Modelling subjective well-being - CiteSeerX

Paper presented at BHPS – 2005 Conference, Essex Work in progress – not for citation without the author’s permission

Modelling subjective well-being

Tessa Peasgood Centre for Well-being in Public Policy University of Sheffield

Abstract The use of income as a proxy for well-being is meeting increasing criticism from social scientists, including economists. Subjective well-being (SWB), measured through direct survey questions, presents a possible alternative proxy for well-being. However, will knowing what effects people’s responses to subjective well-being questions tell us useful information for public policy? To be ‘useful’ the responses to subjective well-being questions must be a valid measure of wellbeing and be responsive to change following changes in circumstances which could be influenced by policy. Furthermore, the response findings need to be robust to different ways of measuring SWB, and different forms of modelling the relationship between an individual’s resources and circumstances and their SWB. This paper considers whether SWB appears to be a sufficiently trustworthy measure of well-being to be useful for public policy and draws on previous empirical work to model SWB responses within the British Household Panel Survey. The measures of subjective well-being used are the response to the question “how dissatisfied or satisfied are you with your life overall?” which was asked in waves 6 to 10 and 12 to 13, the GHQ12 (full score and Caseness score), and one question within the GHQ which asks about feeling ‘unhappy or depressed’. The relationship between these measures of SWB and individual, household and societal characteristics is explored using fixed effects, ordered probit and conditional fixed effects logit models.

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The use of panel data allows the analysis to account for time-invariant individual specific effects. This is particularly important when considering subjective well-being since a large component of subjective well-being is thought to be due to an individual’s personality. However, problems of endogeneity remain unsolved. The results of the different models and SWB measures are broadly consistent, and in line with previous work. Increases in income improve SWB and unemployment, poor health, divorce, widowhood reduce SWB. Despite their importance, such findings are likely to have little new to add to any policy agenda. What is more novel are the findings relating to the importance of social contacts. Across a range of different ways of modelling SWB our contact with other people matters. Those times when we have people who will listen to us, visit us, and create a peaceful local environment are the times when we are more satisfied with our lives and less likely to experience unhappiness. Although people’s social contacts may not appear to have direct policy relevance, these findings emphasise the importance of considering the indirect impact on social capital of other economic policies designed to increase income. Whilst these findings suggest increases in income do improve SWB, increases in income also have negative externalities for others, and negative consequences for other sources of SWB in peoples’ lives.

Acknowledgements Data from the British Household Panel Survey were provided through the Data Archive at the University of Essex. Contact details: Tel: (0)114 2228375, email: [email protected]

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Introduction The use of income as a proxy for well-being is meeting increasing criticism from social scientists, including economists. Subjective well-being (SWB), measured through direct survey questions, presents a possible alternative proxy for well-being. This paper considers the SWB questions within the British Household Panel Survey with a view to whether they have the potential to provide useful information to policy makers. Section one summarises the SWB measures within the BHPS; section two makes some initial comments relating the validity of these measures for measuring well-being; section three considers whether these measures seem potentially useful in terms of responsiveness to change and providing new information; section four discusses expectations arising from previous empirical work; section five and six describe the models and their results. The paper concludes with some policy implications and options for improvements to the models. 1. Subjective well-being in the BHPS Data The BHPS is a longitudinal survey of private households in the United Kingdom1 which began in 1991. It was designed as an annual survey of each adult (16+) member of nationally representative sample of 5,500 households. The same individuals are re-interviewed in successive waves, and if they leave their original household they are also re-interviewed along with all adult members of their new household. The panel nature of the BHPS with repeated observations on the same individual enables analysis to control for unobserved differences between individuals. Furthermore, because information is collected on all adults within a household, this enables individuals to be considered within the context of their households. Since wave 6 the self-completion part of the survey has asked questions about respondent’s satisfaction with various domains in their life and their life overall2. Below is the question wording for income satisfaction. “Here are some questions about how you feel about your life. Please tick the number which you feel best describes how dissatisfied or satisfied you are with the following aspects of your current situation…. .........The income of your household?” 1 Not satisfied

2

3

4

5

6 7 Completely satisfied

Respondents are then asked whether they are “more satisfied with life, less satisfied or feel about the same” as they did a year ago. The self-complete questionnaire also includes the 12-item version of the General Health Questionnaire (GHQ12, see Appendix A). This questionnaire is used to detect the presence of nonpsychotic psychiatric morbidity in community settings. Half the questions (concentration, playing a useful role, capable of making decisions, enjoying day-to-day activities, ability to face problems) use the scale “not at all, no more than usual, rather more than usual and much more than usual”. The other half (loss of sleep, constantly under strain, problem overcoming difficulties, unhappy or depressed, loosing confidence, feeling worthless) use the scale “better or more than usual, same as 1

England, Wales, Scotland south of the Caledonian Canal since wave 1, and including Northern Ireland and the rest of Scotland since wave 7 2 Excluding wave 11

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usual, less than usual, much less than usual.” Scoring of the GHQ responses can be done by combining all responses into a 36 point scale, or a 12 point scale (which converts the four point response scale into a binary response), known as the Caseness score.

2. How valid are these SWB measures as a measure of well-being? In order to use SWB responses for policy purposes they need to accurately measure what we think they are measuring, in other words they should give a true reflection of whether someone's life is going well for them. 2.1 Is SWB well-being? From the perspective of an objective account of well-being, such as Aristotle’s emphasis on virtue and contemplation, or Sen’s capability account (Sen 1993), subjective measures are unlikely to provide a complete picture of an individual’s well-being. Well-being according to Sen is an individual’s capabilities and the choice of functionings they enable an individual to achieve. Although capabilities are not specified by Sen, education, health, autonomy, and security appear to be attributes which would extend or limit an individual’s opportunity set. An increase in capability (such as autonomy), all else equal, would objectively increase one part of an individual’s well-being regardless of the impact it had on their subjective well-being. Objective accounts have the potential to overcome problems of making interpersonal comparisons of well-being in situations where we may have reasons to mistrust an individual’s subjective judgement of their well-being. People may, for example, respond that they are satisfied with poor circumstances of their life due to low (and possibly realistic) expectations. Where an individual is not “informed or autonomous” we have good reason to question the authenticity of their subjective judgements (Sumner 1996)). 2.2 Can we interpret SWB responses? A further problem arises in that it is unclear what is influencing people when they respond to SWB questions. For example, it is not clear what time period respondents use to make the assessment, or whether they are taking concerns beyond their own well-being into consideration, such as an assessment of other members of their family. Interpreting the GHQ responses are particularly problematic since the questions compare the last few weeks to usual, however, what individuals are taking as their reference point is not clear. If a respondent repeatedly reports they are worse than usual, are they saying that prior to a few weeks before the interview on each occasion they were better than they are now, or are they using ‘worse than usual’ as a label for worse than their perception of what is usual in other people. The transition matrix below for waves 12 to 13 shows that about a third of respondents who answer better than, or less than usual in one wave will also do so in the other and of those responding much less than usual in one wave there is a 19 percent change they will also do so in the other. This suggests that some respondents may be using the scale in a more absolute manner. Table 1: Transition matrix of general happiness, for waves 12 and 13, (n=30,165) More than Same as usual Less so than usual usual More than usual 33.09 56.57 8.72 Same as usual 10.77 80.07 8.09 Less so than usual 11.39 52.36 29.72 Much less than usual 12.06 37.74 18.68 14.01 72.91 11.00 Total

Much less than usual 1.63

2.09

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2.3 How big is measurement error? Studies which have found that SWB is influenced by factors which should not affect someone’s underlying judgements of their well-being have brought into doubt the validity of SWB measures. Global life satisfaction questions require a difficult mental task and respondents are unlikely to retrieve all the information that may be relevant to making such assessments, but rather respond when they feel they have enough information to form a judgement. Consequently, what people’s attention is currently being drawn can influence SWB responses. Chance situational factors and question ordering have been found to influence assessments (Schwarz and Strack 1999). The scales – including the time frame over which assessments are made – can affect respondents’ positive and negative affect and life satisfaction. For example, Winkielman et al, found that the frequency of anger differed if respondents are asked how many times they felt angry in the last week compared with over the last year (Winkielman, Knaeuper et al. 1998). There is also some evidence that global life satisfaction responses can be influenced by current feelings, for example, in one study respondents reported being more satisfied with their lives on sunny days than on rainy days – but only when their attention was not drawn to the weather (Schwarz and Clore 2003). However, if life satisfaction responses were being strongly influenced by current mood, we would expect responses to show the same level of stability as mood yet they appear to be more stable than moods (Fujita and Diener 2005). For example, correlations in life satisfaction in the German household panel (GSOP) range from 0.56 over one year to 0.29 over 10 years (Ehrhardt, Saris et al. 2000). Order effects and chance situation effects may be present in the BHPS, however, the life satisfaction questions follow a broad range of other questions, hence there appear to be no obvious framing effects. A further concern exists relating to whether people respond in manner which is social appropriate. For example, some groups may feel uneasy admitting to feelings of sadness. Whilst these tendencies may be consistent across one individual in different periods of time (hence not be problematic if individual effects are controlled for) they are problematic if people deliberately alter SWB responses to conform to socially acceptable responses following changes in circumstances (e.g. unemployed and recently widowed reporting lower levels of life satisfaction). 2.4 Do SWB correlate with other well-being measures? If SWB survey responses are valid we would expect them to show a high correlation to other signs of how well someone’s life is going for them. Life satisfaction responses have been shown to correlate with other indicators of both subjective and objective well-being, such as informant reports, smiling, experience sampling measures and interviewer ratings (Sandvick, Diener and Seidlitz 1993, cited by (Diener and Biswas-Diener 2002). Some support for the validity of selfreported measures of feelings has been taken from findings which show that reports of positive feelings correspond to increased electrical activity in the left side of the pre-frontal cortex (Richard Davison 2000, cited by (Layard 2003). Furthermore, SWB measures have been found to respond to changes in an individual’s circumstances from which we would anticipate a reduction in well-being, such as unemployment and divorce. 2.5 Do SWB measures correlate with behaviour? If subjective measures are able to predict behaviour it implies they must be picking up some real differences between people. Higher subjective well-being has been linked with reduced suicide attempts (Moum 1996, cited in (Diener 2000)). Also low satisfaction with ones job has been found to predict quitting (Clark 2001). 5

2.6 Do the different SWB correlate with each other? If different measures of SWB (global evaluations, current feelings and emotions) are tapping into the same underlying construct we would expect them to correlate highly with each other. In the BHPS the SWB measures do show reasonable correlation, with life satisfaction correlating 0.51 with GHQ12 (36), and -0.50 with happiness/depression question.3 This is fairly consistent with other findings. Whilst it would be worrying if different SWB measures were not correlated, their validity can not be judged on their strength of correlation since they are tapping into different aspects of how well life is currently going. The GHQ is specifically about the last few weeks, which although may be a reasonable sample of the respondents life, could gain a different response to questions asking about the last year. Evaluations of one’s life overall may also include time before the previous 12 month period and anticipation of how life will be in the future. The GHQ taps into affect and mood, whereas global life satisfaction questions are more evaluative, again reinforcing the fact that GHQ is short term assessment, whereas life satisfaction is a reflection on a longer period of time. The responses may also diverge because the life satisfaction question allows the respondent to choose what is important from their own perspective when making the assessment, whereas the GHQ relies on a set of affects and considerations which are externally determined. These may not, however, incorporate all affects which matter to an individual’s subjective well-being. For example, they exclude engagement, flow, and meaning. Unhappiness itself may not necessarily be a sign of sustained low well-being, particularly if the individual perceives that they are learning from the experience. A recent mother, for example, may score poorly on the GHQ but very highly on life satisfaction. The BHPS allows a check for internal consistency by comparing the change in life satisfaction score with the responses to the direction question which asks “Would you say that you are more satisfied with life, less satisfied or feel about the same as you did a year ago?” Ideally one would hope these two gave similar responses. However, as Table 2 shows this is not always the case. Table 2: Perception of changes in life satisfaction and actual changes in life satisfaction score. Change in life satisfaction score Increased Decreased Same Total More satisfied 2,251 (34.4%) 1,058 (16.2%) 3,240 (9.5%) 6,549 Less satisfied 654 (16.5%) 2,002 (51.0%) 1,287 (32.5%) 3,963 3,863 (24.9%) 3,808 (24.5%) 7,864 (50.6%) 15,545 Same Comparisons show that of those who were more satisfied, 16% gave a lower score and of those who thought their life was worse 16% gave a higher score. These inconsistencies could be explained by measurement error in the life satisfaction response (this year, the previous year or both). Alternatively, respondents may have forgotten what life was like for them a year ago, or may remember but with hindsight assess their satisfaction differently to the way they did a year ago. It may of course be that life satisfaction is such a vague concept to individuals that they are unable to give consistent and meaningful responses.

3. How useful are SWB measures likely to be for policy 3

waves 6-13, present in every wave

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3.1 Are SWB measures sensitive to change? Life satisfaction or GHQ as an assessment of well-being will be of little use to policy unless they are sensitive to change, and more specifically sensitive to change following changes in factors which policy can influence. The Minnesota twin studies (Tellegen, Lykken et al. 1988) have contributed to an impression that happiness is strongly influenced by genetic predisposition. However, SWB responses in the BHPS do show considerable change. The average standard deviation of life satisfaction questions for respondents (those in every wave) is 0.75. However, given that life satisfaction is technically a categorical variable, it may be more appropriate to consider change in terms of movement between categories. Table 3: Range and number of moves in life satisfaction responses (wave 6 to 13)4 Range Frequency Number of times Frequency respondents move 0 347 0 347 1 1802 1 401 2 1537 2 876 3 799 3 1,179 4 341 4 1,102 5 110 5 799 6 58 6 290 4,994 4,994 N Only 7% of respondents do not change their life satisfaction response. Most respondents move three or four times during the six waves that the question was asked, suggesting considerable movement. This movement can also be seen in the transition matrix, particularly for respondents who answer at the low end of the scale. Table 4: Transition matrix: Satisfaction with life overall 1 2 3 4 5 6 7 1 28.76 15.03 15.03 14.05 14.05 7.19 5.88 2 7.44 20.12 27.57 20.32 15.29 6.84 2.41 3 3.26 9.91 24.60 30.42 21.21 8.32 2.29 4 1.63 3.12 11.85 33.40 33.58 13.28 3.14 5 0.39 0.92 4.15 15.01 46.66 28.90 3.97 6 0.22 0.39 1.36 5.57 24.59 56.03 11.83 7 0.39 0.26 1.44 3.55 9.21 29.44 55.72 1.12 1.96 5.51 13.46 29.55 34.39 14.02 Whilst this shows that life satisfaction responses move around considerably, this may be due to measurement error. An individual’s satisfaction with their life may truly be a five out of seven, but due to a ‘wobbly hand’ they may answer four, five or six. If movements represent real change and external circumstances matter to people’s assessment of their lives, we would expect changes in observed circumstances to explain some of the variation in life satisfaction responses. 4. Choice of explanatory variables.

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Since there is no life satisfaction question in wave 11 movement is treated as change from waves 10 to 12.

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Much work has already been conducted exploring how SWB measures respond to changes in an individual circumstances, using national level time series data, and international cross sectional data, and increasingly using large household panel surveys. The richness and depth of the BHPS offers the potential to explore how changes in a range of circumstance impact on subjective well-being. The choice of explanatory variables used in the models is based on existing theoretical and empirical work. 4. 1 Income We would anticipate that income would have a positive relationship with subjective well-being measures. If satisfaction with life is taken to represent the utility experienced by an individual, as currently implied by economic theory, it would be expected that utility would increase as an individual’s income rises since this increases opportunities for them to move to (assumed) preferred states of higher consumption, subject to a possible offsetting increase in disutility from increased working hours and accounting for diminishing marginal utility of income. Consequently, we would expect a positive relationship between SWB and income, although non linear. Some SWB research has reached the counterintuitive finding that increased income has not led to increased happiness sparking extensive debate. Many studies suggest that income correlates surprisingly weakly with SWB (Easterlin 1995). Clark and Oswald, using cross sectional analysis of the BHPS, also found no significant affect of income on SWB measured using the GHQ (Clark and Oswald 1996). The importance of income on SWB has been focused on situations when basic needs are not met (Veenhoven 2000) or on avoiding unhappiness with higher incomes corresponding to modest differences in happiness but substantially reducing the risk of unhappiness (Diener and Biswas-Diener 2002). If individuals rapidly adapt to changes in income we would only expect to find a relationship between changes in income in longitudinal data when we have frequent remeasurement, or in cross sectional data where many people had recently experienced changes in income. Inglehard and Rabier (1986) use longitudinal data from 10 Western European countries to show that happiness scores relate to change in income over last 12 months. In Britain, Clark found that job satisfaction was related to changes in wages (Clark 1999). The adaptation process may not be as strong for increases in income compared to decreases in income, which would be consistent with diminishing marginal returns. Frey and Stutzer (1999, 2002) found that increases in income compared with the previous year had a small effect on general satisfaction, whereas reductions in income had a significant negative effect. Adaptation theories are supported by findings that individuals with higher income report higher values for the minimum income required to make ends meet (Stutzer 2004),(Chan, Ofstedal et al. 2002). Consequently, it may be the case that changes in income which are important rather than actual level of income. The theoretical basis for deriving increased utility from increased income as been questioned by those who believe it is that improvements in relative income, rather than absolute income, which increase utility (Duesenberry 1949). Our wants are not static, but determined by external influences, hence in an environment where our expectations and wants are raised by consumption of others, a preferred state can only be attained by increases in income which alter the ranking of our relative income position. Schyns has found that absolute income is less important than whether one is satisfied with ones income which critically depends on ones outgoings and expectations. Expectations are themselves formed by reference to others and one’s own past. Using a comparison income found by both an income function and the average regional hourly wages for ones sex, Clark and Oswald (1996), found that relative income was more important that absolute income. However, how relative 8

comparisons work not clear. Comparison to others on a higher income may reduce well-being if it leads to dissatisfaction with ones own income and life, however, it may be uplifting if it is interpreted as a expectation of what can be achieved in the future (Diener and Fujita 1997). 4.2 Employment status Employment status has also been found to have a strong impact on subjective well-being with unemployment consistently being found to decrease life satisfaction (Clark and Oswald 1996; Frey and Stutzer 2000) 4.3 Health state Health states have been found to have a strong relationship with SWB (Clark and Oswald 2002), however, since the subjective health assessment may include an assessment of one’s mental health there is some circularity in showing mental health explains the variation in the GHQ. There may also be problems with positivity bias in using a subjective assessment as an explanatory variable, since both may be affected by current mood. Using more objective measures of health would help to overcome this problem. 4.4 Age The relationship between age and SWB has generally been found to be U shaped (Blanchflower and Oswald 2004). However, using a fixed effects logit model on life satisfaction from German panel data Winkelmann and Winkelmann found an inverse relationship between age and satisfaction (Winkelmann and Winkelmann 1998). Looking at the mean life satisfaction of each age suggests an S shape within the BHPS. 4.5 Education Findings relating education to life satisfaction are ambiguous. Higher education levels have been linked to reduced overall satisfaction (Clark and Oswald 1996; Anand, Hunter et al. 2004) and Warr 1992, Klein and Maher 1966). However, it has also been linked to increased overall satisfaction (Frey and Stutzer 2000). Education is likely to increase an individual’s chances of having, among other things, higher income, increased wealth and better health. Consequently, separating out the affect of education when controlling for the factors which education has influenced, will not give a good indication of the contribution that education makes to well-being. 4.6 Marriage status Having a strong intimate relationship, specifically marriage has been found to increase satisfaction with life (Argyle 1987). However, using German Socio-Economic Panel (GSOP) Lucas et al found some evidence of adaptation to marriage and widowhood (Lucas, Clark et al. 2003). 4.7 Safety Data on the impact of SWB from victimisation is sparse. Cross sectional analysis of the BHPS has found that the absence of crime has been found to have a small but significant effect on overall life satisfaction for men (Anand, Hunter et al. 2004). However, Michalos and Zumbo (2000) found little significant reduction in quality of life from crime in British Columbia (Michalos and Zumbo 2000). 4.8 Social capital Social capital, namely trust in others and membership of voluntary organisations, has been found to have a positive effect on well-being (Helliwell 2003). Helliwell suggests that the findings relating to divorce and unemployment may in fact be due to reduced social capital (Helliwell 2003). It is difficult to judge the extent of an individual’s social capital, however, membership and involvement in clubs and seeing friends and having people to listen to your problems will tap into this concept.

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4.9 Religion and life purpose A belief in God has been found to increase life satisfaction (Helliwell 2003). 5. The models Since there is strong evidence that SWB is influenced by personality we would expect variations in the levels of SWB to explained partly by the individual’s personality and partly by their life circumstances. If we assume that personality does not change over the time period being modelled, the relationship can be represented as the SWB for the individual (i) at time period (t) being a function of a set of characteristics or explanatory variables (X) for individual (i) at time period (t) and an individual effect (for individual i) plus an error term. SWB here is measured either by the GHQ or by life satisfaction. SWBit = ’Xit + i + uit Where uit is assumed to be independent and identically distributed over individuals and time with mean zero and variance σ2 The individual effect is therefore shifting the level of SWB; a happy disposition for example, would shift the level of SWB upwards in each time period. The inclusion of an individual effect in the model requires the use of either random effects of fixed effects models. Although random effects models are potentially more efficient, they require an additional assumption that the individual effect is not correlated with the other independent variables. However, this seems theoretically unlikely. For example the individual effect may include trait optimism which will influence the SWB responses and the subjective measures of health. It is also likely to influence the level of education attained and the type of relationships the individual has, consequently, it is reasonable to assume that fixed effects are more appropriate.5 Fixed effects approach takes the difference of each variable from its individual level mean, thereby removing the individual effect. As well as reducing degrees of freedom, this method is problematic when looking at the impact of variables which do not change much over time at the individual level (e.g. education level), and can say nothing about those variables which do not change within individuals (e.g. sex and ethnicity at birth). Furthermore, the fixed effects model treats both the GHQ scores and the life satisfaction responses as cardinal variables. The GHQ is a constructed score from categorical responses, and life satisfaction is only cardinal if everyone uses the scale in a cardinal manner, with increase from 5 to 6 representing the same amount of improvement as between 3 and 4. If this assumption is too problematic, one solution is to model life satisfaction as an ordered probit which treats life satisfaction as a latent continuous variable. However, in this format the individual effects are no longer being accounted for and the individual is being compared to all others rather than just themselves at different period of time. Consequently, those factors which are more time invariant such as education are more likely to enter the model as significant. An alternative approach could be to consider the question asked within the GHQ about happiness and depression, this can then be modelled as a dependent binary variable using the absolute response ‘not at all’. Again this can be treated as a latent continuous variable – the amount of unhappiness experienced – which is only observed when it is sufficiently high to make the unhappy variable equal to one. Chamberlain (1980) has shown that it is possible to model such a function

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This is supported by a Hausman test.

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using a fixed effects logit model based on conditional likelihood and to get consistent estimates (Chamberlain 1980). 6. Findings 6.1 Regression results The full regression results can be found in Appendix B. Table 5 shows a summary of the variables which significantly (at 5%) influence the various SWB measures. Table 5: Summary of significant regression results from various SWB models, BHPS waves 6-12 Explanatory variables

Model 1 Fixed Effects GHQ (36)

Model 2 Fixed Effects GHQ Caseness

Log of household income Income increase (>5%) since previous year

-0.219 (0.000)

Having problems paying for accommodation

1.360 (0.000)

Mean income in ones area

0.0001 (0.016)

0.764 (0.000)

Model 3 Fixed Effects Life sat

Model 4 Ordered Probit Life sat

0.218 ( 0.054)

0.039 (0.004)

0.031 (0.013)

0.040 (0.012)

-0.125 (0.001)

-0.265 (0.000)

-0.352 (0.000)

-0.387 (0.000)

-0.00002 (0.047)

0.00003 (0.000)

Commercial education

-0.152 (0.006)

O level

-0.242 (0.000)

A level

-0.271 (0.000)

Degree

-0.270 (0.000)

Model 5 Conditional FE Logit Unhappy

Subjective health state (1-4 scale)

-1.389 (0.000)

-0.763 (0.000)

.0237 (0.000)

0.423 (0.000)

-0.412 (0.000)

Unable to walk 10 minutes

0.876 (0.000)

0.534 (0.000)

-0.130 (0.000)

-0.211 (0.000)

-0.194 (0.030)

Subjective health better than last year

0.138 (0.017)

0.111 (0.001)

-0.436 (0.000)

-0.112 (0.000)

0.045 (0.002)

0.140 (0.000)

Subjective health worse than last year Registered (or considers self to be) disabled

-0.100 (0.006)

Look after someone for more than 50 hrs/week

0.845 (0.000)

Cohabiting

-0.4972 (0.008

Never being married

0.563 (0.000)

-0.266 (0.000)

0.611 (0.000)

-0.110 (0.006) -.1139 (0.034)

-0.376 (0.000)

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Explanatory variables

Widow since last year

Model 1 Fixed Effects GHQ (36) 5.400 (0.000)

Widowed since 2 years ago

Model 2 Fixed Effects GHQ Caseness 3.360 (0.000)




-0.591 (0.000)

-0.872 (0.000)

2.698 (0.000)

0.410 (0.048)

-0.212 (0.009)

-0.459 (0.000)

0.542 (0.036)

Widowed for at least 3 years

-0.237 (0.000)

Will become widowed (next year)

-0.185 (0.033)

-0.369 (0.001)

Will get divorced (next year)

1.633 (0.008)

1.172 (0.001)

-0.696 (0.000)

-0.925 (0.000)

1.063 (0.048)

Will separate (next year)

2.151 (0.000)

1.060 (0.000)

-0.560 (0.000)

-0.752 (0.000)

0.929 (0.000)

Separated since last year

3.4468 (0.000)

2.368 (0.000)

-0.3661 (0.000)

-0.843 (0.000)

1.319 (0.000)

Separated 2 years ago

1.769 (0.000)

0.8395 (0.001)

-0.3695 (0.000)

-0.567 (0.000)

Separated 3 or more years ago

-0.3661 (0.000)

-0.677 (0.000)

Divorced since last year

-0.3020 (0.000)

-0.511 (0.000)

Divorced 3 or more years ago

-0.2100 (0.000)

-0.504 (0.000)

0.2571 (0.000)

0.272 (0.010)

0.1377 (0.001)

0.138 (0.000)

Married since last year

-0.5916 (0.003)

Married for 2 years

-0.331 (0.011)

Age

0.576 (0.000)

0.283 (0.000)

-0.1375 (0.000)

-0.206 (0.000)

0.133 (0.025)

Age squared

-0.012 (0.000)

-0.006 (0.000)

0.0028 (0.000)

0.004 (0.000)

-0.003 (0.007)

Age cubed

0.00007 (0.000)

0.00004 (0.000)

-0.00001 (0.000)

0.00002 (0.000)

0.0002 (0.006)

Problem with noisy neighbours Problem with noise on the street

-0.097 (0.000) 0.144 (0.045)

-0.053 (0.010)

Problem with vandalism or crime Have one person to listen to you

-0.591 (0.000)

-0.299 (0.000)

-0.0443 (0.004)

-0.069 (0.000)

0.063 (0.025)

0.180 (0.000)

0.166 (0.001)

-0.259 (0.005)

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Explanatory variables

Model 1 Fixed Effects GHQ (36)

Have more than one person to listen to you

-0.652 (0.000)

Meets up with friends or family most days

-0.131 (0.010)

Model 2 Fixed Effects GHQ Caseness -0.328 (0.000)




0.132 (0.000)

0.296 (0.000)

-0.338 (0.000)

0.037 (0.002)

0.069 (0.000)

Actively involved in a religious organisation

0.062 (0.021)

Plays sport at least once a month

-0.173 (0.005)

0.057 (0.003)

0.084 (0.000)

Attends a group or voluntary organisation at least once a month Long term sick

-0.277 (0.001)

-0.165 (0.001)

0.057 (0.004)

0.087 (0.000)

1.314 (0.000)

0.833 (0.000)

-0.305 (0.000)

-0.266 (0.000)

Unemployed

1.552 (0.000)

0.966 (0.000)

-0.267 (0.000)

-0.282 (0.000)

0.431 (0.001)

Retired

-0.265 (0.045)

0.135 (0.000)

-0.199 (0.037)

Family carer

0.355 (0.006)

On government training scheme Maternity leave

0.243 (0.001) 1.453 (0.002)

-0.521 (0.009)

-0.562 (0.026)

0.346 (0.000)

0.522 (0.000)

6.1 Income The models suggest a relationship between SWB and income but do not always give a significant result. Since the same data is being used in each model this indicates some of the variation in previous findings relating to income may be due to modelling differences, for example, the fixed effects life satisfaction does not give a significant result yet the ordered probit does echoing the differences between time series and cross sectional findings. Some problems may caused by inaccurate measurement of income. Headey et al (2004) argue that wealth and consumption should also be considered when modelling SWB (Headey, Muffels et al. 2004). Consumption may be less vulnerable to measurement error than income, but also come closer to disposable income. Using a fixed effects model and the BHPS income changes were found to have a significant effect on life satisfaction, but less of an effect than consumption changes (Headey, Muffels et al. 2004). Adding consumption variables to these models shows a significant affect only from expenditure on meals out and leisure expenses. However, this is not included in the final model since as well as reducing the sample size it is particularly problematic to interpret since it is not clear if it is the representing the actual activity – going out – rather than giving a good indication of disposable income.

13

When we, possibly naively, think money will buy us happiness, we may not be thinking of actual (or equivalised) household income. Being wealthier means having greater command over resources, now and in the future. Hence we should include savings, pensions, house equity, benefits in kind, and ‘real’ income allowing for the cost of living in the respondents location and their unavoidable needs (such as living with a disability). Income may not in itself lead to SWB, but financial security may. Even if income were an accurate measure, it still may not give a good indication of effective disposal income. Years when people have problems paying for accommodation show reduced SWB by all measures. This suggests a need to look at stresses people face from failing to meet their necessary expenses. Furthermore, the extent of an individual’s control over household resources is also likely to be important, although the percentage an individuals labour income contributes towards total household income (or total household labour income) has not shown significance. These results support the notion that it is years when income increases that SWB improves, and that mean income of a respondents region has a negative relationship with SWB6. However, this may be due to a reflection of living costs in the area rather than a direct relative income effect. 6.2 Health The relationship between health and SWB is consistently strong. Although, as mentioned above, the finding relating health to GHQ is not surprising, the health variables are also significant for life satisfaction. The significance of the change in health status may arise due to the limited nature of the one to four scale. For example, subjective health status being better than last year significantly lowers SWB. Since the level of subjective health status is already being controlled for, this may be showing that, for example, a three out of four is a lower three when it has followed a two out of four than at other times. This raises an important issue with such regression results – the more explanatory variables that are included the more complex (and potentially misleading) is the interpretation of the results. 6.3 Marital status The findings for marriage status are broadly consistent across the different models, with substantial effect sizes (e.g. the fixed effect modelling of GHQ (36) has an effect size for being widowed since last year of 5.3). The lead variables are also strongly significant and adding in a time dimension does show some adaptation to marriage states in some models, although this may not be best approach to looking at adaptation. 6.4 Employment status The significantly negative impact of unemployed and being long term sick supports other findings. Being retired, a student or on maternity leave increases SWB, although there are very few cases in the latter state. 6.5 Security and environment There is sufficient consistency and strength in the relationship between SWB and security of ones environment to warrant further exploration. A measure of actual victimization or antisocial behaviour in the neighbourhood would be preferable to a subjective measure since both SWB and the security measures could be influenced by current mood. 6.6 Social capital Having friends who will listen to you has a strong effect on SWB in all models, however, there are few cases who do not have anyone to listen to them. Seeing friends or family most days also has a 6

This finding is only significant for males – see Models 6 and 7 in the appendix.

14

significantly positive effect on SWB in most models, as does playing sport and attending a group/organisation at least once a month. 6.7 Religion and education The religion and education variables only become significant in the ordered probit model, since as would be expected, theses variables change very little at the individual level across the time period. They conform to expectations with religion increasing life satisfaction, and the pure education effect lowering life satisfaction. However, as noted above the effect of education on SWB can not be fully interpreted from the significance of the education variables when so much else which education influences has been controlled for. 7. Discussion 7.1 Do models say anything about causality? The problems of endogeneity which arise when modelling the impact of income on health are well versed (Smith 1999). This is addressed either by assuming income to be exogenous (which is the case for lottery winnings, or unexpected changes in pensions/taxation), or through the use of instruments which although correlated with income are not directly related to health. Satisfaction with life also has the potential to be endogenous with some of the explanatory variables used (e.g. income, health, social capital, sporting activities) as the direction of causality may be unclear. For example, an increase in income may improve satisfaction with life, alternatively, increased satisfaction may increase productivity and thereby increase income. Life satisfaction and explanatory variables may both be influenced by another factor which is not included in the model. For example, having a sick child may both reduce an individual’s health and their satisfaction with life. Using instrumental variables is a possible solution to this endogeneity problem but it is conceptually difficult to find appropriate instruments to use for all the potentially endogenous variables. One option is to consider windfall income, which is included in the BHPS, which is does not significantly influence life satisfaction. However, this is unlikely to give a good indication of how income and wealth can influence SWB. 7.2 Do they say anything new or helpful? That well-being is reduced by poor health, unemployment, and marriage break down is not news, neither is it useful additional information to aid policy formulation. If we already know these things are important, modelling SWB to show they are has little additional benefit. So what is new? One very clear message from this range of different modelling techniques on different SWB measures is that the people around you really matter. Those times when we have people who will listen to us, visit us, and create a peaceful local environment are the times when we are more satisfied with our lives and less likely to experience unhappiness. Modelling SWB in this manner enables clear quantification of the effect of social capital. Although people’s social contacts may not appear to have direct policy relevance, these findings emphasise the importance of considering the indirect impact on social capital from other economic policies designed to increase income, such as encouraging a flexible labour force. Using a conditional fixed effects logit model and controlling for age, employment status and health, household income was found to be significantly negatively related to the social capital variables which were found to significantly increase SWB (namely, whether you see friends or family most

15

days; whether you take part in sporting activities, and whether you belong to groups or organisations). Whilst these findings suggest increases in income do improve SWB (both directly and indirectly via incomes contribution to health) increases in income also have negative externalities for others, and negative consequences for other sources of SWB in peoples’ lives. Consequently, policies which focus exclusively on maximising income risk undermining other sources of well-being. 7.3 Future work It appears there is much to be gained from analysis of panel datasets. Clearly, there are issues of measurement error in subjective well-being outcome measures, however, since it is possible to get consistent findings when looking at range of measures using a range of methods the measurement error does not seem too detrimental to the analysis. However, the vulnerability of SWB measures to irrelevant factors (such as question ordering) does mean that policy needs to be based on consistently robust findings gained from variety of different sources. There are various improvements which can be made to the current models: 1. The effect of income needs to be more thoroughly understood. When does income become a dominating influence on people’s lives (e.g. through absolute poverty, or stress about meeting basic expenditures) and what level of income do increases make no difference? How do the effect sizes of income on subject well-being relate to potential fiscal policy and distributional policies? 2. The effect of relative income needs to be more thoroughly understood. Do relative income effects remain after adequately controlling for costs of living? If people are engaging in comparisons who are they comparing themselves to? 3. The effects of poor health need to be more thoroughly understood? Which health conditions are particularly problematic for well-being?

16

References Anand, P., G. Hunter, et al. (2004). Capabilities and well-being: Evidence based on the SeenNussbaum approach to welfare. Open Discussion Papers in Economics, The Open University. Milton Keynes. Argyle, M. (1987). The Psychology of Happiness. London, New York, Methuen & Co Lt. Blanchflower, D. G. and A. J. Oswald (2004). "Well-being over time in Britain and the USA." Journal of Public Economics 88(7-8): 1359-1386. Chamberlain, G. (1980). "Analysis of Covariance with Qualitative Data." Review of Economic Studies 47: 225-238. Chan, A., M. B. Ofstedal, et al. (2002). "Changes in Subjective and Objective Measures of Economic Well-Being and Their Interrelationship among the Elderly in Singapore and Taiwan." Social Indicators Research 57(3): 263-300. Clark, A. E. (1999). "Are wages habit-forming? evidence from micro data." Journal of Economic Behavior & Organization 39(2): 179-200. Clark, A. E. (2001). "What really matters in a job? Hedonic measurement using quit data." Labour Economics 8(2): 223-242. Clark, A. E. and A. J. Oswald (1996). "Satisfaction and comparison income." Journal of Public Economics 61(3): 359-381. Clark, A. E. and A. J. Oswald (2002). "A simple statistical method for measuring how life events affect happiness." Int J Epidemiol 31(6): 1139-44; discussion 1144-46. Diener, E. (2000). "Subjective Well-Being. The Science of Happiness and a Proposal for a National Index." American Psychologist 55(1): 34-43. Diener, E. and R. Biswas-Diener (2002). "Will Money Increase Subjective Well-Being?" Social Indicators Research 57(2): 119-169. Duesenberry, J. S. (1949). Income, saving and the theory of consumer behaviour. Cambridge, Harvard University Press. Easterlin, R. A. (1995). "Will raising the incomes of all increase the happiness of all?" Journal of Economic Behavior & Organization 27(1): 35-47. Ehrhardt, J. J., W. E. Saris, et al. (2000). "Stability of Life-satisfaction over Time." Journal of Happiness Studies 1(2): 177-205. Frey, B. S. and A. Stutzer (2000). "Happiness, Economy and Institutions." Economic Journal 110(466): 918-938. Fujita, F. and E. Diener (2005). "Life Satisfaction Set Point: Stability and Change." Journal of Personality and Social Psychology 88(1): 158-164.

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Headey, B., R. Muffels, et al. (2004). Money doesn't buy happiness... Or does it? A reconsideration based on the combined effects of wealth, income and consumption. Discussion paper series, IZA, Institute for the Study of Labor. Bonn. Helliwell, J. F. (2003). Well-being and social capital: Does suicide pose a puzzle. Hernandex-Quevedo, C., A. M. Jones, et al. (2004). Reporting Bias and Heterogeneity in SelfAssessed Health. Evidence from the British Household Panel Survey. Discussion Papers in Economics. York. Layard, R. (2003). Happiness: Has social science a clue? Lionel Robbins Memorial Lectures 2002/03. Lucas, R. E., A. E. Clark, et al. (2003). "Reexamining adaptation and the set point model of happiness: Reactions to changes in marital status." Journal of Personality and Social Psychology 84(3): 527-539. Michalos, A. C. and B. D. Zumbo (2000). "Criminal Victimization and the Quality of Life." Social Indicators Research 50(3): 245-295. Schwarz, N. and G. L. Clore (2003). "Mood as Information: 20 Years Later." Psychological Inquiry 14(3&4): 296-303. Schwarz, N. and F. Strack (1999). Reports of subjective well-being: judgemental processes and their methodological implications. Well-Being and the Foundations of Hedonic Psychology. D. Kahneman, E. Diener and N. Schwarz. New York, Russell Sage Foundation. Sen, A. K. (1993). Capability and well-being. The quality of life. A. K. Sen and M. Nussbaum. Oxford, Clarendon Press. Smith, J. P. (1999). "Healthy bodies and thick wallets: The dual relation between health and economic status." The Journal of Economic Perspectives 13(2): 145-166. Stutzer, A. (2004). "The role of income aspirations in individual happiness." Journal of Economic Behavior & Organization 54(1): 89-109. Sumner, L. W. (1996). Welfare, Happiness and Ethics. Oxford, Clarendon Press. Tellegen, A., D. T. Lykken, et al. (1988). "Personality similarity in twins reared apart and together." Journal of Personality and Social Psychology 54: 1031-1039. Veenhoven, R. (2000). "Well-being in the Welfare State: Level not higher, distribution not more equitable." Journal of Comparative Policy Analysis 2: 91-125. Winkelmann, L. and R. Winkelmann (1998). "Why are the unemployed so unhappy? Evidence from panel data." Economica 65(1): 1-15. Winkielman, P., B. Knaeuper, et al. (1998). "Looking back at anger: Reference periods change the interpretation of emotion frequency questions." Journal of Personality and Social Psychology 75: 719-728.

18

Appendix A GHQ12 1. Here are some questions regarding the way you have been feeling over the last few weeks. For each question please ring the number next to the answer that best suits the way you have felt. Have you recently.... a) been able to concentrate on whatever you're doing ? Better than usual...........................................................1 Same as usual .............................................................. 2 Less than usual ........................................................... 3 Much less than usual .................................................. 4 b) lost much sleep over worry ? Not at all ....................................................................... 1 No more than usual .................................................... 2 Rather more than usual.............................................. 3 Much more than usual ............................................... 4 c) felt that you were playing a useful part in things ? More than usual.......................................................... 1 Same as usual.............................................................. 2 Less so than usual ...................................................... 3 Much less than usual .................................................. 4 d) felt capable of making decisions about things ? More so than usual..................................................... 1 Same as usual.............................................................. 2 Less so than usual ....................................................... 3 Much less capable ...................................................... 4 e) felt constantly under strain ? Not at all ....................................................................... 1 No more than usual ................................................... 2 Rather more than usual ............................................. 3 (22) Much more than usual .............................................. 4 f) felt you couldn't overcome your difficulties ? Not at all....................................................................... 1 No more than usual ................................................... 2 Rather more than usual.............................................. 3 Much more than usual .............................................. 4 g) been able to enjoy your normal day-to-day activities ? More so than usual..................................................... 1 Same as usual.............................................................. 2 Less so than usual ...................................................... 3 Much less than usual .................................................. 4 h) been able to face up to problems ? 19

More so than usual...................................................... 1 Same as usual.............................................................. 2 Less able than usual.................................................... 3 Much less able............................................................. 4 i) been feeling unhappy or depressed ? Not at all....................................................................... 1 No more than usual .................................................... 2 Rather more than usual.............................................. 3 (26) Much more than usual ............................................... 4 j) been losing confidence in yourself ? Not at all....................................................................... 1 Not more than usual.................................................. 2 Rather more than usual ............................................. 3 Much more than usual .............................................. 4 k) been thinking of yourself as a worthless person ? Not at all........................................................................ 1 No more than usual ................................................... 2 Rather more than usual............................................. 3 Much more than usual ............................................... 4 l) been feeling reasonably happy, all things considered ? More so than usual..................................................... 1 About same as usual................................................... 2 Less so than usual ...................................................... 3 Much less than usual .................................................. 4

20

Appendix B Variables used in the regression models Personal characteristics Age12 Age2 Age3 Health status hldsbld hlltdd hlstatc4 Healthdown Healthup

Education Degreed Alevel Olevel Commerd Relationships Mar1 Mar2 Livtog Nevmar Wid1 Wid2 Wid3 Sep1 Sep2 Sep3 Div1 Div2 Div3 Willmar Willdiv Willwid Willsep Ssupa1d

Age in years at 1st December of current wave Age squared Age cubed

1 if reported themselves as registered disabled (up to wave K) or consider themselves disabled (wave L&M), 0 otherwise 1 if reported themselves as not being able to walk for 10 minutes (waves) or for half a mile (wave) Self reported health status 1-4 recoded (Hernandex-Quevedo, Jones et al. 2004) 1 if self-reported health status (1-4) worse than previous year, 0 otherwise 1 if self-reported health status (1-4) worse than previous year, 0 otherwise

1 if has degree, 0 otherwise 1 if has A levels, 0 otherwise 1 if has O level or equivalent, 0 otherwise 1 if has commercial qualifications, 0 otherwise

1 if married year t but not year t-1 1 if married year t and married t-1 but not year t-2 1 if cohabiting, 0 otherwise 1 if never married, 0 otherwise 1 if widow year t but married in year t-1, 0 otherwise 1 if widow year t and widow in year t-1 and married in year t-2, 0 otherwise 1 if widow year t and widow in year t-1 and widow in year t-2, 0 otherwise 1 if separated in year t but married in t-1, 0 otherwise 1 if separated in year t and t-1 but married in t-2, 0 otherwise 1 if separated in year t and in year t-1 and t-2, 0 otherwise 1 if divorced in year t but married in t-1, 0 otherwise 1 if divorced in year t and t-1 but married in t-2, 0 otherwise 1 if divorced in year t and in year t-1 and t-2, 0 otherwise 1 if not married in year t but married in year t+1 1 if not divorced in year t but divorced in year t+1 1 if not widowed in year t but widowed in year t+1 1 if not separated in year t but separated in year t+1 1 if have 1 person who will listen to you (this year or the last), 0 otherwise 21

Ssupa2d Frnbd

1 if have at least 2 people who will listen to you (this year or the last), 0 otherwise 1 if meets up with friends of family most days, 0 otherwise

HH environment HHsize NCH04 NCH511 NCH1218 Aidhigh

Number of people in household including respondent Number of children in household aged 0 to 4 Number of children in household aged 5 to 11 Number of children in household aged 12 to 18 1 if reported looking after someone for more than 50 hours per week

Income Xphsdfd Lfihhyr

Incomeupd2 Incomedod2 Meanfihhyr

1 if reported having problems paying for accommodation Log of annual household income (adjusted for inflation) Assumes income has a curvilinear effect on satisfaction (as used in Schyns 2001) Income increased by more that 5% since previous year Income decreased by more than 5% since previous year Mean of household income for that region

Employment status Unempd Empd Maternd Famcard Retiredd Ltsickd Studentd Selfempd Govtraind Othactd

1 if unemployed, 0 otherwise 1 if employed, 0 otherwise 1 if on materity leave, 0 otherwise 1 if family carer, 0 otherwise 1 if retired, 0 otherwise 1 if long term sick, 0 otherwise 1 if student, 0 otherwise 1 if self employed, 0 otherwise 1 if on government training scheme, 0 otherwise 1 if other activity, 0 otherwise

Religion Oprlg2d Opgafd

Security and environment Hsprbhd Hsprbid Hsprbqd Activities Lactad Lactkd

1 if attend religious meeting at least once month, this year or last 1 if active in religious organisation, this year or previous year, 0 otherwise

1 if has problem with noisey neighbours 1 if has problem with street noise 1 if has problem with vandalism or crime

1 if plays sport at least once a month, 0 otherwise 1if goes to local group or voluntary organisation at least once a month, 0 otherwise

22

Model 1: GHQ (36 Score) Fixed effects Fixed-effects (within) regression Group variable (i): pid

Number of obs Number of groups

= =

40534 5326

R-sq:

Obs per group: min = avg = max =

1 7.6 8

within = 0.0691 between = 0.2444 overall = 0.1548

corr(u_i, Xb)

= 0.1269

F(58,35150) Prob > F

= =

44.95 0.0000

-----------------------------------------------------------------------------hlghq1 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------lfihhyr | .0755795 .0483941 1.56 0.118 -.0192745 .1704336 incomeupd2 | -.2188389 .0528048 -4.14 0.000 -.3223379 -.1153398 incomedod2 | -.044594 .0570086 -0.78 0.434 -.1563327 .0671448 xphsdfd | 1.360021 .1140434 11.93 0.000 1.136492 1.583549 meanfihhyr | .0001288 .0000535 2.41 0.016 .0000239 .0002336 commerd | .1867963 .4690443 0.40 0.690 -.7325453 1.106138 oleveld | .2788862 .3634255 0.77 0.443 -.4334393 .9912116 aleveld | .1478748 .3665402 0.40 0.687 -.5705555 .866305 degreed | -.1580162 .3246601 -0.49 0.626 -.7943602 .4783279 hlstatc4 | -1.388752 .0480002 -28.93 0.000 -1.482834 -1.29467 hlltdd | .8757499 .1164925 7.52 0.000 .647421 1.104079 hldsbld | .2443024 .1301897 1.88 0.061 -.0108735 .4994783 healthdown | -.0537398 .0623905 -0.86 0.389 -.1760272 .0685476 healthup | .1382633 .0579586 2.39 0.017 .0246626 .2518641 aidhigh | .844791 .2152979 3.92 0.000 .4228003 1.266782 nch1218 | -.0340683 .0713178 -0.48 0.633 -.1738534 .1057169 nch511 | -.1316734 .072495 -1.82 0.069 -.2737659 .0104192 nch04 | -.0863394 .086486 -1.00 0.318 -.2558547 .0831758 hhsize | .0887068 .0490629 1.81 0.071 -.0074581 .1848717 livtog | -.4972154 .1877513 -2.65 0.008 -.8652138 -.129217 nevmar | -.4219725 .2328316 -1.81 0.070 -.8783298 .0343848 wid3 | -.2453398 .2911774 -0.84 0.399 -.8160567 .3253771 wid2 | .5132098 .3539559 1.45 0.147 -.1805549 1.206974 wid1 | 5.399953 .3621066 14.91 0.000 4.690213 6.109693 willwid | .3817296 .3666924 1.04 0.298 -.336999 1.100458 willdiv | 1.632933 .6173289 2.65 0.008 .4229491 2.842917 willsep | 2.150779 .3072349 7.00 0.000 1.548589 2.752969 sep3 | .6046173 .394748 1.53 0.126 -.1691012 1.378336 sep2 | 1.769195 .4297262 4.12 0.000 .9269185 2.611472 sep1 | 3.44686 .3193439 10.79 0.000 2.820936 4.072784 div3 | -.1628803 .2442093 -0.67 0.505 -.6415382 .3157775 div2 | -.5014516 .7062298 -0.71 0.478 -1.885684 .8827811 div1 | .2525906 .3341494 0.76 0.450 -.4023527 .9075339 willmar | -.1515678 .2115537 -0.72 0.474 -.5662197 .263084 mar2 | -.3437147 .18538 -1.85 0.064 -.7070654 .0196359 mar1 | -.5916487 .1957228 -3.02 0.003 -.9752715 -.208026 age12 | .5756824 .0862072 6.68 0.000 .4067136 .7446512 age2 | -.0115386 .0017281 -6.68 0.000 -.0149257 -.0081514 age3 | .0000707 .0000109 6.51 0.000 .0000494 .000092 hsprbhd | .1291198 .0826486 1.56 0.118 -.0328741 .2911137 hsprbid | .1435013 .0716645 2.00 0.045 .0030366 .283966 hsprbqd | .0628887 .0662409 0.95 0.342 -.0669456 .1927229 ssupa1d | -.5909283 .1189218 -4.97 0.000 -.8240188 -.3578378 ssupa2d | -.6522758 .1210427 -5.39 0.000 -.8895232 -.4150283 oprlg2d | .0572934 .0996278 0.58 0.565 -.1379801 .252567 orgafd | -.1579175 .1121449 -1.41 0.159 -.377725 .0618901 frnbd | -.1309843 .050608 -2.59 0.010 -.2301775 -.031791

23

lactad | -.1726371 .0620741 -2.78 0.005 -.2943043 -.0509699 lactkd | -.2769867 .0846329 -3.27 0.001 -.4428698 -.1111035 ltsickd | 1.314011 .2065256 6.36 0.000 .9092148 1.718808 retiredd | -.2648037 .1319877 -2.01 0.045 -.5235037 -.0061037 unempd | 1.552055 .171772 9.04 0.000 1.215376 1.888733 othactd | -.0446872 .3878357 -0.12 0.908 -.8048575 .7154831 maternd | .0660313 .3368094 0.20 0.845 -.5941257 .7261884 selfempd | -.0447315 .1401811 -0.32 0.750 -.3194909 .2300279 famcard | .3553416 .1293355 2.75 0.006 .1018401 .6088432 studentd | -.4517436 .245919 -1.84 0.066 -.9337526 .0302654 govtraind | 1.200446 .846426 1.42 0.156 -.4585755 2.859468 _cons | 4.312154 1.698822 2.54 0.011 .9824091 7.641898 -------------+---------------------------------------------------------------sigma_u | 3.3463781 sigma_e | 3.8478123 rho | .43063708 (fraction of variance due to u_i) -----------------------------------------------------------------------------F test that all u_i=0: F(5325, 35150) = 5.31 Prob > F = 0.0000

24

Model 2: GHQ (Caseness) Fixed effects Fixed-effects (within) regression Group variable (i): pid


= =

40534 5326

R-sq:


1 7.6 8


corr(u_i, Xb)

= 0.1050

F(58,35150) Prob > F

= =

42.42 0.0000

-----------------------------------------------------------------------------hlghq2 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------lfihhyr | .0200861 .0283309 0.71 0.478 -.0354434 .0756156 incomeupd2 | -.0372426 .030913 -1.20 0.228 -.0978331 .0233479 incomedod2 | .026102 .0333741 0.78 0.434 -.0393121 .0915162 xphsdfd | .7644137 .0667634 11.45 0.000 .6335553 .895272 meanfihhyr | .0000595 .0000313 1.90 0.057 -1.84e-06 .0001209 commerd | .0156087 .2745883 0.06 0.955 -.522593 .5538104 oleveld | .2028539 .2127569 0.95 0.340 -.2141562 .6198641 aleveld | .050054 .2145803 0.23 0.816 -.3705301 .4706381 degreed | -.0774194 .1900628 -0.41 0.684 -.4499485 .2951097 hlstatc4 | -.762513 .0281003 -27.14 0.000 -.8175905 -.7074356 hlltdd | .5344076 .0681971 7.84 0.000 .4007391 .6680761 hldsbld | .1190386 .0762158 1.56 0.118 -.0303467 .2684239 healthdown | .0202134 .0365247 0.55 0.580 -.0513763 .091803 healthup | .1111023 .0339302 3.27 0.001 .044598 .1776065 aidhigh | .563095 .1260399 4.47 0.000 .3160528 .8101371 nch1218 | -.0437561 .0417509 -1.05 0.295 -.1255892 .038077 nch511 | -.066725 .0424401 -1.57 0.116 -.1499089 .0164589 nch04 | -.017937 .0506307 -0.35 0.723 -.1171748 .0813008 hhsize | .036562 .0287225 1.27 0.203 -.0197349 .092859 livtog | -.1461555 .1099135 -1.33 0.184 -.3615894 .0692784 nevmar | -.066824 .1363045 -0.49 0.624 -.333985 .2003371 wid3 | -.1053523 .1704613 -0.62 0.537 -.4394618 .2287573 wid2 | .4098976 .2072132 1.98 0.048 .0037533 .8160419 wid1 | 3.360029 .2119848 15.85 0.000 2.944532 3.775525 willwid | .2961669 .2146694 1.38 0.168 -.1245918 .7169256 willdiv | 1.172271 .3613972 3.24 0.001 .463921 1.880621 willsep | 1.060251 .1798617 5.89 0.000 .7077166 1.412786 sep3 | .2609499 .2310937 1.13 0.259 -.1920011 .7139008 sep2 | .8394512 .2515707 3.34 0.001 .3463647 1.332538 sep1 | 2.368259 .1869506 12.67 0.000 2.00183 2.734688 div3 | -.0533535 .1429652 -0.37 0.709 -.3335698 .2268628 div2 | .1125737 .4134417 0.27 0.785 -.697785 .9229324 div1 | .2364121 .195618 1.21 0.227 -.1470053 .6198296 willmar | .0478316 .1238479 0.39 0.699 -.1949143 .2905774 mar2 | -.074575 .1085253 -0.69 0.492 -.287288 .1381381 mar1 | -.1770368 .1145802 -1.55 0.122 -.4016176 .047544 age12 | .282576 .0504675 5.60 0.000 .1836581 .3814938 age2 | -.0060543 .0010117 -5.98 0.000 -.0080372 -.0040714 age3 | .0000385 6.36e-06 6.05 0.000 .000026 .000051 hsprbhd | .0729622 .0483842 1.51 0.132 -.0218724 .1677969 hsprbid | .0791313 .0419539 1.89 0.059 -.0030996 .1613623 hsprbqd | .0382944 .0387788 0.99 0.323 -.0377133 .1143021 ssupa1d | -.2988708 .0696193 -4.29 0.000 -.4353268 -.1624148 ssupa2d | -.3283483 .0708609 -4.63 0.000 -.4672379 -.1894587 oprlg2d | .0333038 .0583242 0.57 0.568 -.0810134 .1476211 orgafd | -.0621324 .065652 -0.95 0.344 -.1908123 .0665475 frnbd | -.0494178 .029627 -1.67 0.095 -.1074876 .008652

25

lactad | -.0635057 .0363395 -1.75 0.081 -.1347322 .0077208 lactkd | -.1652652 .0495459 -3.34 0.001 -.2623767 -.0681538 ltsickd | .8325707 .1209044 6.89 0.000 .5955943 1.069547 retiredd | -.03063 .0772683 -0.40 0.692 -.1820784 .1208183 unempd | .9657583 .1005589 9.60 0.000 .7686596 1.162857 othactd | .163846 .2270471 0.72 0.471 -.2811735 .6088655 maternd | .3601248 .1971753 1.83 0.068 -.0263449 .7465945 selfempd | .0115251 .082065 0.14 0.888 -.1493248 .172375 famcard | .2432107 .0757157 3.21 0.001 .0948056 .3916158 studentd | -.0491932 .1439661 -0.34 0.733 -.3313713 .232985 govtraind | 1.534944 .4955154 3.10 0.002 .5637184 2.50617 _cons | -.8022085 .9945259 -0.81 0.420 -2.75151 1.147094 -------------+---------------------------------------------------------------sigma_u | 1.7912397 sigma_e | 2.2525896 rho | .38737851 (fraction of variance due to u_i) -----------------------------------------------------------------------------F test that all u_i=0: F(5325, 35150) = 4.46 Prob > F = 0.0000

26

Model 3: Life satisfaction fixed effects Fixed-effects (within) regression Group variable (i): pid


= =

35548 5325

R-sq:


1 6.7 7


corr(u_i, Xb)

= 0.1944

F(58,30165) Prob > F

= =

25.91 0.0000

-----------------------------------------------------------------------------lfsato | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------lfihhyr | .0218268 .0113381 1.93 0.054 -.0003964 .04405 incomeupd2 | .0309257 .0124572 2.48 0.013 .006509 .0553424 incomedod2 | .0040009 .0133458 0.30 0.764 -.0221575 .0301593 xphsdfd | -.2645318 .0265134 -9.98 0.000 -.3164993 -.2125644 meanfihhyr | -.0000244 .0000123 -1.98 0.047 -.0000485 -2.80e-07 commerd | .0395438 .1080101 0.37 0.714 -.1721606 .2512481 oleveld | -.0137476 .0838956 -0.16 0.870 -.1781866 .1506914 aleveld | .100956 .0844621 1.20 0.232 -.0645933 .2665052 degreed | .0845267 .07487 1.13 0.259 -.0622217 .2312751 hlstatc4 | .2366634 .0112535 21.03 0.000 .214606 .2587208 hlltdd | -.1295628 .0269839 -4.80 0.000 -.1824523 -.0766732 hldsbld | -.0462001 .0308908 -1.50 0.135 -.1067474 .0143472 healthdown | .0447834 .014617 3.06 0.002 .0161335 .0734334 healthup | -.0435577 .0136471 -3.19 0.001 -.0703067 -.0168088 aidhigh | -.1523754 .0506073 -3.01 0.003 -.2515678 -.053183 nch1218 | .0152734 .0164506 0.93 0.353 -.0169704 .0475173 nch511 | .029488 .0166972 1.77 0.077 -.0032393 .0622152 nch04 | -.0007557 .020014 -0.04 0.970 -.039984 .0384726 hhsize | -.0466322 .0113936 -4.09 0.000 -.0689642 -.0243002 livtog | .035897 .0430668 0.83 0.405 -.0485157 .1203097 nevmar | -.1138988 .0538264 -2.12 0.034 -.2194009 -.0083968 wid3 | -.021793 .0679357 -0.32 0.748 -.1549499 .1113638 wid2 | -.2123305 .0812449 -2.61 0.009 -.371574 -.053087 wid1 | -.5906253 .08277 -7.14 0.000 -.752858 -.4283927 willwid | -.1851185 .086977 -2.13 0.033 -.3555971 -.0146398 willdiv | -.6964513 .1531085 -4.55 0.000 -.9965505 -.396352 willsep | -.5599061 .0721541 -7.76 0.000 -.7013312 -.418481 sep3 | -.3661033 .0919599 -3.98 0.000 -.5463485 -.185858 sep2 | -.3695407 .1014187 -3.64 0.000 -.5683257 -.1707558 sep1 | -.6250491 .0736534 -8.49 0.000 -.7694128 -.4806854 div3 | -.2100661 .056355 -3.73 0.000 -.3205244 -.0996078 div2 | .0803961 .1599251 0.50 0.615 -.2330639 .3938562 div1 | -.3020322 .0786556 -3.84 0.000 -.4562004 -.1478639 willmar | .0620972 .0494525 1.26 0.209 -.0348319 .1590263 mar2 | .1377419 .0427746 3.22 0.001 .0539018 .221582 mar1 | .257104 .0457084 5.62 0.000 .1675136 .3466944 age12 | -.1374896 .0194677 -7.06 0.000 -.1756471 -.099332 age2 | .002871 .0003897 7.37 0.000 .0021073 .0036347 age3 | -.0000183 2.45e-06 -7.49 0.000 -.0000231 -.0000135 hsprbhd | -.0347637 .0192373 -1.81 0.071 -.0724697 .0029423 hsprbid | -.001595 .0166778 -0.10 0.924 -.0342842 .0310942 hsprbqd | -.0443657 .0155086 -2.86 0.004 -.0747632 -.0139681 ssupa1d | .0626078 .027857 2.25 0.025 .0080069 .1172086 ssupa2d | .1315426 .0282479 4.66 0.000 .0761755 .1869098 oprlg2d | .0072097 .0229683 0.31 0.754 -.0378092 .0522286 orgafd | .0179945 .0261499 0.69 0.491 -.0332605 .0692495 frnbd | .0368309 .011783 3.13 0.002 .0137357 .0599261

27

lactad | .0429375 .0144758 2.97 0.003 .0145643 .0713107 lactkd | .0568618 .0197404 2.88 0.004 .0181698 .0955538 ltsickd | -.3053704 .0478187 -6.39 0.000 -.3990971 -.2116438 retiredd | .0426969 .0305098 1.40 0.162 -.0171037 .1024974 unempd | -.2666288 .0396755 -6.72 0.000 -.3443944 -.1888632 othactd | .0283873 .0899878 0.32 0.752 -.1479927 .2047673 maternd | .3462968 .0776989 4.46 0.000 .1940037 .4985899 selfempd | .0172805 .0327303 0.53 0.598 -.0468722 .0814333 famcard | -.0311384 .0300964 -1.03 0.301 -.0901287 .0278518 studentd | .1086824 .0572163 1.90 0.058 -.003464 .2208287 govtraind | -.5217883 .1994235 -2.62 0.009 -.9126668 -.1309098 _cons | 6.745527 .3869234 17.43 0.000 5.987141 7.503913 -------------+---------------------------------------------------------------sigma_u | .86631222 sigma_e | .838376 rho | .51638348 (fraction of variance due to u_i) -----------------------------------------------------------------------------F test that all u_i=0: F(5324, 30165) = 5.93 Prob > F = 0.0000

28

Model 4: Life satisfaction Random Effects Ordered Probit Random Effects Ordered Probit Log likelihood = -46169.479

Number of obs LR chi2(58) Prob > chi2

= = =

35548 2900.21 0.0000

-----------------------------------------------------------------------------lfsato | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------eq1 | lfihhyr | .0389339 .0135884 2.87 0.004 .012301 .0655667 incomeupd2 | .040119 .0159815 2.51 0.012 .0087959 .0714421 incomedod2 | .0051744 .017099 0.30 0.762 -.028339 .0386878 xphsdfd | -.3521643 .0329547 -10.69 0.000 -.4167543 -.2875742 meanfihhyr | -.0000275 7.25e-06 -3.80 0.000 -.0000417 -.0000133 commerd | -.1522174 .0551551 -2.76 0.006 -.2603194 -.0441154 oleveld | -.2418097 .045498 -5.31 0.000 -.3309841 -.1526354 aleveld | -.27075 .0509912 -5.31 0.000 -.3706908 -.1708091 degreed | -.2698577 .0393339 -6.86 0.000 -.3469507 -.1927646 hlstatc4 | .4234055 .0131331 32.24 0.000 .3976651 .4491459 hlltdd | -.2111716 .0331247 -6.38 0.000 -.2760948 -.1462485 hldsbld | -.1005425 .0368415 -2.73 0.006 -.1727506 -.0283344 healthdown | .1400199 .0183237 7.64 0.000 .104106 .1759337 healthup | -.1129964 .0173274 -6.52 0.000 -.1469574 -.0790353 aidhigh | -.2659239 .0605732 -4.39 0.000 -.3846452 -.1472026 nch1218 | -.0073277 .0193966 -0.38 0.706 -.0453444 .0306891 nch511 | .0197336 .0188986 1.04 0.296 -.0173071 .0567743 nch04 | -.0137778 .0238325 -0.58 0.563 -.0604886 .032933 hhsize | -.0482791 .0128767 -3.75 0.000 -.073517 -.0230412 livtog | -.1098078 .0402808 -2.73 0.006 -.1887567 -.0308588 nevmar | -.376019 .0457333 -8.22 0.000 -.4656546 -.2863834 wid3 | -.2374694 .0588338 -4.04 0.000 -.3527815 -.1221572 wid2 | -.4589777 .0967443 -4.74 0.000 -.648593 -.2693624 wid1 | -.8719906 .1000987 -8.71 0.000 -1.068181 -.6758008 willwid | -.3694581 .1094071 -3.38 0.001 -.583892 -.1550241 willdiv | -.9251681 .1889064 -4.90 0.000 -1.295418 -.5549183 willsep | -.7528316 .0877328 -8.58 0.000 -.9247848 -.5808784 sep3 | -.6771403 .1077566 -6.28 0.000 -.8883394 -.4659412 sep2 | -.5674473 .1246272 -4.55 0.000 -.8117122 -.3231824 sep1 | -.8437972 .0886725 -9.52 0.000 -1.017592 -.6700023 div3 | -.5037037 .0525651 -9.58 0.000 -.6067294 -.4006779 div2 | -.0827405 .198868 -0.42 0.677 -.4725145 .3070336 div1 | -.5112765 .0941403 -5.43 0.000 -.6957881 -.3267648 willmar | -.0172788 .0597907 -0.29 0.773 -.1344663 .0999088 mar2 | .1379082 .0536243 2.57 0.010 .0328065 .2430099 mar1 | .2717273 .0560693 4.85 0.000 .1618334 .3816212 age12 | -.2061124 .0189118 -10.90 0.000 -.2431789 -.1690459 age2 | .0040536 .0003765 10.77 0.000 .0033156 .0047916 age3 | -.0000232 2.36e-06 -9.82 0.000 -.0000278 -.0000186 hsprbhd | -.0969351 .0238153 -4.07 0.000 -.1436122 -.0502579 hsprbid | -.0525839 .0204816 -2.57 0.010 -.0927271 -.0124406 hsprbqd | -.0689845 .0193649 -3.56 0.000 -.106939 -.03103 ssupa1d | .1799813 .0345117 5.22 0.000 .1123396 .2476231 ssupa2d | .2956352 .0349542 8.46 0.000 .2271262 .3641443 oprlg2d | .0620673 .0269026 2.31 0.021 .0093392 .1147954 orgafd | .0274361 .0306089 0.90 0.370 -.0325562 .0874284 frnbd | .0686778 .0146138 4.70 0.000 .0400352 .0973203 lactad | .0841722 .0173865 4.84 0.000 .0500953 .1182491 lactkd | .0872885 .0235131 3.71 0.000 .0412038 .1333733 ltsickd | -.2656099 .0542284 -4.90 0.000 -.3718956 -.1593241 retiredd | .1354017 .036487 3.71 0.000 .0638886 .2069149 unempd | -.2823634 .0487423 -5.79 0.000 -.3778967 -.1868302

29

othactd | .0665156 .1141493 0.58 0.560 -.1572128 .2902441 maternd | .5219591 .1019673 5.12 0.000 .3221068 .7218114 selfempd | .0234394 .0362314 0.65 0.518 -.0475728 .0944516 famcard | .0268785 .0342944 0.78 0.433 -.0403373 .0940942 studentd | .128017 .0697999 1.83 0.067 -.0087882 .2648222 govtraind | -.5619475 .2523193 -2.23 0.026 -1.056484 -.0674107 -------------+---------------------------------------------------------------_cut1 | _cons | -5.46237 .3412626 -16.01 0.000 -6.131232 -4.793508 -------------+---------------------------------------------------------------_cut2 | _cons | -4.87131 .3407055 -14.30 0.000 -5.539081 -4.20354 -------------+---------------------------------------------------------------_cut3 | _cons | -4.090116 .3403493 -12.02 0.000 -4.757189 -3.423044 -------------+---------------------------------------------------------------_cut4 | _cons | -3.165954 .3401472 -9.31 0.000 -3.832631 -2.499278 -------------+---------------------------------------------------------------_cut5 | _cons | -1.898871 .3399399 -5.59 0.000 -2.565141 -1.232601 -------------+---------------------------------------------------------------_cut6 | _cons | -.2792137 .3396913 -0.82 0.411 -.9449965 .3865691 -------------+---------------------------------------------------------------rho | _cons | .4975046 .0064989 76.55 0.000 .4847669 .5102423

30

Model 5: GHQ12 response to question asking about experiencing unhappiness conditional fixed effects logistic regression Conditional fixed-effects logistic regression Group variable (i): pid

Log likelihood

= -10528.417


= =

27013 3497


2 7.7 8

LR chi2(58) Prob > chi2

= =

548.26 0.0000

-----------------------------------------------------------------------------unhappy | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------lfihhyr | .0100644 .0352345 0.29 0.775 -.058994 .0791228 incomeupd2 | -.1254744 .037109 -3.38 0.001 -.1982066 -.0527422 incomedod2 | -.0440928 .0404003 -1.09 0.275 -.123276 .0350904 xphsdfd | .3872882 .0889799 4.35 0.000 .2128908 .5616855 meanfihhyr | .0000314 .0000369 0.85 0.395 -.000041 .0001038 commerd | .1782155 .3283742 0.54 0.587 -.4653861 .8218171 oleveld | .1741949 .2672956 0.65 0.515 -.3496948 .6980846 aleveld | .2288609 .2666351 0.86 0.391 -.2937343 .751456 degreed | .0905749 .2454688 0.37 0.712 -.3905351 .5716849 hlstatc4 | -.4122472 .0342042 -12.05 0.000 -.4792863 -.3452081 hlltdd | .1943848 .0893415 2.18 0.030 .0192786 .369491 hldsbld | .2056547 .0977781 2.10 0.035 .0140132 .3972963 healthdown | -.031747 .0446321 -0.71 0.477 -.1192243 .0557303 healthup | .0152195 .0400508 0.38 0.704 -.0632787 .0937177 aidhigh | .6116504 .1745517 3.50 0.000 .2695353 .9537655 nch1218 | .0122712 .0508173 0.24 0.809 -.0873288 .1118713 nch511 | -.110969 .0507639 -2.19 0.029 -.2104644 -.0114736 nch04 | -.0348125 .0596667 -0.58 0.560 -.151757 .082132 hhsize | .0428241 .0346021 1.24 0.216 -.0249947 .1106429 livtog | -.1569269 .1306709 -1.20 0.230 -.4130371 .0991833 nevmar | .0915978 .1626511 0.56 0.573 -.2271925 .4103881 wid3 | .0085651 .214747 0.04 0.968 -.4123313 .4294614 wid2 | .5416577 .2589598 2.09 0.036 .0341057 1.04921 wid1 | 2.698117 .3626048 7.44 0.000 1.987425 3.40881 willwid | .2046466 .2653362 0.77 0.441 -.3154028 .7246961 willdiv | 1.063737 .5369571 1.98 0.048 .0113202 2.116153 willsep | .929269 .2394432 3.88 0.000 .4599689 1.398569 sep3 | .3908333 .2839201 1.38 0.169 -.1656399 .9473065 sep2 | .562589 .3087715 1.82 0.068 -.042592 1.16777 sep1 | 1.319464 .2639706 5.00 0.000 .8020911 1.836837 div3 | .2158048 .1813068 1.19 0.234 -.1395501 .5711596 div2 | -.3877073 .5634514 -0.69 0.491 -1.492052 .7166372 div1 | .4743941 .2531623 1.87 0.061 -.0217949 .9705832 willmar | .024049 .1440598 0.17 0.867 -.2583029 .306401 mar2 | -.1061151 .1254207 -0.85 0.398 -.3519352 .139705 mar1 | -.331713 .1309838 -2.53 0.011 -.5884364 -.0749895 age12 | .1337099 .0598548 2.23 0.025 .0163966 .2510233 age2 | -.003247 .0012046 -2.70 0.007 -.005608 -.000886 age3 | .0000209 7.59e-06 2.76 0.006 6.05e-06 .0000358 hsprbhd | .0528076 .0608249 0.87 0.385 -.0664069 .1720221 hsprbid | .1656543 .0518976 3.19 0.001 .0639368 .2673719 hsprbqd | .0130964 .0475475 0.28 0.783 -.0800949 .1062878 ssupa1d | -.2598378 .0928132 -2.80 0.005 -.4417482 -.0779273 ssupa2d | -.338137 .0944124 -3.58 0.000 -.5231819 -.1530921 oprlg2d | -.0138206 .0705951 -0.20 0.845 -.1521845 .1245433 orgafd | .0394077 .0785532 0.50 0.616 -.1145538 .1933693

31

frnbd lactad lactkd ltsickd retiredd unempd othactd maternd selfempd famcard studentd govtraind

| | | | | | | | | | | |

-.0359227 -.0207055 -.0168583 .2241209 -.1987133 .430861 -.136498 -.1923563 .0007807 -.0653158 -.1530767 -.4011138

.0355001 .0437825 .05872 .1722429 .0953214 .129206 .2850242 .2231781 .0968636 .0907267 .1658548 .5922346

-1.01 -0.47 -0.29 1.30 -2.08 3.33 -0.48 -0.86 0.01 -0.72 -0.92 -0.68

0.312 0.636 0.774 0.193 0.037 0.001 0.632 0.389 0.994 0.472 0.356 0.498

-.1055016 -.1065176 -.1319473 -.113469 -.3855399 .1776218 -.6951351 -.6297773 -.1890685 -.2431369 -.4781461 -1.561872

.0336562 .0651067 .0982307 .5617109 -.0118867 .6841001 .4221391 .2450646 .1906298 .1125052 .1719926 .7596447

32

Model 6: Life satisfaction, fixed effects for males only Fixed-effects (within) regression Group variable (i): pid


= =

15788 2360

R-sq:


1 6.7 7


corr(u_i, Xb)

= 0.1693

F(58,13370) Prob > F

= =

12.64 0.0000

-----------------------------------------------------------------------------lfsato | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------lfihhyr | .024472 .0163858 1.49 0.135 -.0076465 .0565905 incomeupd2 | .0700484 .0172474 4.06 0.000 .036241 .1038557 incomedod2 | .0129866 .0185004 0.70 0.483 -.0232768 .04925 xphsdfd | -.2531036 .0377922 -6.70 0.000 -.3271817 -.1790255 meanfihhyr | -.0000411 .0000171 -2.41 0.016 -.0000745 -7.64e-06 commerd | -.0837131 .1702518 -0.49 0.623 -.4174308 .2500046 oleveld | .0108186 .1218638 0.09 0.929 -.2280517 .2496889 aleveld | -.0532978 .1193411 -0.45 0.655 -.2872233 .1806277 degreed | .0229496 .1072315 0.21 0.831 -.1872394 .2331385 hlstatc4 | .2340508 .0160999 14.54 0.000 .2024927 .2656089 hlltdd | -.1578469 .0413654 -3.82 0.000 -.2389289 -.0767649 hldsbld | -.0713384 .0431916 -1.65 0.099 -.156 .0133232 healthdown | .0605973 .0205262 2.95 0.003 .0203629 .1008316 healthup | -.0702952 .019189 -3.66 0.000 -.1079084 -.0326819 aidhigh | -.1328024 .073133 -1.82 0.069 -.2761533 .0105486 nch1218 | .0015212 .0231503 0.07 0.948 -.0438567 .0468992 nch511 | .0317301 .023105 1.37 0.170 -.0135591 .0770192 nch04 | .0032235 .027596 0.12 0.907 -.0508685 .0573156 hhsize | -.0521143 .0154981 -3.36 0.001 -.0824929 -.0217357 livtog | -.0390619 .0606639 -0.64 0.520 -.1579717 .0798478 nevmar | -.1905904 .0735375 -2.59 0.010 -.3347344 -.0464465 wid3 | .202739 .1184293 1.71 0.087 -.0293992 .4348772 wid2 | .1746979 .1326301 1.32 0.188 -.0852758 .4346715 wid1 | -.2895457 .1346866 -2.15 0.032 -.5535506 -.0255409 willwid | .1692215 .1426814 1.19 0.236 -.1104542 .4488972 willdiv | -.4265013 .2488845 -1.71 0.087 -.9143501 .0613475 willsep | -.587537 .1046343 -5.62 0.000 -.7926351 -.382439 sep3 | -.3783115 .1346109 -2.81 0.005 -.6421679 -.1144551 sep2 | -.2083645 .1549851 -1.34 0.179 -.5121572 .0954281 sep1 | -.7407758 .1073858 -6.90 0.000 -.9512671 -.5302845 div3 | -.3199804 .0904043 -3.54 0.000 -.4971856 -.1427753 div2 | -.2911285 .2484527 -1.17 0.241 -.7781309 .195874 div1 | -.2644994 .120316 -2.20 0.028 -.5003358 -.0286631 willmar | -.0390076 .0680321 -0.57 0.566 -.1723601 .0943449 mar2 | .0561451 .0580006 0.97 0.333 -.0575442 .1698344 mar1 | .1022172 .0635457 1.61 0.108 -.0223414 .2267758 age12 | -.1759402 .0274088 -6.42 0.000 -.2296654 -.122215 age2 | .0034256 .0005496 6.23 0.000 .0023483 .0045028 age3 | -.0000207 3.47e-06 -5.97 0.000 -.0000275 -.0000139 hsprbhd | -.0382608 .0272179 -1.41 0.160 -.0916117 .0150901 hsprbid | -.0140031 .023132 -0.61 0.545 -.0593451 .0313389 hsprbqd | -.0722633 .0220224 -3.28 0.001 -.1154303 -.0290964 ssupa1d | .0728233 .034168 2.13 0.033 .0058491 .1397974 ssupa2d | .121728 .0347874 3.50 0.000 .0535398 .1899162 oprlg2d | -.0428393 .0365518 -1.17 0.241 -.114486 .0288073 orgafd | .0064136 .0423851 0.15 0.880 -.0766671 .0894944 frnbd | .0197244 .0167679 1.18 0.239 -.013143 .0525919

33

lactad | .0494383 .0208416 2.37 0.018 .0085857 .0902909 lactkd | .0229202 .0292702 0.78 0.434 -.0344535 .080294 ltsickd | -.3941821 .0671856 -5.87 0.000 -.5258755 -.2624888 retiredd | .011634 .0455658 0.26 0.798 -.0776815 .1009495 unempd | -.3442022 .050775 -6.78 0.000 -.4437283 -.244676 othactd | -.0528543 .1286002 -0.41 0.681 -.304929 .1992203 maternd | .9106852 .5960674 1.53 0.127 -.2576911 2.079062 selfempd | .0260659 .0385458 0.68 0.499 -.0494894 .1016212 famcard | -.1203719 .1257367 -0.96 0.338 -.3668335 .1260897 studentd | .1787029 .0848621 2.11 0.035 .0123612 .3450447 govtraind | -.3096754 .2566139 -1.21 0.228 -.812675 .1933242 _cons | 7.861685 .5485808 14.33 0.000 6.786389 8.936981 -------------+---------------------------------------------------------------sigma_u | .824823 sigma_e | .77735656 rho | .52960019 (fraction of variance due to u_i) -----------------------------------------------------------------------------F test that all u_i=0: F(2359, 13370) = 6.18 Prob > F = 0.0000 _______________________________________________________________________________

Model 7: Life satisfaction, fixed effects for females only Fixed-effects (within) regression Group variable (i): pid


= =

19760 2965

R-sq:


1 6.7 7


corr(u_i, Xb)

= 0.1445

F(58,16737) Prob > F

= =

15.13 0.0000

-----------------------------------------------------------------------------lfsato | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------lfihhyr | .0190665 .0156937 1.21 0.224 -.0116948 .0498279 incomeupd2 | .0013004 .0177236 0.07 0.942 -.0334397 .0360405 incomedod2 | -.0028632 .0189543 -0.15 0.880 -.0400156 .0342892 xphsdfd | -.2660309 .0368473 -7.22 0.000 -.3382554 -.1938063 meanfihhyr | -8.87e-06 .0000175 -0.51 0.612 -.0000432 .0000254 commerd | .1113964 .1422769 0.78 0.434 -.1674813 .3902742 oleveld | -.0106371 .115184 -0.09 0.926 -.23641 .2151357 aleveld | .2300745 .1183384 1.94 0.052 -.0018813 .4620303 degreed | .1357948 .1036834 1.31 0.190 -.0674357 .3390253 hlstatc4 | .2393292 .0155957 15.35 0.000 .20876 .2698985 hlltdd | -.1098815 .0357523 -3.07 0.002 -.1799597 -.0398033 hldsbld | -.0237538 .0435832 -0.55 0.586 -.1091816 .0616739 healthdown | .0333084 .0205354 1.62 0.105 -.0069432 .0735601 healthup | -.0241366 .0191527 -1.26 0.208 -.0616779 .0134047 aidhigh | -.1672676 .0697695 -2.40 0.017 -.3040231 -.0305121 nch1218 | .0239847 .0231457 1.04 0.300 -.0213832 .0693527 nch511 | .0263581 .0238527 1.11 0.269 -.0203956 .0731118 nch04 | -.0050584 .0286391 -0.18 0.860 -.0611941 .0510774 hhsize | -.0414248 .0165924 -2.50 0.013 -.0739477 -.0089019 livtog | .0807984 .0606203 1.33 0.183 -.0380237 .1996206 nevmar | -.0652909 .0780255 -0.84 0.403 -.2182291 .0876472 wid3 | -.1080321 .085264 -1.27 0.205 -.2751585 .0590943 wid2 | -.3864392 .104289 -3.71 0.000 -.5908566 -.1820217 wid1 | -.7160912 .1064292 -6.73 0.000 -.9247036 -.5074788

34

willwid | -.3430775 .1112124 -3.08 0.002 -.5610656 -.1250893 willdiv | -.8318679 .1967817 -4.23 0.000 -1.217581 -.4461549 willsep | -.534582 .0990704 -5.40 0.000 -.7287705 -.3403934 sep3 | -.3456824 .1259206 -2.75 0.006 -.5925 -.0988648 sep2 | -.4697751 .134729 -3.49 0.000 -.7338582 -.205692 sep1 | -.5426259 .1010154 -5.37 0.000 -.7406268 -.3446251 div3 | -.1539214 .0738599 -2.08 0.037 -.2986946 -.0091481 div2 | .2911671 .2105598 1.38 0.167 -.1215524 .7038865 div1 | -.3159979 .1048277 -3.01 0.003 -.5214712 -.1105246 willmar | .1485777 .0707377 2.10 0.036 .0099244 .287231 mar2 | .2109521 .0619655 3.40 0.001 .0894932 .3324111 mar1 | .3856564 .0647703 5.95 0.000 .2586996 .5126131 age12 | -.1078948 .0274887 -3.93 0.000 -.1617755 -.054014 age2 | .002458 .0005498 4.47 0.000 .0013803 .0035357 age3 | -.0000166 3.43e-06 -4.85 0.000 -.0000234 -9.91e-06 hsprbhd | -.0336979 .0268796 -1.25 0.210 -.0863847 .018989 hsprbid | .0082357 .0236761 0.35 0.728 -.038172 .0546434 hsprbqd | -.0264483 .0215943 -1.22 0.221 -.0687754 .0158788 ssupa1d | .0567105 .0450553 1.26 0.208 -.0316026 .1450237 ssupa2d | .1404288 .0455332 3.08 0.002 .0511789 .2296787 oprlg2d | .0321608 .0299028 1.08 0.282 -.0264518 .0907733 orgafd | .0202835 .0336412 0.60 0.547 -.0456568 .0862238 frnbd | .0481878 .0163991 2.94 0.003 .0160438 .0803319 lactad | .0370544 .0199883 1.85 0.064 -.0021249 .0762337 lactkd | .0800941 .0266975 3.00 0.003 .0277642 .132424 ltsickd | -.2427098 .0675346 -3.59 0.000 -.3750848 -.1103349 retiredd | .0608874 .0411735 1.48 0.139 -.0198171 .1415918 unempd | -.18755 .0615062 -3.05 0.002 -.3081087 -.0669914 othactd | .0780038 .1252058 0.62 0.533 -.1674128 .3234203 maternd | .3477094 .0830014 4.19 0.000 .1850179 .5104008 selfempd | -.0043569 .0565203 -0.08 0.939 -.1151427 .1064289 famcard | -.018202 .034014 -0.54 0.593 -.0848729 .048469 studentd | .0539145 .0776407 0.69 0.487 -.0982694 .2060985 govtraind | -.7294527 .303754 -2.40 0.016 -1.324843 -.1340629 _cons | 5.847417 .5431285 10.77 0.000 4.782828 6.912007 -------------+---------------------------------------------------------------sigma_u | .90782162 sigma_e | .8830697 rho | .51381837 (fraction of variance due to u_i) -----------------------------------------------------------------------------F test that all u_i=0: F(2964, 16737) = 5.70 Prob > F = 0.0000

35