1
You cant be happier than your wifeDivorce
and the distribution of life satisfaction across
spouses

• Cahit Guven (Deakin University) and Claudia Senik
  (Paris School of Economics)

September 4, 2009
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What this paper does

• Ask
• Does the distribution of life satisfaction across
  spouses matters per se?
• Does it predict divorce?
• (beyond the level of individual satisfaction of
  each spouse)
• Try to answer this question using the GSOEP panel
  data 1983-2007
• 359958 observations, 45225 individuals, 13456
  couples

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Motivation 1. Economic consequences of divorce

• Impact of actual and expected divorce on factors
  of GDP growth
• Fertility and number of children
• Capital accumulation in marital specific assets
  (Becker, 1974)
• Human capital of children (education, care,
  expenditure)
• Houses
• Specific human capital of spouses
• Labor market force participation of women
• Implications for public policy concerning family
  and womens labor force participation
• Generalize the evidence on aversion to
  inequality?
• to other contracts of indefinite duration where
  the parties involved have the option of
  termination, perhaps with a penalty (Becker et
  al. 1977)

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Motivation 2. aversion to inequality in
households?

• Literature on income distribution and subjective
  well-being
• Negative association between income inequality
  and SWB
• Literature on income comparisons and well-being
• income comparisons and other types of comparisons
  inside the household (Clark, 2005) associated
  with lower levels of happiness
• Literature on marriage and divorce
• Essentially self-centered decision of
  getting/remaining married
• But no literature on whether the distribution of
  subjective well-being inside the household
  matters.

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Motivation 3. Reliability of subjective data

• Show impact of subjective variables on actual
  choices, decisions and actions
• Inequality in Subjective Well-Being? Divorce

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The economics of marriage and divorce

• Marriage is viewed as a means to maximize
  individual welfare and collective output (Becker,
  1974, 1991)
• Joint production, joint consumption (e.g.
  children)
• Increasing returns, division of labor, risk
  pooling, coordination
• Rational individuals
• look at her level of well-being inside marriage
  versus outside and decides whether to
  become/remain married or not (Becker)
• Other compatible assumptions
• Altruism, intra-household externalities of
  welfare (Powthdawee, 2004)

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Unitary models of household

• Basic unitary model
• One decision-maker
• Consider only aggregate utility for all members
• More sophisticated models (Becker 1974, 1991)
• Head of household is altruistic takes into
  account individual preferences of household
  members
• Gains of marriage shared among members of family
  depending on marriage market (sex ratio)
• Upfront payments in traditional societies
  dowries or bride-price
• Division of labor in modern families

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Unitary models of household (continued)

• Income pooling
• behavior of spouses (labor supply, expenditures)
  only depend on aggregate exogenous income
• Does not depend on the distribution of income
  across members
• But unitary model of household rejected by
  empirical tests
• Phipps and Burton (1992)

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Collective models of the household

• Cooperative models (following Chiappori, 1992)
• 1) Sharing rule depends on individual preferences
  and individual bargaining power (distribution
  factors)
• Bargaining power depends on outside wage, divorce
  legislation, child custody rules, remarriage
  market, etc.
• 2) Each individual maximizes his utility under
  the budget constraint defined in first stage
• Pareto efficiency of all decisions
• Non cooperative models of Nash bargaining
• not necessarily Pareto-efficient

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The economics of marriage and divorce

• But are all equilibria in terms of distribution
  of welfare across spouses stable?
• Beyond purely self-regarding motives, are there
  also concerns for the distribution of well-being?

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Concerns for the distribution of well-being
across spouses?

• We try to answer this question, controlling for
  the classical correlates of the value of
  marriage/ value of outside options (Weiss and
  Willis, 1997)
• Income, education, age, of each spouse, children,
  etc.
• We take life satisfaction as given, as the result
  of bargaining and all intra-household decisions
  and allocations (chores, etc.)
• We find a positive statistical association
  between the difference in life satisfaction
  across spouses and the probability that they will
  divorce in later years.

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Possible mechanisms

• Aversion to inequality in terms of happiness
  inside couples
• The gap in satisfaction is a sign of the
  degrading quality of the marriage technology
• altruism, sharing, spillovers of SWB, pooling
• Impossibility to transfer well-being between
  spouses
• Makes compensation of the less happy spouse
  impossible
• Positive assortative mating in terms of life
  satisfaction more stable
• Matching on the set-point of happiness (Lucas and
  Schimmack, 2006), Fujita and Diener (2005),
  Lucas et al. (2003)

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Other alternative explanations

• Reverse causality the perspective of divorce
  makes one spouse more unhappy and creates the
  happiness gap that we observe
• Infidelity One of the spouses is contemplating
  (or experiencing) forming another couple, and
  this creates the gap between him and his spouse
• ? We try to rule out these mechanisms using long
  distance lagged variables, pre-marital life
  satisfaction levels and other strategies.

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Some related papers on marriage and divorce using
subjective happiness data

• GSOEP
• Lucas et al. (2003), Stutzer and Frey (2006),
  Zimmermann and Easterlin (2006) Marriage makes
  people happy (beyond happier people getting
  married)
• Lucas and Schimmack (2006) Similarity of
  happiness of spouses
• BHPS
• Gardner and Oswald (2002) Marriage increases
  life expectancy
• Gardner and Oswald (2005) Divorcing couples
  become happier
• Powdhtavee (2009) Happiness spillover effect
  between spouses

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Data

• GSOEP panel data 1983-2007
• Individual and partner identification variable
  for 45226 people and 252753 observations
• Number of couples 13456
• Number of divorces 4074
• GSOEP includes a separate spell dataset for
  marital status.
• Constructed dataset sample of women with all
  socio-demographic variables pertaining to
  themselves and their husband. Before, during and
  after marriage.
• Symmetrically sample of men with all variables
  pertaining to themselves and their wife.

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Attrition

• 10 of couples in the sample for the whole
  period (23 years)
• Average duration of a couple in the sample is
  13.4 years
• By men 13.3 years, by women 13.5 years
• Characteristics of those who are more likely to
  leave the sample men, non-German, young,
  unmarried, seperated
• (Kroh and Spieß, 2008)
• We weight the observations by the inverse of the
  probability to remain in the sample.

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Estimates

• We run a dprobit estimate of the probability to
  divorce
• Divorce t1 f (total happinesst, absolute value
  of happiness difference between spousest age t,
  age differencet, household incomet, number of
  childrent) (1)
• Controls classical determinants of marriage and
  divorce (Weiss and Willis, 1997)
• Cluster standard errors at individual level

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Comparability of self-declared happiness of
spouses?

• Individual fixed effects or couple fixed effects
  controls for the anchoring effect
• Interpretation probability of divorce depending
  on the evolution of the gap in SWB
• Impact of subjective representation of happiness
  rather than objective happiness

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Description of the data and main variables
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How happy are you? (scale 0-10)
Not weighted
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Absolute difference in happiness across spouses,
1984-2007
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Couples who marry and do not divorce throughout
the sample (1984-2007)
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Total happiness, happiness gap around the year of
divorce
Married and partnering together
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Individual happiness and happiness gap around the
year of divorce
Married and partnering together
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Total happiness and happiness gap around the year
of divorce
Legally Married Only
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Total residual happiness, residual happiness gap
around the year of divorce
Married and partnering together Residuals of
equation (1)
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of divorces depending on happiness differences
Married and partnering together Residuals of
equation (1)
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OLS estimates of the of people who divorce
T-statistics are reported in absolute values. The
second column is estimated only at the first year
of marriages. Number of observationsnumber of
years.
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ResultsProbability to divorce and absolute value
of happiness difference
One row per control show only wife results
during the whole presentation put interesting
coefficient in bold
Standard errors clustered at individual level
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Happiness difference as a categorical variable

Standard errors clustered at individual level
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Hapiness difference and marriage durationOnly
for those who married in the sample

• Do for those who marry in the sample

Standard errors clustered at individual level
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Avoid the risk of reverse causation or
infidelityHappiness gap in the first year of
marriage predicts divorce
Write Dprobit
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Lagged values of absolute happiness differences
Write Dprobit
Controls total happiness, age, age difference,
number of children, ln household income. Each
coefficient corresponds to a separate regression.
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Robustness additional controlsSample of wives
Write Dprobit
Controls as usual, cluster(individual)
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Robustness continued. Sample of wives
Write Dprobit Split into several tables
Controls as usual. Cluster(individual).
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Robustness continued. Sample of wives
Write Dprobit Split into several tables
Controls as usual. Cluster(individual). Column
5 omitted category one spouse born in Germany
and the other is not.
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Robustess continued. Sample of wives
Write Dprobit
Self-reported health 5 is very good health 1
bad health. Individual fixed effects is estimated
using conditional logit.
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Write Dprobit
Omitted 1) different nationalities, 2) German
origin and living inWest-Germany, 4) Each manages
own money separately. Specification 3 is
estimated by weighting with the inverse of the
individual longitudinal staying probabilities
which is provided in the GSOEP. In specification
5, importance of family1 very unimportant 4 is
very important and, is treated as a continuous
variable.
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Robustness
Same results obtained on the sample of
husbands Unexpected income shocks, such as
disability and unemployment increase the absolute
value of happiness difference but, can not
predict divorce.
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Interpretation

• Aversion for inequality of happiness
• Positive assortative mating in terms of happiness
• Indeed there are signs of assortative mating in
  the data

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Assortative mating by happiness level in the
first year of marriage
1 if happinesslt5 2 if happiness5, 6, 7 3 if
happinessgt7
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Assortative mating by residual happinessin the
first year of marriage
1 if residual happinessgt0 0 if residual
happinesslt0
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Happiness gap between divorced people remains
higher than between married people(although it
decreases)
Attention need to take absdslife not dslife
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Conclusions

• Some evidence suggestive that more equal
  distributions of subjective well-being are more
  favorable to marriage continuation.
• Reflects bargaining inside household or
  assortative matching.
• One additional motive of marriage/divorce beyond
  purely self-regarding motives.
• Predictive power of SWB variables.

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Descriptive statistics of the main variables
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Descriptive statistics of the main variables
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Transition matrix of partnership
The estimates excludes people who lose partners
due to death. 2.02 is the probability of
separation from partner conditional on having the
same partner in the previous period. Ratios are
in percentages.
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Correlation matrix of happiness variables
Happiness difference Happiness of husband
happiness of wife
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Transition matrix of marital status
We do not differentiate between separations and
divorces in the paper. Hence separation/divorce
probability for marital relationships is
0.930.311.24. Ratios are in percentages.
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Correlation matrix of lagged absolute value of
happiness differences