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Unemployment and (un)happiness: Life satisfaction approach to enhance policy efficiency for developing countries

Aliyev, K. (2021). Unemployment and (un)happiness: Life satisfaction approach
to enhance policy efficiency for developing countries. Journal of International Studies,
14(4), 220-235. doi:10.14254/2071-8330.2021/14-4/15
Unemployment and (un)happiness:
Life satisfaction approach to enhance
policy efficiency for developing countries
Khatai Aliyev
Azerbaijan State University of Economics (UNEC),
Baku, Azerbaijan;
Baku Engineering University, Khirdalan, Azerbaijan;
ASERC, Baku, Azerbaijan
ORCID 0000-0001-8161-6269
Abstract. Unemployment decreases happiness in individuals lives, generating
pecuniary and non-pecuniary costs for unemployed individuals, especially for the
least satisfied or most vulnerable groups. The study investigates cognitive aspects
of individual well-being among unemployed people. Based on a pooled cross-
sectional dataset of 689 unemployed respondents and multivariate regression
outputs, the research constructs a vulnerability scale and suggests the use of a
“differentiated supporting system” in developing countries. The proposed system
requires identifying and supporting the least satisfied unemployed individuals
first, as they need that the most. Therefore, applying a differentiated supporting
system can increase policy efficiency and enhance societal life satisfaction in
developing countries with limited resources available for employment agencies.
Use of the scale requires easily observable data (age, gender, marital status,
educational attainment, and unemployment duration) and is readily reproducible
in other cases. Within the conceptual framework of the differentiated supporting
system, employment agencies can construct a measurable vulnerability scale
for unemployed individuals and increase resource use efficiency.
Keywords: unemployment policy, subjective well-being, vulnerability scale,
employment agencies, developing countries, Azerbaijan
JEL Classification: H53, I31, I38, J65
July, 2020
1st Revision:
August, 2021
December, 2021
The primary goal of all economic policy decisions is to enhance the well-being of people, at the
individual level and as a whole (Oishi & Diener, 2014). Ritzen (2019) argues that happiness is key to a
productive economy, and a job is a key to individual happiness. Happier people have better health and live
longer (Zajacova & Dowd, 2014). Being unemployed is a terrible thing, as harmful as a divorce or death in
of International
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Khatai Aliyev
Unemployment and (un)happiness: Life
satisfaction approach to enhance policy
the family (Layard, Clark & Senik, 2013). It is not only income loss, but also the sense that the person is not
fulfilling the duties expected of them as a human being (Akerlof & Shiller, 2010).
Recent studies present strong evidence that unemployment decreases life satisfaction (Lim, 2017; Eren
& Aşıcı, 2017; Frey, 2018; Barros, Dieguez & Nunes, 2019) and mental health (Farré, Fasani & Mueller,
2018), simultaneously increases happiness inequality (Becchetti, Massari & Naticchioni, 2013). Studies reveal
that jobless people are unhappy and stressed, have poorer mental health (Clark & Oswald, 1994), and feel
less valuable.
Figure 1. Average life satisfaction among employed and unemployed people
Source: Author’s calculation based on ASERC (2018a, 2018b, 2019)
According to 3 large social surveys (SS) datasets, figure 1 describes the satisfaction gap between
employed and unemployed people in Azerbaijan. The gap of average life satisfaction was 6.77 points during
March-June, 2018 (SS1), 4.18 points during the last quarter of 2018 (SS2), and 5.84 points during March-
June, 2019 (SS3).
The typical question asks how limited resources can be allocated in the most efficient way to improve
the well-being of unemployed people. The primary contribution of this research is suggesting the use of a
happiness-based relative vulnerability scale predicted by various observable individual-specific factors such
as gender, age, marital status, education level, and unemployment duration to improve the theoretical and
conceptual frame of a “differentiated supporting system” (identifying, supporting and prioritizing the
unhappiest groups first). Employment agencies in the countries with limited employability and
compensation resources are recommended to employ such a supporting system based on this scale to
support first those who need it the most. The list of covariates can be expanded upon data availability.
Parameters of the vulnerability scale model could be estimated using regular representative survey data of
unemployed people. Therefore, employment agencies can assess the vulnerability of each applicant (required
information will be available in the application form).
The research estimates the vulnerability scale model and identifies basic features of higher
vulnerability to unemployment in Azerbaijan. In a broader context, the research can be replicated in other
cases and at different time zones to identify the “vulnerability scale” model parameters and the most
vulnerable unemployed groups. In the short-term, research findings can be used by Azerbaijan’s
employment agencies. The use of the scale can also be beneficial for long-term public policy decisions.
Employed Unemployed
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Vol.14, No.4, 2021
The remaining part of the research is structured as follows: Section II reviews the existing state of
knowledge, Section III explains sampling procedure and the employed empirical methodology. Section IV
presents preliminary research results, while the last section discusses the findings and make a conclusion.
The relationship between the overall unemployment rate and the well-being of the unemployed is
ambiguous. Some researchers refer to the social norm effect for unemployed individuals and argue that the
negative impact on happiness is comparatively less in a society with a higher rate of unemployment (Stutzer
& Lalive, 2004; Ritzen, 2019). On the contrary, others reject this argument and conclude that the negative
impact remains the same (Oesch & Lipps, 2012) or even larger (Chadi, 2014) if being unemployed is more
common. On the other hand, several studies reveal the negative impact of unemployment on the well-being
of employed people (Clark, Knabe & Rätzel, 2010; Schwarz, 2012; Winkelmann, 2014). Ochsen and Welsch
(2011) state that a persons fear of unemployment is not about being jobless but mostly linked to long-term
duration probability, which affects employed and unemployed people alike.
Unemployment has significant psychological effects as well (Boyce et al., 2015). Scar from past
unemployment will become permanent, decreasing the person’s happiness even after re-employment (Clark,
Georgellis & Sanfey, 2001). Knabe and Rätzel (2011) alter the term slightly to scarring effect, implying
that the past unemployment experience worsens the expectations of the re-employed people, constantly
worrying about the possibility of being unemployed again in the future, and finding himself in discontent
and in an in-secured position despite working. In this context, scholars underline the role of job security
perception as a channel for the indirect effect of unemployment over life satisfaction (Clark, Knabe &
Rätzel, 2010; Winkelmann, 2014; Chadi & Hetschko, 2016), especially for those who are temporarily
employed (Helliwell & Huang, 2014; Schöb, 2016). Unemployment causes ineffective job search
(Winkelmann, 2014), decreased motivation (Chadi, 2010), and compels those into psychological scarcity
(Mullainathan & Shafir, 2013), which reduces good decision-making skills. On the contrary, re-employment
probability depends on job search confidence (Petrucci, Blau & McClendon, 2015).
The two main channels that unemployment affects life satisfaction are (1) income loss (pecuniary
costs), (2) deprivation from social rewards such as social relationships, identity in society, and individual
self-esteem (non-pecuniary costs) (Winkelmann & Winkelmann, 1998). Helliwell and Huang (2014) argue
that the second exceeds the first. Regarding the impact of unemployment, Harrison (1976) differentiates
shock-optimism-pessimism-fatalism stages during the duration. Before, Easterlin (1974) had claimed that
the negative impact mainly occurs in the short term that the person adapts to the new situation over time.
However, Clark et al. (2008) argue that the impact remains even after re-employment. Results in Von Scheve,
Esche and Schupp (2017) also reject the adaptation thesis.
A vast amount of previous studies has confirmed the impact of unemployment on life satisfaction. The
relationship is unambiguously clear and negative. For successful active labour market policy implementation
with limited resources, there is a need to identify the most vulnerable groups and design a policy directed
towards the unemployed belonging to such groups. To develop a vulnerability scale, one should consider
factors such as gender, age, marital status, education level, and unemployment duration, among other
potential determinants.
According to Daouli et al. (2015), the most vulnerable groups to long-term unemployment are females,
singles, the elderly, and the urban labour force. However, some studies conclude that males are more
vulnerable to unemployment compared to females. Results display higher life satisfaction of women than
men during unemployment (Stutzer & Lalive, 2004). Broman et al. (1995) identify males as more vulnerable
to long-term unemployment than females in terms of depression and unhealthy mental state.
Khatai Aliyev
Unemployment and (un)happiness: Life
satisfaction approach to enhance policy
Knabe, Schöb and Weimann (2016) address the gender-based happiness difference from a different
perspective. If the partner of a jobless person is unemployed, the life satisfaction of males falls significantly
more than that of females. The result confirms that males with no job are also more vulnerable to the
partners unemployment (Knabe et al., 2016). Stutzer and Lalive (2004) explain the difference by social norm
effect of unemployment, especially in more traditionally oriented societies where males are considered the
familys breadwinners. Zuelke et al. (2018) argue that unemployment increases depression equally, while
Beatty and Ritter (2018) find health costs higher for unemployed males. According to Basbug and Sharone
(2017), long-term unemployment creates a larger negative emotional toll for jobless married males than
females. Previous studies reveal less happiness among widowed or divorced individuals compared to others
(Oshio, Nozaki & Kobayashi, 2011; Chyi & Mao, 2012)
Regarding the impact of age, studies end with the existence of a U-shaped association (Clark and
Oswald, 1994; Oesch and Lipps, 2012), concluding that unhappiness due to unemployment is in the majority
for those in mid-thirties (Clark and Oswald, 1994). According to Clark and Oswald (1994), unemployment
hurts young people less than others. In contrast, Winkelmann and Winkelmann (1998) find a negative
relationship between age and happiness regarding the impact of unemployment, which argues that the young
suffer the most from losing a job. Graham and De Lannoy (2016) support Winkelmann and Winkelmann
(1998), who claim that the most vulnerable age group to unemployment is 15-24. Unemployed individuals
aged 30-44 have a significantly greater chance of finding a job in a year after being unemployed than younger
and older ones who are challenged by the business cycle and age-related issues, respectively (Axelrad, Malul
& Luski, 2018).
Overall, the relationship between educational attainment and happiness is insignificant or negative
(Powdthavee, 2010). It can be positive depending on age (Nikolaev & Rusakov, 2016), positive diminishing
marginal return (Nikolaev, 2018). Clark and Oswald (1994) reveal higher mental distress at a higher
educational level regarding the relationship among unemployed people. In contrast, Daouli et al. (2015) and
Broman et al. (1995) find less educated people as more vulnerable to unemployment.
The conclusion from the previous studies displays the greater vulnerability of males and less educated
people to unemployment than females and those with higher education levels. The results are inconclusive
about the most vulnerable age group in the existing literature. To the best of our knowledge, there is no
prior research investigating the unemploymenthappiness association in Azerbaijan. The current study will
have the following significant contribution to the existing state of knowledge: (1) suggesting the use of
vulnerability scale to identify the unemployed individuals who need to be supported the most and cover
them within “a differentiated supporting system”, (2) identification of more disaggregated vulnerable
groups in a Muslim society on a broader framework, (3) filling the unemployment happiness research gap
in subjective well-being literature on Azerbaijan, (4) provide applicable recommendations for the
unemployment-related policy decision-makers to enhance the efficiency of labour market policy in the
country to overcome negative consequences of being jobless in the society.
The current study applies pooled cross-sectional research methodology based on the combination of
3 different survey results: Social Survey-1 (    , conducted during 01.03.2018-
01.06.2018), Social Survey-2 (    , conducted during 01.10.2018-01.01.2019), and
Social Survey-3 (    , conducted during 01.03.2019-01.06.2019) by ASERC (2018a,
2018b, 2019). The sample size equals 689 (     ).
According to research methodology, the most vulnerable groups among unemployed people are
identified based on selected socio-demographic factors. Therefore, the sample covers only unemployed
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Vol.14, No.4, 2021
individuals. The suggested framework attempts to provide a roadmap for policy officials to determine (based
on a survey-based independent research finding) and firstly support those with the least life satisfaction.
Regarding data collection methodology, respondents are selected randomly in surveys from all parts of the
Republic, with comparatively limited access to rural areas.
In the data cleaning stage, we filter respondents who mention their employment status as
unemployed. Later, the second filtering process is applied to remove all voluntary “unemployed” people
following the responses to the question what do you think, why you are unemployed. Those mention I
am still studying, "I do not want to work", "due to my family (especially some married females)" or any
similar other notes are removed from the list to find the number of involuntary unemployed.
In the analysis stage, we employ both descriptive and multivariate regression techniques.
3.1. Conceptual framework
Unemployment has heterogeneous well-being effects on unemployed individuals. In this context,
vulnerability to being unemployed is different across unemployed groups. The degree of vulnerability can
depend on numerous individual factors such as gender, age, marital status, educational attainment level,
duration of unemployment, etc., in line with cultural and regional determinants.
Conceptually, employment agencies can use the “life satisfaction approach” to determine vulnerability
among unemployed people in two ways. Firstly, an unemployed applicant should report "how much he/she
is happy" according to a 1-10 scale. However, the applicant will try to show how “unhappiest he/she is” to
maximize the potential gains. Therefore, this approach would yield biased results. On the contrary, the
second way requires survey-based pre-determination of vulnerable group features by independent studies.
The survey might be randomly selected, representative, and repeated regularly. In this case, the employment
agency could use a "vulnerability scale" and apply the "differentiated supporting system” (identifying,
supporting and prioritizing the unhappiest groups first).
The second strategy looks more reliable and practically applicable if the determined features can be
observable (easily collected without subjective evaluation) and back-checked.
3.2. Measuring happiness
According to Oishi and Diener (2014), self-reported happiness is reliable enough and valid, which
"tracks objective societal and economic conditions fairly well". The measurement scale for self-reported happiness
varies in different studies. Many empirical studies use a single Likert scale alike question such as "how
satisfied are you at present with your life as a whole? (1 to 10)" (See Winkelmann and Winkelmann, 1998).
However, self-reported happiness with a single question may not be reliable to measure the well-being of
unemployed people, particularly in Muslim societies where gratitude behaviour (thanks to God) dominates
largely. To measure approximate true happiness, the Satisfaction with Life Scale (SWLS) methodology by
Pavot and Diener (1993) is more powerful which determines well-being according to 5 questions (p. 172):
(1) In most ways, my life is close to my ideal, (2) The conditions of my life are excellent, (3) I am satisfied with my life, (4) So
far, I have achieved the important things I want in life, and lastly (5) If I could live my life over, I would change almost
nothing. The first three questions address measuring current satisfaction, while the remaining two cover the
effect of past events on current happiness.
Answer choices are the same for all 5 questions strongly agree, agree, slightly agree, neither agree nor disagree,
slightly disagree, disagree, and strongly disagree. The participant is allowed to choose only one option. Each answer
option is coded as 1 to 7, starting from "strongly disagree" (equals 1) while 7 stands for "strongly agree".
Next, the life satisfaction (LS) index for each respondent is calculated as the sum of response values to all 5
questions, varying between 5 (the respondent chooses the “strongly disagree” option in all questions) and
Khatai Aliyev
Unemployment and (un)happiness: Life
satisfaction approach to enhance policy
35 (the respondent chooses the “strongly agree” option in all question). The reliability of the scale reached
conventional levels of acceptability (  ).
According to Pavot and Diener (1993), the respondent is: Extremely dissatisfied if     ;
Dissatisfied if   ; Slightly dissatisfied if   ; Neutral if  ; Slightly satisfied
if   ; Satisfied if   ; Extremely satisfied if   .
Note that SWLS is a multi-item scale intended to assess the cognitive (happiness) rather than affective
(life satisfaction) component of subjective well-being (Pavot and Diener, 1993). SWLS relies on Diener's
(1984) early concept to create a global life-satisfaction scale based on.
3.3. Model building
3.3.1. Variables
Life satisfaction (LS) is the dependent variable. Independent variables include unemployment duration
(UD), age, gender status, a set of dummy variables displaying the respondent’s highest educational
attainment level, marital status. Two more dummy variables are added to account for the time difference
among the wave of surveys. The primary logic of independent variable selection is to be easily observable
from individual’s personal records which makes the use of the suggested supporting system based on a
“vulnerability scale”.
Brief definitions are given in Appendix A. Table 1 presents major descriptive statistics about each
variable. Due to missing values, the number of observations varies across variables.
Table 1
Descriptive statistics of variables
No. of
Std. Dev.
Bachelor (Ref.)
Single (Ref.)
Source: Author's own completion
3.3.2. Model
For reliability of empirical results, we employ 3 estimation methods: Ordinary Least Squares (OLS),
Robust Least Squares, and Ordered Logit. The final model specification includes a quadratic association
between unemployment duration and happiness as well as between age and happiness. The model for
estimation is as follows:
Journal of International Studies
Vol.14, No.4, 2021
   
  
   
           
   
is the dependent variable, LS, which is in natural logarithmic form () for OLS and Robust
Least Squares, while different (
) in Ordered Logit. denotes regression coefficient for each
explanatory variable. is the error term. stand for i-th observation.
Considering Harrison's (1976) "shock-optimism-pessimism-fatalism" stages during the unemployment
period, previous findings on the age-happiness relationship (see Clark and Oswald, 1994; Oesch and Lipps,
2012), and in accordance with the results of descriptive analyses, U-shaped association is expected between
UD and LS (   ), and age and LS (   ) while   and  .
4.1. Descriptive results
This introductory overview also displays the level of unhappiness among unemployed people in
Azerbaijan. In the sample of 689 unemployed individuals, 75% are dissatisfied with their life, while 4% are
neutral, and 13% are just slightly satisfied. The total average score is 14.46, which is slightly more than the
upper axis of dissatisfaction.
It is highly noteworthy to underline that the largest portion belongs to the extremely dissatisfied
category - 204 jobless, 128 males, and 76 females. Among those, males are slightly more unhappy (
 ;     ). The age distribution is almost equal for
each gender group. Initial descriptive analyses outcomes display signs of females being less vulnerable to
unemployment compared to unemployed males.
Regarding the role of marital status, educational attainment level, and age, results show that the gender
happiness gap is 3.29 among married respondents ( ;  ) while the
difference is 2.52 for single / engaged ( ;  ) and 4.2 among widowed
participants (  ;  ). Gender happiness gap ( ) is always
negative against males. The least gap is among singles in absolute value, and the most considerable difference
is for widowed respondents. All widowed males are extremely dissatisfied with life, while overall
dissatisfaction is 92% (60% extremely dissatisfied) among widowed females. The average LS score of
unemployed widowed individuals is extremely low, especially for males (twice more). Therefore, widowed
males should be at the center of the unemployment policy focus as a more vulnerable group.
Brief descriptive results display an increasing return to life satisfaction and gender happiness gap
expansion at higher educational attainment levels. The gap is 2.22 points among comprehensive school
graduates ( ;  ), 2.76 points at college graduation level ( ;
 ), 2.38 points among bachelor degree holders ( ;  ),
and the largest, 3.25 point at graduate (master or Ph.D.) level ( ;  ).
Therefore, comprehensive school graduates or less educated individuals are more vulnerable to
Khatai Aliyev
Unemployment and (un)happiness: Life
satisfaction approach to enhance policy
Figure 2. Disaggregated life satisfaction: general overview
Source: Author's own creation
Figure 3. Average life satisfaction vs unemployment duration
Source: Author's own creation
Disaggregated age distribution of life satisfaction among the unemployed people in Azerbaijan displays
a U-shaped association. Average LS score decreases until the age group 35-39 and turns upward after.
Among the youngest group of unemployed (17-24 ages), the dissatisfaction share is 75% (out of which 23%
are extremely dissatisfied) while the percentage is 77% (out of which 32%are extremely dissatisfied) among
25-29 aged youth, 86% (out of which 38% extremely dissatisfied) among age 30-34, and 91% (out of which
48% are extremely dissatisfied) among age 35-39. In older age groups (40-44, and 45 and older), the share
of dissatisfied individuals is 74% (out of which 39% are extremely dissatisfied) and 72% (out of which 25%
are extremely dissatisfied), respectively. The gender happiness gap is negative in all age groups, relatively
larger at 25-39 ages. Females' average LS score is nearly 3-point higher than males' score within these age
The overall evaluation result is that people at thirties are more vulnerable to unemployment, especially
males. Males are also more vulnerable to unemployment duration (see figure 3).
Following Harrison's (1976) "shock-optimism-pessimism-fatalism" stages, shock or immediate effect
of being jobless is approximately the same females and males are almost equally unhappy. As the
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Vol.14, No.4, 2021
unemployment duration lasts longer, males become pessimistic and report higher unhappiness, while the
"optimism" stage is valid for females during 1-3 months of unemployment followed by sharp pessimism (3-
6 months), a little optimism (6-12 months), small (1-2 years) and large pessimism (2-3 years). Adaptation to
the unemployment or fatalism stage only starts after approximately 3 years of being unemployed.
4.2. Empirical results
Table 2 includes results from OLS, Robust Least Squares, and Ordered Logit estimation methods. The
findings of all methods are logically very close to each other. Hence, the causality from unemployment
duration towards the life satisfaction of an unemployed person is like U-shaped (   ).
Contrary to Clark and Oswald (1994), we do not find any significant association between age and happiness
Simultaneously, comprehensive school graduates are substantially less happy compared to those with a
bachelor degree ( ). Regarding marital status, empirical results reveal a huge happiness gap
between widowed and unmarried (single) individuals ( ). The gender happiness gap is significant
against males (  ). Overall, results are generally close to the findings of previous studies.
Reminding the primary goal to identify the main features of a more vulnerable group among the
unemployed individuals, we do that here for Azerbaijan. Considering the U-shaped association between the
duration of unemployment and life satisfaction, we should calculate the threshold level. Finding the first
derivative of the estimated model by OLS according to  yields the marginal impact equation. Therefore,
we can calculate the threshold:
       
  
OLS finds 4.71 years as the threshold level of unemployment duration in Azerbaijan, after which
adaptation happens. Analogous results by Robust Least Squares and Ordered Logit methods are 4.96 (
  ) and 9.89 years (
  ), respectively. More precisely, there is a diminishing marginal
return to an additional year of unemployment in absolute value. Unemployment hurts more in earlier years.
Starting the adaptation requires too much time at least nearly five years, ceteris paribus.
Regarding individual-specific characteristics of vulnerable group members, the research reveals that
less-educated individuals (graduation from 9-year comprehensive school is compulsory in the Azerbaijan
education system) are nearly 13% less satisfied with life than bachelor degree holders. However, there is no
significant satisfaction gap among unemployed individuals with college and master's degrees compared to
those with a bachelor's degree (  ). On average, unemployed widows are 43-46% less happy than
singles, ceteris paribus. While holding other fixed factors, an unemployed female is 23-24% more satisfied
with life than males, on average. There is no significant well-being difference between married and single
unemployed individuals (  ).
For robustness, models are also estimated without quadratic term of age variable, as well as for different age groups. In all cases,
no significant causality is revealed from age to happiness.
Khatai Aliyev
Unemployment and (un)happiness: Life
satisfaction approach to enhance policy
Table 2
Empirical results
Robust Least Squares
Ordered Logit
  
Note: ***, **, and * denote statistical significance at 1%, 5%, and 10%, respectively. Standard errors are in ( ).
a Dependent variable is .
b Dependent variable is .Method: M-estimation. M settings: weight=Bisquare, tuning=4.685, scale=MAD (median centered),
Huber Type I Standard Errors & Covariance.
c Dependent variable is 
. The number of ordered indicator values: 7. Convergence achieved after 5 iterations. Coefficient covariance
computed using observed Hessian.
Meanwhile, it is also essential to underline the time-related difference of unhappiness/dissatisfaction
among unemployed individuals. The coefficient of a time-specific dummy variable () means that in
average, ceteris paribus, life satisfaction has been 17.4% higher among unemployed participants of “Social
Survey -1” than “Social Survey -3”. The time difference is approximately 1 year. Although the coefficient of
 is statistically insignificant, the positive sign still confirms that life satisfaction has a decreasing
tendency among unemployed people in Azerbaijan.
Journal of International Studies
Vol.14, No.4, 2021
4.3. Sensitivity analyses
The robustness check requires residual and stability diagnostics (results are available upon request).
Test results confirm that the estimated models have no functional misspecification (Ramsey-Reset test is
employed) and heteroscedasticity (Breusch-Pagan-Godfrey test is applied) problems. Although residuals are
not normally distributed, it should have no significant effect on t-test results due to the large sample size.
Meanwhile, recursive estimates confirm the stability of OLS results.
To avoid omitted variable biasedness, we re-estimated the models by adding religiosity and regional
dummies. Although religiosity dummies were significant, other variables' coefficients (and statistical
significance) are not affected substantially. Because religiosity is not an observable indicator, we did not
keep it in the model. On the contrary, regional dummies significantly impact neither the dependent nor the
coefficients of independent variables, so not added.
The research provided robust evidence about the severe unhappiness of unemployed people in
Azerbaijan, which may have substantial social effects. A review of existing studies confirms how much
unemployment can be harmful. The official unemployment rate is around 5%, while the number of people
who received “unemployed status” at employment agencies is less than 1.6% of the total active labour force.
However, the country has a large informal sector (Ismayilov, 2020) and hidden unemployment (Guney,
Sabiroglu and Bulut, 2013) problems. According to the State Statistical Committee of Azerbaijan Republic,
the average unemployment duration is 6.3 months. In 2019, the monthly unemployment benefit had been
within 127-163 USD (1USD = 1.7 AZN (Azerbaijan national currency)), which was 36-40% of the average
nominal salary in the country and paid only to 540 individuals. Interestingly, compared to the previous year,
4 times more people received official “unemployed status” in 2019 while beneficiaries of unemployment
benefit decreased 2 times. All these confirm that employment agencies have limited resources in Azerbaijan
(probably in many other developing countries). There is a need for “a differentiated supporting system” to
enhance policy efficiency.
In this context, the current research provides valuable policy insights, suggesting that employment
agencies should focus on unemployed people with higher vulnerability who need to be supported the most.
The study follows the life satisfaction approach to vulnerability, arguing that unhappy people are more
vulnerable to unemployment.
It becomes clear that males are more vulnerable to unemployment in Azerbaijan. The result is
consistent with previous studies (Broman et al., 1995; Stutzer and Lalive, 2004; Knabe et al., 2016; Basbug
and Sharone, 2017; Beatty and Ritter, 2018). With dominating Muslim society, males are viewed as
breadwinners. In this sense, supporting the argument of Stutzer and Lalive (2004), unemployment policy
should identify males as a more vulnerable group than females. However, results identify being divorced or
widowed as the most influential factor to determine a specific vulnerable group to unemployment. Being
less educated is another determined feature of a vulnerable group to unemployment, inconsistent with
Broman et al. (1995) and Daouli et al. (2015), among others. To sum up, major individual-specific
determinants of the most vulnerable group to unemployment in Azerbaijan are being widowed, male, and
less educated completing only compulsory education.
Without specifying any individual, but another essential factor is the duration of unemployment.
Despite reviling diminishing marginal return in absolute value, the threshold duration level is very long, until
which each additional year of being unemployed reduces the life satisfaction further. Therefore, long-term
unemployment duration also should be added to the features of the most vulnerable group in the country.
Khatai Aliyev
Unemployment and (un)happiness: Life
satisfaction approach to enhance policy
5.1. Implications for research and policy
Research findings have significant research and policy implications. Contributions to the existing
literature are (1) confirmation of the significant negative significant effect of unemployment over life
satisfaction in Azerbaijan, and (2) identification features of the most vulnerable groups among unemployed
individuals and suggesting the use of "a differentiated supporting system" to enhance policy efficiency.
Those features can be case and time-sensitive and require further empirical evidence in different societies.
In this context, the second contribution also opens a new field for future research.
Regarding policy implications, Oishi and Diener (2014) underline that "self-reported happiness can be used
to evaluate public policies such as taxation and unemployment benefits" and describe an ideal society as "in which citizens
are happy, feel satisfied, and find their lives meaningful". Current research creates a scientific impression about the
unhappiness of unemployed individuals for Azerbaijani policymakers. Considering the vulnerable group's
features, the government should re-evaluate its unemployment policy and consider applying “a
differentiated supporting system”.
Official employment agencies are recommended to build a strategy based on the individual’s
vulnerability degree. The agency can order an independent research agency to conduct an anonymous survey
among both employed and unemployed people regularly. In this way, it will be possible to update the main
futures of more vulnerable groups. According to the scale, limited resources and available jobs might be
used: from the most to the least vulnerable. The scale can refer to the current research findings at earlier
5.2. Limitations
Firstly, the research does not consider the strength of family ties, income support from other family
members, and whether husband/wife works or not (if works, how much salary do they earn) due to data
unavailability. The second limitation is about not controlling for an individual's health-related (personal or
family) issues. However, these factors are essential to assess actual vulnerability among unemployed people.
The first one may decrease an individual's vulnerability to unemployment, while the second most probably
affects vice-versa. Relatively less important, another limitation can be the perception of an unemployed
person about the socio-economic situation and living conditions of others.
5.3. Conclusion
The overall conclusion of empirical findings is that the most vulnerable group to unemployment in
Azerbaijan is (1) widowed/divorced, less educated (with only comprehensive school graduation) males with long-term (4-5
years) unemployment duration. The most influential factor seems to be widowed/divorced and gender status.
Calculations based on estimated equations display that more vulnerable (high-to-low) subsequent groups
Widowed/divorced males with higher educational attainment
Widowed/divorced less-educated females
Widowed/divorced females with higher educational attainment
Less-educated males (not widowed/divorced)
Less-educated females (not widowed/divorced).
Assessment of individual vulnerability among unemployed people and prioritizing those with the least
life satisfaction should increase the quality and efficiency of services provided by employment agencies with
limited available resources. Parameters of the "vulnerable scale" model are case sensitive and should be
updated over time. The countries with a high unemployment rate can use this scale to identify and support
Journal of International Studies
Vol.14, No.4, 2021
those who need it the most. The use of "a vulnerability scale" or “a differentiated supporting system” based
on a life satisfaction approach will have many practical and social implications and positive externalities.
Akerlof, G. A., & Shiller, R. J. (2010). Animal spirits: How human psychology drives the economy, and why it matters for global
capitalism. Princeton university press.
ASERC. (2018a). Social Survey -1. Unpublished dataset.
ASERC. (2018b). Social Survey -2. Unpublished dataset.
ASERC. (2019). Social Survey -3. Unpublished dataset.
Axelrad, H., Malul, M., & Luski, I. (2018). Unemployment among younger and older individuals: does conventional
data about unemployment tell us the whole story?. Journal for Labour Market Research, 52(1), 3.
Barros, A., Dieguez, T., & Nunes, P. (2019). How Unemployment May Impact Happiness: A Systematic Review.
In Emerging Economic Models for Global Sustainability and Social Development (pp. 237-259). IGI Global.
Basbug, G., & Sharone, O. (2017). The emotional toll of long-term unemployment: Examining the interaction effects
of gender and marital status. RSF: The Russell Sage Foundation Journal of the Social Sciences, 3(3), 222-244.
Beatty, T. K., & Ritter, J. A. (2018). Measuring the Health Cost of Prolonged Unemployment: Evidence from the Great
Recession (No. 1698-2018-7891).
Becchetti, L., Massari, R., & Naticchioni, P. (2013). The drivers of happiness inequality: suggestions for promoting
social cohesion. Oxford Economic Papers, 66(2), 419-442.
Broman, C. L., Hamilton, V. L., Hoffman, W. S., & Mavaddat, R. (1995). Race, gender, and the response to stress:
Autoworkers' vulnerability to long-term unemployment. American Journal of Community Psychology, 23(6), 813-842.
Boyce, C. J., Wood, A. M., Daly, M., & Sedikides, C. (2015). Personality change following unemployment. Journal of
Applied Psychology, 100(4), 991.
Chadi, A. (2010). How to Distinguish Voluntary from Involuntary Unemployment: On the Relationship between the
Willingness to Work and UnemploymentInduced Unhappiness. Kyklos, 63(3), 317-329.
Chadi, A. (2014). Regional unemployment and norm-induced effects on life satisfaction. Empirical Economics, 46(3),
Chadi, A., & Hetschko, C. (2016). Flexibilization without hesitation? Temporary contracts and job satisfaction. Oxford
Economic Papers, 68(1), 217-237.
Clark, A. E., & Oswald, A. J. (1994). Unhappiness and unemployment. The Economic Journal, 104(424), 648-659.
Clark, A., Georgellis, Y., & Sanfey, P. (2001). Scarring: The psychological impact of past
unemployment. Economica, 68(270), 221-241.
Clark, A. E., Diener, E., Georgellis, Y., & Lucas, R. E. (2008). Lags and leads in life satisfaction: A test of the baseline
hypothesis. The Economic Journal, 118(529), F222-F243.
Clark, A., Knabe, A., &Rätzel, S. (2010). Boon or bane? Others' unemployment, well-being and job insecurity. Labour
Economics, 17(1), 52-61.
Chyi, H., & Mao, S. (2012). The determinants of happiness of China's elderly population. Journal of Happiness
Studies, 13(1), 167-185.
Daouli, J., Demoussis, M., Giannakopoulos, N. L., & Lampropoulou, N. (2015). The incidence of long-term
unemployment in Greece: Evidence before and during the recession. In Crete 14th Conference on Research on
Economic Theory & Econometrics Chania (pp. 12-16).
Diener, E. (1984): Subjective Well-Being, Psychological Bulletin, 95 (3):
Easterlin, R. A. (1974). Does economic growth improve the human lot? Some empirical evidence. In Nations and
households in economic growth (pp. 89-125). Academic Press.
Eren, K. A., & Aşıcı, A. A. (2017). The determinants of happiness in Turkey: Evidence from city-level data. Journal of
Happiness Studies, 18(3), 647-669.
Farré, L., Fasani, F., & Mueller, H. (2018). Feeling useless: the effect of unemployment on mental health in the Great
Recession. IZA Journal of Labor Economics, 7(1), 8.
Khatai Aliyev
Unemployment and (un)happiness: Life
satisfaction approach to enhance policy
Frey, B. S. (2018). What Makes People Happy?. In Economics of Happiness (pp. 13-20). Springer, Cham.
Graham, L. & De Lannoy, A. (2016) Youth unemployment: What can we do in the short run? Available at:
%20unemployment%20FINAL.pdf. 16.08. 2019.
Guney, A., Sabiroglu, I. M.,& Bulut, C. (2013). What Kind of Capitalism for Azerbaijan? A Comparative Analysis from
Economic View. Available at: (Accessed 24.11.2021)
Harrison, R. (1976). The demoralising experience of prolonged unemployment. Department of Employment Gazette, 84,
Helliwell, J. F., & Huang, H. (2014). New measures of the costs of unemployment: Evidence from the subjective well
being of 3.3 million Americans. Economic Inquiry, 52(4), 1485-1502.
Ismayilov, I. E. (2020). Problems of employment of young labor force in Azerbaijan. Economic and Social Development:
Book of Proceedings, 4, 319-327.
Knabe, A., & Rätzel, S. (2011). Scarring or scaring? The psychological impact of past unemployment and future
unemployment risk. Economica, 78(310), 283-293.
Knabe, A., Schöb, R., & Weimann, J. (2016). Partnership, gender, and the well-being cost of unemployment. Social
Indicators Research, 129(3), 1255-1275.
Lim, H. E. (2017). Estimating Psychological Impact of Unemployment: the Case of Malaysian Graduates. Malaysian
Journal of Economic Studies, 47(1), 33-53.
Layard, R., Clark, A., & Senik, C. (2013). The causes of happiness and misery. In: Helliwell, J., R. L ayard, and J. Sachs
(eds). World Happiness Report 2013. Columbia: Earth Institute.
Mullainathan, S., & Shafir, E. (2013). Scarcity: Why having too little means so much. Macmillan.
Nikolaev, B. (2018). Does higher education increase hedonic and eudaimonic happiness?. Journal of Happiness
Studies, 19(2), 483-504.
Nikolaev, B., & Rusakov, P. (2016). Education and happiness: an alternative hypothesis. Applied Economics
Letters, 23(12), 827-830.
Ochsen, C., & Welsch, H. (2011). The social costs of unemployment: accounting for unemployment duration. Applied
Economics, 43(27), 3999-4005.
Oishi, S., & Diener, E. (2014). Can and should happiness be a policy goal?. Policy insights from the Behavioral and Brain
Sciences, 1(1), 195-203.
Oesch, D., & Lipps, O. (2012). Does unemployment hurt less if there is more of it around? A panel analysis of life
satisfaction in Germany and Switzerland. European Sociological Review, 29(5), 955-967.
Oshio, T., Nozaki, K., & Kobayashi, M. (2011). Relative income and happiness in Asia: Evidence from nationwide
surveys in China, Japan, and Korea. Social Indicators Research, 104(3), 351-367.
Pavot, W., & Diener, E. (1993). Review of the Satisfaction With Life Scale. Psychological Assessment, 5, 164172.
Petrucci, T., Blau, G., & McClendon, J. (2015). Effect of Age, Length of Unemployment, and ProblemFocused
Coping on Positive Reemployment Expectations. Journal of Employment Counseling, 52(4), 171-177.
Powdthavee, N. (2010). How much does money really matter? Estimating the causal effects of income on
happiness. Empirical Economics, 39(1), 77-92.
Ritzen, J. (2019). Happiness as a guide to labor market policy. IZA World of Labor.
Schwarz, P. (2012). Neighborhood effects of high unemployment rates: Welfare implications among different social
groups. The Journal of Socio-Economics, 41(2), 180-188.
Schöb, R. (2016). Labor market policies, unemployment, and identity. IZA World of Labor.
Stutzer, A., & Lalive, R. (2004). The role of social work norms in job searching and subjective well-being. Journal of the
European Economic Association, 2(4), 696-719.
Winkelmann, L., & Winkelmann, R. (1998). Why are the unemployed so unhappy? Evidence from panel
data. Economica, 65(257), 1-15.
Winkelmann, R. (2014). Unemployment and Happiness. Successful Policies for Helping the Unemployed Need to
Confront the Adverse Effects of Unemployment on Feelings of Life Satisfaction. IZA World of Labor, 94.
Von Scheve, C., Esche, F., & Schupp, J. (2017). The emotional timeline of unemployment: anticipation, reaction, and
adaptation. Journal of Happiness Studies, 18(4), 1231-1254.
Journal of International Studies
Vol.14, No.4, 2021
Zajacova, A., & Dowd, J. B. (2014). Happiness and health among US working adults: is the association explained by
socio-economic status?. Public Health, 128(9), 849.
Zuelke, A. E., Luck, T., Schroeter, M. L., Witte, A. V., Hinz, A., Engel, C., Enzenbach, C., Zachariae, S., Loeffler, M.,
Thiery, J., & Villringer, A. (2018). The association between unemployment and depressionResults from the
population-based LIFE-adult-study. Journal of Affective Disorders, 235, 399-406.
Khatai Aliyev
Unemployment and (un)happiness: Life
satisfaction approach to enhance policy
Brief definitions of the variables
Dependent variable
Life satisfaction / happiness / well-being of the unemployed individuals, measured in units changing
between 5 and 35.
Independent variables
Duration of unemployment for each corresponding jobless, measured in years.
The age of each respondent, measured in years.
Dummy variable, equals 1 if the jobless is female, 0 otherwise. Reference group is males.
Educational dummies (Ref. bachelor degree holders.)
Equals 1 if the respondent's highest educational attainment level is graduation from comprehensive
schools, 0 otherwise.
Equals 1 if the respondent's highest educational attainment level is graduation from vocational
schools / colleges (2.5-year education), 0 otherwise.
Equals 1 if the respondent has master or higher degree, 0 otherwise.
Marital status dummies (Ref. Singles)
Equals 1 if the respondent is married, 0 otherwise.
Equals 1 if the respondent is widowed / divorced, 0 otherwise.
Time specific dummies (Ref. Social Survey -3, 01.03.2019-01.06.2019)
Equals 1 if the respondent belongs to Social Survey -1 (01.03.2018-01.06.2018), 0 otherwise.
Equals 1 if the respondent belongs to Social Survey -2 (01.10.2018-01.01.2019), 0 otherwise.
Source: Author's own completion
© 2021. This work is licensed under (the “License”).
Notwithstanding the ProQuest Terms and Conditions, you may use this
content in accordance with the terms of the License.
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The paper examines the role of sovereign wealth funds in the national economy and analyzes the reasons for establishing such funds from the economic aspects. A sovereign wealth fund is an investment fund, and the main priority of these funds is to invest in foreign currency reserves of a country effectively. In 2020, the total assets of the sovereign wealth funds exceeded 9 trillion dollars, which is six times higher than the 2003 figure. The main targets of governments are to ensure macroeconomic stability and provide effective management of public assets with the use of sovereign wealth funds. Proper implementation of the functions of sovereign wealth funds mainly depends on a correct investment strategy, and when such strategy is determined the macroeconomic policy should be considered. Managing sovereign wealth funds highly depends on monetary, fiscal, and exchange rate policies.
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Вступ. Гармонізація взаємозв’язку між рин­ком освітніх послуг та ринком праці сприяє збалансуванню структури попиту і пропо­зиції робочої сили та робочих місць у націо­нальній економіці. Проблема. Пандемія коронавірусу COVID-19 та умови воєнного стану призвели до істот­них дисбалансів на ринку праці, міграційних впливів на ринок освітніх послуг, втрати інте­лектуального потенціалу людського капі­талу, що і визначає актуальність теми та її прак­тичну значущість для подальших досліджень. Метою статті є обґрунтування інстру­ментарію гармонізації ринків праці та освіт­ніх послуг. Методи. Використано загальнонаукові та спеціальні методи, зокрема: економіко-мате­матичні методи, графічно-аналітичний метод, методи статистичного та структурно-функ­ціонального аналізу. Результати дослідження. Проаналізовано особливості функціонування ринку освітніх послуг та ринку праці в умовах сучасних ви­кликів. Розкрито загальні тенденції, що ви­никли на цих ринках під впливом пост­пан­демічної кризи та в умовах воєнного стану. Запропоновано алгоритм функціонування ринку праці та ринку освітніх послуг на основі взаємодії держави, найманого працівника, роботодавця, освітніх закладів. Висновки. Взаємодія ринку освітніх послуг та ринку праці реалізується через гармо­ні­зацію відносин бізнесу та вищих навчальних закладів і є головною умовою для впрова­дження позитивних змін, які задовольняють усіх суб’єктів. Імплементація дуальної форми здобуття вищої освіти сприятиме побудові відносин соціального партнерства між ви­щими навчальними закладами, здобувачами освіти та роботодавцями, що посилюватиме взаємозв’язок ринку освітніх послуг та ринку праці. Перспективами подальших наукових до­сліджень є розроблення дієвого механізму взаємодії ринку освітніх послуг та ринку праці, ефективного інструментарію гармоні­зації структури робочої сили та робочих місць в умовах постпандемічного і після­воєнного відновлення.
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Məqalədə İKT-nin ölkə iqtisadiyyatında rolu, İnformasiya Cəmiyyətinə keçidin daha da sürətləndirilməsi, cəmiyyətin bütün sahələrinin elektronlaşdırılması işi təhlil edilmişdir. Burada əsas meyar, müasir İKT-dən istifadə etməklə istifadəçi rahatlığının təmini, vaxta qənaət, daha çox informasiya ilə təminatlılıq və eyni zamanda əhalinin sosial həyatında vacib rol oynaya biləcək məlumatlara (səhiyyə, təhsil) birbaşa çıxış imkanının olmasıdır. Məhz bu səbəbdən də cəmiyyətin müxtəlif sferalarını əhatə edən xidmət sahələrinin vahid elektron bazaya çıxışının təmini əsas hədəflərdəndir. Bununla yanaşı, İKT sektorunun, eləcə də digər sosial sahələrin buna hazır olmasına və bu yönümdə inkişafı xarakterizə edən dövlət proqramlarının (təhsil, səhiyyə, məşğulluq sahələrində) qəbuluna da məqalədə xüsusi yer ayrılmışdır. Ölkənin İKT sektorunun məşğulluq sahəsindəki rolu önə çəkilmiş, ölkədə işsizlik probleminin azaldılmasına vermiş olduğu töhfədən bəhs edilmişdir. Məhz bütün bunlara görə də, ölkə İKT-nin inkişafı sahəsində qəbul olunmuş dövlət proqramlarına geniş yer ayrılmış və detallı şəkildə vurğulanmışdır. Həmçinin məqalədə nəinki ölkə əhəmiyyətli, eləcə də qlobal əhəmiyyət kəsb edən layihələrdən də bəhs edilmişdir. Nəhayət, bu sektor üzrə qarşıda duran hədəflərə çatmaq üçün bir sıra təkliflər qeyd olunmuşdur.
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Primary goal of public policy is to enhance individual and societal well-being. Public policy decision-makers are the representatives of public institutions and there is a clear positive linkage between the trust to public institutions and self-reported life satisfaction of individuals. Therefore, this research aims to explore the current state of institutional trust and life satisfaction of individuals, and empirically estimate the relationship for the group of people with different employment status (employed, unemployed, people not in the labour force). Using a nationally representative cross-sectional dataset of 2698 respondents, we find that life satisfaction and the trust to public institutions significantly vary among the people with different employment status. Particularly, unemployed people report lower trustworthiness to public institutions and less satisfaction with life. Overall, research findings confirm existence of a positive causality from the trust to public institutions and self-reported individual life satisfaction. The impact is larger for employed people, followed by unemployed and inactive labour force. Azerbaijan government should work on strengthening the public trust, especially among the unemployed people in order to enhance the individual and societal well-being. Public policy decision makers can consider the research findings to increase the effectiveness of policy initiatives.
The subject of this article is to research the role of trade unions in the hotel industry in this century. Looking at trade union density across the Horeca sector in the EU27 plus Norway, it can be seen that overall density is relatively low (less than 15%), irrespective of the diversity within the Horeca sector in each country. Accordingly, the main objective of this article is to investigate the future of the trade unions in the hotel industry considering social and economic changes of the last few decades. It starts from four hypotheses, which refer to the attitudes of employees and union members towards the role of unions in the hotel industry and their power. The results of field research have served as primary data. The survey has been carried out among hotel employees (N=452) along the Adriatic coast in the summer season before the COVID-19 crises. In order to evaluate, formulate and present the findings, the following scientific methods have been applied: analysis and synthesis together with descriptive and inferential statistics.. The main finding of this paper points to the conclusion that employees who achieve high productivity do not have faith in the power of unions in the hotel industry. In terms of demographic variables, union employees above the age of 50 believe the least in the power of unions. These employees mostly point out that they are not adequately rewarded, that their superiors do not help them, and do not respect their working hours. The findings obtained may be significant for trade unions and their representatives to support them to form a new strategy that is necessary for unions to reach their goals.
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Abstract This article documents a strong connection between unemployment and mental distress using data from the Spanish National Health Survey. We exploit the collapse of the construction sector to identify the causal effect of job losses in different segments of the Spanish labor market. Our results suggest that an increase of the unemployment rate by 10 percentage points due to the breakdown in construction raised reported poor health and mental disorders in the affected population by 3 percentage points, respectively. We argue that the size of this effect responds to the fact that the construction sector was at the center of the economic recession. As a result, workers exposed to the negative labor demand shock faced very low chances of re-entering employment. We show that this led to long unemployment spells, stress, hopelessness, and feelings of uselessness. These effects point towards a potential channel for unemployment hysteresis.
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The wealth of an economy is traditionally measured by its level of productivity. However, countries with the highest level of productivity do not always report equal levels of happiness and general wellbeing. In fact, there is no direct relationship between both variables and sometimes less wealthy productive countries report higher levels of happiness. Recent studies and theories are trying to demonstrate that the term happiness has made its way into economics literature as the result of economist dissatisfaction who believe happiness should become a matter of study in the field. Unemployment is one of the most recently researched variables in economics and has a direct relationship with happiness. Potentially some other variables such as autonomy, reliability, and added value of happiness would help researchers to better complete economic analysis via a multidisciplinary perspective.
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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.
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How much does a year of unemployment affect a person's health? Previous studies estimate the health effects of job loss after a follow-up period, but the length of unemployment spells within the follow-up is an implicitly variable treatment. Thus estimates based on a fixed follow up average over unemployment spells of different lengths, which implicitly depend on macroeconomic conditions. We estimate the effects of time unemployed and find robust negative effects of duration on men's self-assessed health. For women the estimated effects are smaller and less precise. We use an instrumental variables approach to account for dynamic selection driven by feedback from health to duration via search intensity or reservation wages. Combining these effects with prior estimates of the relationship between self-assessed health and specific-cause mortality suggests the effects correspond to large social costs.
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An increasing number of studies suggest that the relationship between higher education and subjective well-being (SWB) is either insignificant or negative. Most of these studies, however, use life satisfaction as a proxy for SWB. In this study, using longitudinal data from the Household Income and Labor Dynamics in Australia survey, I examine the link between higher education and three different measures of subjective well-being: life satisfaction and its different sub-domains (evaluative), positive and negative affect (hedonic), and engagement and purpose (eudaimonic). Three substantial results emerge: (1) people with higher education are more likely to report higher levels of eudaimonic and hedonic SWB, i.e., they view their lives as more meaningful and experience more positive emotions and less negative ones; (2) people with higher education are satisfied with most life domains (financial, employment opportunities, neighborhood, local community, children at home) but they report lower satisfaction with the amount of free time they have; (3) the positive effect of higher education is increasing, but at a decreasing rate; the SWB gains from obtaining a graduate degree are much lower (on the margin) compared to getting a college degree.
Background: Unemployment is a risk factor for impaired mental health. Based on a large population-based sample, in this study we therefore sought to provide detailed information on the association between unemployment and depression including information on (i) differences between men and women, (ii) differences between different types of unemployment, and (iii) on the impact of material and social resources on the association. Methods: We studied 4,842 participants (18-65 years) of the population-based LIFE-Adult-Study. Depression was assessed using the Center for Epidemiological Studies Depression Scale. Employment status was divided into three groups: being employed, being unemployed receiving entitlement-based benefits, being unemployed receiving means-tested benefits. Multivariate logistic regression models were applied to assess the association between employment status and depression. Results: Statistically significantly increased depression risk was solely found for unemployed persons receiving means-tested benefits. Adjusting for differences in sociodemographic factors, net personal income and risk of social isolation, comparable associations of being unemployed and receiving means-tested benefits with elevated depression risk were found for men (Odds Ratio/OR = 2.17, 95%-CI = 1.03-4.55) and women (OR = 1.98, 95%-CI:1.22-3.20). Limitations: No conclusions regarding causality can be drawn due to the cross-sectional study design. It was not possible to assess length of unemployment spells. Conclusion: Unemployed persons receiving means-tested benefits in Germany constitute a risk group for depression that needs specific attention in the health care and social security system. The negative impact of unemployment on depression risk cannot be explained solely by differences in material and social resources. Contrasting earlier results, women are equally affected as men.
Happiness research determines, isolates, and measures the various determinants of human well-being. The data collected on the subjective life satisfaction of individuals are related to possible determinants of happiness by multiple regressions. The personality structure determined by one’s genetic inheritance has a strong influence on happiness. Among economic factors, people with higher incomes unambiguously consider themselves to be more satisfied with their lives than do people with low income, and people losing their job are much more dissatisfied with their lives than are those holding a job. Prominent among the socio-demographic influences is a U-shaped relationship between age and life satisfaction; married people are happier than those living alone; and intensive and regular social contacts within the family and among friends and acquaintances contribute strongly to happiness. Physical and psychological health contribute strongly to well-being. Cultural differences matter, and religious persons are demonstrably happier than those who do not belong to a religious community. Happiness is positively influenced by democracy and political decentralization.
Prior research shows that long-term unemployment (LTU) generates a negative emotional toll but leaves unexplored how such toll varies by gender and marital status. Using a mixed-methods approach we examine how the negative emotional toll of LTU is shaped by the interaction of gender and marital status. Our qualitative findings suggest that more unemployed married men than women experience marital tensions that exacerbate the emotional toll of unemployment. Our analysis of survey data show that while marriages improve the well-being of both unemployed men and women, for married men but not women such benefits disappear once we control for household income. These findings contribute to the existing literature by deepening our understanding of how gender and marital status mediate the emotional toll of LTU.