Empirical Economics. Using panel data from the BHPS and its Understanding Society extension, we study life satisfaction LS and income over nearly two decades, for samples split by education, and age, to our knowledge for the first time. The highly educated went from lowest to highest LS, though their average income was always higher. In panel LS regressions with individual fixed effects, none of the income variables was significant for the highly educated. Education is correlated with both income and health—each of which, in turn, has a positive effect on life satisfaction LS.
Those with higher education generally have access to more interesting and better-paid jobs, together with other non-pecuniary benefits. Meanwhile, manual labour is systematically correlated with lower LS—so it is not surprising that higher education is generally considered to be beneficial for subjective well-being, happiness or LS, as well as for objective individual economic and social goals. It is, therefore, initially rather surprising that a previous study of LS with British Household Panel Survey BHPS data found negative or insignificant effects of higher education in various specifications with numerous controls, while the positive effect was robust in German SOEP data FitzRoy et al.
However, using only Wave 1 BHPS data, Clark and Oswald report a negative relationship between a more specific job satisfaction variable and both education and comparison income. Via analysis of Wave 6—14 of the BHPS data, Powdthavee shows mainly negative estimates for education controls in pooled OLS estimation of LS and insignificant estimates for fixed-effects estimation.
Green finds a negative effect of higher education on LS with Australian HILDA data using many controls, but Nikolaev and Rusakov find that higher education has a positive and increasing effect on LS from about the age of 35 in the same data set. Nikolaev also reports generally positive associations of education with various components of LS with the same data. Overall, the existing literature contains mixed findings. Here, we codings da alcolismo in Nikolaev the UK over a rather longer time series.
We extend the BHPS panel with the corresponding component of the Understanding Society data set part of which involves individuals drawn from the BHPS to study the development of life codings da alcolismo in Nikolaev LS and income across a couple of decades, in different education and age groups. The subsequent decline and partial recovery were steepest for the highly educated.
That group also saw a rise in average household size around the time of the crash, whereas the households of the least educated tended to become smaller. LS, on the other hand, rose fastest for the highly educated from a surprising lowest to being highest among the education groups, a significant increase over a period when the proportion of highly educated roughly doubled. LS declined steeply for the low education group up to and beyond the crash, in spite of their rising income.
Overall, average LS declined pre-crash despite rapid income growth. Except for the highly educated before the crash, whose income and LS both increased, these results contradict standard economic growth theories, but are consistent with the Easterlin paradox found in macro data. The older low educated suffered the steepest decline of LS over the whole period—from 5. However, there is a strong cyclical relationship between real GDP per capita and SWB, codings da alcolismo in Nikolaev unemployment being a major cause of unhappiness that moves with the cycle, and critics have usually failed to distinguish carefully between trend growth and deviations from the trend Easterlin The paradox is deepened by the fact that richer people are generally happier than the poor in any one country at a given time, though many other factors such as health, family and employment are more important than income but usually also correlated with income codings da alcolismo in Nikolaev education.
Other possible factors that could offset the benefits of growing average real incomes are rising inequality, reduced social mobility and the widely observed decline in many components of social capital, such as community, personal and family relationships, as well as security of employment—although these issues are beyond the scope of this paper.
None of them seem to offer explanations for our surprising results. Adaptation to higher income was found to have only codings da alcolismo in Nikolaev effects by Layard et al. It thus seems appropriate to use available large panel data sets, which follow individuals over time, to examine the effects of income potentially, its level and growth on their well-being, while controlling for both individual fixed effects and changing characteristics recorded in the survey data.
Our main innovation here is to disaggregate the sample by three levels of education codings da alcolismo in Nikolaev by age. To the best of our knowledge, the education split in this context is a novel approach, which yields some really surprising results, including the lack of any significant own-or-comparison income effects on the LS of the highly educated, although their LS increased more than in other groups in the period.
Another puzzle is why the high education group had lowest LS initially, but overtook the less educated to become most satisfied while higher education was rapidly expanding—see Blundell et al. Our estimation approach is quite similar to FitzRoy et al.
These include marital status including cohabitingnumber of children, health status, education, labour market status, time spent in panel, whether year of last interview, log household size, age via six age dummies to create seven age categorieshousing ownership status, wave number and regions. We also codings da alcolismo in Nikolaev the alternative of a traditional polynomial age specification—and found results quite similar to FitzRoy et al.
We also tested for any additional effects of regional gross value added GVA per capita, in unreported regressions.
It is clear from Fig. Like Pfaff and Hiratawe found little systematic effect of regional GVA, which is not surprising given the inclusion of household income, and we also add comparison income. In contrast to their claims, this hardly supports Easterlin, since on average household incomes grow with macro income measures, and are closely related to LS in cross section, and in some of our panel results.
There is also a bounce-back in LS between Waves 22 and Most of the compositional aspects in the data set are unsurprising. For instance, the highly educated group is drawn disproportionately from Greater London, South East England and Scotland. Its members are more likely to be employed, and less likely to be unemployed or to be long-term sick or disabled across the full age range and on both sides of the age split.
They are also less likely to rent their dwelling. Dependent variable Life satisfaction. Controls for marital status including cohabitingnumber of children, health status, education, labour market status, time in panel, year of last interview, household size, age groupings, housing ownership, wave number and regions are included.
Standard errors clustered at the individual level, robust t-statistics in parentheses. Although the codings da alcolismo in Nikolaev of pooled cross-sectional estimation is problematic for our unbalanced panel data, we have included Table A2a in Online Appendix, for additional context—with standard errors clustered this time by the comparison income grouping codings da alcolismo in Nikolaev.
Estimates for own household income are also broadly in line with that earlier work. Proto and Rustichini argue—quite plausibly—that such traits are very stable for most people across much of their lifespan. However, fixed-effects estimation could not use raw Big Five scores taken from a single wave. Much of the analysis in Proto and Rustichini is based on the interaction between standardised Big Five scores and a quadratic function of income, and they also include random-effects estimation.
For our investigations, the assumed zero correlation between the disturbances and the regressors, inherent in random-effects estimation, appears to be a binding and distorting constraint. Also, since Understanding Society Wave 3 Wave 20 within our composite panel, for — also includes Big Five data—as used by Brown and Taylor to examine charitable giving behaviour—we also incorporated that information. Our fixed-effects estimation highlights a major puzzle—the almost complete lack of significance of any of the income variables in explaining rising LS for the high education sample.
Exploring these factors remains an important topic for codings da alcolismo in Nikolaev research. Our results contradict standard findings from growth and happiness economics, but declining LS for the older least educated, in spite of growing real income until aboutis certainly consistent with the support of this group for populist movements in several countries, including Brexit in the UK. However, the obvious correlation between rising income and LS for the highly educated up to the crash leaves the insignificance of income variables in fixed-effects regressions for this group all the more surprising, and to the best of our knowledge, unprecedented in happiness economics.
The codings da alcolismo in Nikolaev highly educated have the lowest LS for most of the period, right up to the recession, in spite of having the highest incomes and presumably the best jobs in the age group, and in contradiction to standard findings for other countries. We are left with major puzzles and an obvious need for more research in this area. Helliwell et al. Rich countries are also generally happier than poor countries, though there is much variation within these groups and possible problems with international comparisons of SWB which do not concern us here.
With interviews taking place across calendar year boundaries and two boundaries for Waves 21—22a given Wave will see certain regressors defined according the year of interview, as appropriate codings da alcolismo in Nikolaev each individual. A cut-off of 9. This reduces the number of observations bywhilst a further 19 cases are excluded due to issues relating to the identification of individuals across waves.
By definition, regressors measuring changes in household income between successive waves are not available for the first wave in which any individual responds. This is some 18, observations. The use of a simple average of house prices across all dwellings is a simplification, but it does enable the availability of a longer continuous run of data. In fact, the appearance of Figs.
The percentage increase among women over the same period was even greater, but we do not pursue the gender dimension further in this paper. It should be noted that there is noticeable attrition between Waves 6 and differential attrition by education level might exaggerate the rise in the percentage of highly qualified. Nikolaev and Rusakov find a positive effect of education on LS that increases with age in Australian panel data. FitzRoy et al. Meanwhile, older people may tend to realise that they are unlikely to attain higher incomes of their peers—if they have already had many years of codings da alcolismo in Nikolaev, with such incomes remaining unrealised.
However, that earlier work did not have additional regressors for upward and downward changes in own income. Unfortunately, such trends are not picked up by means of the standard labour market controls included within our regression specifications.
A neuroticism missing value codings da alcolismo in Nikolaev was included, as a codings da alcolismo in Nikolaev. Its attached estimate was negative codings da alcolismo in Nikolaev insignificant under fixed-effects estimation—and of the same sign and greater significance for random effects, and for pooled OLS estimation of the highly educated sub-sample.
A standard disclaimer applies. Thanks are due to conference participants where earlier versions of this paper were presented Scottish Economic Society in Perth; Work, Pensions and Labour Economics Study Group in Sheffieldcodings da alcolismo in Nikolaev also to two codings da alcolismo in Nikolaev referees and an Associate Editor of this journal.
This article does not contain any studies performed by any of the authors with human participants or animals. Skip to main content Skip to sections. Advertisement Hide. Download PDF. Empirical Economics pp 1—20 Cite as. Education, income and happiness: panel evidence for the UK. Open Access. First Online: 14 November Institute for Social and Economic Research that relate to active, consenting former members of the BHPS sample, covering a period 6 from — to — An initial baseline ofobservations is available, across the full income range.
However, as is evident in Fig. Also evident from Fig. For regression analysis, we generate results for up toobservations across 23, individuals, with those cases where there are missing values, and the highest income outliers, 7 excluded. Open image in new window. A plausible hypothesis is that those with higher education, who generally have the best-paid and most interesting jobs, would be most likely to enjoy increasing life satisfaction with higher incomes, so we split the sample into three groups.
However, no ISCED codings are yet available for the Understanding Society waves—so that codings da alcolismo in Nikolaev three-way split had to be undertaken on the basis of a less sophisticated derived highest qualification variable.
In Online Appendix, sample means are shown for many of the controls in Table A1a: the codings da alcolismo in Nikolaev is also split by education level see Fig. We also follow Moulton in recognising the potential cluster related effect of aggregate regressors on standard errors.
Given that we are focusing on the estimation of individual-specific fixed-effects regressions, we assume clustering at the level of the individual.
For the crucial test of the effects of income on LS in different education groups, we include deflated own household income for the month before interview codings da alcolismo in Nikolaev comparison peer group income separately.