Financial, Job and Health Satisfaction: A Comparative Approach on Working People

societies Article Financial, Job and Health Satisfaction: A Comparative Approach on Working People María Navarro Department of Economic Theory and History, University of Granada, 18071 Granada, Spain; [email protected] Received: 30 March 2019; Accepted: 20 April 2019; Published: 30 April 2019   Abstract: The determinants of domain satisfactions could be differently evaluated depending on the aspect of life considered, which would lead to different implications for public policies. To test this hypothesis, using the German Socio − Economic Panel (GSOEP), we analyse the effect of different economic and non − economic factors on satisfaction with financial situation, job and health status. The main results confirm that several determinants exert different effects depending on the aspect of life that people are evaluating. For instance, household income only improves satisfaction with financial situation but it does not explain job or health satisfaction. However, those people with an active social life, who are less worried and distrustful, are more satisfied regardless of the aspect of life considered. These findings reflect the importance of studying the main determinants of the domain satisfactions using a comparative perspective to design and evaluate specific public policies, since some measures could be effective for improving satisfaction in one area of an individual’s life but not for others. Keywords: domain satisfactions; income characteristics; social capital; cultural capital; psychological capital; public policies 1. Introduction The literature related with subjective well − being has demonstrated that it is a multidimensional concept encompassing different areas of life called domain satisfactions, that is, subjective well − being can be seen as an aggregate of different domains (for more detail, see [ 1 – 4 ]). Moreover, people are able to differentiate the domains and to evaluate them separately. Previous studies have considered domain satisfactions as different areas of individual life, such as the financial situation, job, health status, housing, leisure, environment, marriage, friendships, safety and social relationships [3,5–12]. Given the relationship between subjective well − being and the different domain satisfactions, the study of domain satisfactions and their determinants is also useful for policy making. Knowing what produces satisfaction in different areas of individual life could be fundamental for measuring consumer preferences and social welfare, as well as for the design and assessment of public policies [12–18]. Especially, the knowledge obtained in this research can be used to complement traditional measures of welfare, since the subjective vision provides information of non − material aspects of people’s satisfaction [ 6 , 19 , 20 ]. For instance, the analysis of job satisfaction and health satisfaction should be relevant for public policies related to the labour market, health care and medical expenditure. Several studies have analysed the main determinants of different domains of life but the comparisons between the common determinants and the possible implications for public policies using this comparison are scarcer. Thus, as something new and given the relevance for the public policies, our main goal is to do a comparative analysis about the three most relevant domains, namely financial, job and health satisfaction, as well as, we focus on the common and specific implications for public policies. Specifically, using the German Socio − Economic Panel (GSOEP) over the period 1998–2014, we compare the effect of common factors, that is, those which are used to analyse the three different Societies 2019,9, 34; doi:10.3390/soc9020034 www.mdpi.com/journal/societies Societies 2019,9, 34 2 of 18 domains, to determine whether these affect the domains in a similar way or, in the opposite case, whether it depends on the aspect of life that people are evaluating. Moreover, we analyse the effect of several specific factors, which are included to analyse each domain. Therefore, our paper contributes to the literature about satisfaction and its implications on public policies. Our main results show that the effect of several common factors depends on the aspect of life that people consider. For instance, absolute income (own income at the moment of the interview) only exerts effects on financial satisfaction but a higher income does not affect job, neither health satisfaction. Thus, the policy marker should consider what they want to get to design specific public policies based on the results of these studies, because, for instance, a high economic growth is better for financial satisfaction but it is not relevant to improve the satisfaction of workers with their job or their health. Nonetheless, other factors such as the social contacts explain the different domains analysed in this paper in the same direction. For instance, having more social relationships, being less worried or distrustful improves the satisfaction regardless the aspect of life that they are evaluating. Hence, our evidence confirms the relevance of social contacts to improve the satisfaction with different aspects of the individual life. The remainder of this paper is structured as follows. The literature on domain satisfactions and determinants is reviewed in Section 2. The empirical strategy is presented in Section 3. The data and variables used to analyse the domain satisfactions are explained in Section 4. The main results of our analysis are shown and discussed in Section 5. Finally, Section 6concludes. 2. Literature Review 2.1. Domain Satisfactions As described in previous literature, domain satisfactions refer to the individual satisfaction in different aspects of life, such as financial, job, health, housing, leisure, environment, marriage, friendships, safety, standard of living or social relationships [1,3,5–9,12].1 Although the domains of life considered in the literature differ and there is not agreement on which domains are conceptually preferable, it has been demonstrated that the most standard and relevant as determinants of subjective well − being are financial situation, work and health [ 1 , 8 , 11 , 20 – 25 ]. Thus, we consider financial situation, job and health status as domains of life to analyse in this study. Financial satisfaction is related to the current level of individual satisfaction with several aspects of their financial situation. It is well known that individual financial satisfaction could have an impact on different factors such as the choice of the consumers, productivity of the job and social contacts [ 26 ]. On the other hand, job satisfaction concerns how satisfied people are with their main activity, taking into consideration different aspects of the workplace such as wages, working hours, or relationships with co − workers and employer, among others. It is also known to be a great predictor of labour market behaviours, such as mobility, worker performance and productivity, health, longevity and social illnesses [ 27 – 31 ]. Concerning health satisfaction, it is related to the satisfaction with the current health status. It has been studied by many health economists to evaluate possible effects from illnesses and medical treatments (see, for instance, [ 32 ]), being relevant to design and assess public policy related to health care and medical spending. 2.2. Common and Specific Determinants of Domain Satisfactions We consider both predictive and hedonistic approaches to explain the three different domains considered here (for more detail, see, [ 11 ]). In this vein, first, considering that subjective well − being can be seen as an aggregated concept of domain satisfactions [ 1 , 4 ], we review the factors used in 1 In this work, the terms, on the one hand, “domains of life” and “domain satisfactions”, and on the other hand, “subjective well−being”, “general satisfaction”, “happiness” and “life satisfaction” are considered synonymous. Societies 2019,9, 34 3 of 18 subjective well − being studies, which are considered common factors of all domains and represent the predictive approach. In line with the literature, we classify them into three groups: (1) income characteristics; (2) social, cultural and psychological capital; and (3) socio − economic characteristics. Secondly, we briefly examine specific determinants, which are used for each domain in related studies and encompassing the hedonistic approach. 2.2.1. Income Characteristics As has been demonstrated in related studies, absolute income (own income in the current period) is not the only income measure which matters in order to be satisfied with general life. Indeed, literature related to subjective well − being has widely accepted the Easterlin Paradox, that is, increases in income are not always related to increases in satisfaction, which could be explained by the individual comparisons including both internal and external. We consider that domain satisfactions could also be affected by: firstly, absolute income and secondly, the comparisons that individual makes with oneself in the past in income terms, that is, internal comparison and with peers, that is, external or social comparison [ 16 , 33 – 35 ]. For that, we not only include absolute income but also the relative income in the sense of a measure of internal and external comparison. Regarding internal comparisons, it has been demonstrated for subjective well − being that increases in past income may only have a transitory effect, since either people adapt to their past experiences or new aspirations appear [ 36 ]. As a consequence, they would return to the same level of satisfaction than in an initial moment after a period of adaptation [37–40]. This process is known as hedonic adaptation. Concerning external comparisons, these refer to the fact that comparisons are made with peers belonging to a demographic group, for instance, co − workers, neighbours, friends, family members or people with similar socio − demographic characteristics (same gender, age, education, etc.). This is usually called as relative income hypothesis. People are affected by the comparison with the economic situation of those around them, normally, their reference group. Commonly, researches impose it exogenously groping people with observable and common characteristics [ 41 ]. Moreover, the social comparisons can be modelled using two different methods, namely symmetric and asymmetric, that is, a change in the reference income influences individuals’ well − being in a similar and different manner, respectively. 2.2.2. Social, Cultural and Psychological Capital According to the scheme proposed by [ 42 ] based on the model of Sen’s capabilities [ 43 ], the related literature has distinguished between social, cultural and psychological capital. Although these factors have received increasing attention in the literature as determinants of satisfaction [ 44 – 46 ], they are less used as determinants of domain satisfactions but we consider that these could affect them. For instance, health satisfaction could improve when people have a good mood or an active social life. Social capital has been a debated topic, but currently there is not a common definition or consensus about how to measure it [ 47 ]. [ 48 ] based on [ 49 ] defines it as “networks together with shared norms, values and understandings that facilitate cooperation within or among groups”. As [ 37 ] pointed out, social capital includes measures of a person or group of networks, personal relationships, general trust and civic participation, called relational goods. The literature has differentiated two types of social capital: bonding and bridging. The first concerns closed networks of people with relatives or friends and the latter is more formal and it implies cross − cutting ties such as associations, trade union or the attending different social and cultural events. Previous evidence has shown that people with more social relationships experiment higher levels of satisfaction [33,35,37,45]. Regarding cultural capital, it can be defined as the values and goals in the individual’s life. The literature has shown that while the objectives social and family make to people more satisfied, the effect of economic goals is less conclusive [ 42 ]. Concerning the psychological capital, following [ 42 ], we consider the personality traits related to the so − called “Big Five Indicators” (BFI), namely neuroticism, extraversion, openness, agreeableness and conscientiousness; the LOC index as an external measure of Societies 2019,9, 34 4 of 18 the degree of control over an individual’s life; and a reciprocity measure (positive and negative). The existing results on subjective well − being have shown that people with more extraversion, openness, agreeableness and conscientiousness, with less neuroticism, the lower LOC (they think that external circumstances only play a small role in their life), more positive reciprocity and less negative reciprocity are more satisfied [33,50]. 2.2.3. Socio−Economic Characteristics A set of socio − economic characteristics, such as gender, place of residence, age, marital status, years of education and household characteristics, is included to analyse satisfaction. Evidence has shown that, in general, females, people who live in West Germany, with a partner or who are the owner of dwelling are more satisfied (see, for instance, [ 1 , 35 , 37 , 41 ]). The most extended result about age is that it has a quadratic relationship with U − shape or inverted U − shape with satisfaction [ 11 , 37 ]. No conclusive effects have been found for years of education. While some studies have obtained negative effects due to the fact that more educated people have more aspirations and expectations [ 33 ], others have found that more educated individuals are more satisfied [ 51 ]. The presence of children and adults in the household could have positive effects [1,33,37], negative [52] or null [34,35]. 2.2.4. Specific Determinants for Each Domain For financial satisfaction, the evidence has shown that the savings and the presence of a second earner in the household exert a positive effect [ 1 ]. Variables such as working income, working hours, extra money, extra hours or the rate between the household income and working income have been included in related papers to study job satisfaction, where a larger working income, extra money and proportion between household income and working income lead to higher job satisfaction [ 1 ]. The effect of working hours is less conclusive. While [ 11 ] found that these do not affect job satisfaction, [ 53 ] stated that a reduction of working hours could have either positive effects, since it helps to work − life balance, or negative by the association with lower working income. For health satisfaction, the factors considered have been practicing sport, where a positive effect is found since those people who do more sports, they have a better health status and the frequency of visiting to the doctor, where more visits imply less satisfaction (see, for instance, [54,55]). 3. Empirical Strategy In line with the existing literature related to subjective well − being, the empirical model for the determination of domain satisfactions can be written as follows: DSit =α0+α1yit +α2yi,t−k+α3f(yit,yjt) + α4SCit +α5CCit +α6PCit +ρ0Xit +η0Qit +γ0TDt+εit (1) for i=1 . . . .N, t=1 . . . T, where y it denotes the absolute income; y i,t−k is the k − periods lagged income, that is, hedonic adaptation; f(y it ,y jt ) represents the social comparisons between the i’s income (y it ) and individual j’s income (y jt ); SC it ,CC it and PC it are, respectively, social, cultural and psychological capital; X it stands for a set of socio − economic characteristics; Q it stands for a set of specific characteristics considered in each domain; TD t includes time dummies which account yearly changes that are the same for all individuals; and εit the error term. Following [ 11 ], we cardinalize our dependent variables and, then, to make use of the panel structure of the dataset, we estimate random effects model with Mundlak’s correction to control for individual heterogeneity for each domain (see, for instance, [ 52 ]). Therefore, first, we cardinalize the reported answers about the different domain satisfactions to account for the fact that pass differences among categories of satisfaction may not have the same meaning [ 35 ]. And secondly, the error term is assumed to be εit =λizi+ωi+πit , where λizi+ωi is Mundlak’s correction and πit the error term, with, ωi∼N( 0, σ2 ω) , πit ~N(0,1), and Cov (ωi,πit) =0. The Mundlak variables ( zi ) used in this work are time − average values of years of education and number of adults and children in household. Societies 2019,9, 34 5 of 18 4. Data and Variables 4.1. Data The empirical analysis of this study is based on the data from the German Socio − Economic Panel (GSOEP) over the period 1998 − 2014. The main reasons for choosing GSOEP are its longitudinal structure and the inclusion of private households’ data to study the different domain satisfactions, such as hedonic adaptation, social, cultural and psychological capital and different socio − economic characteristics and specific aspects. To avoid the duplication of observations, we consider the responses of the household head, that is, the responses of the household member with better knowledge of the conditions in the household. Also, as [ 34 ], to control for potential panel, we consider people with three or more interviews as a proxy for the interviewing experience in the panel. Moreover, we only consider people with consecutive observations. Note that for people who are not working, there is not information on job satisfaction. Hence, to compare the different domains, we only take the specific subsample of employed people. The final number of observations is 29,430. Specifically, there are 5063 individuals, of which 32% are women and 22% of them are living in the East of Germany. 4.2. Variables 4.2.1. Dependent Variables In the GSOEP the respondents can distinguish several aspects of life, which can be evaluated separately in terms of how satisfied people are with respect to each domain. In particular, we study financial, job and health satisfaction. Different questions in the GSOEP about the degree of satisfaction with each domain are approximately the same “How satisfied are you with your (financial, job, health,) situation?” measured on an 11 − point scale ranging from 0 (completely dissatisfied) to 10 (completely satisfied). Domains are denoted by Financial Satisfaction (FS),Job Satisfaction (JS) and Health Satisfaction (HS). Table 1reports Pearson’s correlation across the three domain satisfactions considered in this study plus general satisfaction. As in [ 3 , 8 , 25 ], all correlations are positive and statistically significant but they are not relatively high. Job and health satisfaction report a 0.443 coefficient (the highest), while health and financial show a 0.344 coefficient (the smallest). In line with previous studies, the correlation between general satisfaction and domain satisfactions is also positive, where the highest correlation is found for health satisfaction (0.508) and the smallest for job satisfaction (0.473). Table 1. Pearson’s correlation across domain satisfactions and general satisfaction. General Financial Job Health General 1.000 Financial 0.496 1.000 (0.000) Job 0.473 0.429 1.000 (0.000) (0.000) Health 0.508 0.344 0.443 1.000 (0.000) (0.000) (0.000) Note: These are the pairwise correlation coefficients between the domain satisfactions used in this study for the whole period and general satisfaction, with p−or the whole period and gen. Moreover, Table 2shows the main descriptive statistics of these dependent variables and of the explanatory variables whose definitions are presented in the following sections. We observe that working people report the highest average of satisfaction with their job situation and the lowest one with their financial situation (6.94 and 6.63, respectively). Societies 2019,9, 34 6 of 18 Table 2. Descriptive statistics of domain satisfactions and explanatory variables. Variables Mean SD Min Max Financial Satisfaction 6.632 1.891 0 10 Job Satisfaction 6.938 1.841 0 10 HealthSatisfaction 6.757 1.876 0 10 Income Characteristics Absolute income(a) 19.18 8529 1.135 130.1 Absolute income(a)(b) 23.74 15.16 0.200 51.48 Adaptation(a) 18.20 8.447 1.417 306.9 Adaptation(a)(b) 26.09 17.25 0.238 51.48 Poorer 0.156 0.219 0 2.77 Richer 0.122 0.200 0 1.81 Poorer(b) 0.271 0.399 0 5.129 Richer (b) 0.169 0.278 0 2.724 Social Capital Bonding 0.431 0.495 0 1 Bridging 0.378 0.157 0 1 Cultural Capital Eco_ goals 0.651 0.154 0 1 Fam_goals 0.819 0.202 0 1 Soc_goals 0.545 0.139 0 1 Worries 0.553 0.235 0 1 Mistrust 0.527 0.177 0 1 Risk 4.751 2.079 0 10 Psychological Capital Neuroticism 3.724 1.150 1 7 Extraversion 4.766 1.111 1 7 Openness 4.469 1.116 1 7 Agreeableness 5.308 0.951 1 7 Conscientiousness 5.936 0.844 1 7 LOC 3.567 0.889 1 7 Positive_Rep 5.883 0.864 1 7 Negative_Rep 3.144 1.392 1 7 Socio−Economic Characteristics Male 0.675 0.468 0 1 East 0.213 0.409 0 1 Age 45 9.340 21 74 Living _partner 0.658 0.474 0 1 Number_children 0.648 0.929 0 9 Number_adults 2.102 0.815 1 7 Years _education 12.82 2.771 7 18 Owner_dwelling 0.557 0.497 0 1 Specific Variables Financial Secondearner 0.892 0.310 0 1 Job Unemployment experience 0.401 1.030 0 23 Working hours 41 8.772 1.5 80 Equivalent_extra_money(a) 25.34 42.07 0.455 145.06 Prop. Household_inc/working_inc 1.059 1.241 0.061 60 Health Visits_doctor 8.300 13.30 0 396 Sport 3.242 1.345 1 5 Note: a These variables are measured in hundreds of euros. b These variables are built considering working income rather than household income. Adapted from the German Socio−Economic Panel. Societies 2019,9, 34 7 of 18 4.2.2. Income Characteristics Following [ 34 ], absolute income (y it in Equation (1)) is obtained using household income, except in job satisfaction. This provides a measure of the more regular income components received by all household members. In the particular case of job satisfaction, we obtain absolute income using the working income. 2 All income measures are real and converted into Euros for the year 2011 using consumer price index (CPI). Additionally, to control economics of scale, we calculate the equivalent income using the OECD−modified equivalence scale. We denote it as Absolute income. We control the adaptation process including one’s own past income (y i,t−k in Equation (1)). Although different periods have been considered in related studies, given that we do not have the same number of past observations for all individuals, we have decided to consider the lags three incomes in order not to lose a lot of observations.3This variable is denoted as Adaptation. Concerning external comparisons (f(y it ,y jt )in Equation (1)), following Ferrer − i − Carbonell (2005), first, we built the reference group by grouping together all people with a similar education level, in the same age bracket and of the same region. 4 Secondly, we distinguish between upward and down comparisons considering whether the individual’s absolute income is higher or lower than the average income of the reference group. Particularly, we define Poorer, when the individual absolute income is lower than the average reference income and Richer, when the individual absolute income is higher than the average reference income. They are specified as follows: Poorer =(yt−yit if yit <yt 0if yit ≥yt and Richer =(yit −ytif yit >yt 0if yit ≤yt (2) where y it is the individual absolute income and yt is the average income of the reference group to which he/she belongs. 4.2.3. Social, Cultural and Psychological Capital Concerning social capital (SC it in Equation (1)), in line with the literature related to subjective well − being, we distinguish two different dimensions: bonding and bridging social capital. The respondents are asked about the frequency with which they meet with relatives and friends and their participation in different type of events, where the answers to all these questions take values between 1 “every day” and 5 “never”. We consider the categorical variable Bonding, which takes the value of 1 if the respondent meets with relatives and friends at least once a month. Bridging social capital is a linear index built using individual’s answer relating to the attendance to different types of events. Following [ 35 ], we recode the variables used to obtain bridging social capital and then, a principal components analysis is used to get the variable Bridging which is standardized between 0 and 1. Regarding cultural capital (CC it in Equation (1)), in accordance with the related literature, we consider three life goals: economic (success at work, having a home and affording things), family (importance of having a partner or children), and social (helping others, being fulfilled, having good relationships with friends, travel or political activity). In this case, every question is of the type “Importance of” and answers take values into the scale from 1 “very important” to 4 “unimportant”. Again, recoding this scale and using a principal component analysis, we get the normalized variables Eco_goals, Fam_goals and Soc_ goals. Following [ 33 ], we also consider a group of variables which reflect whether people are concerned about different aspects, such as economic development, finances, peace and the environment. These 2Working income is the sum of gross wages, gross self−employment income and gross income from second job. 3This decision carries out that the final analyzed period is 1998−2014, in spite of we have data from 1995. 4 Particularly, following [ 35 ], for education, we have used three categories according to years of formal education: less than 10 years, between 10 and 12 years and 12 or more years. Similarly, the age brackets are: younger than 25, 25 − 34, 35 − 44, 45 − 65 and 66 or older. The regions distinguished are West and East Germany. Societies 2019,9, 34 8 of 18 variables take values between 1 “very concerned” and 3 “not concerned at all”. As in [ 35 ], we rearrange this scale and we use a principal component analysis to build the standardized variable Worries. We also take into account a variable about the mistrust of people, where the answers take values between 1 “totally agree” and 4 “totally disagree”. Using the same procedure than above, we obtain the variable Mistrust. Additionally, we include the variable Risk, which reflects the risk attitudes and takes values between 0 means the lowest risk willingness and 10 means the highest risk willingness. It is standardized to take mean zero and unit variance. We include personal traits as part of psychological capital (PC it in Equation (1)). Following [ 50 ], we include the BFI (Neuroticism, Extraversion, Openness, Agreeableness and Conscientiousness), the LOC index to capture the degree of control over their own life and the positive (Positive_Rep) and negative (Negative_Rep) reciprocity with others. The BFI have been obtained aggregating a total of 15 items included in the GSOEP. The external LOC is obtained after aggregating six items. Reciprocity measures, both negative and positive, are modelled by aggregation across three items each one. All these variables take values between 1 “does not apply”, and 7 “does apply”, that is, if people consider that they enclose that personal trait. Also, to facilitate the interpretation of the results, all these measures are standardized to take mean zero and variance 1. The information of all these variables was not collected every year in the GSOEP but following [ 35 ], we impute the values for the missing year with the immediately preceding year with information and, when this is the first year, we replace it with the first data available. 4.2.4. Socio−Economic Characteristics We use the socio − economic characteristics which are commonly considered in previous studies (X it in Equation (1)). We define the dummy variable Male, which is coded with 1 if the respondent is man. The variable East takes the value of 1 when the respondent lives in East of Germany. The age of the respondent is included with the variable Age. To test the non-linearity in the relationship between age and domain satisfactions, we also include age squared, which is denoted as Age2. The dummy variable Living_partner takes the value of 1 if the respondent is currently living with his/her partner. We include information related to the number of children and adults in the household, which are denoted as Number_children and Number_adults. The variable Years_education measures the number of years of formal education. We also incorporate the dummy variable Owner_dwelling which takes the value of 1 if the respondent currently owns a dwelling. 4.2.5. Specific Variables for Each Domain We consider the specific variables for each domain that have been previously used in related studies (Q it in Equation (1)). For financial satisfaction, we consider the dummy variable Second earner which takes the value of 1 if there is more than one earner in the household. For job satisfaction, we include Unemployment experience which measures the number of years of unemployment in the respondent’s career up to the point of the interview. We also include Working hours measured as the average number of hours worked weekly. The variable Equivalent_extra_money is the sum of extra working income, including Christmas bonus, holiday bonus, 13th and 14th month and profit − sharing. It is real and converted in Euros for the year 2011. Moreover, it is corrected whit the OECD − modified equivalence scale to control the economies of scale and we consider it in logarithmic form. We also consider the ratio of household income over working income (Prop_Household_inc/working_inc). For the analysis of health satisfaction we incorporate the variable Visits_doctor which is referred to the number of visits to the doctor during the previous year and a variable about the frequency of participating in sports, which takes values between 1 “daily”and 5 “never”. Recoding this scale we obtain the variable Sport, which is standardized to take mean zero and variance 1. Societies 2019,9, 34 9 of 18 5. Results In Table 3, we present the estimated results for domain satisfactions. For the sake of simplicity, we omit the estimated coefficients of time dummies and Mundlak’s correction from the table.5 Table 3. Estimation results for domain satisfactions of German citizens, 1998–2014. FS JS HS Common Variables IncomeCharacteristics a Absolute income 6.065 *** 0.491 0.526 (0.628) (0.634) (0.696) Adaptation 0.798 *** −0.447 *** 0.424 ** (0.155) (0.119) (0.172) Poorer −0.114 * −0.011 −0.015 (0.063) (0.061) (0.069) Richer 0.084 0.139 ** 0.022 (0.066) (0.062) (0.073) Social Capital Bonding −0.002 0.018 * 0.037 *** (0.009) (0.010) (0.010) Bridging 0.159 *** 0.071 * 0.171 *** (0.037) (0.040) (0.046) Cultural Capital Eco_ goals −0.018 0.340 *** 0.122 ** (0.036) (0.039) (0.040) Fam_ goals 0.025 0.075 ** 0.025 (0.029) (0.031) (0.032) Soc_ goals 0.029 −0.050 0.056 (0.041) (0.044) (0.045) Worries −0.458 *** −0.341 *** −0.212 *** (0.020) (0.022) (0.022) Mistrust −0.242 *** −0.303 *** −0.180 *** (0.033) (0.036) (0.037) Risk −0.003 0.014 ** 0.017 ** (0.005) (0.006) (0.006) Psychological Capital Neuroticism −0.031 *** −0.071 *** −0.093 *** (0.007) (0.007) (0.007) Extraversion −0.002 0.003 −0.008 (0.007) (0.007) (0.008) Openness 0.007 0.013 * 0.019 ** (0.007) (0.007) (0.008) Agreeableness 0.020 ** 0.036 *** 0.042 *** (0.007) (0.008) (0.008) Conscientiousness 0.025 *** 0.050 *** 0.038 *** (0.006) (0.007) (0.007) LOC −0.061 *** −0.051 *** −0.039 *** (0.007) (0.007) (0.008) Positive_Rep 0.035 *** 0.020 ** 0.006 (0.006) (0.007) (0.007) Negative_Rep −0.017 ** −0.023 ** 0.001 (0.007) (0.007) (0.008) 5These results are presented in Table A1 of the Appendix A. Societies 2019,9, 34 16 of 18 8. Gandelman, N.; Piani, G.; Ferre, Z. Neighborhood Determinants of Quality of Life. J. Happiness Stud. 2012 , 13, 547–563. [CrossRef] 9. Headey, B.; Wearing, A. Understanding Happiness: A Theory of Subjective Well-Being; Longman Cheshire: Melbourne, Australia, 1992. 10. Mallard, A.; Lance, C.; Michalos, A. Culture as a Moderator of Overall Life Satisfaction—Life Facet Satisfaction Relationships. In Connecting the Quality of Life Theory to Health, Well-Being and Education; Springer: Cham, Switzerland, 2017; pp. 59–79. 11. Van praag, B.M.S.; Ferrer-i-Carbonell, A. Happiness Quantified: A Satisfaction Calculus Approach, Reviseded; Oxford University Press: Oxford, UK, 2008. 12. Wills-Herrera, E.; Orozco, L.; Forero-Pineda, C.; Pardo, O.; Andonova, V. The relationship between perceptions of insecurity, social capital and subjective well-being: Empirical evidences from areas of rural conflict in Colombia. J. Socio-Econ. 2011,40, 88–96. [CrossRef] 13. Dolan, P.; Metcalfe, R. Measuring subjective well-being: Recommendations on measures for use by national governments. J. Soc. Policy 2012,41, 409–427. [CrossRef] 14. Frey, B.; Stutzer, A. Public Choice and Happiness; No. 2017-03; Center for Research in Economics, Management and the Arts (CREMA): Basel, Switzerland, 2017. 15. Kahneman, D.; Sugden, R. Experienced utility as a standard of policy evaluation. Environ. Resour. Econ. 2005,32, 161–181. [CrossRef] 16. Navarro, M.; S á nchez, A. Ingreso y bienestar subjetivo: El efecto de las comparaciones sociales. Rev. Econ. Mund. 2018,48, 139–156. 17. Odermatt, R.; Stutzer, A. Subjective Well-Being and Public Policy. IZA Work Pap. 2017, p. 11102. Available online: https://ssrn.com/abstract=306944 (accessed on 1 March 2019). 18. Stiglitz, J.; Sen, A.; Fitoussi, J. Mismeasuring Our Lives: Why GDP Doesn’t Add Up; The New Press: New York, NY, USA, 2011. 19. Stutzer, A.; Frey, S. Recent Advances in the Economics of individual subjective Well-Being. Soc. Res. 2010 ,77, 679–714. 20. Vera-Toscano, E.; Ateca-Amestoy, V. The relevance of social interactions on housing satisfaction. Soc. Indic. 2008,86, 257–274. [CrossRef] 21. Lepp, A. Correlating leisure and happiness: The relationship between the leisure experience battery and the Satisfaction with Life Scale. Ann. Leis. Res. 2018,21, 246–252. [CrossRef] 22. Pinquart, M.; Schindler, I. Change of leisure satisfaction in the transition to retirement: A latent-Class analysis. Leis. Sci. 2009,31, 311–329. [CrossRef] 23. Plagnol, A. Financial satisfaction over the life course: The influence of assets and liabilities. J. Econ. Psychol. 2011,32, 45–64. [CrossRef] 24. Vera-Toscano, E.; Ateca-Amestoy, V.; Serrano-Del-Rosal, R. Building financial satisfaction. Soc. Indic. Res. 2006,77, 211–243. [CrossRef] 25. Wolbring, T. Home sweet home! Does moving have (lasting) effects on housing satisfaction? J. Happiness Stud. 2017,18, 1359–1375. [CrossRef] 26. Joo, S.; Grable, J. An exploratory framework of the determinants of financial satisfaction. J. Fam. Econ. Issues 2004,25, 25–50. [CrossRef] 27. Clark, A. What really matters in a job? Hedonic measurement using quit data. Labour Econ. 2001 ,8, 223–242. [CrossRef] 28. Freeman, R. Job satisfaction as an economic variable. NBER Work. Pap. 225 1977 ,68, 135–141. Available online: https://www.nber.org/papers/w225 (accessed on 1 March 2019). 29. Mangione, T.; Quinn, R. Jobs atisfaction, counterproductive behaviour and drug use at work. J. Appl. Psychol. 1975,60, 114. [CrossRef] 30. Ostroff, C. The relationship between satisfaction, attitudes, and performance: An organizational level analysis. J. Appl. Psychol. 1992,77, 963. [CrossRef] 31. Seara, T.; Pollnac, R.; Poggie, J. Changes in job satisfaction through time in two major New England fishing ports. J. Happiness Stud. 2017,18, 1625–1640. [CrossRef] 32. Cutler, D.; Richardson, E.; Keeler, T.; Staiger, D. Measuring the Health of the US Population. Brook. Pap. Econ. Act. Microecon. 1997,1997, 217–282. [CrossRef] 33. B á rcena-Mart í n, E.; Cort é s-Aguilar, A.; Moro-Egido, A. Social Comparisons on Subjective Well-Being: The Role of Social and Cultural Capital. J. Happiness Stud. 2017,18, 1121–1145. [CrossRef] Societies 2019,9, 34 17 of 18 34. D’Ambrosio, C.; Frick, J.R. Individual well-being in a dynamic perspective. Economica 2012 ,79, 284–302. [CrossRef] 35. Moro-Egido, A.; Navarro, M.; S á nchez, A. Changes in Subjective Well-Being over Time in Germany. Dpto. Toer í a Hist. Work. Pap. 17/05 2017 . Available online: https://ideas.repec.org/p/gra/wpaper/17-05.html (accessed on 1 March 2019). 36. Frey, B.; Stutzer, A. What Can Economists Learn from Happiness Research? J. Econ. Lit. 2002 ,40, 402–435. [CrossRef] 37. Bartolini, S.; Bilancini, E.; Sarracino, F. Predicting the trend of well-being in Germany: How much do comparisons, adaptation and socia bility matter? Soc. Indic. Res. 2013,114, 169–191. [CrossRef] 38. Clark, A.; Frijters, P.; Shields, M. Relative income, happiness and utility: An explanation for the Easterlin paradox and other puzzles. J. Econ. Lit. 2008,46, 95–144. [CrossRef] 39. Conceicao, P.; Bandura, R. Measuring Subjective Wellbeing: A Summary Review of the Literature; Office of Development Studies, United Nations Development Programme (UNDP) Research Paper: New York, NY, USA, 2008. 40. Di Tella, R.; Haisken-De New, J.; MacCulloch, R. Happiness adaptation to income and to status in an individual panel. J. Econ. Behav. Organ. 2010,76, 834–852. [CrossRef] 41. Ferrer-i-Carbonell, A. Income and well-being: An empirical analysis of the comparison income effect. J. Public Econ. 2005,89, 997–1019. [CrossRef] 42. Muffels, R.; Headey, B. Capabilities and choices: Do they make Sen’s for understanding objective and Subjective Well-Being? An Empirical Test of Sen’s Capability Framework on German and British Panel Data. Soc. Indic. 2013,110, 1159–1185. [CrossRef] 43. Sen, A. Human rights and capabilities. J. Hum. Dev. 2005,6, 151–166. [CrossRef] 44. Helliwell, J.F. Well-being, social capital, public policy: What’s new? Econ. J. 2006,116, 34–45. [CrossRef] 45. Helliwell, J.; Putnam, R. The social context of well-being. Philos. Trans. R. Soc. 2004 ,359, 1435–1446. [CrossRef] 46. Sabatini, F. Social capital as social networks: A new framework for measurement and an empirical analysis of its determinants and consequences. Socio-Econ. 2009,38, 429–442. [CrossRef] 47. Sarracino, F. Social capital and subjective well-being trends: Comparing 11 western European countries. J. Socio-Econ. 2010,39, 482–517. [CrossRef] 48. OECD. The Well-Being of Nations: The Role of Human and Social Capital; OECD: Paris, France, 2001. 49. Putnan, R. Bowling Alone: The Collapse and Revival of American Community; Simon and Schuster: New York, NY, USA, 2000. 50. Budria, S.; Ferrer-i-Carbonell, A. Income Comparisons and Non-Cognitive Skills. SOEP Pap. 2012 ,441. [CrossRef] 51. D’Ambrosio, C.; Frick, J.R. Income satisfaction and relative deprivation: An empirical link. Soc. Indic. Res. 2007,81, 497–519. [CrossRef] 52. Ferrer-i-Carbonell, A.; Frijters, P. How important is methodology for the estimates of the determinants of happiness? Econ. J. 2004,114, 641–659. [CrossRef] 53. Gash, V.; Mertens, A.; Romeu-Gordo, L. Women between part-Time and full-Time work: The influence of changing hours of work on happiness and life-Satisfaction. SOEP Pap. 2010 ,268. Available online: https://ssrn.com/abstract=1553702 (accessed on 1 March 2019). 54. Jones, A.M.; O’Donnell, O. (Eds.) Econometric Analysis of Health Data; Wiley: Chichester, UK, 2002. 55. Wicker, P.; Coates, D.; Breuer, C. The effect of a four-Week fitness program on satisfaction with health and life. Int. J. Public Health 2015,60, 41–47. [CrossRef] 56. Alessie, R.; Crossley, T.; Hildebrand, V. Estimating a collective household model with survey data on financial satisfaction. In Discussion Paper Series/Tjalling C; Koopmans Research Institute: Utrecht, The Netherlands, 2006; Volume 6. 57. Bartolini, S.; Sarracino, F. Happy for how long? How social capital and economic growth relate to happiness over time? Ecol. Econ. 2014,108, 242–256. [CrossRef] 58. Veenhoven, R. The Four Qualities of Life Ordering Concepts and Measures of the Good Life. J. Happiness Stud. 2000,1, 1–39. [CrossRef] © 2019 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).