DPRIETI Discussion Paper Series 11-E-062

A Network Approach to the Economic Models of Fertility

YOUM Yoosik
RIETI

The Research Institute of Economy, Trade and Industry

http://www.rieti.go.jp/en/

RIETI Discussion Paper Series 11-E-062
August 2011

A Network Approach to the Economic Models of Fertility

YOUM Yoosik
RIETI／Yonsei University
Abstract
Since its first appearance in the late 1950s, Neoclassical economic theory of fertility, especially as
exemplified by Gary Becker’s model of household production function that assumed a unitary utility
function of the household, has become one of the most popular paradigms to examine fertilitychanges. This paper intends to expand the economic model by incorporating the social network
approach into the original paradigm. Social networks are crucial in determining the fertility rate of asociety above and beyond parameters originally included in the neoclassical economic model in two
ways. First, the extent that separate utilities of spouses could be treated in one function is, in part,
dependent on the network embeddedness of spouses: intra-household network. If spouses are notembedded into each other’s networks, it would be natural to drop the assumption of the unitaryutility function and reformulate the decision process based on bargaining. Second, in addition to theintra-household network, inter-house networks also play a role in couple’s decisions with regard to
fertility. Couples need information about other couples’ fertility decisions for their own and also
normative pressures from other couples or friends are crucial in the dynamic process of fertilitychange. Social networks are a major conduit both for information and normative constraints. This
paper focuses on the first kind of networks (intra-household networks) with illustrative empiricalresults by using the two waves of Korean Longitudinal Survey of Women and Families (KLOWF).

Keywords: fertility, economic model of fertility, neoclassical economic model, social network
approach, unitary utility function, information networks, and normative pressures.
JEL classification: A10; A12; A14; D10; D13
RIETI Discussion Papers Series aims at widely disseminating research results in the form of professional
papers, thereby stimulating lively discussion. The views expressed in the papers are solely those of theauthor(s), and do not represent those of the Research Institute of Economy, Trade and Industry.

* The author would like to thank Mitsuhide Hoshino, Masayuki Morikawa, Atsushi Nakajima, Kazuo
Yamaguchi, and other participants of the RIETI seminar for their helpful comments. They were crucial to
improve the draft. Especially Kazuo Yamaguchi pushed me to develop a series of elaborated hypotheses. This
research was carried out by the financial support from the RIETI and Asia Research Fund in the year of 2011.

1

1. INTRODUCTION

Japan and South Korea have been exemplary countries in East Asia that showed extremely
low fertility rates for almost a decade. Usually the total fertility rate (TFR) of 2.1 is believed
to be necessary to maintain the current population size (replacement rate) and somescholars coined the term ‘lowest-low fertility rate’ to refer to 1.3 of TFR. The rate of 1.3
implies a reduction of the birth cohort by 50% and a halving of the stable population size
every 45 years (Kohler, Billari, and Ortega 2001, 2). Figure 1 shows that since 2007, thetotal fertility rates in both countries have dropped below 1.3 and never came back.

Especially the quite dramatic change of South Korea’s fertility rate is pretty interesting.
When South Korea’s TFR reached six in 1960, the Korean government newly established
the Family Planning Association of Korea in 1962 for extremely strong and persistent
government-initiated family planning policy. The policy remained one of the most successful
cases in family planning history in the world (Freedman and Berelson 1976; Lapham and
Mauldin 1972). During only four-year period between 1966 and 1970, the fertility rate
dropped half among women aged 15 to 19 and women aged among 20 to 24 showedalmost a quarter reduction (Lapham and Mauldin 1972). This kind of strong familyplanning policy for the fertility reduction continued till 1996 and then, only several years
later, South Korea faced one of the lowest fertility rates in human history.

Now South Korean government initiated another and opposite strong policy to increase thefertility rate: it enacted a new law of ‘Basic Regulations for Aging Society with Low Fertility
Rate’ in 2005 and has implemented a series of government programs. However, withcompared to numerous and various policies, it is relatively hard to find theoreticalframeworks to examine the change of the fertility rate in South Korea. Unlike South Korea in
1960s when the government could implement strong nation-wide policies across virtually
any societal domains including firms, families, and communities with little resistance, South
Korea is now a relatively well modernized, individualized, and de-centralized society.
Successful policy operation needs a robust empirical basis guided by well-elaborated
theoretical frameworks. Various economic models have proven to be effective inunderstanding fertility behaviors in general for the last 50 years but still elusive in some
aspects and has not lived up to early expectations (Robinson 1997: 63). This study tries to
add some explanatory powers by introducing social network approach to the models in aharmonious way. The purpose of this study is not to criticize economic models by any sense
but rather to improve them by taking couples’ social embeddedness into account in theirdecision making processes.

22. TWO ECONOMIC MODELS OF FERTILITY
The economic model of fertility can be traced back to Harvey Leibenstein’s original workwhere he observed the fertility decline and formulated an economic model where families
balance utilities against disutilities with regard to an nth child (1957). Based on many works
following a paper in 1960, Becker elaborated the economic model to the fuller extent where
the family is working as a factory that produces household products such as housework,
children, etc. (Becker 1960; Becker and Lewis 1973; Becker and Barro 1988; Becker 1991).
He proposed a household production function where all familiar neo-classical assumptionsof maximizing behavior and equilibrium solutions are applied. Especially he proposed ananswer one of the hottest puzzles of fertility at that time: why did fertility fall as income
increases? Common sense told that as income increases the fertility would increase too
unless the child-service is an inferior good with negative income-elasticity of demand, which
seemed unreasonable (Robinson 1997). He came up with the concept of ‘quality’ of children
and formulated a model where total child-services equals the number of children times anaverage quality per child and successfully showed that as income increases people
maximize their utilities by increasing average quality rather than the number of children(Becker and Lewis 1974). It has been a dominating theory of fertility within social sciences
because of its simplicity and rigor.

Until the early 1990s, following the Becker’s model, most economic models treated thehousehold as one economic unit in which family members behave as if they share acommon utility or at least as if they agree on every economic activity. This ‘common
preference’ (or unitary utility of the couple) is guaranteed by consensus (Samuelson 1956)
or altruism (Becker 1991). Different terms are used to describe this approach: the‘neoclassical model’ (McElroy and Horney 1981; Schultz 1990), the ‘common preference
model’ (Thomas 1990), the ‘unitary model’ (Alderman et al. 1995), or the ‘consensus model’(Behrman 1997).
One of the major reasons this approach gained popularity was that economic consumption
data were typically reported only at the level of the household. It also allowed economists toanalyze household data with relative ease by putting aside the theoretical task of showing
how individual preferences are collectively aggregated at the household level (the so- called‘preference aggregation problem’). Becker provided the seminal theoretical basis for thisanalytical convenience in studies of the family. As a possible mechanism to ensure theunitary utility of the household, Becker paid attention to ‘care’. In his well-known ‘rotten-kid’
theorem, he proved that even if only one of the members is caring for others so that his or
her preference depends on the other’s utility function, every family member (including arotten kid) will try to maximize the joint family utility or dynastic utility (Becker 1991). This
provided a theoretical justification for studying family behavior as if its members share one
utility although they have separate utilities in reality.

3

Although Becker avoids the preference aggregation problem by assuming ‘caring’ (or‘altruism’), the issue over who has the last word (or ultimate power) in the household is not
solved (Ben-Porath 1982). A caring person (or a parent) must have the ultimate power inthe household in order for the rotten kid theorem to be valid. It disregards the fact that thefamily is also often a place for conflict and struggle. In economics, many different
approaches, denoted ‘collective models’ (Alderman et al. 1995; Behrman 1997;
Bourguuignon and Chiappori 1992) or 'bargaining models' (Lundberg and Pollak 1993;
1994; 1996; Stark 1984), have been developed that treat the family as a collection ofindividuals with separate utilities and that show how the aggregated outcome emerges fromdifferent and even conflicting utilities.
Based on the Nash bargaining model, bargaining models assume a cooperative gamebetween two persons with separate utility functions who bargain over the intra-household
resource allocation (Manser and Brown 1980; McElroy and Horney 1981). All these are
efforts to explain intra-household decisions without relying on the assumption of a single
household utility function

Many empirical results have challenged the unitary utility argument, especially by showingthat family members do not look like they pool their income (Lundberg, Pollak, and Wales
1997; Schultz 1990; Thomas 1990; especially Alderman et al. 1995 for a summary) andnow many believe that the burden of proof should shift to those who argue for the unitaryutility of the family (Alderman et al. 1995; Lundberg and Pollak 1996).1

An important lesson to be drawn from the discussion on the two competing economic
paradigms is that we must conceptualize the family as the place where both love andbargaining (or care and struggle) coexist (Stark 1984). We must identify the conditions
under which reciprocity and altruism emerge, rather than assume that they are always ornever characteristic of families (Ferree 1990, p. 879). Following this dictum, I try to show therelevance of the level of structural embeddedness of the couple as a contingent factor in
predicting fertility behavior of the family. I develop a bargaining model where in the
beginning, the spouse has his or her own distinctive utility functions and the magnitude of
the couple’s embeddedness is incorporated as a deciding factor of transfer between
spouses. Contingent on the level of structural embeddedness of the couple, two distinctive
equilibria emerge. Among weakly embedded couples, the model predicts that the fertility
behavior that is the result of the bargaining between partners with separate and conflicting
utilities. In contrast to this pure bargaining situation, strongly embedded couples behave as
if they share a unitary utility in deciding the fertility as assumed in Becker’s model.
1

For a fuller discussion of two competing economic paradigms in household behavior, please see anotherpaper (Youm 2005).
43. INTER-HOUSEHOLD NETWORK EMBEDDEDNESS AS A CONTINGENT FACTOR

The following five assumptions are necessary to develop a bargaining model where thecouple’s social embeddedness is a contingent factor to predict the divergence of fertility
behaviors.

Assumption 1: (Utility function of the spouse) The household comprises a married husband
and wife and they decide the number of children to be born. The utilities of the spouses are
separate and dependent on (1) the divisible private goods (x) and (2) a public good (B) (i.e.
the flow of child-services from the birth).

U h  Vh ( x)  Vh ( B)(1)

U w  Vw ( x)  Vw ( B)

(2)

Assumption 2: (Cost for the child) In order to produce children, both wife and husband pay
some costs but we assume that wife’s cost is higher than husband’s. Some examples
include (mental and physical) cost for bearing and rearing children and the stoppage ofcareer. We simplify the model by assigning the cost only for the wife. This won’t change themajor conclusions of the model. We denote this cost as C(B).
Assumption 3: (Network-contingent transfer) I also assume that since the children are apublic good of the household and thus, there should be some transfer of utility from thehusband to the wife in order to produce it. For example, the wife wants the fair reward forbearing and rearing the child from the husband later even when she can’t get back to work
after a certain period of career breakdown.
However, the wife can’t be always sure that there will be a fair transfer. Sometimes there
could be unfair transfer or betrayal of the husband. When the husband betrays, he has topay the cost that refers to the value of emotional strain, loss of love, companionship, andreputation etc. We assume that this cost the husband must pay is systematically dependent
on the strength of couple’s network embeddedness. The stronger the spouse is embedded
into each other’s social networks, the more expensive the cost from the betrayal is.2 Thus, I
denote β as the level of couple’s embeddedness (0 ≤ β ≤ 1) then, the expected transfer
amount from the husband to the wife will be a fraction of the full amount, β*T.

2

Youm and Laumann discussed in detail the cost from the betrayal between spouses with regard to the intrahousehold resource allocation (Youm and Laumann 2003). Their study examined the effects of spousal
embeddedness on the division of housework rather than fertility.
5Assumption 4: (Transfer function) The amount of the transfer from the husband to the wife
is determined by the traditional bargaining model based on each spouse’s bargaining power(Lundberg and Pollak 1996; Rasul 2008; Suen and Chan 2003). We denote the transfer as
T and the husband’s bargaining power as α (0 < α < 1).

T  (1   )U h   [U w  C ( B)](3)

Now, the wife will maximize the following with respect to the number of children (B) while
taking account of the transfer she receives.

Max( B) : U w   * T  C ( B)(4)

If we substitute eq. (3) into eq. (4), then we will get the following.

(1   )U w   (1   )U h  (  1)C ( B)

(5)

Once eq. (1) and eq. (2) are substituted into eq. (5), then the first order condition for the
maximization would be;(1   )Vw ( B* )   (1   ) Vh ( B* )  (1   ) C ( B* )
B
B
B

(6)

From the equation (6), we can show that depending on the embeddedness level, the fertility
behaviors are diverging.
When the spouse is strongly embedded into each other’s networks and thus, we can expect
fair transfer from the husband to the wife (when β = 1), the number of children at equilibrium
will be decided regardless of the bargaining power of the husband (α disappears in theequation (6) ). Actually the wife will behave as if she maximizes the unitary utility function of
the couples that is the sum of two spouses’ utilities, the LHS of the equation (6). Thus, thepredicted fertility behavior will be identical with the one Becker’s model expected. In theirdecision process, the bargaining power of each spouse must be insignificant. They will
behave as if they share one utility function without bargaining.

Assumption 5: (Nash product solution) When the spouse is weakly embedded into eachother’s social networks and thus, the wife can’t expect a fair reward from the husband, now
the couple is in the bargaining situation. The wife will maximize her own utility based on theequation (6) while the husband will do the same based on corresponding utility structure
and constraints. We do not have a universal economic model to predict the result of thebargaining. A series of different economic bargaining models of intra-household resource
allocation were developed and they showed the differences in the assumptions of essential
parameters such as utility functions, transfer rules, and equilibrium concepts. However allmodels agree on one thing: the final equilibrium must be a result of maximizing weighted
average of each spouse’s utility. For example, the Nash solution, one of the most popular6

bargaining solution concept, expects that the bargaining equilibrium must be a geometricmean of each spouse’s utility maximization weighted by ach own threat point.3

Thus, when the spouses are not quite embedded into each other’s social networks andthus, the low cost of betrayal lead to only small fraction of the full transfer (when β is close
to zero), now the equilibrium is decided by bargaining power as predicted in manybargaining models. Now each spouse’s threat point that is usually measured by wage or
educational level must be crucial to decide the couple’s fertility behavior. In a nutshell, when
the couples are strongly embedded, we can expect that they will behave just like they share
one utility function of the household as Becker assumed. When the couples are only weakly
embedded, however, spouse will be in bargaining situation where the final fertility behavior
is a product of maximizing an average of each spouse’s utility weighted by each own threat
points and bargaining powers. In addition to this type of intra-household networks of
spouses, the social networks across household are also crucial to determine the speed offertility change. Let me briefly discuss it now.

4. INTRA-HOUSEHOLD NETWORKS FOR INFORMATION AND NORMS

Although I believe that the socio-economic parameters such as increasing earning power ofwomen and the cost of rearing children had a crucial role in the fertility drop, the concrete
dynamics of fertility changes must be also dependent on the social network patterns acrosshouseholds. Unless each household decides the fertility of its own in an independent way
without interacting with other household, the way the households interact with each othermust be crucial in fertility change path. Now many demographers do not believe thatsocioeconomic change in itself cannot explain fertility drop in Europe and found out thattime path of fertility change is different from one predicted by models that take socioeconomic parameters into account only. Increasing number of studies has actively paid
attention to the diffusion processes of knowledge, information, attitudes, and norms to
explain the fertility change fully (Bongaarts and Watkins 1996; Buhler and Philipov 2005;
Coale and Watkins 1986; Easterlin 1978; Kohler 2000).

It is well-known that depending on network patterns, the diffusion process can take quite
different paths. For example, Burt illustrated two distinctive hypothetical diffusion processes
3

The solution of the bargaining will be to maximize the product of each spouse’s utility in a form ,(U w  Dw )(U h  Dh ) , where Ui and Di is the utility and threat point of each spouse, respectively (Nash 1953;

Binmore, Rubinstein, and Wolinsky 1986). The solution is a type of geometric mean weighted by each
spouse’s threat point.
7

of new pill: the one without network contagion and the other one with network contagion
where the odds of doctors adopt a new drug is systematically dependent on the proportionof colleagues who already adopt it (Burt 1987). Clearly the latter one fits to the S-patternthat has been known to be actual diffusion process of many products, ideas, diseases, andsocial phenomena.
Intra-household networks provide two related but distinctive mechanisms through which
fertility change can be accelerated or deterred: information and norms. First, households
need to know the fertility intensions of other households since their own optimum fertility
behavior itself is dependent on the behaviors of others (Kohler 2000). As illustrated in
Kohler’s paper, multiple-equilibria situation constitutes a coordination problem: the limitationof fertility is not a rational decision, given (expectations about) a prevailing high fertility levelin the population (Kohler 2000, 241). Let’s take an opposite example. If substantial
proportion of household decides to invest a lot of money and efforts for the education of
their children and thus focus on quality over quantity (just like in South Korea), otherhouseholds also want to change their focus from quantity to quality.

Another mechanism that could be responsible for fertility change is social normative
pressure through inter-household networks. Networks convey attitudes, beliefs, and norms
in addition to information: they must be responsible for changing preferences, beliefs, andattitudes.

Numerous social network studies showed that strong and intensive ties are believed to
produce greater mutual commitment and social influences and pressures while weak ties
provide more information than control (Burt 1992; Kincaid 2000; Sandefur and Laumann1998). The speed and robustness of diffusion are also contingent on the level ofhomogeneity of social networks. If social networks are strongly assortative and thus, peopleinteract mainly with other people with similar backgrounds, the diffusion will be slow but also
robust in the sense that it won’t die out easily. Otherwise, if social ties are formed in a very
dissortative way and thus, people get acquainted with many others with different
backgrounds, the diffusion will be fast but non-robust. In order to understand the full
picture of fertility change, we need to turn our attention to the way that households interact
with each other, especially with regard to different regions and social-classes in a country.

5. AN EMPIRICAL ILLUSTRATION
In order to carry out empirical studies based on the frameworks I proposed above, we needto consider the following issues on data availability and measurement. First, with regard todata-availability, intra-household network data is much more available than inter-household
one. Most network studies have focused on individual-level (or ego-level) ties yet and it is8relatively rare to find network data at the level of household. Second, the measure of social
embeddedness of spouses is not quite straightforward. Since there are only a few networks
studies to focus on embeddedness of spouses, we do not have a set of universal
measurements. One study successfully utilized two dimensions of couples’ networks tomeasure the strength of embeddedness and used it to explain the division of housework:
the number of mutual friends and shared time (Youm and Laumann 2003). We need morestudies to be confident about proper empirical measures of embeddedness of couples.

Data
In this paper, I would like to present an empirical illustration that an intra-household network
is working as a key contingent factor in predicting the patterns of the fertility behavior. The
Korean Longitudinal Survey of Women and Families (KLOWF) provides one of the best
available empirical data of Korean society to examine the hypothesis although it is far fromperfect. The data first collected a representative sample of 9,068 households where at least
one woman aged from 19 to 64 lived in South Korea from September 2007 to February
2008. The second wave was collected from October 2008 to June 2009 for the 7,704
households who participated in the first wave (15% attrition rate).

The third wave data collection is done in June 2011 and they are expecting a public releaseby the end August 2011. There exist several limitations to examine the current hypothesis.
(1) Although the data contained some measures of couples’ social networks, they were notelaborated. Thus, available social network measures are very limited. (2) It did not collectmatch-paired data. Only women were interviewed and this means that all the variables
describing the partners, including their wages and social networking patterns, must be
estimated from the women’s report about them. (3) Although it is a panel data, the current
data set contained only two-wave and furthermore, the waves were only one year apart. It
does not provide enough number waves across enough time-lag to fully observe andanalyze fertility behavior. As a result, the following empirical analysis can only be taken as
illustrative of an empirical examination of the model’s implication.

Measures
Since there were only 313 people who actually gave a birth within one year period, I
decided to consider also people who had a plan to give a birth in five years. The number ofthe respondents who actually gave a birth or had a plan to give a baby in five years is 575
in the sample. I adopted a logistic regression to predict this group of people.

A set of independent variables that were included for control purpose includes age, age
9squared, respondent’s educational level, use of housemaid, cohabiting with parents-in-law,
cohabiting with parents, logarithm of total asset, household income as a categoricalvariable, marriage satisfaction level.

In order to measure the amount of embeddedness of the couple, I used the questionnairethat asked if the couple met the parents or siblings of the wife at least once a month. I
believe this is a good measure to measure the amount of husband’s embeddeness into
wife’s intimate social networks. If the husband’s investment is high in the wife’s social
networks, she can expect a fair reward from giving a birth.
I used the logarithm of wage ratio of the couples as a proxy for bargaining power of thewife. It is calculated as [log(wage of the wife + 1) – log(wage of the husband + 1]. Sincemany wives were not working at the time of interview, I ran a regression to predict the wageof non-working woman based on their educational level, age, and career history andreplace the wage by it. I also added one to avoid log(0). The higher logarithm of the wage
ratio, the more bargaining power of the wife.

Hypothesis

H1: Since the couple’s embeddedness is the key contingent factor on which the couple’s
fertility behavior follows either Becker’s unitary utility model or bargaining models withconflicting utilities, there exist the interaction effect between the embeddedness andbargaining power. In other words, depending on the strength of the embeddedness, theeffect of the bargaining power differs. In the empirical analysis below, this implies theexistence of statistical significance of the interaction term between the couple’s visit towife’s family at least once a month and the logarithm of wage ratio of the wife (withcompared to the husband).

H2: When the embeddedness is strong, the couples would behave just like they share one
utility function and thus, bargaining power measured by the wage ratio loses its significancein predicting the fertility. Thus, when the couples visit the wife’s family at least once amonth, the effect of the wage ratio is statistically insignificant.

H3: When the embeddedness is weak, now the logarithm of wage ratio becomes significant
since the couple is bargaining now to maximize each own utility. Given the fact that on
average the wife would prefer less number of children than the husband due to gender10specific cost4, the higher the logarithm of the wage ratio of the wife (with compared to thehusband), the lower the odds that the couple had a childbirth or plan to have a childbirth.

Results and Discussion
Table 1 summarized the all the coefficients from the logistical regression and figure 2
summarized the interaction effect. The predicted probability is calculated given othervariables are fixed as average. The first column of the table 1 revealed that there exists
strong interaction effect between the logarithm of wage ratio of the wife and the husband’s
embeddedness: the coefficient is 0.388 and the t-value for the hypothesis that thecoefficient is statistically insignificant is 3.26. As we proposed in the H1, the logistic
regression confirmed that the effect of the wage ratio is contingent on the strength of theembeddedness.

This hypothesis is also confirmed on the second and the third column. The logistic
regression only among weakly embedded couples showed statistically significant effect of
the wage ratio while the one among strongly embedded couples did not identify thestatistical significance. Among the couples who are strongly embedded would behave justlike they share unitary utility and thus, there is no statistical significance of the wage ratio:
the coefficient is 0.0566 with 0.82 t-value. This result is consistent with the H2.

Also the figure 2 confirmed the H3. When we calculated the predicted probability of having
a childbirth or plan to childbirth, the direction of the logarithm of the wage ratio is consistent
with the H3. Among the couple with strong embeddedness, there is no effect of wage ratio
but when the couple is weakly embedded, the logarithm of the wage ratio becomessignificant. Furthermore, among the couple who were weakly embedded, the higher the
logarithm of the wife’s wage ratio, the lower the odds that the couple had a childbirth of plan
to have one.

Some major limitations of the empirical examination must be discussed. First, as I
mentioned, I would like to take the result as an empirical illustration rather than robust
evidence since the date is incomplete in two senses. Any two-wave data in one year apartis not enough for examining fertility behaviors. Also since the data was not built for network
analysis, many possible measures of embeddedness could not be utilized. For example, if
4

It would be natural to assume that the wife prefers less children if the wife pay cost more than the husband
from the childbirth as we assumed. Although there is no available data for Korea, according to the Malaysian
Family Life Survey (MFLS), Malaysian men prefer more children than Malaysian women (Rasul 2008).
11

the spouses maintain each own bank account or not usually provides a good measure of
the trust within the household (Heimdal and Houseknecht 2003; Treas 1993). We mightwant to wait until the data with rich measures of social networks of the couples are available
for more robust and detailed answers. Also the current model of network embeddedness
was not elaborated and developed to the full extent. A fuller model could incorporate thecouple’s embeddedness into even bargaining processes so that we can predict the effectsof the social embeddedness on the bargaining equilibrium. But I believe the current
empirical results would suffice as an illustrative example for my model.

This paper also has strong policy implications to improve fertility rates in a society. One
possible policy direction is to increase the husband’s embeddedness with the wife’s social
life. We can consider longer paternity leave at work or even obligatory observation ofpaternity leave. This will increase the actual (and expected) transfer amount from thehusband to the wife. Or the reduction of working hours on average could be helpful.
Although this kind of suggestions is consistent with work-family balance approach (Brewster
and Rindfuss 2000; MacIness 2006; Shreffler, Pirretti, and Drago 2010), the model argued
that this kind of policies will help to improve the fertility rates above and beyond work-lifebalance through different mechanisms. Also the current theoretical model implies that thiseffect will be maintained regardless of the levels of employment and college education
among women: under the strong embeddedness, the wage ratio has no effect. This could
be specifically important for the countries like Korea that observed extremely highproportion of employment and college education among women.

None of the above discussions is intended to criticize economic approaches to fertility
behavior. I believe, in fact, economic models are essential to understand the processes offertility decision. I also believe if we can carefully incorporate some social network
components to the economic models, the explanatory power increases.

12References
Alderman, Harold, Pierre-Andre Chiappori, Lawrence Haddad, John Hoddinott, and Ravi Kanbur.
1995. “Unitary versus Collective Models of the Household: Is it time to shift the burden of proof?”
The World Bank Research Observer 10:1-19.
Becker, G.S. 1960. “An economic analysis of fertility,” in Demographic and Economic Change in
Developed Countries. Princeton: National Bureau of Economic Research.
___________. 1991. A Treatise on the Family, Revised and Enlarged Edition. Cambridge, ,Mass:
Harvard University Press.
Becker, G.S. and Lewis, H.G. 1973. “On the interaction between quantity and quality of children,”
Journal of Political Economy. 82: 279-288.
Becker, G.S. and Tomes, N. 1976. “Child endowment and the quantity and quality of children,”
Journal of Political Economy. 84: S143-162.
Behrman, J.R. 1997. "Intrahousehold Distribution and the Family." Pp. 125-87 in Handbook of
Population and Family Economics, edited by M.R. Rosenzweig and O. Stark. New York: Elsvier.
Ben-Porath, Yoram 1982. "Economics and the Family - Match or Mismatch? A Review of Becker's
A Treatise on the Family." Journal of Economic Literature 20 (March): 52-64.
Binmore, Ken, Rubinstein, Ariel, and Wolinsky, Asher. 1986. “The Nash Bargaining Solution in
Economic Modeling,” The RAND Journal of Economics. 17(2): 176-188.
Bongaarts, J. and Watkins, S.C. 1996. “Social Interactions and Contemporary Fertility Transitions,”
Population and Development Review 22(4): 639-682.
Bourguignon, Francois and Pierre-Andre Chiappori. 1992. “Collective models of householdbehavior.” European Economic Review 36:355-64.
Brewster, K.L. and Rindfuss, R.R. 2000. “Fertility and Women’s Employment in industrialized
Nations,” Annual Review of Sociology 26: 271-296.
Buhler, Christopher and Philipov, Dimiter. 2005. “Social Capital Related to Fertility: Theoretical
Foundations and Empirical Evidence from Bulgaria,” Vienna Yearbook of Population Research:
53-81.
Burt, R.S. 1987. “Social Contagion and Innovation: Cohesion Versus Structural Equivalence,”
American Journal of Sociology, 92(6): 1287-1335.
_____________. 1992. Structural Holes: The Social Structure of Competition. Cambridge,
Massachusetts: Harvard University Press.
Central Intelligence Agency. 2011. Factbook.
Chiappori, Pierre-Andre. 1992. “Collective Labor Supply and Welfare.” Journal of Political
Economy 100:437-67.
Coale, A.J., Watkins, S.C., 1986. The Decline of Fertility in Europe. Princeton Univ. Press,
Princeton.
Easterlin, R.A., 1978. The economics and sociology of fertility: a synthesis. In: Tilly, C. Ed. ,Historical Studies of Changing Fertility. Princeton Univ. Press, Princeton, pp. 57–133.
13

Ferree, Myra Marx. 1990. “Beyond Separate Spheres: Feminism and Family Research.” Journal of
Marriage and the Family 52:866-84.
Freedman, R. and Berelson. 1976. “The Record of Family Planning Programs,” Studies in Family
Planning. 7(1): 1-40.
Heimdal, K.R. and Houseknecht, S.K. “Cohabiting and married couples’ income organization:
approaches in Sweden and the United States,” Journal of Marriage and Family. 65(3): 525-538.
Kinkaid, D. L. 2000. Social Networks, ideation, and contraceptive behavior in Bangladesh: alongitudinal analysis, Social Science & Medicine. 50: 215-31.
Kohler, Hans-Peter. 2000. “Fertility decline as a coordination problem,” Journal of Development
Economics. 63: 231-263.
Kohler, Hans-Peter, Billari, F.C. and Ortega, J.A. 2001. “Towards a Theory of Lowest-Low Fertility,”
MPIDR Working Paper WP 2001-032. Max Planck Institute for Demographic Research.
Lapham, R.J. and Mauldin, W.P. 1972. “National Family Planning Programs: Review and
Evaluation,” Studies in Family Planning. 3(3): 29-52.
Leibenstein, Harvey. 1957. Economic Backwardness and Economic Growth. New York: Wiley.
Lundberg, Shelly and Robert A. Pollak. 1993. “Separate Spheres Bargaining and the Marriage
Market.” Journal of Political Economy 101:988-1010.
---------------------------------. 1994. “Noncooperative Bargaining Models of Marriage.” American
Economic Review - Papers and Proceedings 84:132-7.
---------------------------------. 1996. “Bargaining and Distribution in Marriage.” Journal of Economic
Perspectives 10:139-58.
Lundberg, Shelly, Robert A. Pollak, and Terrence J. Wales. 1997. "Do husbands and wives pool
their resources? Evidence from the United Kingdom child benefit." The Journal of Human
Resources. 32: 463-80.
MacInnes, John. 2006. “Work-life balance in Europe: a response to the baby burst or reward forthe baby boomers?,” European Societies. 8(2): 223-49.
Manser, Marilyn and Murray Brown. 1980. “Marriage and Household Decision Making: A
Bargaining Analysis.” International Economic Review 21:31-44.
McElroy, Marjorie B. and Mary Jean Horney. 1981. “Nash-Bargained Household Decisions: Toward
A Generalization of the Theory of Demand.” Internatinal Economic Review 22:333-49.
Nash, John. 1953. “Two-Person Cooperative Games,” Econometrica 21(1): 128-140.
Robinson, W.C. 1997. “The Economic Theory of Fertility Over Three Decades,” Population
Studies. 51: 63-74.
Rasul, Imran. 2008. “Household bargaining over fertility: Theory and evidence from Malaysia,”
Journal of Development Economics 86: 215-241.
Samuelson, Paul A. 1956. “Social Indifference Curve.” Quarterly Journal of Economics 70:1-22.
Sandefur, Becky and Edward Laumann. 1998. A Paradigm for Social Capital, Rationality &14Society. 10: 481-502.
Schultz, T. Paul. 1990. "Testing the neoclassical model of family labor supply and fertility", Journalof Human Resources 25: 599-634
Shreffler, K.M., Pirretti, A.E., and Drago, R. 2010. “Work-Family Conflict and Fertility Intentions:
Does Gender Matter?,” Journal of Family and Economic Issues 31(2): 228-240.
Stark, Oded. 1984. “Bargaining, Altruism, and Demographic Phenomena.” Population andDevelopment Review 10:679-92.
Suen, Wing and William Chan. 2003. “Marital transfer and intra-household allocation: a Nashbargaining analysis,” Journal of Economic Behavior & Organization. 52(1): 133-146.
Thomas, Duncan. 1990. "Intra-household resource allocation: an inferential approach", Journal of
Human Resources 25: 635-64.
Treas, Judith. 1993. “Money in the bank: transaction costs and the economic organization of
marriage,” American Sociological Review 58(5): 723-734.
Youm, Yoosik. 2005. “Household Behavior and Family Economics,” in Encyclopedia of Social
Measurements (edited by Kimberly Kemp-Leonard): 243-248.
Youm, Yoosik and Edward Laumann. 2003. “The Effect of Structural Embeddedness on the
Division of Household Labor: A Game-theoretic Model Using a Network Approach,” Rationality
and Society. 15(2): 243-280.

15

Tables and Figures

Figure 1. The total fertility rates of Japan and South Korea. (CIA World Factbook 2010).

16

Predicted probability of having a childbirth orplan to have a childbirth
0.05
0.04
0.03
0.02
0.01
0
‐4

‐3

‐2

‐1

0

1

2

Husband's Embeddedness : Low

3

4

Husband's Embeddedness : High

Figure 2. The interaction between embeddedness and bargaining power

17

5

6

(1)
Total sample
R's ageR's age^2
R's educ : vocational highR's educ : general highR's educ : college
R's educ : above univ
Number of births >=3
Number of births = 2
Number of birth = 1
Number of birth = 0
household maid
cohabitee : R's parent
cohabitee : Partner's
parentlog(Husband’s Household
Labor(min)Marriage Satisfaction
Conversation with
Partner
Shared Activity with
Partner
Husband’s
embeddedness
Log (Wage Ratio)
Household income >4720 (0-27%)

0.740***(3.74)-0.0150***(-4.87)-0.143(-0.23)-0.200(-0.31)0.0498(0.08)-0.128(-0.20)-4.375***(-10.85)-3.970***(-16.64)-1.400***(-6.37).
.
0.210(0.85)
0.506(1.25)
0.214

(2)
Weak
embeddedness
0.788*(2.56)-0.0156***(-3.30)-0.946(-1.33)-0.728(-0.99)-0.290(-0.40)-0.825(-1.11)-4.309***(-6.71)-3.847***(-9.11)-1.221**(-3.21).
.
0.318(0.66)-0.140(-0.16)-0.346(3)
Strongembeddedness
0.694**(2.65)-0.0144***(-3.51)
14.63(0.01)
14.39(0.01)
14.59(0.01)
14.58(0.01)-4.590***(-8.55)-4.123***(-13.78)-1.540***(-5.52).
.
0.234(0.79)
0.629(1.36)
0.609+(0.82)
0.106*(-0.76)0.128+(1.78)
0.101+(2.54)
0.159*(2.36)
0.120(1.76)
0.0450(0.38)
0.364+(1.92)
0.217*(2.56)
0.00110(1.07)
0.00430(1.84)-0.0890+(0.01)
0.0369(0.17)
0.424**(-1.69).

(1.25).

(2.66)-0.316**(-3.04)-0.272.
-0.250*(-2.36)0.696.
0.0566(0.82)-0.853**18

Household income <4720 (27-52%)
Household income <3560 (52-83%)
Household income <2400 (83~100%)log(Household's total
asset)Log (Wage Ratio) *Husband’s
embeddedness
Constant
Observations(-1.12)-0.246(1.62)
0.326(-2.76)-0.651*(-1.08)0.112(0.84)
0.792*(-2.24)-0.348(0.58).

(2.52).

(-1.38).

.
0.0537+.
-0.00754.
0.101*(1.73)
0.388**(-0.16)(2.41)-9.504+(-1.95)2025-22.39(-0.02)2345(3.26)-9.095**(-2.91)4370

t statistics in parentheses+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001

Table 1. Logistic Regression on New Birth and Birth Planning19