Abstract
There are strong two-way links between parent and child happiness (life satisfaction), even for ‘children’ who have grown up, moved to their own home and partnered themselves. German panel evidence shows that transmission of (un)happiness from parents to children is partly due to transmission of values and behaviors known to be associated with happiness (Headey et al. in Proc Natl Acad Sci 107(42):17922–17926, 2010, in Soc Indic Res doi:10.1007/s11205-012-0079-8,2012). These values and behaviors include giving priority to pro-social and family values, rather than material values, maintaining a preferred balance between work and leisure, active social and community participation, and regular exercise. Both parents have about equal influence on the values and behaviors which children adopt. However, the life satisfaction of adult ‘children’ continues to be directly influenced by the life satisfaction of their mothers, with the influence of fathers being only indirect, via transmission of values and behaviors. There appears to be a lifelong happiness dividend (or unhappiness dividend) due to parenting. Structural equation models with two-way causation indicate that the life satisfaction of offspring can significantly affect the satisfaction of their parents, as well as vice versa, long after the ‘children’ have left home. Data come from 25 waves of the German Socio-Economic Panel Survey (SOEP 1984–2008). SOEP is the only panel survey worldwide in which data on life satisfaction have been obtained from parents and an adequate sub-sample of children no longer living in the parental home.
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Notes
It is also more common for women to be the immediate instigators of marital break-up than men (Headey et al. 2005).
Only 64 daughters and 35 sons, who had been raised by single mothers for five or more years during their childhood and adolescence, were now living separately from their parents, and had provided necessary data on life satisfaction, traits, values and behaviors.
Lucas (2008) notes that some studies report modest correlations between happiness and traits A and C. Trait O is usually found to be unrelated to happiness.
Ten items were included in 1990, 1992 and 1995 and then nine in 2004 and 2008. The item dropped in 2004 and 2008 related to the importance of having a wide circle of friends, which loaded on the pro-social factor.
The correlations have varied from year to year but are usually around 0.3.
Mean values are imputed for the nearest two adjacent years in which the questions were asked.
By definition, no bias occurs if cases are missing completely at random (MCAR). However, it is more usual for cases to be missing at random (MAR); that is, missingness can be associated with particular values of variables. For example, it is known that very high and very low income earners are less likely than others to answer income questions. If data are MAR, it appears preferable to impute rather than use the standard computing option of ‘listwise deletion’, which removes any case missing for any single variable in the model.
Regression analysis is essentially a single equation technique. Regression estimates derived from multi-equation systems are likely to be biased, due to correlations between explanatory variables and error terms in some or all equations. A key assumption of OLS regression is that such correlations are zero.
ML estimates are usually consistent and asymptotically normal under the (not very restrictive) assumption of conditional normality (STATA 2011). Only paths or covariances linking conditioning (i.e. control) variables may not be consistent and asymptotically normal (even then, the main problem lies just with estimates of standard errors). These paths are not usually of substantive interest; substantive interest lies in paths (1) linking exogenous with endogenous variables and (2) between endogenous variables.
From a mathematical standpoint, a model can be viewed as a set of constraints—or a set of restricted paths—limiting the possibilities of simply reproducing the input data. Attempts by a researcher to improve his/her model involve modifying these constraints to improve model fit, subject to the theory/hypotheses underlying the model.
The standardized root mean squared residual (SRMR) is also based on comparing coefficients in the input and output matrices. However it is not applicable when missing data have been imputed, as is the case in this article.
Sample numbers of ‘children’ living apart from their parents are barely adequate for longitudinal analysis of changes in life satisfaction. This is particularly the case for dyad analysis and analysis involving two-way causation.
Specifically, we estimates covariances between (1) the error terms of neuroticism and extroversion (2) the error terms of the 3 measures of values (3) the error terms of the 3 measures of behavioral choices and (4) the error terms of the life satisfaction measures for parents and ‘children’.
If they are not included, model fit is always extremely poor, because covariances that are present in the input data are only partially accounted for by the causal (structural) links in the model.
Socio-economic variables relating to parents are included in the equations where a parent measure is the dependent variable. Socio-economic variables for ‘children’ are included in equations where the dependent variable is a ‘child’ measure.
It is also common to include variables for age-squared and age-cubed, if one’s research involves ‘following’ respondents into and out of middle age. In our equations only the age variable itself was found to be statistically significant; the reason being that we only ‘follow’ respondents for a few years after ‘children’ have left the parental home. In early analyses we also included a variable measuring ‘years since left the parental home’. This proved not to be statistically significant and was dropped.
These results are for 2006, when respondents were asked questions about where (how far away) their parents lived.
The Pearson correlations are: father’s neuroticism = −0.15, fathers’ extroversion = 0.03, fathers’ household net income = 0.12, father unemployed = −0.08 and father in ‘bad health’ = −0.08.
The correlation between daughter and father life satisfaction now (for daughters who left home more than 5 years ago) is 0.24, whereas the correlation between daughter life satisfaction now and father satisfaction 10 years ago is 0.13. The parallel correlations for daughters and mothers are, respectively, 0.24 and 0.15. Differences between both these pairs of correlations are significant at the 0.001 level.
The correlation between son and father life satisfaction now is 0.15, compared with 0.16 for son now and father 10 years ago. The parallel correlations for sons and mothers are 0.22 and 0.20. Differences between these pairs of correlations are not significant even at the 0.05 level.
Results for the full model are available as computer print-out from the authors.
The total effect appears not to exactly equal the sum of direct and indirect effects only due to rounding.
The total effect appears not to equal the sum of direct and indirect effects only due to rounding.
References
Aguche, A., & Trommsdorff, G. (2010). Transmission of well-being between mothers, fathers and adolescent children: The role of parenting and personality factors. In 9th international German socio-economic panel user conference, June 30–July 1.
Andrews, F. M., & Withey, S. B. (1976). Social indicators of well-being. New York: Plenum.
Argyle, M. (2001). The psychology of happiness (2nd ed.). London: Taylor and Francis.
Bentler, P. M. (1990). Comparative fit indices in structural models. Psychological Bulletin, 107, 238–246.
Bentler, P. M., & Freeman, E. H. (1983). Tests for stability in linear structural equation systems. Psychometrika, 48, 143–145.
Bentler, P. M., & Raykov, T. (2000). On measures of explained variance in non-recursive structural equation models. Journal of Applied Psychology, 85, 125–131.
Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley.
Bookwala, J., & Schulz, R. (1996). Spousal similarity in subjective well-being: The cardiovascular health study. Psychology and Aging, 11, 582–590.
Bourgeois, M., & Friedkin, N. E. (2001). The distant core: Social solidarity, social distance and interpersonal ties in core-periphery structures. Social Networks, 23, 245–260.
Bowlby, J. (1988). A secure base: Clinical applications of attachment theory. London: Routledge.
Bradburn, N. M. (1969). The structure of psychological well-being. Chicago: Aldine.
Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models. Newbury Park, CA: Sage.
Campbell, A., Converse, P. E., & Rodgers, W. R. (1976). The quality of American life. New York: Sage.
Clark, A. E., Diener, E., Georgellis, Y., & Lucas, R. E. (2008). Lags and leads in life satisfaction: A test of the baseline hypothesis. Economic Journal, 118, 222–243.
Costa, P. T., & McCrae, R. R. (1980). Influences of extroversion and neuroticism on subjective well-being. Journal of Personality and Social Psychology, 38, 668–678.
Costa, P. T., & McCrae, R. R. (1991). The NEO PI-R. Odessa, FL: PAR.
Diener, E., & Biswas-Diener, R. (2008). Happiness: Unlocking the mysteries of psychological wealth. Oxford: Blackwell.
Diener, E., & Seligman, M. E. P. (2004). Beyond money: Toward an economy of well-being. Psychological Science in the Public Interest, 5, 1–31.
Diener, E., Suh, E. M., Lucas, R. E., & Smith, H. L. (1999). Subjective well-being: Three decades of progress. Psychological Bulletin, 25, 276–302.
Duncan, O. D., Haller, A. O., & Portes, A. (1968). Peer influences on aspirations: A reinterpretation. American Journal of Sociology, 74, 119–137.
Dunn, E. W., Aknin, L. B., & Norton, M. I. (2008). Spending money on others promotes happiness. Science, 319, 1687–1688.
Emmons, R. A. (1986). Personal strivings: An approach to personality and subjective well-being. Journal of Personality and Social Psychology, 51, 1058–1068.
Frey, B. S., & Stutzer, A. (2002). What can economists learn from happiness research? Journal of Economic Literature, 40, 402–435.
Frijters, P., Haisken-DeNew, J. P., & Shields, M. A. (2004). Money does matter! Evidence from increasing real incomes and life satisfaction in East Germany following reunification. American Economic Review, 94, 730–741.
Fujita, F., & Diener, E. (2005). Life satisfaction set-point: Stability and change. Journal of Personality and Social Psycholgy, 88, 158–164.
Gerlitz, J. -Y., & Schupp, J. (2005). Zur Erhebung der Big-Five-basierten Persoenlichkeitsmerkmale im SOEP. http://www.diw.de/deutsche/produkte/publikationen/researchnotes/docs/papers/rn4.pdf. Accessed 12 April 2012.
Gottman, J., & Declaire, J. (1998). Raising an emotionally intelligent child. New York: Simon and Schuster.
Greene, W. H. (2008). Econometric analysis (7th ed.). New York: Prentice Hall.
Guven, C., Senik, C., & Stichnoth, H. (2010). You can’t be happier than your wife: Happiness gaps and divorce. SOEP Paper, 261.
Harlow, R. E., & Cantor, N. (1996). Still participating after all these years: A study of life task participation in later life. Journal of Personality and Social Psychology, 71, 1235–1249.
Headey, B. (1987). The subjective well-being and ill-being of families. In D. Tait (Ed.), Family well-being (pp. 14–18). Melbourne: Australian Institute of Family Studies.
Headey, B. W. (2008). Life goals matter to happiness: A revision of set-point theory. Social Indicators Research, 86, 213–231.
Headey, B. W., Muffels, R. J. A., & Wagner, G. G. (2010). Long-running German panel survey shows that personal and economic choices, not just genes, matter for happiness. Proceedings of the National Academy of Sciences, 107(42), 17922–17926.
Headey, B. W., Muffels, R. J.A., & Wagner, G. G. (2012). Choices which change life satisfaction: Similar results for Australia, Britain and Germany. Social Indicators Research (24 May), 1–24. Published online. doi:10.1007/s11205-012-0079-8.
Headey, B. W., Warren, D., & Harding, G. (2005). Changes in marital status 2001–2003; and satisfaction levels just before separation in families, incomes and jobs: A statistical report of the HILDA survey. Melbourne: Melbourne Institute of Applied Economic and Social Research.
Joreskog, K. G., & Sorbom, D. (1986). LISREL VI: Analysis of linear structural relationships by the method of maximum likelihood. Mooresville, IN: Scientific Software.
Kasser, T., & Kanner, A. D. (Eds.). (2004). Psychology and consumer culture: The struggle for the good life in a materialistic world. Washington D.C.: American Psychological Association.
Kessler, R. C., & Greenberg, D. F. (1981). Linear panel analysis. New York: Academic Press.
Kind, M., & Haisken-DeNew, J. P. (2010). Unexpected victims—how parents’ unemployment affects their children’s life satisfaction. In 9th international German socio-economic panel user conference.
Kluckhohn, F. R., & Strodtbeck, F. L. (1961). Variations in value orientations. Evanston, Illinois: Row, Peterson.
Little, I. M. D. (2002). A critque of welfare economics. Oxford: Oxford University Press.
Lucas, R. E. (2008). Personality and subjective well-being. In M. Eid & R. J. Larsen (Eds.), The science of subjective well-being (pp. 171–194). New York: Guilford Press.
Lyubomirsky, S. (2008). The how of happiness: A scientific approach to getting the life you want. New York: Penguin.
Nickerson, C., Schwarz, N., Diener, E., & Kahneman, D. (2003). Zeroing in on the dark side of the American dream: A closer look at the negative consequences of the goal for financial success. Psychological Science, 14, 531–536.
Ostwald, S. K., Godwin, K. M., & Cron, S. G. (2009). Predictors of life satisfaction in stroke survivors and their caregivers after inpatient rehabilitation. Rehabilitation Nursing, 34, 160–174.
Powdthavee, N. (2008) I can’t smile without you: Spousal correlation in life satisfaction. York University, Department of Economics, Discussion Paper 08/16.
Putnam, R. D. (2000). Bowling alone: The collapse and revival of American Community. New York: Simon & Schuster.
Roberts, B. W., Walton, K., & Viechtbauer, W. (2006). Patterns of mean-level change in personality traits across the life course: A meta-analysis of longitudinal studies. Psychological Bulletin, 132, 3–27.
Schimmack, U., & Lucas, R. E. (2010). Environmental influences on well-being: A dyadic latent panel analysis of spousal similarity. Social Indicators Research, 98, 1–21.
Scollon, C. N., & Diener, E. (2006). Love, work and changes in extroversion and neuroticism over time. Journal of Personality and Social Psychology, 91, 1152–1165.
STATA. (2011). Structural equation modeling reference manual. College Station, Texas: Stata Press.
Thoits, P. A., & Hewitt, L. N. (2001). Volunteer work and well-being. Journal of Health and Social Behavior, 42, 115–131.
Wagner, G. G., Frick, J. R., & Schupp, J. (2007). The German socio-economic panel study (SOEP)—scope, evolution and enhancements. Schmoellers Jahrbuch, 127(1), 139–169.
Winkelmann, R. (2004) Subjective well-being and the family: Results from an ordered probit model with multiple random effects. IZA DP No. 1016.
Winkelmann, L., & Winkelmann, R. (1995). Happiness and unemployment: A panel data analysis for Germany. Konjuncturpolitik, 41, 283–307.
Wooldridge, J. M. (2010). Econometric analysis of cross-section and panel data (2nd ed.). Cambridge, MA: MIT Press.
Acknowledgments
Our thanks for valuable discussions about this paper to Alexander Wearing of Melbourne University, Stephen Headey of Monash University and Gisela Trommsdorff of the University of Konstanz. Thanks also to Simon Freiden and Markus Hahn of Melbourne Institute for assistance in preparing linked parent–child panel survey files.
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Appendix
Appendix
Full results for all models in the paper are available from the authors. The model below, which underlies Fig. 3 in the text, is printed to assist understanding and comment on the structural equation models with 2-way causation, which form the core of the paper. This model includes 2-way causal links between father and son life satisfaction, mother and son life satisfaction, and father and mother life satisfaction. Also included are estimates of the covariances of four pairs of error terms: son neuroticism and son extroversion, father and son life satisfaction, mother and son life satisfaction, and father and mother life satisfaction.
See Table 3.
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Headey, B., Muffels, R. & Wagner, G.G. Parents Transmit Happiness Along with Associated Values and Behaviors to Their Children: A Lifelong Happiness Dividend?. Soc Indic Res 116, 909–933 (2014). https://doi.org/10.1007/s11205-013-0326-7
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DOI: https://doi.org/10.1007/s11205-013-0326-7