Are Happy Youth Also Satisfied Adults? an Analysis of the Impact of Childhood Factors
Total Page：16
File Type：pdf, Size：1020Kb
Are happy youth also satisfied adults? An analysis of the impact of childhood factors on adult life satisfaction
1. Introduction
Is adult life satisfaction linked to childhood happiness? If so, is this because environmental factors are, on average, relatively constant over a lifetime or because personality factors do not change significantly after a certain age? There is some evidence that life satisfaction is influenced by how well individuals are doing relative to their peers (Frey and Stutzer, 2002) and relative to their own past (Easterlin, 2001). On the other hand, there is also evidence in the literature that personality has a significant impact on the life satisfaction of individuals and, more controversially, that it results in a baseline level of happiness to which individuals will revert (Fujita and Diener, 2005; Veenhoven, 1994). Either (or both) of these factors could result in adult life satisfaction being influenced by childhood happiness levels.
In this paper, we consider this issue using data from the British Household Panel Survey (BHPS) for 1994-2008. We begin by estimating a model of happiness for a sample of young people and use this to extract variables that reflect youthful personality as well as happiness predicted by childhood socio-economic conditions alone. These variables are then included in an adult model to analyse their impact on adult life satisfaction.
This paper makes a number of contributions to the literature. First, while there is a large literature that considers the impact of childhood conditions on adult health, education and labour market outcomes more generally (Currie et al, 2010; Goodman et al, 2011; Ostberg and Modin, 2008; 2012), there are fewer papers that consider the impact on adult life satisfaction (Frijters et al, 2011 and Modin et al, 2011 being exceptions). Second, we provide a potential measure of personality which has been seen as an omitted variable in recent studies of happiness in the economics literature and is commonly captured simply as individual heterogeneity (Boyce, 2010 being an exception). Since our measure of personality is extracted from a youth sample, it avoids reverse causality that might arise in using contemporaneous personality traits (Diener et al, 1999; John and Srivastava, 1999)). Thus, the paper offers a methodological contribution to the life satisfaction literature. Third, it compares the size of the impact of personality relative to the impact that socio-economic
1 conditions may have to see which is larger and more significant. Finally, we use a panel-data methodology (a Hausman-Taylor instrumental variable approach) that allows us to include both exogenous and endogenous variables (which are correlated with this unobserved effect but not the time variant error term) in our model. This is a significant innovation given the endogeneities that are common in all models of life satisfaction.
The rest of the paper is set out as follows. In Section 2, we review the general literature on the role of childhood factors and personality in particular on happiness/life satisfaction amongst adults. In Section 3, we discuss the data and in Section 4, the methodology, particularly the problems of unobserved heterogeneity and the two-stage method we propose to follow. Finally, in Section 5, we discuss our results first for the youth models (happiness) and then for the adult life satisfaction models. Section 6 concludes.
2. Literature Review
The economic literature on well-being and happiness has largely concentrated on the socio- economic correlates of happiness like age (Blanchflower and Oswald, 2008), income (see Clark, Frijters et al, 2008 for a review), marriage (Frey and Stutzer, 2006) and unemployment (Clark, 2003, 2006). In recent years, a number of these studies (e.g. Clark et al., 2008; Senik, 2009; Mentesoglu and Vendrik, 2009)) have also controlled for the influence of unobserved traits using individual fixed effects (Ferrer-i-Carbonell and Frijters, 2004) and have found this to influence the results quite significantly. While these individual effects are often interpreted as personality (e.g. Winkelmann and Winkelmann, 1998; Clark 2003; Ferrer-i- Carbonell and Frijters, 2004; Frey and Stutzer, 2006), most studies have included them only to control for their effects on the other determinants of happiness rather than because these fixed effects are of intrinsic interest (the exception being Boyce, 2010 as we will see below).
The impact of personality on subjective well-being has been considered by many studies which analyse adaptation to life events (like marriage, divorce, lottery wins or unemployment). Many of these studies (Heady and Wearing, 1989; Lucas, Clark et al, 2003; Clark, Diener et al, 2008) provide evidence of a baseline level of well-being to which individuals seem to revert over time. This has variously been termed the hedonic treadmill, set point levels of well-being etc. Many researchers have concluded that this long term stability is accounted for by personality and genetic pre-disposition. In a longitudinal analysis 2 of personality and life events, Magnus and Diener (1991) find that measures of personality had an influence on life satisfaction four years later, even after controlling for the influence of intervening life events. Graef (1978) argued that while all emotions are trait-determined, this is especially true of happiness: a person’s average level of happiness explains more of the variance in his or her responses over the week than is explained by what the person is doing, where or with whom he/she spends time.
There is, however, some disagreement about whether personality changes over time or is fixed. A study by the University of Manchester (Boyce, 2013) shows that participants’ character traits changed during 4 years at least as much as demographic factors and these small personality shifts influenced life satisfaction very significantly. Heady and Wearing (1989), one of the first longitudinal analyses of adaptation, followed individuals for 8 years and found that life satisfaction was set by a baseline level. While events can move people above and below the baseline, they will revert to it. Kozman, Stone and Stone (1997) analysed the stability of subjective well-being by studying the influence of stability in the environment, in personality and in affective styles. They found that all 3 factors contributed to the stability of subjective well-being (SWB) to some degree but the stable environmental component was the smallest. The notion of a baseline level of SWB has been confirmed by researchers in the context of major life events like divorce, widowhood and having children (Angeles, 2010; Clark et al., 2008: Clark and Georgellis, 2010). Only one life event – unemployment – has been shown to have a lasting effect on life satisfaction (Lucas et al (2003) and Diener et al. (2006)). Clark and Georgellis (2010) further find that such adaptation itself is influenced by personality.
Boyce (2010) goes beyond such analyses by attempting to decompose the individual heterogeneity or fixed effect into an observable and unobservable component using a fixed effect vector decomposition methodology. This involved estimating a life satisfaction fixed effects residual and then using this as a dependent variable in a second estimation in which a number of personality measures and other average characteristics are included as predictors. An error term from this second model was included as an additional variable in a third stage life satisfaction estimation. This methodology allowed Boyce to show that contemporaneous personality measures can explain 20% of unobserved heterogeneity in life satisfaction. The personality measures he used, however, were contemporaneous fixed effects. Our
3 methodology, on the other hand, uses youth fixed effects as an instrument for adult personality and avoids any problems of reverse causality.
These studies raise questions relating to the set point of happiness or the baseline level: where does it originate? Is it determined during childhood? There is a relatively large literature that looks at the impact of childhood conditions on a range of objective adult outcomes including those in the labour market (Smith, 2009) and health (Frijters et al, 2010; Mensah and Hobcraft, 2008; Carneiro et al, 2007; Case et al, 2008 and Currie et al, 2010; Almquist, 2011; Almquist and Ostberg, 2012). There are fewer papers that, to our knowledge, consider the impact of childhood conditions on subjective wellbeing amongst adults. One of these is a paper by Frijters et al (2011) which uses data from the National Child Development Survey (NCDS) in Britain. The authors find that a range of childhood factors are important predictors of adult life satisfaction. In particular, childhood behavioural-emotional problems and social maladjustment are significant predictors of whether a child grows up to be a satisfied adult. They also find some aspects of personality (timidness, introversion etc.) are significant while conventional socio-economic variables are not very good predictors. Another study by Modin et al. (2013) uses data from the Stockholm Birth Cohort Study and finds that amongst women low peer status in childhood is associated with an increased risk of anxiety and depression in later life (Modin et al, 2013). They also find that marginalisation increased the odds of being hospitalised in later life (Almquist, 2011).
3. Data The British Household Panel Survey (BHPS) provides longitudinal data for around 5,500 households and over 10,000 individuals representative of the UK. It began in 1991 and has collected annual data on social and economic variables at both the individual and household level until 2008. Since 1994 (wave d), children aged 11-15 years living in the sampled households were administered a youth questionnaire and at 16 years became eligible to join the adult part of the panel. For more information on the BHPS and the sampling procedures see Volume A of the BHPS user guides1
We utilise data from the individual youth and adult panels, matching the youth data to their later adult data. In addition, we combine the youth data with household data and data for
1 Available from https://www.iser.essex.ac.uk/bhps/documentation/pdf_versions/volumes/bhpsvola.pdf 4 parental characteristics. This allows us to match youth happiness traits and predictions extracted from estimation on the youth sample, with subsequent adult life satisfaction scores to see if they are correlated in any way. To our knowledge, this has not been done to date.
To estimate the determinants of youth happiness in Stage 1, we use a sample of 5,124 young panel members (between 11-15 years) who had at least one youth questionnaire during the period 1994-2008. 49% of these sample members had 4 observations or more. The number of youth observations for each individual will partly depend on how old they were in 1994 and 2008 and when they entered the panel. We used the relationship between household members to identify the family structure, which the literature indicates is crucial to youth well-being (e.g. Robson, 2008). In particular, we identify whether they lived with both parents, step parents, in a single parent family or with no parents2. 60% of our sample lived with biological parents, 18% as part of a step family and 22% as part of a single-parent family. Robson (2008) found using BHPS data that changes in family structure have a negative impact on youth happiness and self-esteem. We also extract information regarding siblings including how many lived in the household and their ages. Our final sample then has 7,865 female youth observations (from 2,422 individuals) and 8,022 male youth observations (from 2,519 individuals) with full information on their household relatives and parents, as well as a full youth interview.
In the second stage of our analysis, we follow our sample of youth participants into the adult part of the panel using information relating to their well-being from 18 years onwards (allowing a gap of 2 years between the youth sample and the adult sample). This gives us a sample of 942 female and 883 male youth participants who also entered the adult panel and reached the age of 18 years and who had full information on their adult variables. The number of observations an individual has will depend on their age – with the oldest individual in the sample being 29 years. We acknowledge the limitation of our sample only reaching their late 20s, since personality traits are less stable for those under the age of 25 (Cobb-Clark and Schurer, 2011). However, our methodology does not require personality to be stable, only for adult personality to be correlated with youth personality. In addition, the
2 We exclude those who live with same sex parents or did not live with at least one parent e.g. lived with grandparents, other relatives or non-relatives, since some of our variables relate to the relationship with their parent(s), and it was not always obvious who their main guardian was. We also removed the few (189 cases) who had “other” parents (e.g. adopted, foster) as it was difficult to identify where to place these children (with biological parents or step parents). 5 BHPS has the advantage of annual information on individuals which cohort studies do not and our adult sample has an average of 4 years of adult data and a maximum of 11 years, providing us with considerable variation across individuals and over time.
4. Methodology
In what follows, we test two basic hypotheses. First, that youth (11-15 year olds’) happiness influences early adult life satisfaction. Second, this relationship arises not only because there is some constancy in socio-economic conditions but also because there is some stability in personality factors. In order to analyse the link in life satisfaction between youth and adulthood, we use a two-stage modelling technique. In the first stage, we model the determinants of youth happiness. We use this estimation to extract, (i) an unobserved individual effect (which we call the happiness trait) that captures individual heterogeneity, and (ii) to extract the youth happiness levels predicted from the model (with and without individual effects). The happiness levels predicted without the fixed effects capture the effect of demographic and socio-economic conditions alone on happiness while those predicted with fixed effects capture the effect of both personality and demographic and socio-economic conditions. In the second stage, we include each of these variables in various models for the adults in our sample to analyse their relative impact on adult life satisfaction.
We make use of panel data techniques in both stages although neither random nor fixed effects estimation is appropriate for our purpose. The random effects approach assumes that the explanatory variables in a model are uncorrelated with the unobserved individual effect. This assumption is unlikely to be appropriate in our case because a number of these explanatory variables (household income, adult employment etc.) are likely to be correlated with personality especially in the adult models. On the other hand, the fixed effects approach would not be suitable either, since some of the variables we are interested in (like race, family background, youth happiness measures) are time invariant. We therefore apply a Hausman- Taylor (HT) estimator (Hausman and Taylor, 1981), a Generalised Least Squares (GLS) instrumental variable estimator which allows us to include time invariant variables (unlike the fixed effects approach) and also allows some of the explanatory variables to be correlated with the unobserved individual effect (unlike the random effects approach).
The HT model is set up as follows: 6 Yit X1it 1 X 2it 2 Z1i, 3 Z 2i 4 i it (2)
Where X1it is a vector of exogenous time varying variables i.e. variables not correlated with the unobserved individual effects (μi) for person i in time period t, whist X2it is a vector of time varying variables that are correlated with the individual effects, (μi) but not with the idiosyncratic error of εit. Z1i are time-invariant variables that are uncorrelated with the individual effect and Z2i are those time-invariant variables that are correlated with the
3 individual effect but not εit . μi are the fixed effects commonly included to capture individual heterogeneity.
The exogenous variables serve as their own instruments and the endogenous time varying variables are instrumented by their deviations from the group means (X1it – X 1i and X2it – X 2i ).The endogenous time invariant variables are instrumented by the average of the exogenous time varying variables ( X 1i ) and time invariant variables (Z1). The HT model is identified provided that the number of exogenous time-varying variables is equal to the endogenous time invariant variables. However in our estimation, there are no endogenous time invariant variables, resulting in a HT estimator that is less efficient but still consistent (as shown by Hausman tests). Since the HT estimator allows the inclusion of exogenous time invariant variables and corrects for endogeneity of some variables, we opt for this approach in both stages.
We estimate our models separately by gender, since previous research has shown that there are systematic differences in the determinants of life satisfaction by gender (Della Giusta et al, 2011) and a Chow test confirms this for our sample too.
Dependent Variables Before we move on, it is worth discussing our well-being variables in more detail. The empirical literature on happiness and life satisfaction sees them as synonymous and with the correlation between the two quite high, there are good reasons for this. However, while happiness is subjective and largely affective, life satisfaction is both objective and evaluative. It gives weight to objective conditions of well-being and also includes an evaluation of one’s
3 Note that an exogenous variable in the Hausman-Taylor context is defined as a variable that is not correlated with the unobserved individual effect (μi) while an endogenous variable is one that is. Neither is correlated with the idiosyncratic error term (εit). 7 life over the longer term. Individuals can have moments or even periods of happiness under quite adverse circumstances. While they may claim happiness in these circumstances, they are less likely to express satisfaction with their lives. In this paper, we are concerned with longer term life satisfaction rather than more variable measures of happiness. However, while the BHPS asks adults about their life satisfaction, young respondents are only questioned about their happiness levels, possibly because it is not expected that young people will be reflective or evaluative about their well-being. We model happiness amongst the 11-15 year old sample but life satisfaction for the same individuals as adults.
The BHPS asks young people about their happiness, measured on a scale of 1 (completely happy) to 7 (completely unhappy). We reverse code this scale so that higher happiness levels relate to higher values, making the scale comparable to the adult life satisfaction variable. It also makes interpretation of our results more intuitive. Adult life satisfaction in the BHPS is measured asking ‘How satisfied or dissatisfied are you with your life overall?’ Answers are provided along a seven point scale with 1 being not satisfied at all and 7 being completely satisfied.
A final methodological point relates to whether we model happiness and life satisfaction as ordinal or cardinal measures. As can be seen from our data, these variables are ordered rather than continuous variables requiring that they be modelled using limited dependent variable methods. However, it is very difficult to include fixed effects in limited dependant variable models. There is some consensus in the literature (Ferrer-i-Carbonell and Frijters, 2004; Clark et al., 2008; Senik, 2009; Mentesoglu and Vendrik, 2009) that whether a life satisfaction dependent variable is treated as continuous or an ordered variable makes little difference to the conclusions of the results but controlling for individual effects is important. In our estimations, therefore we treat happiness (in Stage 1) and life satisfaction (in Stage 2) as continuous variables.
Stage 1 – Youth Happiness Model
A review of the literature indicates that there are broadly 3 factors that might influence happiness amongst young people - satisfaction with family, satisfaction with friends and self (which becomes more important as children mature) and the types of activities in which young people participate (especially those involving work for groups or organisations and 8 helping others)4 . In this context, Ferguson (2006) has highlighted authoritative parenting, parental support and family social capital as important in young people’s happiness. He highlights the links between children and parents and identifies family structure, quality of parent-child relations, adult’s interest in the child, parent’s monitoring of the child and extended family members as part of family social capital. Our data allows us to test for family structure and also closeness of parent-child relations.
Appendix 1 provides an overview of the variables used in our analysis and the section of the BHPS they were obtained from, while Appendix 2 provides descriptive statistics of these variables. In our youth analysis, we include the number of older siblings5, the number of younger siblings and an indicator of whether the respondent is the only child in the house. In addition, we also include some variables relating to structure of parental unit - whether they live with natural parents, step family or single parent family. Finally, we include variables relating to whether both parents worked and whether the mother worked full or part time. We interact parent’s employment status with family structure, since the effect of employment may be mediated by whether the young person lives with a single parent or two parents.
To analyse parent-child relations, we used information on 3 variables. First, how often young people quarrelled with their mother/father and second, how often they talked with their parents about things that matter. Third, we included dummy variables relating to whether they had a meal together with their family (“in the past 7 days how many times have you eaten an evening meal together with your family?”)6. These variables capture closeness of child with parent and we would expect that the more often they have meals, talk with parents and the less they argue, the happier they are likely to be.
Finally, the literature is clear that, for young people in addition to their relationship with their parents, emotional highs are associated with having friends. To capture this, we include variables relating to how many friends they had and dummy variables of the intensity of their interaction with friends. Thus, respondents were asked: “Thinking back over the last 7 days,
4 see Land et al (2001) for an index of Child and Youth Well-being based on a combination of these domains of life. 5 Including twins (only 120 cases of being a twin) and step siblings of the same age 6 In some cases, it was evident that the answers did not necessarily relate to parents living within the household. Thus, young people answered the question even when they had earlier indicated that parents were not living in their household. The answer might therefore relate to their parents living in other households (after divorce or separation for example). We took the answers as given but made the interpretation conditional on the child stating having that parent. 9 how many times have you had friends round to your house?”. Finally, we also include the log of household annual income7.
In stage 1, we therefore estimate the following model for the youth sample using the
y Hausman-Taylor approach described earlier. The superscript y refers to the youth sample, Yit refers to the youth happiness measure described above for individual i in period t.
y y y y y y y y y Yit X 1it 1 X 2it 2 Z1i 3 i it (2)
y The exogenous, time variant variables ( X1it ) are log of household income, family structure, parent’s employment status, age and wave dummies; the exogenous and time invariant
y variable is ethnicity (included in Z1i ). All other variables including relationship with family
y and friends are seen as endogenous and time varying ( X 2it ). They are correlated with the
y unobserved individual effect ( i ) which is also our measure of youthful personality.
Stage 2 – Adult Life Satisfaction
In stage 2, we analyse life satisfaction amongst adults, using in particular our sample of youth participants who entered the adult sample. As mentioned earlier, we include a variety of measures to capture the links between childhood happiness and adult life satisfaction. The
y 8 first of these is ˆi (extracted from equation 2) which can be interpreted as a set of
y systematic traits that influence happiness. ˆi is assumed to be fixed for each i and to have zero mean and a finite variance and to be independently and identically distributed across individuals i. Including this in the adult model will allow us to test whether these traits (which can be thought of as personality traits) captured when young would also influence life satisfaction when older. There is also some disagreement about whether personality traits of this kind change over time or are fixed. Lieberman (1970:74) argues that ‘at some point in life, even before the age of 18, an individual becomes geared to a certain stable level of satisfaction, which – within a broad range of environmental circumstances – he maintains
7 Household income was adjusted for household size (weighting adults by 1 and children by 0.5). 8 This fixed effect is obtained in Stata through predict u, u after the regression specified in equation 2 – which is fixed for each individual. 10 throughout life.’ Costa and McCrae (1994, 2006) and Cobb-Clark and Schurer (2011) also argue that the five factor model traits are fairly stable in adulthood though Cobb-Clark and Schurer (2011) indicate that they may be less stable for the under 25’s (see also Borghans, et al., 2008). On the other hand, a recent study by Boyce (2013) shows that participants’ character traits changed during 4 years as much as demographic factors did and these personality changes, in turn, influenced life satisfaction. In our analysis, we make no prior assumption regarding the changeability or otherwise of personality traits. Instead, it is sufficient for our methodology for youthful personality to be correlated with adult personality without them having to be the same. In our estimations, therefore, the effect of youthful
y personality on adult life satisfaction will be captured by the coefficient of ˆi whereas the
y impact of changes in personality during adulthood will be captured in ˆi .
Second, we extract the average (across years in the sample) of the fitted values of happiness (
ˆ y1 Yi ) from the youth model estimated without the individual fixed effect (the average across the fitted values as estimated by equation 3):
ˆ y1 y ˆ y y ˆ y y ˆ y Yit X 1it 1 X 2it 2 Z1i 3 (3)
This variable captures youth happiness as predicted only by socio-economic and demographic characteristics and excludes all individual heterogeneity. To the extent that it is significant in the adult model, it captures the impact of youthful environmental factors on adult well-being. If significant, it would imply that the socio-economic conditions (income levels, family structure etc.) of childhood affect life satisfaction into adulthood.
Third, we extract the average of the fitted values from our estimation of equation 2 including
ˆ y2 the individual fixed effect (Yi ). This is given by equation 4 as:
ˆ y2 y ˆ y y ˆ y y ˆ y y Yit X 1it 1 X 2it 2 Z1i 3 ˆ i (4)
11 ˆ y1 While the prediction from equation 3 (Yi ) gives us youth happiness as determined by the
ˆ y2 socio-economic variables in our model, the prediction from equation 4 (Yi ) gives us youth happiness as determined both by socio-economic and personality factors. The significance of the variable in an adult life satisfaction model would indicate the extent to which adult life satisfaction was determined by youthful personality and socio-economic conditions i.e. the longer term impact of personality and socio-economic conditions. Fourth, we take an average
y 9 of the residual for each individual from equation 2 (ˆi ) to see if there are some excluded variables that might influence happiness both during childhood and adulthood. Finally, for completeness we consider whether the average across the raw measure of youth happiness (
y ˆ y1 ˆ y2 ˆ y Yi ) influences adult life satisfaction. Thus, we extract four different variables (Yi ,Yi , i ,
y ˆi ) from the youth happiness model which we include in the adult model to test for various versions of inter-temporal dependence.
One advantage of estimating a youth happiness model is that a number of variables that are endogenous in adult models are exogenous in the youth estimations. Thus, family income, neighbourhood and family structure are all exogenous to the child making the estimation
ˆ y1 more reliable and therefore making the four variables extracted from this estimation ( Yi ,
ˆ y2 ˆ y ˆ y Yi , i , i ) more reliable.
In addition, as indicated above, given that it is no longer contemporaneous personality, this method avoids the problem of reverse causality between personality and life satisfaction that has plagued many studies in this area. Youthful personality, in this way, provides a good instrument for adult personality.
a Our adult life satisfaction model is defined as follows. Yit refers to adult life satisfaction with the superscript ‘a’ denoting adult.
a a a a a a a y a a a Yit X 1it 1 X 2it 2 Z1i 3 X 3i 4 i it (5)
9 The predicted values without the individual effect is obtained in Stata through predict, xb; the predicted value with the individual fixed effect through predict xbu and the residual through predict, e 12 We include the following control variables (see Appendix 1 for more variable details): age, age squared and ethnicity, education (higher or no qualifications), indicators of whether they live with a spouse or partner, whether they have any of their own children living with them, employment status (employed, in full time education versus not being in the labour market or in education) and log of household income. In our estimation, the variables treated as endogenous are whether the individual lives with a spouse/partner, whether they have a child, Log of Household income, whether employed or in full time education and qualifications
a (included in X 2i ). The following control variables are considered exogenous: ethnicity
a a y (included in Z1i ), age, and wave dummies (included in X1it ). X 3i includes the effect of the
ˆ y1 ˆ y2 ˆ y ˆ y y various childhood variables discussed above (Yi ,Yi , i , i ,Yi ) which will help capture dependence between youth and adult life satisfaction. Each of these variables is separately
a included in various versions of the model. μi is the residual adult fixed effect after allowing
a for the childhood path dependence and εit is the time variant random error term.
Since our main aim is to analyse the extent, and source, of dependence between youth and adulthood in a person’s life satisfaction, we estimate seven versions of the adult life satisfaction model (equation 5). In the baseline model (Table 4), we exclude all measures of dependence across the life cycle. In Table 5, we separately include a range of the childhood variables discussed above to the baseline model. In Model 1, our childhood-related variable (
y y X 3i ) is the raw average youth happiness (Yi ) as calculated from the raw data. In Model (2),
ˆ y ˆ y1 we include the estimated happiness personality trait ( i ); in Model 3, we include (Yi ) which is the average youth happiness predicted by socio-economic conditions alone. In Model 4, we include youth happiness predicted both by socio-economic conditions and the
ˆ y2 ˆ y personality trait for happiness (Yi ) and Model 5 includes the average residual ( i ) from the youth model to see if there are any variables excluded in the youth model that continue to influence adult life satisfaction. Model 6 includes both the happiness trait from the youth
y ˆ y1 model ( ˆi ) and also the predicted happiness (without the individual fixed effect) Yi i.e. the effect attributed only to socio-economic and demographic factors. This model allows us to estimate the relative effect of each of these factors, while Model 7 is the same as Model 6 but
13 y also includes the average residual, ˆi . In order to make interpretation and comparison easier we standardise all the measures extracted from the youth models.
5. Results:
As discussed in the previous section, we estimate our model in two stages – the youth happiness model and the early adulthood life satisfaction models for the corresponding individuals in Stage 2. In this section, we will discuss the results of these two sets of estimations.
5.1. Stage 1: Determinants of Youth Happiness Table 1 presents our results for youth happiness separately for boys and girls. A Chow test confirms this separation of sub-samples (chi2 = 131.39, p<0.00). A Hausman test indicated that the fixed effects approach is better than the random effects approach but a second Hausman test confirmed that the Hausman Taylor estimate is preferred to either (see the last few rows of Table 1 for the Hausman tests). Given this and the centrality of the time- invariant fixed effect to our estimation, we estimate the model using the Hausman-Taylor method. We note this is also the case for the adult regressions.
Our results (see Table 1) indicate that age has different effects by gender. As girls grow older (after 12 years), they are less happy, whilst for boys this dip in happiness occurs only when they reach 15/16 years. Ethnicity generally has no significant effect on happiness in this sample.
Both boys and girls are less happy if they are living in a step family compared to living with both natural parents. Boys are also less happy if they are living with a single parent though this effect is insignificant for girls10. As the number of older siblings increases, happiness decreases for both boys and girls though the effect is larger and more significant for girls. Neither being an only child nor having younger siblings has an impact on happiness for boys or for girls. Household income11 has no significant impact on happiness for young people of 10 In estimations not shown in this paper, we find that when the parent employment variables are excluded, being part of a single parent family reduces happiness for both sexes but the effect is smaller than that for being part of a step family. The fact that the parent employment variables affect the single parent variable could indicate that time spent with the child could mitigate any effects of only having one parent. 11 Other measures of income - monthly income and income quintiles – were also tested but all turned out to be insignificant. 14 either gender, with prior research suggesting that the relationship between income and happiness is weaker for children than adults (Burton and Phillips, 2010). This is in contrast to Mayer (1997), who highlights the importance of income to children’s well-being.
For both genders, happiness is significantly related to family structures and relationship with parents. For girls, arguing with either parent reduces happiness though this effect is much larger if they argue with their mother. For boys too, arguing with their mother reduces happiness though arguing with their father does not. Talking to a parent of the same sex increases happiness for both genders though talking with a parent of the opposite sex is not significant. Having regular family meals increases happiness for both boys and girls though the effect is larger and shows up earlier for boys. Thus, for boys, happiness increases with every meal they have with their family and the effect is larger, the more evening meals they have at home. For girls, this effect is smaller and only significant if they have at least 3 meals at home.
Our results confirm that relationships with friends are important for both boys and girls, though the pattern is different by gender. For girls, having friends visit between 1-5 times a week increases happiness and the effect reaches a peak with 3-5 visits a week. For boys, the effect is positive and increases monotonically from 1-2 times to more than 6 times a week.
Table 1: Hausman-Taylor Happiness Regressions by Gender, Youth Sample (11-15 year olds)
Girls Boys White -0.017 0.048 [0.067] [0.047] Age (ref: 11) 12 -0.037 0.037 [0.036] [0.035] 13 -0.177*** -0.013 [0.038] [0.037] 14 -0.266*** -0.034 [0.042] [0.038] 15/16 -0.331*** -0.101** [0.046] [0.040] Log(annual household income) 0.041 0.05 [0.035] [0.031] Family situation (ref: both natural parents) Step family -0.278*** -0.138***
15 [0.065] [0.049] Single parent -0.139 -0.171** [0.090] [0.081]
Number of older siblings -0.073** -0.047* [0.033] [0.026] Number of younger siblings -0.046 0.002 [0.030] [0.024] Only child -0.006 -0.066 [0.074] [0.061] Relationship with Parents Argue with parent same sex -0.129*** -0.025 [0.017] [0.019] Argue with parent opposite sex -0.053*** -0.096*** [0.018] [0.017] Talk with parent same sex 0.095*** 0.065*** [0.015] [0.017] Talk with parent opposite sex 0.027 -0.005 [0.017] [0.016] Parents Employment
Father work*two parent 0.058 0.043 [0.059] [0.055] Mother work full time* two parent 0.004 -0.023 [0.054] [0.047] Mother work part time* two parent 0.016 0.05 [0.050] [0.045] Parent work full time*single parent 0.027 0.083 [0.088] [0.077] Parent work part time*single parent 0.126 0.083 [0.088] [0.079] Evening meals with family (ref: none) 1-2 times 0.067 0.130*** [0.048] [0.050] 3-5 times 0.087* 0.161*** [0.052] [0.053] 6-7 times 0.197*** 0.272*** [0.055] [0.055] Friends No of friends 0.009*** 0.006*** [0.003] [0.002] Times friends round house (ref: none) 1-2 times 0.072** 0.091*** [0.033] [0.032] 3-5 times 0.142*** 0.118*** [0.045] [0.044]
16 6 or more times 0.081 0.143** [0.055] [0.056] Constant 5.501*** 5.320*** [0.345] [0.301] Observations 7,865 8,022 Number of Individuals 2,422 2,519 Hausman FE vs. RE 110.86 95.15 p value 0.0000 0.0000 Hausman FE vs. HT 14.23 28.39 Pvalue 0.9999 0.8952 Robust standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1 Includes wave dummies Exogenous variables: white, age, household income, family situation and siblings, parents employment, wave dummies Endogenous variables: relationship with parents, family meals, number of friends, times friends round house
Unlike most other studies of youth happiness, our results above are based on panel data and
y therefore control for personality fixed effects ( i ). They allow us to confirm that, even after controlling for the role played by personality fixed effects, family structure, relationships with parents and peers are important in determining youth happiness.
Provided the socio-demographic and economic controls included in the model account for the
y main environmental factors, the individual fixed effect ( ˆi ) that we recover from this model is likely to be largely made up of personality effects. To the extent that a major fixed environmental control is missing from our model, the fixed effect will also include the effect of this variable. As mentioned earlier, we extract a series of variables from this model to include into our adult life satisfaction model. Summary statistics for each of these variables are presented in Table 2 below.
y Table 2 confirms that ˆi , the youth happiness trait, has a zero mean and finite standard deviation for both men and women, as would be expected from a white noise error term. The standard deviation of this variable is 0.74 for females and 0.52 for males confirming that there is a difference in the spread of this variable across genders. The average predicted happiness both with and without fixed effects is similar: it is 5.66 for women and 5.92 for men. However, the standard deviation of these predictions is much lower when fixed effects
17 are excluded than when they are included indicating that the impact of individual heterogeneity is more variable than that of socio-economic conditions.
Table 2: Summary Statistics for the Youth Happiness Measures Std. Mean Dev. Min Max Women (n=942) y Youth Average Happiness, Yi 4.79 1.48 1.00 7.00 y Youth Happiness Trait , ˆi 0.00 0.74 -3.73 1.49 Average Predicted Happiness without FE; ˆ y1 Yi 5.66 0.27 4.71 6.40 Average Predicted Happiness with FE; ˆ y2 Yi 5.66 0.83 1.70 6.91 y Average Residual , ˆi 0.00 0.20 -1.63 0.70
Std. Mean Dev. Min Max Men (n=883) y Youth Average Happiness, Yi 5.18 1.35 1.00 7.00 y Youth Happiness Trait, ˆi 0.01 0.52 -2.73 1.03 Average Predicted Happiness without FE, ˆ y1 Yi 5.92 0.20 5.14 6.45 Average Predicted Happiness with FE, ˆ y2 Yi 5.93 0.58 2.86 6.87 y Average Residual, ˆi 0.00 0.36 -2.61 1.10
5.2. Stage 2: Youth Happiness and Adult Life Satisfaction We now move on to our primary objective: analysing the extent of dependence between youth and adult life satisfaction. Table 5 presents 7 models of adult life satisfaction, while Table 4 presents the results for the baseline model which has only the common controls included in every model. Since the coefficients of these controls do not vary significantly across the models, they are only presented once in Table 4. We will begin by considering the
ˆ y1 ˆ y2 ˆ y ˆ y y correlations between each of the above measures (Yi ,Yi , i , i ,Yi ) and adult life satisfaction in Table 3 below.
Table 3: Correlations of Youth Measures and Average Adult Life Satisfaction*
18
y Yi Raw ˆ y1 y y ˆ y2 Yi ˆi ˆi Yi Happines Predicted youth Individual youth Average Predicted youth Women (n=942) s happiness; no FE heterogeneity; FE residual happiness with FE ˆ y1 Predicted youth Yi happiness; no FE 0.2702 1 y ˆi Individual youth heterogeneity; FE 0.8027 0.1529 1 y ˆi Average residual 0.6776 0.1171 0.8386 1 ˆ y2 Predicted youth Yi happiness with FE 0.8082 0.4606 0.9475 0.7912 1 Average Adult Life satisfaction 0.3012 0.2243 0.31 0.2603 0.3517
Men (n=883) ˆ y1 Predicted youth Yi happiness; no FE 0.2511 1 y ˆi Individual youth heterogeneity; FE 0.808 0.1044 1 y Average residual ˆi 0.7155 0.1214 0.8211 1 ˆ y2 Predicted youth Yi happiness with FE 0.8167 0.438 0.9398 0.7838 1 Average Adult Life satisfaction 0.2576 0.2081 0.3226 0.2764 0.3631 * Note: All correlations are significant at the 1% level
The table indicates that, for women, the predicted values from our youth model (without the
ˆ y1 individual fixed effect, Yi ) have only a 0.27 correlation with raw happiness measures. However, the correlation of the raw happiness measure with predicted youth happiness with
ˆ y2 FE (Yi ) is 0.81 indicating that our model (equation 2) predicts youth happiness very well. However, the difference between the two correlations indicates that the FE plays a very important role in this prediction because the individual heterogeneity alone has a 0.80 correlation with the raw happiness measure.
Turning to the correlation between these youth measures and average adult life satisfaction,
ˆ y2 we find from Table 3 that the youth happiness predicted by our model (Yi ) has a 0.35 correlation with adult life satisfaction. If we exclude fixed effects and predict only from
19 ˆ y1 objective socio-economic factors (Yi ), then the correlation is smaller at 0.22. The youth
y happiness trait on its own ( ˆi ) has a correlation of 0.31 with adult life satisfaction. The patterns are very similar for the men in our sample. Adult life satisfaction is most closely
ˆ y2 correlated with Yi (i.e. youth happiness with fixed effects) for women as well as men. This is not surprising because it captures the constancy in both socio-economic factors and
y personality traits. The next closest correlation is with individual heterogeneity or ˆi .
Table 4: Hausman Taylor Model of Adult Life Satisfaction – Baseline Model (without Youth effects) Women Men
Age -0.209** -0.365*** [0.088] [0.090] Age squared 0.004** 0.007*** [0.002] [0.002] White 0.279* -0.325** [0.156] [0.147] Qualifications (ref: school level) Higher/first degree 0.397*** -0.02 [0.127] [0.129] Other tertiary 0.331*** 0.153* [0.099] [0.093] A levels 0.364*** 0.221** [0.096] [0.098] No qualifications 0.259 -0.043 [0.287] [0.230] Live with spouse/partner 0.200*** 0.224*** [0.051] [0.070] Have child 0.137* -0.087 [0.079] [0.102] Employment status (ref: non-participation) Employed 0.139* 0.549*** [0.074] [0.134] In full time education 0.263*** 0.469*** [0.086] [0.138] Unemployed -0.179** 0.182 [0.089] [0.139] Log(household income) 0.023 0.029 [0.026] [0.024] Health Limits Daily Activities -0.446*** -0.392*** [0.067] [0.082]
20 Observations 3,887 3,595 Number of Individuals 942 883 Standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1 Includes wave dummies Exogenous variables: white, age, health limits daily activities, wave dummies Endogenous variables: qualifications, employment status, household income, whether live with partner/spouse, whether have children.
Table 4 above presents the baseline model excluding all our childhood variables. It indicates that for both men and women, life satisfaction decreases with age though this effect stabilises after a point. White women are more satisfied with their lives than their non-white counterparts but white men are significantly less satisfied with their lives. Educated women are significantly more satisfied with their lives than uneducated women. For men, this effect is smaller and does not exist at all for tertiary level education. Living with a spouse/partner makes both men and women happier, while having children only makes women happier. Men and women who are employed are happier than the non-employed though the effect is significantly larger for employed men (0.549) than for employed women (0.139). Similarly, men in full time education are happier (coefficient 0.469) than women (the coefficient is 0.263) and unemployed women are significantly less happy than employed women though this is not true for unemployed men. Finally, for both men and women when health limits daily activities, life satisfaction is lower.
The above controls are included in all the models that we discuss in Table 5. Since the coefficients of the controls vary very little when we include childhood factors, we do not repeat the results for these variables in Table 5. In addition to these controls, each of the models in Table 5 includes (a set) of childhood related happiness variables, whose results are presented below.
Table 5: Hausman Taylor Models of Adult Life Satisfaction with Youth Happiness Measures Women 1 2 3 4 5 6 7 Youth Happiness (standardised) Youth Average Raw Happiness 0.295*** [0.037] y Youth Happiness Trait, ˆi 0.284*** 0.267*** 0.261*** [0.035] [0.035] [0.065] Average Predicted Happiness 0.193*** 0.149*** 0.149***
21 without FE, ˆ y1 Yi [0.046] [0.044] [0.044] Average Predicted Happiness with FE, ˆ y2 Yi 0.318*** [0.036] Average Residual, y ˆi 0.261*** 0.008 [0.041] [0.073] Men 1 2 3 4 5 6 7 Youth Happiness (standardised) Youth Average Raw Happiness 0.253*** [0.041] y Youth Happiness Trait, ˆi 0.283*** 0.267*** 0.252*** [0.036] [0.036] [0.063] Average Predicted Happiness without FE, ˆ y1 Yi 0.178*** 0.143*** 0.142*** [0.041] [0.039] [0.040] Average Predicted Happiness with FE, ˆ y2 Yi 0.313*** [0.036] Average Residual, y ˆi 0.260*** 0.019 [0.041] [0.070] Controls (as in Table 4) Yes Yes Yes Yes Yes Yes Yes Standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1 Includes wave dummies Exogenous variables: white, age, youth happiness, health limits daily activities, wave dummies Endogenous variables: qualifications, employment status, household income, whether live with partner/spouse, whether have children
Our results in Table 5 indicate that, whichever youth measure we include, it is highly significant in influencing life satisfaction for both men and women. Not surprisingly, the
ˆ y2 largest effect is from Yi the average youth happiness level predicted from both the socio- economic conditions and personality fixed effects. This variable has a coefficient of 0.32 indicating that a 1 standard deviation change in average predicted youth happiness leads to an increase in adult life satisfaction of 0.32 (of 1 unit on a scale of 1-7) for women and 0.31 (of 1 unit on a scale of 1-7) for men.
22 ˆ y1 ˆ y In Model 6 we include Yi and i separately into the same model. We find that youth happiness predicted purely from socio-economic factors (excluding FE) has a coefficient of
y 0.15 for adult women and 0.14 for adult men. The youth happiness trait ( ˆi ), on the other hand, has a coefficient of 0.267 both for adult women and men. This indicates that a 1 standard deviation change in the youth happiness trait causes a 0.267 (of 1 unit on a scale of 1-7) increase in life satisfaction amongst adults while a 1 standard deviation change in happiness caused by socio-economic factors during childhood leads to only approximately 0.15 (of 1 unit on a scale of 1-7) increase in life satisfaction. These results indicate that youthful personality traits continue to have a larger impact on life satisfaction during adulthood than constancy in socio-economic conditions.
Our results to both Models 5 and 6 lead us to conclude that there is significant dependence in life satisfaction between youth and adulthood. This dependence is caused partly by the long term impact of objective youth factors and partly by the impact of youthful personality characteristics that continue to influence life satisfaction into adulthood. The impact of the latter is significantly larger than the former leading us to conclude that a large proportion of the dependence arises from persistent personality factors. There is very little difference between the results for men and women.
6. Discussion of Results and Conclusion
This paper aimed to consider whether youth happiness levels influence early adult life satisfaction. It also aimed to consider whether such an impact arises from environmental factors that influence youthful happiness levels or because the effect of personality factors on youth happiness persists into adulthood. To do this, we estimated a model of life satisfaction for a sample of young people and used this to predict and extract a range of variables which we then include in the adult life satisfaction model. Both models are estimated using data from the BHPS for 1994 to 2008.
This paper adds to the literature of the impact of childhood conditions (both socio-economic and personality) on adult well-being (Frijters et al, 2011; Modin et al, 2011). It does so by providing a potential measure of personality which avoids reverse causality because it is extracted from a youth sample. The paper therefore offers a methodological contribution to
23 the life satisfaction literature which also allows it to compare the relative magnitude of the impact of personality and of socio-economic conditions on adult life satisfaction. In using the Hausman-Taylor instrumental variable approach, it also allows us to include both exogenous and endogenous variables, an innovation that helps correct for the endogeneities that are common in all models of life satisfaction.
Our results lead us to conclude that there is a relatively large and significant impact of youth happiness on early adulthood life satisfaction. This confirms the importance of getting things right for the young. This result stands whatever youth measure we use in our adult model. However, when we compare the long run impact of socio-economic factors on youth happiness as against the long run impact of personality factors, we find that the latter have a larger impact than the former. This finding confirms the result of Frijters et al (2011) that childhood behavioural-emotional problems, and social maladjustment are much stronger predictors of whether a child grows up to be a satisfied adult. Unlike us, however, Frijters et al also found that family socioeconomic status (parental education, occupational class and family income) do not predict adult life satisfaction very well and they argue that this implies that there is high equality of opportunity to live a satisfied life, at least for individuals born in Britain in 1958 and 1970. Almquist (2011) found that both friendlessness and marginalisation were related with significantly more hospital care events in adulthood in Stockholm though marginalisation alone was associated with the probability of being admitted into hospital. Modin et al (2011) find in a study of Stockholm that women who held low peer status positions in childhood were at higher risk of anxiety and/or depression later in life compared to women in average status positions even after controlling for a range of family and socio- economic conditions. Their result, once again, confirms the importance of relationships with peers during childhood, the impact that this has on childhood happiness and through this (as well as independently of this) on adult life satisfaction. The Field Report (2010) on poverty and life chances in the UK considers that ‘children’s life chances are most heavily predicated on their development in the first 5 years of life’ (p.5). Our paper extends this finding to argue that it is not only material outcomes that are influenced by conditions in the early years but also the outcomes in relation to subjective well-being.
While this paper makes a number of contributions to the literature, much remains to be done. A more detailed study of the precise personality traits that influence life satisfaction is likely to be illuminating as also the link between the socio-economic conditions and these
24 personality traits. Such a link is likely to be best addressed using cohort studies which follow the same group of individuals from birth to adulthood and into old age.
References
Almquist, Y. B., Modin, B. & Augustine, L., 2013, Peer acceptance in the school class and subjective health complaints. A multilevel approach, Journal of School Health.
Almquist,A., 2011, Social isolation in the classroom and adult health: A longitudinal study of a 1953 cohort, Advances in Life Course Research, Volume 16, Issue 1, March 2011, Pages 1- 12, ISSN 1040-2608,
Angeles, L. (2010). Adaptation and Anticipation Effects to Life Events in the United Kingdom, Department of Economics, and University of Glasgow
Argyle, M., (1999). Causes and Correlates of Happiness, in Kahnemann,D., Diener, E., and Schwarz, N., (Eds), Well-being: The Foundations of Hedonic Psychology, New York: Russell Sage Foundation.
Argyle, M. and L. Lu (1990). The happiness of extroverts, Personality and Individual Differences 11, 1011–1017.
Blanchflower, D., and Oswald, A. (2008) Is well-being U-shaped over the life cycle?, Social Science & Medicine, vol. 66(8), 1733-1749
Borghans, L, Duckworth, A,L., Heckman, J and Weel, B. (2008) The Economics and Psychology of Personality Traits, Journal of Human Resources, 43, 817-860.
Boyce, C. J. (2010). Understanding fixed effects in human well-being. Journal of Economic Psychology, 31, 1-16
Boyce, C., (2013). Is Personality Fixed? Personality Changes as Much as “Variable” Economic Factors and More Strongly Predicts Changes to Life Satisfaction, Social Indicators Research. 25 Burton, P., and Phipps, S. (2010). The well-being of immigrant children and their parents in Canada. Paper presented at the 31st General Conference of the International Association for
Research in Income and Wealth.
Buss, A. and Plomin, R. (1984) Temperament: Early developing personality traits. Erlbaum.
Carneiro, P., Crawford, C. and Goodman, A. (2007). The impact of early cognitive and non- cognitive skills on later outcomes. Centre for the Economics of Education, London School of Economics.
Case, A., Fertig, A. and Paxson, C. (2005). The lasting impact of childhood health and circumstance. Journal of Health Economics, 24, 365-89.
Clark, A. (2003). Unemployment as a Social Norm: Psychological Evidence from Panel Data, Journal of Labor Economics, 21, 323-351.
Clark, A. (2006). A Note on Unhappiness and Unemployment Duration. Applied Economics Quarterly, 52(4), 291-308.
Clark, A, Diener, E., Georgellis, Y. and Lucas, R. (2003). Re-Examining Adaptation and the Setpoint Model of Happiness: Reaction to Changes in Marital Status, Journal of Personality and Social Psychology, 84, 527-539.
Clark, A, Diener, E., Georgellis, Y. and Lucas, R. (2008). Lags and Leads in Life Satisfaction: A Test of the Baseline Hypothesis. Economic Journal, 118 (526), 222-243
Clark, A.E., Frijters, P., and Shields, M.A. (2008). Relative Income, Happiness and Utility: An Explanation for the Easterlin Paradox and Other Puzzles, Journal of Economic Literature, Vol.46, no.1, 95-144
Clark, A, and Georgellis, Y. (2010). Back to Baseline in Britain: Adaptation in the BHPS, Paris School of Economics Working Paper No 2010-02
26 Cobb-Clark, D and Schurer, S. (2011). The Stability of Big-Five Personality Traits, Melbourne Institute Working Paper No. 21/11.
Contoyannis, P. and Dooley, M. (2010). The role of child health and economic status in educational, health and labor market outcomes in young adulthood. Canadian Journal of Economics, 43, 323-346.
Costa, P, T. and R.R, McCrae. (1994). Set like plaster: Evidence for the stability of adult personality, in Todd F. Heatherton and Joel Weinberger, eds., Can personality change? pp. 21-40, Washington D.C: American Psychological Association
Costa, P, T. and McCrae, R.R,. (2006). Age Changes in personality and Their Origins: comment on Roberts, Walton and Viechtbauer (2006), Psychological Bulletin, 132, 26-28.
Csikszentmihalyi, M., and Hunter, J. (2003). Happiness in Everyday Life: The Uses of Experience Sampling. Journal of Happiness Studies, 4, 185-199.
Currie, J., Stabile, M., Manivong, P. and Roos, LL. (2010). Child health and young adult outcomes. Journal of Human Resources, 45, 517-548.
Diener, E., Larsen, R. J., and Emmons, R. A. (1984). Person x situation interactions: Choice of situations and congruence response models. Journal of Personality and Social Psychology, 47, 580-592.
Diener, E, Lucas, R.E., and Scollon, C,. (2006). Beyond the Hedonic Treadmill: revising the adaptation theory of well-being. Psychological Science, 61, 305-324.
Diener, E., Suh, E.M., Lucas, R.E., and Smith, H.L. (1999). Subjective Well-Being: Three Decades of Progress. Psychological Bulletin, 125 (2), 276-302.
Ferguson, K.M. (2006). Social Capital and Children’s Well-being: A Critical Synthesis of the International Social Capital Literature. International Journal of Social Welfare, 15, 2-18.
27 Ferrer-i-Carbonell, A, and . Frijters. (2004). How Important is Methodology for the estimates of the determinants of Happiness? Economic Journal, Royal Economic Society, vol. 114(497), 641-659
Field, F. (2010). The Foundation Years: Preventing Poor Children becoming Poor Adults: The Report on the Independent Review on Poverty and Life Chances. HM Government, London.
Frey, B.S., and Stutzer, A. (2006). Does Marriage Make People happy or Do Happy People get Married? Journal of Socioeconomics, 35(2), 326-347
Frijters, P., Johnston, D. and Shields, A. (2011), Destined for (Un)Happiness: Does Childhood Predict Adult Life Satisfaction?, IZA Discussion paper no 5819.
Fujita, F. and Diener, E. (2005). Life satisfaction set point: Stability and change. Journal of Personality and Social Psychology, 88, 158-164.
Goodman, A., Joyce, R. and Smith, JP. (2011). The long shadow cast by childhood physical and mental problems on adult life. Proceedings of the National Academy of Sciences, 108, 6032-37.
Graef, R. (1978). An analysis of the person by situation interaction through repeated Measures. Unpublished doctoral dissertation, The University of Chicago.
Hausman, J. and Taylor, W. (1981). Panel data and unobservable individual effects. Econometrica, 49 (6), 1377–98.
Headey, B. and Wearing, A. (1989). Personality, life events, and subjective well-being: Toward a dynamic equilibrium model. Journal of Personality and Social Psychology, 57, 731-739.
Hogan, R. and Hogan, J. (2007). Hogan Personality Inventory Manual, Third Edition, OK: Hogan Assessment Systems
28 John, O and Srivastava, S. (1999). The Big-Five Trait Taxonomy: History, Measurement, and Theoretical Perspectives in Pervin and O.P. John (Eds.), Handbook of Personality: Theory and Research (2nd ed.). New York: Guilford
Kozman, Stone and Stone. (1997). Stability in Components and Predictors of Subjective Well-being. Paper Delivered at First Conference of the International Quality of Life Studies: 1997, Charlotte, North Carolina.
Land, K.C., Lamb, V.L., and Mustillo, S.K. (2001). Child and Youth Well-Being in the United States 1975-1998: Some Findings from a New Index. Social Indicators Research, 56, 241-320.
Larsen, R.J., & Ketelaar, T. (1991). Personality and susceptibility to positive and negative emotional states. Journal of Personality and Social Psychology, 61, 132-140.
Lucas, R, Clark, A., Diener, E. and. Georgellis, Y. (2003). Re-Examining Adaptation and the Setpoint Model of Happiness: Reaction to Changes in Marital Status, Journal of Personality and Social Psychology, 84, 527-539.
Lucas, R., Clark, A., Georgellis, Y. and Diener, E. (2004). Unemployment alters the set-point for life satisfaction. Psychological Science, 15, 8-13.
Lykken,D.T., and Tellegen, A., 1996, Happiness is a Stochastic Phenomenon, Psychological Science, 7, 186-189.
Magnus, K and Diener, E. (1991) A Longitudinal Analysis of Personality, Life Events and Subjective Well-being. Paper presented at the 63rd Annual Meeting of the Midwestern Psychological Association, Chicago.
Mentesoglu, G. and M. Vendrik. (2009). Adaption, Anticipation and Social Interaction in Happiness: An Integrated Error-Correction Approach. Department of Economics, Maastricht University
29 Modin, B., Ostberg, V., and Almquist,Y., 2011, ‘Childhood Peer Status and Adult Susceptibility to Anxiety and Depression. A 30 year Hospital Follow Up.’ Journal of Abnormal Child Psychology, Vol.39, pp.187-199.
Östberg, V., Modin, B., 2008, ‘Status relations in school and their relevance for health in a life course perspective: Findings from the Aberdeen children of the 1950's cohort study,’ Social Science & Medicine, Volume 66, Issue 4, February 2008, Pages 835-848, ISSN 0277- 9536.
Östberg, V., Modin, B., 2012, ‘Social relationships and subsequent health-related behaviours: linkages between adolescent peer status and levels of adult smoking in a Stockholm cohort,’ Addiction.
Pavot, W., Diener, E., and Fujita, F. (1990). Extraversion and happiness. Personality and Individual Differences, 11, 1299-1306.
Robson, K. (2008). Changes in Family Structure and the Well-Being of British Children: Evidence from a Fifteen-Year Panel Study, Child Indicators Research, 3 (1), 65-83
Schaefer, E.S. and Bayley, N. (1963). Maternal Behavior, Child Behavior and their Intercorrelations. Monographs of the Society for Research in Child Development, 28, 3.
Senik, C. (2009). Direct Evidence on Income Comparisons and their Welfare Effects. Journal of Economic Behavior and Organisation, 72 (1), 408-424
Smith. J. (2009). The impact of childhood health on adult labor market outcomes. Review of Economics and Statistics, 91, 478-489.
Tellegen,A., Lykken, D.T., Bouchard ,T.J., Wilcox, K.J., Segal, N.L., and Rich,S. (1988). Personality Similarity in Twins Reared Apart and Together. Journal of Personality and Social Psychology, 54, 1031-1039.
Veenhoven,R. (1994). Is Happiness a trait? Social Indicators Research, 32, 101-160.
30 Veenhoven, R. (1991). Is happiness Relative? Social Indicators Research, 24, 1991, 1-34
Weiss, A., Bates, TC. and Luciano, M. (2008). Happiness is a personal(ity) thing: The genetics of personality and well-being in a representative sample. Psychological Science, 19, 205-10.
Wilson, W. (1967). Correlates of Avowed Happiness. Psychological Bulletin, Vol.67, 294- 306.
Winkelmann, L., & Winkelmann, R. (1998). Why are the unemployed so unhappy? Evidence from panel data, Economica, 65(257), 1-15.
Appendices Appendix 1:
Variable Definition/questions Source Stage 1 Variables
Happiness How do you feel about your life as a Youth questionnaire whole ? On a scale of scale of 1 (completely happy) to 7(completely unhappy)
Family structure Whether live with both natural Household members parents, part of a step family or relationship records single parent. Excluded cases of same sex parents or where did not live with a parent e.g. lived with grandparents , other relatives or non- relatives Siblings Number of younger and older Household members siblings in household relationship records and main adult questionnaire for siblings age Relationship with parents How often do you quarrel with your Youth questionnaire mother (father)? How often do you talk to your mother (father), about things that matter to you?
31 Responses: Most days (1) More than once a week (2) Less than once a week (3) Hardly ever (4) Don’t have a father/mother (5) (Reverse coded to aid interpretation so higher values reflect a closer relationship for the talking variable and more arguments for the arguments variable) Family relationship In the past 7 days how many times Youth questionnaire have you eaten an evening meal together with your family?
Responses: None, 1-2, 3-5, 6-7 Relationship with peers Thinking back over the last 7 days, Youth questionnaire how many times have you had friends round to your house?”
Responses: none, 1-3, 4-6 and 7
How many friends do you have? Household income Log (Annual income) adjusted for Household questionnaire household size (weighted 1 for adults and 0.5 for children) and inflation Parents employment Whether father is employed, whether Adult questionnaire status mother is employed and working full (matched through parents or part time personal identifier) Gender Gender Fixed wave information Age Age indicator variables – 11, 12, 13, Youth questionnaire 14, 15/16 Ethnicity Race of respondent: white or other Fixed wave information record
Stage 2 Variables Life satisfaction Overall life satisfaction – Adult questionnaire individuals were asked on a scale of 1(not satisfied at all) to 7(completely satisfied) “How satisfied or dissatisfied are you with your life overall?”. Average youth happiness Average across an individual’s Youth questionnaire happiness values in youth panel
32 Youth happiness See methodology section Own calculations measures Age Age and age squared Fixed wave record Gender Sex of respondent Fixed wave record Ethnicity Race of respondent: white or other Fixed wave record Whether live with a An indicator dummy or whether they Adult questionnaire spouse partner live with a spouse or partner Qualifications Highest level academic qualification Adult questionnaire – divided into higher/first degree, other tertiary qualifications, A level, school level qualifications and no qualifications Log of Household income Log of annual household income Adult questionnaire divided by the number of adults in the household and adjusted for inflation Labour market status Current labour market status with Adult questionnaire options of: employed, unemployed, retired, family care, education/training, sick/disabled and other – divided into employed, full time education, unemployed and out of the labour market. Children An indicator variable of whether Adult questionnaire they have any children who live with them Health limits daily Whether health limits daily activities Adult questionnaire activities
Appendix 2: Variable Descriptive Statistics
Youth Variables
Girls Boys Mean St.Dev Mean St.Dev Happiness 5.77 1.28 5.97 1.17 White 0.61 0.49 0.59 0.49 Aged 11 0.18 0.38 0.18 0.39 Aged 12 0.20 0.40 0.20 0.40 Aged 13 0.20 0.40 0.20 0.40 Aged 14 0.20 0.40 0.20 0.40
33 Aged 15/16 0.22 0.41 0.21 0.41 Age 13.09 1.43 13.07 1.43 Log(annual household income) 9.03 0.61 9.06 0.65 Live with both natural parents 0.61 0.49 0.60 0.49 Step family 0.18 0.38 0.19 0.39 Single Parent family 0.21 0.41 0.22 0.41 Number of older siblings 0.71 0.84 0.66 0.81 Number of younger siblings 0.80 0.96 0.83 0.93 Only child 0.14 0.34 0.12 0.33 Argue with parent same sex 2.06 1.10 1.48 1.05 Argue with parent opposite sex 1.49 1.10 1.87 1.06 Talk with parent same sex 2.71 1.19 1.79 1.21 Talk with parent opposite sex 1.62 1.16 2.31 1.20 Father work - two parent family 0.67 0.47 0.69 0.46 Mother not work - two parent family 0.42 0.49 0.42 0.49 Mother work full time - two parent family 0.29 0.46 0.30 0.46 Mother work part time- two parent family 0.28 0.45 0.28 0.45 Single parent - not work 0.87 0.33 0.87 0.34 Single parent - work full time 0.07 0.26 0.08 0.27 Single Parent - work part time 0.05 0.22 0.05 0.22 0 evening meals with family 0.12 0.32 0.11 0.31 1-2 evening meals with family 0.24 0.43 0.24 0.43 3-5 evening meals with family 0.25 0.43 0.26 0.44 6-7 evening meals with family 0.39 0.49 0.39 0.49 No of friends 6.75 5.78 7.28 7.35 Friends round house 0 times 0.32 0.47 0.34 0.47 Friends round house 1-2 times 0.43 0.49 0.41 0.49 Friends round house 3-5 times 0.16 0.37 0.16 0.37 Friends round house 6+ times 0.10 0.30 0.09 0.29
No of obs. 7,865 8,022
Adult Variables
Women Men Mean St.Dev Mean St.Dev Life satisfaction 5.14 1.2 5.28 1.17 Age 21.1 2.79 21.06 2.79 Age Squared 452.86 124.46 451.21 123.9 White 0.95 0.21 0.94 0.24 Higher/first degree 0.13 0.33 0.09 0.29 Other tertiary 0.22 0.42 0.21 0.41 A levels 0.33 0.47 0.31 0.46 No Qualifications 0.04 0.19 0.05 0.22 Live with spouse/partner 0.25 0.43 0.14 0.35
34 Have child 0.18 0.39 0.06 0.23 Employed] 0.59 0.49 0.64 0.48 In Full time education 0.23 0.42 0.24 0.43 Unemployed 0.07 0.25 0.10 0.30 Log(household income) 9.12 0.79 9.17 0.84 Health Limits Daily Activities 0.08 0.27 0.05 0.22
No of obs. 3,887 3,595
Appendix 3: Descriptive Statistics: Youth Measures (non-standardised values) Std. Mean Dev. Min Max Women (n=942) Youth Average Happiness 4.79 1.48 1.00 7.00 Youth Happiness Trait 0.00 0.74 -3.73 1.49 Average Predicted Happiness without FE 5.66 0.27 4.71 6.40 Average Predicted Happiness with FE 5.66 0.83 1.70 6.91 Average Residual 0.00 0.20 -1.63 0.70
Std. Mean Dev. Min Max Men (n=883) Youth Average Happiness 5.18 1.35 1.00 7.00 Youth Happiness Trait 0.01 0.52 -2.73 1.03 Average Predicted Happiness without FE 5.92 0.20 5.14 6.45 Average Predicted Happiness with FE 5.93 0.58 2.86 6.87 Average Residual 0.00 0.36 -2.61 1.10
35