Discussion Paper No.255

The Gender Difference in the Burden of Having Children:
Evidence from Life Satisfaction Data

Tsukasa Matsuura

Junji Kageyama

July 2015

INSTITUTE OF ECONOMIC RESEARCH
Chuo University
Tokyo, Japan

The Gender Difference in the Burden of Having Children:
Evidence from Life Satisfaction Data
Tsukasa Matsuura†and Junji Kageyama‡

Abstract
This paper uses life satisfaction data from World and European Integrated Values
Survey 1981–2008 and analyzes the gender difference in the relationship between thenumber of children and life satisfaction across welfare regimes. In doing so, we identify
the gender difference in the parenting burden. Our results show that the gender withhigher parenting costs, generally women, obtain lower satisfaction from having
children. In particular, we find that the women’s disadvantage in life satisfaction issmaller in social democratic and liberal counties where extensive childcare supports
are provided. We also find that the opposite is true in developing countries and NIEs inwhich public childcare supports are not widely available.

Keywords:

Life Satisfaction, Ideal Number of Children, Gender Difference,
Welfare Regime
JEL Classification Number: J13, J16, I31† Associate Professor, Department of Economics, Chuo University; t-matsu@tamacc.chuo-u.ac.jp‡ Associate Professor, Department of Economics, Meikai University, and Department of Demography,

University of California, Berkeley; kagejun@meikai.ac.jp

11. Introduction
The primary purpose of this paper is to assess the difference in parenting costs
between women and men. In this regard, while a large body of theoretical research
exists (e.g., Becker 1960, Becker and Lewis 1973), few empirical analyses have been
conducted. One reason for this deficit may be that directly defining parenting costs isdifficult because they involve opportunity costs and various other costs.
To fill this gap, this paper uses life satisfaction data and analyzes the relationshipbetween the number of children and life satisfaction separately for women and men.
More specifically, this paper first develops a simple theoretical model based on a Nashbargaining model that assumes that parenting costs differ between women and men,and illustrates the correlation between the gender difference in the effect of having
children on life satisfaction and the gender difference in parenting costs. In particular,
the theoretical model demonstrates that, when parenting costs differ for women andmen, the optimal number of children differs for each gender.
Then, using life satisfaction data from World and European Integrated Values
Survey 1981–2008, Wave 1–5 (WVS 2009, EVS 2011), it compares how the genderdifference in parenting costs varies for each welfare regime. In welfare regimes where
the burden of parenting is relatively high for women, having children likely lowers
women’s satisfaction more than men’s satisfaction. The empirical analyses employ thefollowing two approaches. First, defining the percentage of people who have morechildren than their ideal number as the ratio of individuals with excess births (REB),
we demonstrate that, in welfare regimes where a large gender difference in theparenting costs exists, the gender difference in REB is high. Second, using regression
method, we estimate the impact of the number of children on life satisfaction acrosscountries separately for women and men. Previous studies comparing the genderdifference were limited to single-country analyses1 and did not make international
comparisons.
It is worth nothing that, in this type of analyses, endogeneity is a major issue.
Regressing life satisfaction on the number of children would potentially capture thereverse causal effect of life satisfaction on the number of children. To address this issue,
Stanca (2012) added the ideal number of children as an independent variable to control
for the endogeneity. However, this method is not a perfect solution because the ideal
number of children is also an endogenous variable that potentially depends on life
1Matsuura (2007) is one study that analyzed the impact of the number of children on satisfactionlevels using data from one country. The study found that the number of children affects life
satisfaction levels negatively for women and positively for men. The hypothesis that the burden of
educating children differs between women and men is presented as an explanation.

2

satisfaction. Therefore, this paper employs the instrumental variable method using thebirth rate as an instrumental variable.
The rest of the paper is organized as follows. The next section reviews previous
studies. Section 3 develops a theoretical model using a Nash bargaining model toobtain testable hypotheses. Section 4 discusses the empirical strategies and Section 5
tests the derived hypotheses. The results demonstrate that the number of children has
a different effect for women and men. In particular, the results show that the impact of
the number of children on life satisfaction for women and men varies across countries
because of the variability in the gender differences in parental costs. Section 6
concludes.
2. Previous Studies
While research on subjective wellbeing is relatively new in economics, it is wellestablished in the field of sociology. This trend partly explains why research thatanalyzes the relationship between children and life satisfaction is more prevalent insociology and related fields than in economics. White and Edwards (1990) investigatedwhether parents’ satisfaction with marriage increases after their children have grown
up and left home. This question relates to the hypothesis known as the “U-shaped
curve of marital satisfaction.” Briefly, the supposition is that marital satisfaction
declines for some time after couples get married but increases again after theirchildren leave home. In addition, McLanahan and Sorensen (1985) analyzed data fromthe Panel Study of Income Dynamics (PSID) to investigate whether parents’ life
satisfaction increases when their children leave home. McLanahan and Adams (1987)
found lower levels of life satisfaction and wellbeing among parents whose children
were living together with them. In particular, the satisfaction levels of mothers were
low relative to those of fathers; economic and time constraints were given as thereasons. Yet, none of these studies controls for variables such as income or working
hours. One example of prior research in the field of economics that analyzed therelationship between children and satisfaction levels is Tao (2005), which analyzed therelationship between the number of children and marital satisfaction using data from
Taiwan.
To control for unobserved factors such as an individual’s temperament, Kohler et al.
(2005) used data on twins in Denmark to analyze the effects that marriage andchildren have on an individual’s wellbeing. The authors found that the wellbeing ofboth the father and the mother increases with the first child and that fathers’
wellbeing increases more when the first child is a boy than when the first child is a girl.
3

For the second and subsequent children, the wellbeing of mothers is negatively
affected by the number of children but also has no impact on the wellbeing of fathers.
The study that is closest to the concerns of the present paper is Margolis andMyrskyla (2011). They used the World Values Survey (WVS) to study the relationshipbetween the number of children and life satisfaction across countries and acrosswelfare regimes, and found that women experience higher levels of stress from having
children and that unmarried individuals experience more stress than married
individuals. Moreover, they found no significant correlation between the number ofchildren and life satisfaction among older parents. Whereas they covered similar
themes and used the same data as the present study, they differs from the present
study in that it did not compare women and men in each welfare regime.
Stanca (2012) also used the same WVS data as in the present study to analyze therelationship between children and life satisfaction in 94 countries. Novel aspects of the
Stanca’s study include its distinction between financial satisfaction and non-financial
satisfaction and its consideration of endogeneity. Stanca found that children have anegative impact on financial satisfaction but a positive impact on non-financial
satisfaction. However, Stanca did not consider differences between women and men
and did not analyze different welfare regimes.
Hansen (2012) conducted a survey of approximately 30 studies to analyze therelationship between children and life satisfaction and presented the following
findings. Although it was often considered that people without children were lesshappy, the empirical evidence suggests that individuals with no children have higher
levels of life satisfaction. However, most of the studies reviewed in Hansen (2012)
analyzed only developed countries.
3. Theory and Hypotheses
To examine the gender differences in parental costs, we employ an economic
framework. The utility and costs that accrue from having children are formulated asfollows. The utility obtained by women and men from the number of children n isdefined as Um(n) and Uf(n) where the superscripts indicate the gender. We assume thatthe utility is increasing in the number of children but at a diminishing rate, i.e.,

2
m
2
2
f
2
dU m (n) / dn  0、 d U (n) / dn  0 、 dU f (n) / dn  0、 d U (n) / dn  0 . (1)On the other hand, parenting costs for women and men, cf and cm, which are4measured at the utility term and assumed independent of n, include opportunity costs
and other costs involved with having children, such as the loss of leisure time and oftime needed to purchase other assets. Leibenstein (1974) identified three basic sourcesof utility from having children: consumption utility, old age security, and labor utility.
He postulated that having children results in increases in both direct costs, such as
expenses associated with childrearing and education, and indirect costs, such asopportunity costs—things parents could have gained if they had not had any children.
Leibenstein (1974) theoretically illustrated how the ideal number of children isdetermined based on the literature of household financial behavior. However, it isempirically impossible to calculate each type of parental costs with great accuracy.
In this study, we define the net of utility gained from having children and the costs
generated by rearing children as pure utility, NU, such that

NU m  U m (n)  cmn(2)

NU f  U f (n)  c f n .

(3)Subsequently, an individual’s optimal number of children is determined by maximizing
equations (2) and (3) and given bydUm(n) m
 c . (4)dn
dU f (n) c f . (5)dnThese equations demonstrate that the optimal number of children declines as the costof children rises. By setting the optimal number of children for a man as nm* and the
optimal number of children for a woman as nf *, we obtain the relationship:
If cm< cf, then nm *>nf * .

(6)Now, to incorporate the dimension that the actual number of children is a joint
decision of the couple, assume that the actual number of children is determined by aNash bargaining solution, which can be obtained by differentiating the following
formula with respect to n:
5

Fam(n)  (U m (n)  c m n) (U f (n)  c f n)1 .

(7)

where 0< α <1 represents the bargaining power between women and men. In thismanner, we obtain the optimal number of children for a couple, n*. With theassumption of equation (6), we obtain the relationship:nm *＞n*＞nf *. (8)for any α.
Note that a couple’s actual number of children is not necessarily equal to theiroptimal number of children. This is because, first, achieving the optimal number ofchildren in an instant is obviously impossible. Therefore, the current number ofchildren is likely not the household’s optimal number of children n*, especially, foryounger couples. Second, physiological capabilities play an important role for having
children. Third, reproductive outcomes are stochastic. Thus, the actual number can beeither greater or smaller than the optimal number, and once it is greater, the numberof children cannot decrease.
Nevertheless, we can later use the actual number of children for testing equation (8).
To do this, define the percentage of individuals whose current number of children ishigher than their ideal number of children as the ratio of individuals with excess births(REB) separately for women and men in each country. Then, we can expect that
Proposition 1: Comparing women and men in the same country, the higher areparenting costs, the greater is the REB.
In addition, together with the assumption (6), equations (4) and (5) demostrate that,for the actual number of children nr, the effect of children on the utility for women andmen is expressed as

If cm<cf, thendNUm(n)dNUf (n). (9)dn nnrdn nnr

Then, assuming that life satisfaction reflects pure utility, NU, we can expect that6

Proposition 2: Comparing women and men in the same country, the higher areparenting costs, the lower is the level of life satisfaction.
We now direct attention to the gender difference in parenting costs across countries.
More specifically, following the view put forward by Esping-Andersen (1990) and
McDonald (2000) that the status of women varies across welfare regimes, we divide thesample using welfare regimes and analyze the gender differences in the REF and in
the effect of the number of children on life satisfaction. Hypotheses we test are:Hypothesis 1: A more traditional family system in a country results in a greater
childcare burden for women; therefore, women’s REB is higher than that ofmen.
Hypothesis 2: A more traditional family system in a country results in a greater
childcare burden for women; therefore, the impact of children on life
satisfaction is lower for women than for men.
4. Empirical Strategies and Data
To test the hypotheses obtained in the previous section, we conduct an ordered probit
analysis, employing life satisfaction as the dependent variable and the number ofchildren as the main independent variable. We are particularly interested in thegender difference in the coefficient of the number of children. Thus, we either regress
separately for women and men or incorporate the gender dummy with the interactionterm.
To perform the regression analysis, we also divide the sample by welfare regimes.
We expect that, in welfare regimes where women have a high status, the burden ofparenting is shared relatively equally between women and men. This empiricalstrategy enables us to examine the gender difference in the subjective burden ofparenting across welfare regimes.
With respect to the classification of welfare regimes, we follow Esping-Andersen(1990), in which developed countries are divided into liberal, conservative, and social
democratic countries. When classifying the welfare state regimes of Western nations,
Esping-Andersen designated the United States, the United Kingdom, and Canada as
liberal; Germany, Australia, France, and Italy as conservative; and the Scandinavian
countries

associaldemocratic.

Esping-Andersen
7

introduced

the concept of“externalization,” whereby the market increasingly provided resources for childcare
and education—traditionally provided by the family—and set out social democracy,
liberalism, and conservatism as the order of progress in this direction. The
externalization and commercialization of childcare and educational services result in alessening of the burden imposed by these services for women. Furthermore, McDonald(2000) argued that a country with a more traditional family system has a lower birthrate.
Regarding the specific country classifications, this paper follows quite closely theclassification used by Margolis and Myrskyla (2011), which was based on the
Esping-Andersen framework. However, although they distinguish between southern
European and conservative nations, we treat southern European countries asconservative in accordance with Esping-Andersen’s treatment. We also include in our
sample the categories of former communist nations, newly industrialized economies(NIEs),2 and developing countries. Although Margolis and Myrskyla (2011) classified
South Korea and Taiwan (both NIEs) as developing countries, their birth rates andeconomic environments were different from those of other developing countries, evenin the 1980s. NIEs are also particularly interesting from the research perspective of
this paper because of their shared characteristics. They belong to the same East Asian
cultural sphere with a traditional family system; they have achieved rapid economic
growth and they have very low birth rates.
Classifying with welfare regimes has a technical advantage as well. This is because
welfare regimes can control cultural differences, which would potentially explain muchof the variation in levels of satisfaction among countries. An ideal solution is to use
panel data. However, since the available data are limited, we control for latent factorsthat could affect life satisfaction and the number of children using welfare regimes.
Next, to address the endogeneity problem of reverse causality running from life
satisfaction to the number of children, we later use the instrumental variable method,
employing crude birth rate (CBR) as an instrumental variable. While CRB is expected
to positively correlate with the number of children, there is no reason to expect any
relationship between CBR and life satisfaction. Therefore, it is a suitable instrumental
variable.
In previous studies, Stanca (2012) used the ideal number of children to address
endogeneity. However, the ideal number of children is not suitable as an instrumental
variable in the present study because, as presented in the theoretical model, the ideal
number of children is simultaneously determined with life satisfaction and thus
2 Hong Kong, Singapore, South Korea, and Taiwan.

8

endogenous. To assess the explanatory power of the excluded instrumental variable,
we use the F-statistics and test if it is higher than 10 (Stock et al. 2002).
The data employed in this study are from World and European Integrated Values
Survey 1981–2008, Wave 1–5 (WVS 2009, EVS 2011), which were gathered from
respondents aged 15 and older between 1981 and 2009. The data were obtained frominterviews containing questions on socioeconomic, cultural, and political values, andmorals and other areas. The data encompass questions on the number of children andsubjective wellbeing for countries throughout the world, and contain respondents’ ratingsof overall life satisfaction, which range from 1 (dissatisfied) to 10 (satisfied). With the exceptionof some non-responders, we use the entire sample of 423,084 respondents.
5. Results
5-1. Basic Statistics
Before conducting the regression analysis, we first observe the gender difference inthe burden of parenting by examining the differences in what women and men regard
as the optimal number of children in each welfare regime. Figure 1 shows the ideal
number of children for each welfare regime. The ideal number of children is highest for
both women and men in developing countries and second highest in liberal countries.
In contrast, NIEs have the lowest values for the ideal number of children, and thevalues for conservative and former communist countries are also low. In terms of thegender difference regarding the ideal number of children, women reported a higher
ideal number of children than men in liberal, conservative, and social democratic
countries. In former communist countries, NIEs, and developing countries, the ideal
number of children was higher for men than it was for women.
To further investigate the relationship between the ideal number of children and theactual number of children, we examine REB by gender. Figure 2 shows the results. As
the graph indicates, REB for men is slightly higher than that for women in social
democratic countries, but is higher for women than for men in all other welfare
regimes. The order (from the largest gap to the smallest between women and men) is
as follows: NIEs, developing countries, conservative countries, former communist
countries, liberal countries, and social democratic countries. Based on the theoreticalmodel in Section 2, we can infer that this order would reflect the gender difference inparenting costs.
5-2. Ordered Probit Model
Next, we conduct a regression analysis using an ordered probit model without the9instrumental variable. The dependent variable is life satisfaction and the main
independent variable is the number of children. The descriptive statistics are shown in
Table 1. At 52.5%, woman respondents slightly outnumbered man respondents. The
mean age is 41.7 and the mean number of children is 1.88. Other explanatory variables
appear in Table 2, which presents regression results.
The results of the estimation using the entire sample are shown in columns 1 and 2of Table 2. 3 We observe a positive significance at the 10% level for men but nosignificance for women. The results related to the other variables are as follows. While
age is of negative significance, age-squared has positive significance, indicating thatlife satisfaction is U-shaped in age. Both education and annual income are of positive
significance, which corresponds to the results of previous studies. The signs for thesevariables are positive for both women and men.
Next, we investigate the gender difference in the effect of children on parents’ life
satisfaction across welfare regimes. We first examine the social democratic countries.
The results, presented in columns 3 and 4 of Table 2-1, indicate a positive significancefor women. Although the coefficient for men is negative, it is not significant. For
women, life satisfaction increases as the number of children increases. This result is
consistent with the result presented in Figure 2 that REB is lower for women than formen, and can be attributed to the high status of women and the low parenting costs for
women in social democratic countries.
These results are different in other regimes. The finding that parenting costs arelower for women than for men is unique in social democratic countries. In liberalcountries, the results, shown in columns 5 and 6 of Table 2-1, indicate positivecoefficients for the number of children for both women and men. Moreover, thecoefficients are almost the same. Although REB is higher for women than for men inliberal regimes, the gap is smaller than that in the other regimes, particularly
conservative countries. Therefore, we argue that the results in the present regression
analysis and the results with respect to REB are in agreement.
As for conservative countries, the results are shown in columns 7 and 8 of Table 2-1
and indicate a negative coefficient for women and a positive coefficient for men, butneither is significant. In relation to the gender difference in REB, Figure 2 shows thatthe conservative countries are ranked between developing countries and liberalcountries. The sign condition obtained in the regression analysis is consistent with thisresult.
The results for former communist countries are shown in columns 9 and 10 of Table
3 The estimates used six marital dummies and seven job dummies.

102-1. The coefficient for children is positive for men and negative for women, but neither
is significant.
Columns 11 and 12 of Table 2-1 show results for NIEs. The coefficient for men is
positive and significant and that for women is positive but not significant. The
difference between the coefficients is also large. Together with the analysis of REB,
these results suggest that the large gender difference in parenting costs in NIEs
results in a large gender difference in the impact of having children on life satisfaction.
The results for developing countries are shown in columns 13 and 14 of Table 2-1.
The results demonstrate that the coefficient for women is negative and significant. The
coefficient for men is negative but not significant. Therefore, having more children
results in lower life-satisfaction for women in these countries. This result is consistent
with the results presented in Figure 2 that developing countries have the greatest
gender difference in REB.
Finally, we conduct an analysis without a country dummy, and the results are shownin Tables 3-1 and 3-2. Two differences emerge, i.e., positive significance for women for
the entire sample and a negatively significant coefficient for men in developing
countries. However, no other significant variations are observed, supporting therobustness of the persent analysis. Nevertheless, because the results for the entire
sample and for developing countries differ, controlling for cross-country differences isnecessary for the entire sample and for developing countries.
5-3. Investigation of gender differences in the impact of children on life satisfaction
Next, we assess the statistical significance of the gender difference in the effect of
the number of children on life satisfaction. To do this, we use the gender dummy andthe interaction term with the number of children.
The results are shown in Table 4. Regarding the entire sample, the number ofchildren is of positive significance, and the interaction term for the number of children
and the woman dummy is negative and significant at the 10% level. This result
indicates that, although satisfaction levels increase as the number of children
increases, the effect is less pronounced for women than for men.
In social democratic countries, the number of children is not significant but theinteraction term is of positive significance. These results indicate that, consistent withthe result with respect to the gender difference in REB, the levels of satisfaction
obtained from children are greater for women than for men in these countries. In
liberal countries, the number of children is also of positive significance, but theinteraction term is not significant. These results suggest that, although satisfaction
11

levels increase with the number of children, no gender difference exists regarding thiseffect. Conversely, in conservative countries and former communist countries, neither
the coefficient for the number of children nor the interaction term is significant. In the
NIEs, the coefficient for the number of children is positive and significant, but theinteraction term was not significant. Finally in developing countries, the interaction
term was negative and significant, supporting the idea that the burden of having
children is greater for women than for men. This result is consistent with the large
gender difference in REB. Although total fertility rates (TFRs) are often high indeveloping countries, the present results indicate that life satisfaction for women indeveloping countries declines as their number of children increases.
5-4. Instrumental variable estimation
To assess the robustness of the results and address the endogeneity problem
between life satisfaction and the number of children, we apply an instrumental
variable method using the crude birth rate (CBR) of each country as an instrumental
variable.
Tables 5-1 and 5-2 present the estimation results.4 With respect to the entire sample,
the results are similar to the results obtained in the ordered probit model,
demonstrating that, while the coefficient for women is not significant, the one for men
is significant.5 For social democratic countries, the values for both women and men areof positive significance, but the value of the coefficient for women is larger. These
results are consistent with those in Figure 2. For liberal countries, the coefficients forboth women and men are positive, but the coefficients for men are not significant.
Moreover, the coefficient for women is larger. For conservative countries, a positively
significant correlation exists for both women and men. The coefficients are almost
equal, but the coefficients for men are slightly larger. For former communist countries,
a negatively significant correlation exists for both women and men, and the greater
negative effect of having children on women’s satisfaction levels than on men’s isconsistent with the results in Figure 2. For NIEs, the coefficient for the number ofchildren is negative and significant for women and positive but not significant for men.
This result indicates that children have a strikingly different effect on the satisfaction
4 A country dummy is not included except for the entire sample and developing countries because

including both a country dummy and a year dummy causes a large decline in the sample when CBR
is employed as an instrumental variable. However, as was previously shown, this phenomenon does
not cause much of a problem in the estimation because the presence or absence of a country dummy
had little effect when the sample was divided into social democratic, liberal, conservative, former
communist, and NIE countries.
5 However, the F-statistic (the F-value of the excluded instrumental variable) is on the low side.

12

levels of women and men, suggesting that the cost of children is much higher for
women than for men. This estimation result supports the analysis of REB that thedifference between women and men in terms of the parenting costs is largest for NIEs.
Moreover, these results imply that the gender difference in the parenting costs could be
one reason for the current low birth rates in the NIEs. Finally, for developing countries,
the coefficient for women is negative, whereas the coefficient for men is positive, which
is a different result from that shown in Figure 2. However, neither coefficient issignificant. The instrumental variables method takes endogeneity into account and,
although not all of the results obtained are significant, many are consistent with theresults for the excess-ideal number of children.
6. Conclusion
To identify the gender differences in the parenting burden, the present study
examines the gender difference in the relationship between the number of children andlife satisfaction across welfare regimes. This study also analyzes the gender differencein the ratio of individuals who have more births than their ideal number (REB) acrosswelfare regimes to verify the robustness of the results.
In particular, the theoretical model predicts that the gender with higher parenting
costs, generally women, obtain lower satisfaction from having children and tend tohave more children than their ideal. The empirical results support these predictions.
In welfare regimes where extensive childcare supports are provided, particularly insocial democratic and liberal counties, both the women’s disadvantages in life
satisfaction and REB are smaller. The opposite is true in developing countries and
NIEs. The lower fertility in NIEs, a major difference between developing countries and
NIEs, can be explained by higher levels of parenting costs for both women and men.
The present study also provides one possible explanation for the differing birth rates
across countries. For instance, McDonald (2000) found that a lower birth rate prevails
in countries that uphold a traditional family-oriented system and have more prevalent
traditional divisions of labor within the family. By analyzing the relationship between
traditional family-oriented systems and the gender difference in the levels ofsatisfaction with having children, the present study provides an explanation, albeit
indirectly, to the reason for low birth rates in traditional family-oriented countries.
This study demonstrates that in NIEs in particular, as viewed from the perspective ofboth REB and the different effects of the number of children on the satisfaction levelsof women and men, the parenting costs can be significantly higher for women than it is13for men. This result has policy implications for NIEs that are currently experiencing
extremely low birth rates.

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15

Figure 1 Ideal number of children, by Regime
2.9
2.8

femalemale

conservative

formersocialist

2.7
2.6
2.5
2.4
2.3
2.2
socialdemocratic

liberalNIEs

developing
countries

Figure 2 The rate of massive ideal number of children, by Regime
30
female

25

male

20
15
10
5
0
socialdemocratic

liberalconservative

16

formersocialist

NIEs

developing
countries

Table１

Descriptive Statistics

N.obs.

Mean

std.dev.

Min

Max

Life Satisfaction

213163

6.429

2.516

1

10

Children

213163

1.944

1.777

0

8

Female213039

0.517

0.500

0

1

Age

21283341.37415.970

15

99

lower

213163

0.362

0.481

0

1

middle213163

0.426

0.495

0

1

upper

213163

0.212

0.409

0

1

Income213163

4.591

2.410

1

10

Married213163

0.598

0.490

0

1

Living together as married

213163

0.054

0.226

0

1

Divorced

213163

0.039

0.193

0

1

Separated

213163

0.017

0.129

0

1

Widowed213163

0.065

0.247

0

1

Single/Never married

213163

0.227

0.419

0

1

Divorced, Separated or Widow

213163

0.000

0.000

0

0

Full time

213163

0.359

0.480

0

1

Part time

213163

0.074

0.262

0

1

Self employed

213163

0.105

0.306

0

1

Retired213163

0.136

0.343

0

1

Housewife

213163

0.145

0.352

0

1

Students

213163

0.067

0.251

0

1

Unemployed

213163

0.092

0.290

0

1

Other

213163

0.021

0.145

0

1

Education dummy

Marital Status dummy

Job dummy

17

Table 2-1

Parenthood and Life Satisfaction, ordered probit
full sample

childrenage
age2
educ dummy(middle)
educ dummy(upper)
Scale of incomesjob dummy
marital dummy
country dummy
year dummy
Observations

female[1]0.001[0.003]-0.029[0.001]**
0.029[0.001]**
0.065[0.008]**0.104[0.010]**0.085[0.002]**
yes
yes
yes
yes
109962

male
[2]
0.006
[0.003]*-0.03[0.001]**
0.032[0.002]**0.045[0.008]**0.077[0.010]**0.081[0.002]**
yes
yes
yes
yes
102753social democrat
female[3]0.036[0.013]**-0.049[0.007]**0.049[0.007]**0.031[0.037]0.034[0.041]
0.059[0.007]**
yes
yes
yes
yes
5387

male
[4]
-0.013[0.014]-0.04[0.007]**0.043[0.007]**0.028[0.037]0.021[0.041]
0.049[0.007]**
yes
yes
yes
yes
5450

liberalfemale[5]0.026[0.010]**-0.02[0.005]**0.024[0.005]**0.005[0.035]0.044[0.039]0.045[0.006]**
yes
yes
yes
yes
7324

male
[6]
0.024
[0.011]*-0.035[0.005]**0.04[0.006]**-0.055[0.038]-0.06[0.039]0.05[0.006]**
yes
yes
yes
yes
6187

+p<0.10; *p<0.05; **p<0.01

Table 2-2

Parenthood and Life Satisfaction, ordered probit
former socialist

childrenage
age2
educ dummy(middle)
educ dummy(upper)
Scale of incomesjob dummy
marital dummy
country dummy
year dummy
Observations

female[9]-0.004[0.006]-0.036[0.003]**0.033[0.003]**0.102[0.017]**0.22[0.021]**
0.091[0.003]**
yes
yes
yes
yes
30437

male
[10]
0.005
[0.007]-0.041[0.003]**0.039[0.003]**0.093[0.018]**0.224[0.021]**
0.085[0.003]**
yes
yes
yes
yes
25849

NIEs
female[11]0.024[0.019]0.009[0.010]-0.011[0.011]-0.003[0.055]0.077[0.067]0.095[0.010]**
yes
yes
yes
yes
3420

+p<0.10; *p<0.05; **p<0.01

18

male
[12]
0.053
[0.021]*-0.01[0.010]0.012[0.010]-0.021[0.055]0.072[0.065]0.099[0.010]**
yes
yes
yes
yes
3335

developing
female[13]-0.007[0.003]*-0.022[0.002]**0.024[0.002]**0.04[0.012]**0.055[0.016]**0.097[0.002]**
yes
yes
yes
yes
52552

male
[14]
-0.004[0.003]-0.02[0.002]**0.023[0.002]**0.037[0.012]**0.031[0.014]*0.091[0.002]**
yes
yes
yes
yes
52027

conservative
female[7]-0.006[0.010]-0.031[0.004]**0.029[0.005]**0.108[0.026]**0.109[0.032]**0.044[0.005]**
yes
yes
yes
yes
10842

male
[8]
0.005
[0.010]-0.034[0.005]**0.034[0.005]**0.044[0.028]0.124[0.031]**0.05[0.005]**
yes
yes
yes
yes
9905

Table 3-1

Parenthood and Life Satisfaction, ordered probit, no country dummy
full sample

childrenage
age2
educ dummy(middle)
educ dummy(upper)
Scale of incomesjob dummy
marital dummy
country dummy
year dummy
Observations

female[1]
0.02[0.002]**-0.028[0.001]**
0.032[0.001]**
0.027[0.008]**0.121[0.010]**0.078[0.001]**
yes
yes
no
yes
109962

male
[2]
0.019
[0.003]**-0.027[0.001]**0.03[0.002]**0.003[0.008]0.08[0.009]**0.079[0.002]**
yes
yes
no
yes
102753social democrat
female[3]0.046[0.013]**-0.05[0.007]**0.049[0.007]**-0.021[0.037]-0.009[0.041]
0.051[0.007]**
yes
yes
no
yes
5387

male
[4]
-0.005[0.013]-0.041[0.007]**0.043[0.007]**-0.016[0.036]-0.015[0.040]0.043[0.007]**
yes
yes
no
yes
5450

liberalfemale[5]0.031[0.009]**-0.019[0.005]**0.025[0.005]**-0.002[0.035]0.039[0.037]0.046[0.006]**
yes
yes
no
yes
7324

male
[6]
0.027
[0.011]*-0.034[0.005]**0.039[0.006]**-0.072[0.038]+-0.075[0.038]+0.049[0.006]**
yes
yes
no
yes
6187

conservative
female[7]-0.002[0.010]-0.028[0.004]**0.027[0.004]**0.109[0.025]**0.054[0.031]+0.05[0.005]**
yes
yes
no
yes
10842

male
[8]
0.009
[0.010]-0.032[0.005]**0.032[0.005]**0.051[0.027]+0.077[0.030]**0.057[0.005]**
yes
yes
no
yes
9905

+p<0.10; *p<0.05; **p<0.01

Table 3-2

Parenthood and Life Satisfaction, ordered probit, no country dummy
former socialist

childrenage
age2
educ dummy(middle)
educ dummy(upper)
Scale of incomesjob dummy
marital dummy
country dummy
year dummy
Observations

female[9]-0.003[0.006]-0.031[0.003]**0.028[0.003]**-0.002[0.016]0.106[0.020]**0.083[0.003]**
yes
yes
no
yes
30437

male
[10]
0.006
[0.007]-0.033[0.003]**0.032[0.003]**0.008[0.017]0.145[0.021]**
0.079[0.003]**
yes
yes
no
yes
25849

NIEs
female[11]0.018[0.019]0.008[0.010]-0.01[0.010]-0.011[0.055]0.04[0.065]0.095[0.010]**
yes
yes
no
yes
3420

+p<0.10; *p<0.05; **p<0.01

19

male
[12]
0.054
[0.021]**-0.01[0.010]0.012[0.010]-0.02[0.055]0.078[0.063]0.099[0.010]**
yes
yes
no
yes
3335

developing
female[13]-0.015[0.003]**-0.02[0.002]**0.026[0.002]**0.127[0.011]**0.188[0.015]**0.08[0.002]**
yes
yes
no
yes
52552

male
[14]
-0.015[0.003]**-0.018[0.002]**0.024[0.002]**0.106[0.011]**0.104[0.014]**0.085[0.002]**
yes
yes
no
yes
52027

Table 4

Parenthood and Life Satisfaction, ordered probit, interaction term

childfemalefemale*childage
age2
educ dummy(middle)
educ dummy(upper)
Scale of incomesjob dummy
marital dummy
country dummy
year dummy
Observations

full
[1]
0.007
[0.002]**0.041[0.007]**-0.005[0.003]+-0.03[0.001]**
0.031[0.001]**
0.055[0.006]**0.09[0.007]**0.083[0.001]**
yes
yes
yes
yes
212715sdemo[2]-0.002[0.012]0.07[0.031]*
0.032[0.015]*-0.045[0.005]**0.046[0.005]**0.028[0.026]0.027[0.029]0.053[0.005]**
yes
yes
yes
yes
10837

liberal[3]
0.024[0.009]*0.079[0.027]**0.004[0.012]-0.027[0.004]**0.032[0.004]**-0.022[0.025]-0.004[0.028]0.047[0.004]**
yes
yes
yes
yes
13511

+p<0.10; *p<0.05; **p<0.01

20

conserv[4]
0.005[0.009]0.051[0.022]*-0.01[0.011]-0.033[0.003]**0.032[0.003]**0.085[0.019]**0.121[0.022]**0.047[0.003]**
yes
yes
yes
yes
20747

fsocial[5]
0.001[0.006]0.018[0.014]-0.002[0.007]-0.038[0.002]**0.035[0.002]**0.098[0.012]**0.221[0.015]**0.088[0.002]**
yes
yes
yes
yes
56286

nies
developing
[6]
[7]
0.04
-0.003[0.017]*[0.003]0.082
0.044[0.036]*[0.010]**0.000-0.006[0.018][0.003]+0.000-0.022[0.007][0.001]**-0.001
0.024[0.007][0.002]**-0.0070.037[0.039][0.008]**
0.082
0.041
[0.047]+ [0.010]**
0.097
0.094
[0.007]** [0.002]**
yes
yes
yes
yes
yes
yes
yes
yes
6755
104579

Table5-1

Parenthood and Life Satisfaction, instrumental variable estimation
full sample

childrenage
age2
educ dummy(middle)
educ dummy(upper)
Scale of incomesjob dummy
marital dummy
country dummy
year dummy
F value
Observations

female[1]
10.093[6.870]-1.28[0.829]0.972[0.619]4.774[3.147]6.965[4.559]0.448[0.176]*
yes
yes
yes
yes
2.22
107014

male
[2]
4.815
[1.694]**-0.53[0.165]**0.339[0.097]**1.728[0.572]**2.786[0.915]**0.237[0.022]**
yes
yes
yes
yes
9.07
99868

social democrat
female[3]0.415[0.167]*-0.111[0.019]**0.103[0.017]**0.055[0.065]0.127[0.083]0.087[0.011]**
yes
yes
no
yes
88.22
5387

male
[4]
0.262
[0.128]*-0.079[0.013]**0.077[0.012]**0.045[0.057]0.059[0.063]0.064[0.010]**
yes
yes
no
yes
137.045450

liberalfemale[5]0.303[0.119]*-0.061[0.013]**0.064[0.012]**0.113[0.071]0.281[0.088]**0.102[0.012]**
yes
yes
no
yes
135.9
6965

male
[6]
0.122
[0.117]-0.063[0.011]**0.068[0.010]**-0.074[0.072]-0.022[0.077]0.094[0.011]**
yes
yes
no
yes
126.465865

conservative
female[7]
0.732[0.171]**-0.084[0.012]**0.068[0.010]**0.406[0.069]**0.356[0.085]**0.099[0.010]**
yes
yes
no
yes
115.54
10370

male
[8]
0.768
[0.220]**-0.086[0.013]**0.069[0.010]**0.254[0.069]**0.279[0.068]**0.111[0.010]**
yes
yes
no
yes
70.04
9426

+p<0.10; *p<0.05; **p<0.01

Table 5-2

Parenthood and Life Satisfaction, instrumental variable estimation
former socialist

childrenage
age2
educ dummy(middle)
educ dummy(upper)
Scale of incomesjob dummy
marital dummy
country dummy
year dummy
F value
Observations

female[9]-1.06[0.152]**-0.001[0.012]0.01[0.011]-0.300[0.062]**-0.259[0.095]**0.185[0.007]**
yes
yes
no
yes
227.57
28320

male
[10]
-0.617[0.113]**-0.035[0.010]**0.043[0.009]**-0.084[0.046]+0.138[0.063]*0.169[0.007]**
yes
yes
no
yes
378.7523765

nies
female[11]-0.864[0.345]*0.037[0.022]+-0.004[0.026]-0.38[0.182]*-0.403[0.226]+0.179[0.020]**
yes
yes
no
yes
41.06
3420

male
[12]
0.209
[0.264]-0.018[0.018]0.016[0.021]
0.002[0.123]0.206[0.148]0.196[0.018]**
yes
yes
no
yes
65.07
3335

+p<0.10; *p<0.05; **p<0.01

21

developing
female[13]
0.32[0.683]-0.102[0.105]0.091[0.076]0.307[0.379]0.465[0.562]0.244[0.029]**
yes
yes
yes
yes
6.420
52552

male
[14]
-1.259[0.993]0.094[0.109]-0.015[0.052]-0.38[0.389]-0.737[0.678]0.18[0.030]**
yes
yes
yes
yes
4.740
52027

Appendix Welfare regime categorization
SocialDemocratic
Denmark
Finland
IcelandNetherlands
NorwaySwedenLiberalAustralia
CanadaIreland
New ZealandGreat Britain
United States
Northern Ireland

Conservative
AndorraAustria
Belgium
FranceGermany,west
GreeceItaly
Japan
Luxembourg
Malta
Portugal
Spain
Switzerland

Former Socialist
Albania
Azerbaijan
Armenia
Bosnia and Herzegovina
Bulgaria
Belarus
Croatia
Czech Republic
Estonia
Georgia
Germany,east
Hungary
Kyrgyzstan
LatviaLithuania
Moldova
Montenegro
PolandRomania
Russian Federation
Slovakia
Slovenia
UkraineMacedonia
Serbia and Montenegro
SerbiaKosovo22

NIEs
Hong Kong
Singapore
South Korea
TaiwanDeveloping Countries
Algeria
MoroccoArgentina
Nigeria
Bangladesh
PakistanBrazilPeru
Chile
Philippines
China
Puerto Rico
Colombia
RwandaCyprusSaudi Arabia
Dominican Republic Viet Nam
El Salvador
South Africa
Ethiopia
Zimbabwe
Ghana
Thailand
GuatemalaTrinidad and Tobago
India
TurkeyIndonesia
Uganda
Iran
Egypt
Iraq
Tanzania
JordanBurkina Faso
Malaysia
Uruguay
Mali
Venezuela
MexicoZambia