessity for a systematic review of existing evidence. The current meta-analysis addresses the association between emotion regulation strategies and student burnout.

A secondary objective is to test the moderating effect of school level and female percentage in the sample, as well as study quality, in order to identify possible sources of heterogeneity among effect sizes. By analyzing the moderating effect of school level and gender, we may determine if the strength of the association between student burnout and emotion regulation is contingent upon the educational setting and participant characteristics. This offers information on the findings' generalizability to all included student demographics, including those in elementary, middle, and secondary education and of different genders. Additionally, the reliability and validity of meta-analytic results rely on the evaluation of research quality, and the inclusion of study quality rating allows us to determine if the observed association between emotion regulation and student burnout differs based on the methodological rigor of the included studies.

Materials and Methods

Study protocol.

The present meta-analysis has been carried out following the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) statement (Moher et al., 2009 ). The protocol for the meta-analysis was pre-registered in PROSPERO (PROSPERO, 2022 CRD42022325570).

Selection of Studies

A systematic search was performed using relevant databases (PubMed, Web of Science, PsychINFO, and Scopus). The search was carried out on 25 May of 2023 using 25 key terms related to the variables of interest, such as: (a) academic burnout, (b) school burnout, (c) student burnout (d) education burnout, (d) exhaustion, (e) cynicism, (f) inadequacy, (g) emotion regulation, (h) coping, (i) self-blame, (j) acceptance, and (h) problem solving.

Studies of any design published in peer-reviewed journals were eligible for inclusion, provided they used empirical data to assess the relationship between student burnout and emotion regulation strategies. Only studies that employed samples of children, adolescents, and youth were eligible for inclusion. For the purpose of the current paper, we define youth as people aged 18 to 25, based on how it is typically defined in the literature (Westhues & Cohen, 1997 ).

Studies were excluded from the meta-analysis if they: (a) were not a quantitative study, (b) did not explore the relationship between academic burnout and emotion regulation strategies, (c) did not have a sample that can be defined as consisting of children and youth (Scales et al., 2016 ), (e) did not utilize Pearson’s correlation or measures that could be converted to a Pearson’s correlation, (f) include medical school or associated disciplines samples.

Statistical Analysis

For the data analysis, we employed Comprehensive Meta-Analysis 4 software. Anticipating significant heterogeneity in the included studies, we opted for a random effects meta-analytic approach instead of a fixed-effects model, a choice that acknowledges and accounts for potential variations in effect sizes across studies, contributing to a more robust and generalizable synthesis of the results. Heterogeneity among the studies was assessed using the I 2 and Q statistics, adhering to the interpretation thresholds outlined in the Cochrane Handbook (Deeks et al., 2023 ).

Publication bias was assessed through a multi-faceted approach. We first examined the funnel plot for the primary outcome measures, a graphical representation revealing potential asymmetry that might indicate publication bias. Furthermore, we utilized Duval and Tweedie's trim and fill procedure (Duval & Tweedie, 2000 ), as implemented in CMA, to estimate the effect size after accounting for potential publication bias. Additionally, Egger's test of the intercept was conducted to quantify the bias detected by the funnel plot and to determine its statistical significance.

When dealing with continuous moderating variables, we employed meta-regression to evaluate the significance of their effects. For categorical moderating variables, we conducted subgroup analyses to test for significance. To ensure the validity of these analyses, it was essential that there was a minimum of three effect sizes within each subgroup under the same moderating variable, following the guidelines outlined by Junyan and Minqiang ( 2020 ). In accordance with the guidance provided in the Cochrane Handbook (Schmid et al., 2020 ), our application of meta-regression analyses was limited to cases where a minimum of 10 studies were available for each examined covariate. This approach ensures that there is a sufficient number of studies to support meaningful statistical analysis and reliable conclusions when exploring the influence of various covariates on the observed relationships.

Data Extraction and Quality Assessment

In addition to the identification information (i.e., authors, publication year), we extracted data required for the effect size calculation for the variables relevant to burnout and emotion regulation strategies. Where data was unavailable, the authors were contacted via email in order to provide the necessary information. Potential moderator variables were coded in order to examine the sources of variation in study findings. The potential moderators included: (a) participants’ gender, (b), grade level (c) study quality, and (d) mean age.

The full-text articles were independently assessed using the Standard Quality Assessment Criteria for Evaluating Primary Research Papers from a Variety of Fields tool (Kmet et al., 2004 ) by a pair of coders (II and SM), to ensure the reliability of the data, resulting in a substantial level of agreement (Cohen’s k  = 0.89). The disagreements and discrepancies between the two coders were resolved through discussion and consensus. If consensus could not be reached, a third researcher (OD) was consulted to resolve the disagreement.

The checklist items focused on evaluating the alignment of the study's design with its stated objectives, the methodology employed, the level of precision in presenting the results, and the accuracy of the drawn conclusions. The assessment criteria were composed of 14 items, which were evaluated using a 3-point Likert scale (with responses of 2 for "yes," 1 for "partly," and 0 for "no"). A cumulative score was computed for each study based on these items. For studies where certain checklist items were not relevant due to their design, those items were marked as "n/a" and were excluded from the cumulative score calculation.

Study Selection

The combined search terms yielded a total of 15,179 results. The duplicate studies were removed using Zotero, and a total of 8,022 studies remained. The initial screening focused on the titles and abstracts of all remaining studies, removing all documents that target irrelevant predictors or outcomes, as well as qualitative studies and reviews. Two assessors (II and SA) independently screened the papers against the inclusion and exclusion criteria. A number of 7,934 records were removed, while the remaining 88 were sought for retrieval. Out of the 88 articles, we were unable to find one, while another has been retracted by the journal. Finally, 86 articles were assessed for eligibility. A total of 20 articles met the inclusion criteria (see Fig.  1 ). Although a specific cutoff criterion for reliability coefficients was not imposed during study selection, the majority of the included studies had alpha Cronbach values for the instruments assessing emotion regulation and school burnout greater than 0.70.

Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flowchart of the study selection process

Data Overview

Among the included studies, four focused on middle school students, two encompassed high school student samples, and the remaining 14 articles involved samples of university students. The majority of the included studies had cross-sectional designs (17), while the rest consisted of 2 longitudinal studies and one non-randomized controlled pilot study. The percentage of females within the samples ranged from 46% to 88.3%, averaging 65%, while the mean age of participants ranged from 10.39 to 25. The investigated emotional regulation strategies within the included studies exhibit variation, encompassing other-blame, self-blame, acceptance, rumination, catastrophizing, putting into perspective, reappraisal, planning, behavioral and mental disengagement, expressive suppression, and others (see Table  1 for a detailed study presentation).

Study Quality

Every study surpasses a quality threshold of 0.60, and 75% of the studies achieve a score above the more conservative threshold indicated by Kmet et al. ( 2004 ). This indicates a minimal risk of bias in these studies. Moreover, 80% of the studies adequately describe their objectives, while the appropriateness of the study design is recognized in 50% of the cases, mostly utilizing cross-sectional designs. While 95% of the studies provide sufficient descriptions of their samples, only 10% employ appropriate sampling methods, with the majority relying on convenience sampling. Notably, there is just one interventional study that lacks random allocation and blinding of investigators or subjects.

In terms of measurement, 85% of the studies employ validated and reliable tools. Adequacy in sample size and well-justified and appropriate analytic methods are observed across all included studies. While approximately 50% of the studies present estimates of variance, a mere 30% of them acknowledge the control of confounding variables. Lastly, 95% of the studies provide results in comprehensive detail, with 60% effectively grounding their discussions in the obtained results. The quality assessment criteria and results can be consulted in Supplementary Material 4 .

Pooled Effects

A sensitivity analysis using standardized residuals was conducted. Provided that the residuals are normally distributed, 95% of them would fall within the range of -2 to 2. Residuals outside this range were considered unusual. We applied this cutoff in our meta-analysis to identify any outliers. The results of the analysis revealed that several relationships had standardized residuals falling outside the specified range. Re-analysis excluding these outliers demonstrated that our initial results were robust and did not significantly change in magnitude or significance. As a result, we have moved on with the analysis for the entire sample.

The calculated overall effects can be consulted in Table  2 . The correlation between ER difficulties and student burnout is a significant one, with significant positive associations between ER difficulties and overall burnout (k = 13), r  = 0.25 (95% CI = 0.182; 0.311), p  < 0.001, as well as individual burnout dimensions: cynicism (k = 9), r  = 0.28 (95% CI = 0.195; 0.353) p  < 0.001, lack of efficacy (k = 8), r  = 0.17 (95% CI = 0.023; 0.303), p  < 0.05 and emotional exhaustion (k = 11), r  = 0.27 (95% CI = 0.207; 0.335) p  < 0.001. Regarding the relationship between adaptive ER strategies and student burnout, a statistically significant result is observed solely between overall student burnout and adaptive ER (k = 17), r  = -14 (95% CI = -0.239; 0.046) p  < 0.005. The forest plots can be consulted in Supplementary Material 1 .

Heterogeneity and Publication Bias

Table 3 shows that all Q tests were significant, indicating that there is significant variation among the effect sizes of the individual studies included in the meta-analysis. Further, all I 2 indices are over 75%, ranging from 83.67% to 99.32%, which also indicates high heterogeneity (Borenstein et al., 2017 ). This consistently high level of heterogeneity indicates substantial variation in effect sizes, pointing to influential factors that significantly shape the outcomes of the included studies. Consequentially, subgroup and meta-regression analyses are to be carried out in order to unravel the underlying factors driving this pronounced heterogeneity. The results of the publication bias analysis are presented individually below and, additionally, you can consult the funnel plots included in Supplementary Material 2 .

Adaptive ER and School Burnout

Upon visual examination of the funnel plot, asymmetry to the right of the mean was observed. To validate this observation, a trim-and-fill analysis using Duval and Tweedie’s method was conducted, revealing the absence of three studies on the left side of the mean. The adjusted effect size ( r  = -0.17, 95% CI [0.27; 0.68]) resulting from this analysis was found to be higher than the initially observed effect size. Nevertheless, the application of Egger’s test did not yield a significant indication of publication bias ( B  = -5.34, 95% CI [-11.85; 1.16], p  = 0.10).

Adaptive ER and Cynicism

Following a visual examination of the funnel plot, a symmetrical arrangement of effect sizes around the mean was apparent. This finding was contradicted by the application of Duval and Tweedie's trim-and-fill method, which revealed two missing studies to the right of the mean. The adjusted effect size ( r  = 0.04, 95% CI [-0.21; 0.13]) is smaller than the initially observed effect size. The application of Egger’s test did not yield a significant indication of publication bias ( B  = -2.187, 95% CI [-8.57; 4.19], p  = 0.43).

ER difficulties and Lack of Efficacy

The visual examination of the funnel plot revealed asymmetry to the right of the mean. This finding was validated by the application of Duval and Tweedie's trim-and-fill method, which revealed two missing studies to the left of the mean and a lower adjusted effect size ( r  = 0.08, 95% CI [-0.07; 0.23]), the effect becoming statistically non-significant. The application of Egger’s test did not yield a significant indication of publication bias ( B  = 7.76, 95% CI [-16.53; 32.05], p  = 0.46).

Adaptive ER and Emotional Exhaustion

The visual examination of the funnel plot revealed asymmetry to the left of the mean. The trim-and-fill method also revealed one missing study to the right of the mean and a lower adjusted effect size ( r  = 0.00, 95% CI [-0.13; 0.12]). The application of Egger’s test did not yield a significant indication of publication bias ( B  = 7.02, 95% CI [-23.05; 9.02], p  = 0.46).

Adaptive ER and Lack of Efficacy; ER difficulties and School Burnout, Cynicism, and Exhaustion

Upon visually assessing the funnel plot, a balanced distribution of effect sizes centered around the mean was observed. This observation is corroborated by the application of Duval and Tweedie's trim-and-fill method, which also revealed no indication of missing studies. The adjusted effect size remained consistent, and the intercept signifying publication bias was found to be statistically insignificant.

Moderator Analysis

We performed moderator analyses for the categorical variables, in the case of significant relationships that were uncovered in the initial analysis. These analyses were carried out specifically for cases where there were more than three effect sizes available within each subgroup that fell under the same moderating variable.

Students’ grade level was used as a categorical moderator. Pre-university students included students enrolled in primary and secondary education, while the university student category included tertiary education students. The results, presented in Table  4 , show that the moderating effect of grade level is not significant for the relationship between adaptive ER and overall school burnout Q (1) = 0.20, p  = 0.66. At a specific level, the moderating effect is significant for the relationship between ER difficulties and overall burnout Q (1) = 9.81, p  = 0.002, cynicism Q (1) = 16.27, p  < 0.001, lack of efficacy Q (1) = 15.47 ( p  < 0.001), and emotional exhaustion Q (1) = 13.85, p  < 0.001. A particularity of the moderator analysis in the relationship between ER difficulties and lack of efficacy is that, once the effect of the moderator is accounted for, the relationship is not statistically significant anymore for the university level, r  = -0.01 (95% CI = -0.132; 0.138), but significant for the pre-university level, r  = 0.33 (95% CI = 0.217; 0.439).

Meta-regressions

Meta-regression analyses were employed to examine how the effect size or relationship between variables changes based on continuous moderator variables. We included as moderators the female percentage (the proportion of female participants in each study’s sample) and the study quality assessed based on the Standard Quality Assessment Criteria for Evaluating Primary Research Papers from a Variety of Fields tool (Kmet et al., 2004 ).

Results, presented in Table  5 , show that study quality does not significantly influence the relationship between ER and school burnout. The proportion of female participants in the study sample significantly influences the relationship between ER difficulties and overall burnout ( β , -0.0055, SE = 0.001, p  < 0.001), as well as the emotional exhaustion dimension ( β , -0.0049, SE = 0.002, p  < 0.01). Mean age significantly influences the relationship between ER difficulties and overall burnout ( β , -0.0184, SE = 0.006, p  < 0.01). Meta-regression plots can be consulted in detail in Supplementary Material 3 .

A post hoc power analysis was conducted using the metapower package in R. For the pooled effects analysis of the relationship between ER difficulties and overall school burnout, as well as with cynicism and emotional exhaustion, the statistical power was adequate, surpassing the recommended 0.80 cutoff. The analysis of the association between ER difficulties and lack of efficacy, along with the relationship between adaptive ER and school burnout, cynicism, lack of efficacy, and emotional exhaustion were greatly underpowered. In the case of the moderator analysis, the post-hoc power analysis indicates insufficient power. Please consult the coefficients in Table  6 .

The central goal of this meta-analysis was to examine the relationship between emotion-regulation strategies and student burnout dimensions. Additionally, we focused on the possible effects of sample distribution, in particular on participants’ age, education levels they are enrolled in, and the percentage of female participants included in the sample. The study also aimed to determine how research quality influences the overall findings. Taking into consideration the possible moderating effects of sample characteristics and research quality, the study aimed to offer a thorough assessment of the literature concerning the association between emotion regulation strategies and student burnout dimensions. A correlation approach was used as the current literature predominantly consists of cross-sectional studies, with insufficient longitudinal studies or other designs that would allow for causal interpretation of the results.

The study’s main findings indicate that adaptive ER strategies are associated with overall burnout, whereas ER difficulties are associated with both overall burnout and all its dimensions encompassing emotional exhaustion, cynicism, and lack of efficacy.

Prior meta-analyses have similarly observed that adaptive ER strategies tend to exhibit modest negative associations with psychopathology, while ER difficulties generally presented more robust positive associations with psychopathology (Aldao et al., 2010 ; Miu et al., 2022 ). These findings could suggest that the observed variation in the effect of ER strategies on psychopathology, as previously indicated in the literature, can also be considered in the context of academic burnout.

However, it would be an oversimplification to conclude that adaptive ER strategies are less effective in preventing psychopathology than ER difficulties are in creating vulnerability to it. Alternatively, as previously underlined, researchers should consider the frequency, flexibility, and variability in the way ER strategies are applied and how they relate to well-being and psychopathology. Further, it’s important to also address the possible directionality of the relationship. While the few studies that assume a prediction model for academic burnout and ER treat ER as a predictor for burnout and its dimensions (see Seibert et al., 2017 ; Vizoso et al., 2019 ), we were unable to identify studies that assume the role of burnout in the development of ER difficulties. Additionally, the studies identified that relate to academic burnout have a cross-sectional design that makes it even more difficult to pinpoint the ecological directionality of the relationship.

While the focus on the causal role of ER strategies in psychopathology and psychological difficulties is of great importance for psychological interventions, addressing a factor that merely reflects an effect or consequence of psychopathology will not lead to an effective solution. According to Gross ( 2015 ), emotion regulation strategies are employed when there is a discrepancy between a person's current emotional state and their desired emotional state. Consequently, individuals could be likely to also utilize emotion regulation strategies in response to academic burnout. Additionally, studies that have utilized a longitudinal approach have demonstrated that, in the case of spontaneous ER, people with a history of psychopathology attempt to regulate their emotions more when presented with negative stimuli (Campbell-Sills et al., 2006a , 2006b ; Ehring et al., 2010 ; Gruber et al., 2012 ). The results of Dawel et al. ( 2021 ) further solidify a bidirectional model that could and should be also applied to academic burnout research.

Following the moderator analysis, the results indicate that the moderating effect of grade level did not have a substantial impact on the relationship between adaptive ER and school burnout. In the context of this discussion, it is important to note that regarding the relationship between adaptive ER and overall burnout, there is an imbalance in the distribution of studies between the university and pre-university levels, which could potentially present a source of bias or error.

When it comes to the relationship between ER difficulties and burnout, the inclusion of the moderator exhibited notable significance, overall and at the dimensions’ level. Particularly noteworthy is the finding that, within the relationship involving ER difficulties and lack of efficacy, the inclusion of the moderator rendered the association statistically insignificant for university-level students, while maintaining significance for pre-university-level students. The outcomes consistently demonstrate larger effect sizes for the relationship between ER difficulties and burnout at the pre-university level in comparison to the university level. Additionally, the mean age significantly influences the relationship between ER difficulties and overall burnout.

These findings may imply the presence of additional variables that exert a varying influence at the two educational levels and as a function of age. There are several contextual factors that could be framing the current findings, such as parental education anxiety (Wu et al., 2022 ), parenting behaviors, classroom atmosphere (Lin & Yang, 2021 ), and self-efficacy (Naderi et al., 2018 ). As the level of independence drastically increases from pre-university to university, the influence of negative parental behaviors and attitudes can become limited. Furthermore, the university-level learning environment often provides a satisfying and challenging educational experience, with greater opportunities for students to engage in decision-making and take an active role in their learning (Belaineh, 2017 ), which can serve as a protective factor against student’s academic burnout (Grech, 2021 ). At an individual level, many years of experience in navigating the educational environment can increase youths’ self-efficacy in the educational context and offer proper learning tools and techniques, which can further influence various aspects of self-regulated learning, such as monitoring of working time and task persistence (Bouffard-Bouchard et al., 1991 ; Cattelino et al., 2019 ).

The findings of the meta-regression analysis suggest that the association between ER and school burnout is not significantly impacted by study quality. It’s important to interpret these findings in the context of rather homogenous study quality ratings that can limit the detection of significant impacts.

The current results underline that the correlation between ER difficulties and both overall burnout and the emotional exhaustion dimension is significantly influenced by the percentage of female participants in the study sample. Previous research has shown that girls experience higher levels of stress, as well as higher expectations concerning their school performance, which can originate not only intrinsically, but also from external sources such as parents, peers, and educators (Östberg et al., 2015 ). These heightened expectations and stress levels may contribute to the gender differences in how emotion regulation difficulties are associated with school burnout.

The results of this meta-analysis suggest that most of the included studies present an increased level of methodological quality, reaching or surpassing the quality thresholds previously established. These encouraging results indicate a minimal risk of bias in the selected research. Moreover, it’s notable that a sizable proportion of the included studies clearly articulate their research objectives and employ well-established measurement tools, that would accurately capture the constructs of interest. There are still several areas of improvement, especially with regard to variable conceptualization and sampling methods, highlighting the importance of maintaining methodological rigor in this area of research.

Significant Q tests and I 2 identified in the case of several analyses indicate a strong heterogeneity among the effect sizes of individual studies in the meta-analysis's findings. This variability suggests that there is a significant level of diversity and variation among the effects observed in the studies, and it is improbable that this diversity is solely attributable to random chance. Even with as few as 10 studies, with 30 participants in the primary studies, the Q test has been demonstrated to have good power for identifying heterogeneity (Maeda & Harwell, 2016 ). Recent research (Mickenautsch et al., 2024 ), suggests that the I 2 statistic is not influenced by the number of studies and sample sizes included in a meta-analysis. While the relationships between Adaptive ER—cynicism, ER difficulties—cynicism, Adaptive ER—lack of efficacy, and ER difficulties—lack of efficacy are based on a limited number of studies (8–9 studies), it's noteworthy that the primary study sample sizes for these relationships are relatively large, averaging above 300. This suggests that despite the small number of studies, the robustness of the findings may be supported by the substantial sample sizes, which can contribute to the statistical power of the analysis.

However, it's essential to consider potential limitations such as range restriction or measurement error, which could impact the validity of the findings. Despite these considerations, the combination of substantial primary study sample sizes and the robustness of the Q test provides a basis for confidence in the results.

The results obtained when publication bias was examined using funnel plots, trim-and-fill analyses, and Egger's tests were varying across different outcomes. In the case of adaptive emotion regulation (ER) and school burnout, no evidence of publication bias was found, suggesting that the observed effects are likely robust. The trim-and-fill analysis, however, indicated the existence of missing studies for adaptive ER and cynicism, potentially influencing the initial effect size estimate. For ER difficulties and lack of efficacy, the adjustment for missing studies in the trim-and-fill analysis led to a non-significant effect. Additionally, adaptive ER and emotional exhaustion displayed a similar pattern with the trim-and-fill method leading to a lower, non-significant effect size. This indicates the need for additional studies to be included in future meta-analyses. According to the Cochrane Handbook (Higgins et al., 2011 ), the results of Egger’s test and funnel plot asymmetry should be interpreted with caution, when conducted on fewer than 10 studies.

The results of the post-hoc power analysis reveal that the relationship between ER difficulties and cynicism, as well as emotional exhaustion, meets the threshold of 0.80 for statistical power, as suggested by Harrer et al. ( 2022 ). This implies that our study had a high likelihood of detecting significant associations between ER difficulties and these specific outcomes, providing robust evidence for the observed relationships. However, for the relationship between ER difficulties and overall burnout, the power coefficient falls just below the indicated threshold. While our study still demonstrated considerable power to detect effects, the slightly lower coefficient suggests a marginally reduced probability of detecting significant associations between ER difficulties and overall burnout.

The power coefficients for the remaining post-hoc analyses are fairly small, which suggests that there is not enough statistical power to find meaningful relationships. This shows that there might not have been enough power in our investigation to find significant correlations between the variables we sought to investigate. Even if these analyses' power coefficients are lower than ideal, it's important to consider the study's limitations and implications when interpreting the results.

Limitations and Future Directions

One important limitation of our meta-analysis is represented by the small number of studies included in the analysis. Smaller meta-analyses could result in less reliable findings, with estimates that could be significantly influenced by outliers and inclusion of studies with extreme results. The small number of studies also interferes with the interpretation of both Q and I 2 heterogeneity indices (von Hippel, 2015 ). In small sample sizes, it may be challenging to detect true heterogeneity, and the I 2 value may be imprecise or underestimate the actual heterogeneity.

The studies included in the current meta-analysis focused on investigating how individuals generally respond to stressors. However, it's crucial to remember that people commonly use various ER strategies based on particular contexts, or they could even combine ER strategies within a single context. This adaptability in ER strategies reflects the dynamic and context-dependent nature of emotional regulation, where people draw upon various tools and approaches to effectively manage their emotions in different circumstances.

Given the heterogeneity of studies that investigate ER as a context-dependent phenomenon in the context of academic burnout, as well as the diverse nature of these existing studies, it becomes imperative for future research to consider a number of key aspects. First and foremost, future studies should aim to expand the body of literature on this topic by conducting more research specifically focusing on the context-dependent and flexible nature of ER in the context of academic burnout and other psychopathologies. Taking into account the diversity of educational environments, curricula, and student demographics, these research initiatives should also include a wide range of academic contexts.

Furthermore, it is advisable for researchers to implement a uniform methodology for assessing and documenting ER strategies. This consistency in measurement will simplify the process of comparing results among different studies, bolster the reliability of the data, and pave the way for more extensive and comprehensive meta-analyses.

Insufficient research that delves into the connection between burnout and particular emotional regulation (ER) strategies, such as reappraisal or suppression, has made it unfeasible to conduct a meaningful analysis within the scope of the current meta-analysis, that could further bring specificity as to which ER strategies could influence or be affected in the context of academic burnout. Consequentially, the expansion of the inclusion criteria for future meta-analyses should be considered, along with the replication of the current meta-analysis in the context of future publications on this topic.

Future interventions aimed at addressing academic burnout should adopt a tailored approach that takes into consideration age or school-level influences, as well as gender differences. Implementing prevention programs in pre-university educational settings can play a pivotal role in equipping children and adolescents with vital emotion regulation skills and stress management strategies. Additionally, it is essential to provide additional support to girls, recognizing their unique stressors and increased academic expectations.

Implications

Our meta-analysis has several implications, both theoretical and practical. Firstly, the meta-analysis extends the understanding of the relationship between emotion regulation (ER) strategies and student burnout dimensions. Although the correlational and cross-sectional nature of the included studies does not allow for drawing causal implications, the results represent a great stepping stone for future research. Secondly, the results highlight the intricacy of ER strategies and their applicability in educational contexts. Along with the identified differences between preuniversity and university students, this emphasizes the importance of developmental and contextual factors in ER research and the necessity of having an elaborate understanding of the ways in which these strategies are used in various situations and according to individual particularities. The significant impact of the percentage of female participants on the relationship between ER strategies and academic burnout points to the need for gender-sensitive approaches in ER research. On a practical level, our results suggest the need for targeted interventions aimed at the specific needs of different educational levels and age groups, as well as gender-specific strategies to address ER difficulties.

In conclusion, the findings of the current meta-analysis reveal that adaptive ER strategies are associated with overall burnout, while ER difficulties are linked to both overall burnout and its constituent dimensions, including emotional exhaustion, cynicism, and lack of efficacy. These results align with prior research in the domain of psychopathology, suggesting that adaptive ER strategies may be more efficient in preventing psychopathology than ER difficulties are in creating vulnerability to it, or that academic burnout negatively influences the use of adaptive ER strategies in the youth population. As an alternative explanation, it might also be that the association between ER strategies, well-being, and burnout can vary based on the context, frequency, flexibility, and variability of their application. Furthermore, our study identified the moderating role of grade level and the sample’s gender composition in shaping these associations. The academic environment, parental influences, and self-efficacy may contribute to the observed differences between pre-university and university levels and age differences.

Despite some methodological limitations, the current meta-analysis underscores the need for context-dependent ER research and consistent measurement approaches in future investigations of academic burnout and psychopathology. The heterogeneity among studies may suggest variability in the relationship between emotion regulation and student burnout across different contexts. This variability could be explained through methodological differences, assessment methods, and other contextual factors that were not uniformly accounted for in the included studies. The included studies do not provide insights into changes over time as most studies were cross-sectional. Future research should aim to better understand the underlying reasons for the observed differences and to reach more conclusive insights through longitudinal research designs.

Overall, this meta-analysis contributes to a deeper understanding of the intricate relationship between ER strategies and student burnout and serves as a good reference point for future research within the academic burnout field.

Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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This work was supported by two grants awarded to the corresponding author from the Romanian National Authority for Scientific Research, CNCS—UEFISCDI (Grant number PN-III-P4-ID-PCE-2020-2170 and PN-III-P2-2.1-PED-2021-3882)

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Iuga, I.A., David, O.A. Emotion Regulation and Academic Burnout Among Youth: a Quantitative Meta-analysis. Educ Psychol Rev 36 , 106 (2024). https://doi.org/10.1007/s10648-024-09930-w

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