Tsukuba Economics Working Papers
No. 2010-001	
 Marriage Relationships Among Households in the mid 19th Century Tama, Japanby
Nobuyuki Hanaki and Satomi Kurosu	 	
 December 2009

UNIVERSITY OF TSUKUBA
Department of Economics
1-1-1 Tennodai
Tsukuba, Ibaraki 305-8571
JAPANMarriage Relationships Among Households in themid 19th Century Tama, Japan
Nobuyuki Hanakia, b and Satomi Kurosuc, *a

GREQAM and Université de la Méditerranée
2 Rue de la Charite, 13002, Marseille, France
nobuyuki.hanaki@univmed.frb

University of Tsukuba
1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8571
hanaki@dpipe.tsukuba.ac.jpcReitaku University
2-1-1 Hikarigaoka Kashiwa, Chiba, 277- 8686, Japan
Phone: +81-4-7173-3477. Fax: +81-4-7173-3403
skurosu@reitaku-u.ac.jp* Corresponding Author
December 31, 2009
Abstract
This paper studies the formation of marriage relationships between twohouseholds in 19th century, Tama, Japan. Previous studies on marriage market or
partner selection in the Japanese past tended to rely either on information from asingle village in case of statistical analysis, or on collection of oral histories. By using
the information from a household register that covers 35 villages, and applying themethod of social network analysis, this paper goes beyond the limitation of previous
studies. Our empirical results show that there was a tendency for socio-economic
homogamy and endogamy (within kinship and within village) among peasants in themid 19th century Tama, Japan.
Keyword: Marriage, Japan, Network, Household, Household registers

11. Introduction
Marriage is an important social institution that unites two individuals but also binds
two families or groups. Marriage, therefore, has been considered a social reproduction(Bourdieu 1976) or strategy for alliance (Levi-Strauss 1976). Marriage was crucial in
pre-industrial rural Japan. Given the high mortality of the times, marriage offered themost reliable and least expensive way of achieving both the optimal household size
and the optimal gender balance of labor for family farming (Smith 1972; Tsuya andKurosu 2000).
When one marries (timing), whether one marries or not (celibacy), and towhom one marries (partner selection) are three fundamental questions that have beentackled from various perspectives. Accumulated studies on Japanese peasants showthat (1) peasants married early and universal---they married at least once in their life
as long as they survived; (2) however, their marriage did not last long due to highmortality and divorce rate; and (3) they often experienced remarriage. In all, regionalvariations were extremely large (Kurosu, Tsuya and Hamano 1999).
This study deals with the third question of partner selection in marriage, or in
our terms, marriage network (formation of relationships via marriage). In the context
of historical analysis, at least three approaches to examine partner selection arerecognized: socioeconomic background, geographic location, and family alliance.
First, finding a spouse from the same socioeconomic status (SES homogamy,
versus to heterogamy) could be seen as an important strategy, especially among
landholding farmers to maintain the landholding status and secure social reproduction(Bourdieu 1976). In European agrarian contexts, by finding a spouse from a higher
SES origin, an individual increased the chances of advancing to a higher SES, while,2

on the contrary, marrying someone with a lower SES potentially increased the risk ofsocioeconomic mobility downwards (Dribe and Lundh 2008). In Japanese agrariancontexts, homogamy is thought to have been important in securing and assuring an
individual living standard as well as an equal economic relationship and exchange
between two households. Anthropological studies suggest the importance of having
similar pedigrees of households for a good match (Segawa 2006) but demographic
studies are scarce on this matter.
Second, finding a spouse from the same village (endogamy, versus toexogamy) was probably considered vital in rural organization in preindustrial Japan.
Many anthropological studies point to the fact that partners from the same village
were preferred and even some sanctions were practiced against marriage between awoman from the village and outsiders (Segawa 1974). Some studies also indicate that
there were customary laws to prohibit (Uematsu 1996: 614) or to require special
rituals for exogamous marriage (Yonezaki 1987). Some argue that endogamous
marriages fit the best for integrating the functions of two households to cooperatewith each other in cases of rituals, festivals, and in exchanging agricultural tools andhorses in daily basis (Yagi 1996: 577). It is interesting to note, however, thathouseholds with SES at both ends---extremely high SES and extremely low SES,
tended to seek marriage partners outside of one’s village (i.e. exogamous in order tomaintain homogamy) as they could not find any good match within one’s villages(Segawa 2006: 58-59).
Some studies examined the geographic distance of marriage migration beyond
village boundaries (e.g. Segawa 2006; Kawaguchi 1984). These studies demonstrated
that the majority of marriage took place within 3 to 4-ri (12-16km)---commuting or a
day trip distance. Therefore, the marriage market geographically coincided with the3sphere of daily activities and within neighboring communities, if not within the samevillage.
Third, family alliance or kinship organization is pivotal in the studies offamily sociology and rural sociology in Japan. Numerous studies examine andcontrast the two kinship organizations---dozokudan (a group of households based on acommon ancestor and with hierarchical order based on economic relationship) andshinzokudan (a group of individuals related by blood or kinship formed by marriage
or adoption1). However, how they are related to the formation of marriage networks
has not been made clear. Oto (1996: 55) mentions that in order to maintain andstrengthen dozokudan organization in preindustrial Japan, it was necessary to findmarriage and adoption partners within the same dozokudan, thus resulting that, in
reality, dozokudan and shinzokudan overlapped. We could assume that these ruralkinship organizations, whether it to be dozokudan or shinzokudan, served toencourage endogamous marriage as well as marriage between relatives.
Previous studies on marriage market or partner selection in the Japanese past
had at least two limitations. One, since most longitudinal historical data, that would
allow us to examine detailed marriage-related exchanges over generations, come fromone village, the study of marriage market tended to be confined to the alliance amonghouseholds within one village. Two, since many anthropological frameworks onmarital customs, which provide us with many important variations in the past, arebased on collections of oral histories from pre-war Japan, it is difficult to specify timeand space where the customs apply to. The use of 1870 household registers in Tama1

The adoption here includes both adoption of children and that of adults. The latteroften took place together with marriage with daughters in adopting households(called, mukoyoshi). In this paper, we include the latter "mukoyoshi" in marriage.
4

and applying the method of social network analysis allow us to go beyond theselimitations.
To study which types of households tend to form marriage relationships
between themselves, one needs to study not only the relationships that have beencreated, but also those that did not materialize. To do so, we construct a pool ofpotential pairs of household and study which pair actually established a tie. The poolof potential matching would allow us to go beyond existing conceptual frameworks
and examine what are the driving factors of households to ally.
Our empirical results show that there was a tendency for socio-economic
homogamy and endogamy (both in terms of kinship and locality---within kin
members and within village) in the mid 19th century Tama, Japan, but not exclusively
so. We also find that households are connected, either directly or indirectly, beyond
the village boundaries and socio-economic status. In particular, the network ofhousehold demonstrates that about a half of the households from more than a half ofthe villages in our data formed a large connected component.
The rest of the paper is organized as follows. Section 2 describes the data
source, a brief summary of concepts used in network analysis is provided in Section 3.
Section 4 illustrates our empirical framework as well as definitions of variables thatare used in the analysis. The results are summarized and discussed in Section 5.
Section 6 concludes.

2. Data
The data used in this study is constructed from the Commoners’ Household Registerof Hinojuku, Tama, compiled in 1870. It derives from one of the experimental

5

household registers conducted in several areas in Japan around 1870, prior to the firstnation-wide household register in 1872 (known as Jinshin-koseki)(Ishii 1981: 291).
The register covers 35 villages that belonged to Hinojuku-group located in Tama,
Musashi Province, the areas that correspond to what currently are the cities of Hino
and Tama in western Tokyo. In total, there are 2120 households registered.
The household register records the information regarding all the members ofthe 2120 households who were alive in 1870 (there were 13965 such individuals). The
recorded information of individuals includes their age, their relationship to thehousehold head, their presence at the household in 1870, their origin in cases they
became a member of the household due to, for example, marriage or adoption.
In this paper, as noted in footnote 1 above, a marriage refers to either a woman
moving into a new household as a wife (virilocal marriage) or a man moving into a
new household as a husband (uxorilocal marriage). It is important to note that these
two forms of post nuptial residence were practiced in early modern Japan where thestem family organization2 was considered ideal and practiced. That is, in earlymodern Japan, marriage did not mean a formation of a new household, but rather asuccession of household either at the natal household of husband (virilocal) or wife
(uxorilocal).
As we will discuss in detail in the next section, we are going to exploit thisinformation regarding the origin of individuals in constructing a network among these2120 households who are linked through marriage relationships. In addition to thesedetailed information at individual level, the socio-economic status (SES) of

2

The stem family system in pre-industrial Japan emphasized non-partible inheritanceand succession by an eldest child. While the eldest child could have been either maleor female, in reality, males were preferred; and thus, the majority of marriage (around80%) was virilocal than uxorilocal marriage.
6

households such as household landholdings or whether the household heads wereacting as village officials are kept.
In rural Tama where life was centered around agricultural activities,
landholding was the basis for political and economic control of the governors(Yasuzawa 1972: 18), and village officials took important roles representing andleading the rural organization. Whether head of a household served as village officers,
therefore, indicated social, not always economic, status of the household. Household
landholdings are the most frequently used indicator of household SES in the studies of
peasants in preindustrial Japan. They are measured as the sum of their agricultural
yield as expressed in measure of rice (koku or go), or where other crops were grown,
in rice equivalents (Smith 1977: 30). Although the development of market economy
eventually undermined the role of landholding in determining household SES,
landholding remains the best indicator of household SES during the period of thisstudy.
With its size and detailed coverage of 35 villages, the household register inTama provides us with a rare opportunity for statistical examination of various issues
including marriage, adoption, and leaving home (Kurosu and Ochiai 1995; Kurosu
1996). In the current study, we exploit the information about marriage relationships.
However, because of the nature of the household register at the time, we have to beaware of some limitations of the data. First, since we are constructing our variables
based on the information of survivors in 1870, we are not free from the sample
selection problem. Therefore, we must assume that the marital behaviors of thosesurvivors are similar to those not present in 1870 because of death. We also constructa separate dataset by restricting the age in order to partially control the mortality
effect. Second, when there are a series of exits (a person exits once, comes back, and7

exits again), only the most recent reason for exit or entry is recorded in the register.
Most of these cases concern those who moved for service and that should not affectour analysis of marriage network. However, we also expect quite a number ofdivorces and remarriages took place as usual among population in early modern Japan(Kurosu, Tsuya and Hamano 1999). Thus, the new marriage relationship we identify
is likely to be a subset of all the marriage relationships that existed. We have tried toovercome possible effects this may have on our result by focusing on marriages thattook place in the last 10 years in the data. Of course, if we had information on divorce
and re-marriage as well, we could have analyzed not only the formation of new
relationship but also voluntary terminations of existing ones. But that is beyond thescope of the current paper.

3. Networks of households
Households are linked, directly and indirectly, through marriages and adoption.
In this section, we describe the construction of networks of kinships among 2120
households in our data in year 1870 and discuss their properties.
A network consists of sets of nodes and links. For the network we construct,nodes are households and a link between two nodes exists if there has been at least
one individual reported as moved from one node (household) to the other due tomarriage or adoption.
Although the household register is a very rich data source, a few limitations
should be noted. The major limitation of the data is the lack of information for those
who have past away before 1870. This is especially so in trying to construct a kinshipnetworks among households, because the links between households, as they aredefined based on individual movements, will be lost with a death of those who created8the link. This limitation prevents us from creating a complete genealogical graph,such as p-graph (White and Jorion 1996) that is often used in analyzing the structureof kinship network in anthropology.
Another limitation is that we have no information regarding households otherthan those recorded in the household register. For example, there are 1507 individual
movements due to marriage or adoption reported between 1861 and 1870, among
which 640 (42.5%) were within 35 villages and other 867 (57.5%) were with thosehouseholds outside. Such a limitation, however, cannot be avoided in any kind of the
network analysis that is based on the subgroup of the entire population. Many
previous researches have therefore focused on the part of the population they haveinformation about.3 And, at least, the household register provides the full information
regarding those individuals from these 35 villages who were alive in 1870.
The network we construct is, therefore, based on the all marriages andadoptions among 2120 households that are reported to have taken place based on
those members of the households who were alive in 1870. Before proceeding todiscuss the properties of the so created network of households, we introduce a few
terminologies from the network analysis.
Two nodes in the network are directly connected if there is a link between the
two. Although two nodes are not directly connected, they can be indirectly connected
if there exists a chain of links that connects them. We call such a chain of links, apath. For example, consider a network with three nodes, 1, 2, and 3. And assume
there is a link between 1 and 2, 2 and 3, but not between 1 and 3. In this case, node 1
3

For example, a seminal work of an evolving social network by Kossinets and Watts(2006) that studies the e-mail exchanges among university students, faculties, andstaff members confines their analysis to those exchanges within the university
because the authors do not have the information about e-mail exchanges with thoseinside the university and those who are outside.

9and 2 are directly connected (or linked, and so are 2 and 3), but not 1 and 3. But since
there exists a path between 1 and 3, they are indirectly connected. Two nodes in the
network are connected if there is a path between them. When all the nodes in the
network are connected with each other, we say that the network is connected.
One can calculate the distance between any two nodes in the network bycounting the number of links in the path connecting the two. In the above example,
distance between nodes 1 and 2 is one, and that between 1 and 3 is two. When there
are multiple paths between two nodes, the distance is defined as the length of theshortest path between the two. If there is no path between two nodes, they are notconnected and the distance between them is infinity.
The average distance between two nodes is not well defined when the network
is not connected. In such cases, the network can be decomposed into several
connected components. A connected component of the network is a subset of nodes
that is connected with each other. The size of a connected component is the number ofnodes that belong to it. When a network is not connected, instead of defining the
average distance of the network, one can calculate the average distance between two
nodes that belong to the largest connected component.
The number of links a node has, i.e., the number of nodes the node is directly
connected to, is called the degree of the node. Thus, the average degree of the
network is the average number of link a node has in the network.
While the size of and the average distance between two nodes in the largest
connected component measure the connectedness and closeness of nodes in the
network, they do not capture local properties. It has been demonstrated that many
networks, both social and physical, demonstrate dense local connections. Clustering
coefficient (Watts, 1999) measures the likelihood of nodes that are directly connected10

to a node are connected with each other. If nodes in a network are connected
randomly, the clustering coefficient will be very low (it approximately becomesaverage degree/number of nodes), while many networks, not only social networks,
exhibit much higher clustering coefficients than that of comparable random networks.
A network that has both the average path length close to and the clustering
coefficient that is much larger than those of random graphs of the same size and theaverage degree is called “small world network.” Small world property is found invarious networks ranging from social networks such as co-authorship networks(Newman, 2004) to more natural networks such as a network of neurons in bacteria(Watts and Strogatz, 1998).

[Table 1 around here]

Table 1 provides these network statistics for marriage network in Tama.
Among 2120 households in 35 villages, 1455 households had at least one link withother households within 35 villages based on marriage or adoption among those who
were alive in 1870. The size of the largest connected network was 1010, in otherwords, close to a half of the entire households in these 35 villages are connected, atleast indirectly, through chain of marriages or adoptions. These 1010 households arefrom 19 different villages. On average, those households, which had at least one
kinship relation with other households in these 35 villages, had about 2 links. There
was a household that had 9 links. Note that this network demonstrates ten times larger
clustering coefficient than, while the average path length of similar magnitude to, thatof a random graph of the same size and the average degree. Thus the marriage
network in Tama has the properties that are closer to a “small-world network” than

11

being a random graph. These descriptive statistics, especially the high clustering
coefficient, suggest that there existed a tendency for endogamy in terms of kinshipamong households in our data. Also the large connected component that includes
1010 households and spans 19 villages suggests that marriages in Tama in the period
under study were not constrained too strongly by village boundaries.

4. Formation of new marriage relationships
Our dependent variable is whether there has been a new marriage relationshipbetween two households between year 1861 and 1870. We do not consider creationsof kinship relationships through adoption4 in this part of analysis. The dependent
variable is defined for every pair of households, and takes value 1 if there existed a
new marriage relationship between the pair of households, and 0, otherwise. Here, a“new” marriage is not confined to the first marriage of an individual. It could be one’s
second or higher order marriage. Notice also that we are not going to consider the
direction of marriage relationships nor its nature. The reason why we ignore thedirection of the movement is because it is reasonable to believe that both households
have agreed if a marriage relationship is formed between them. Also note that we areinterested in whether a marriage relationship existed or not, and therefore, we do not
analyze how many relationships being created, if any. As will be discussed in detail in
the result section, there were cases where more than one marriage relationships being
established between a pair of households within the 10 years that we consider.

4

As noted in footnote 1 above, in this paper, we consider a mukoyoshi, i.e., adopting
an adult male who will be married with a daughter of adopting household, as amarriage and not as an adoption.
12

For the analysis below, we restrict our attentions to those pairs of households
in which there are at least one non-married individuals of different sexes in the year
1860 (Non-married individuals (currently not married) are those without identifiable
spouses in the data, and include those unmarried, divorced, and widowed).
Therefore, we are excluding, from our analysis, cases in which a householdthat had no non-married individuals in 1860. In particular, we exclude cases such thata household adopted or gave a birth to a child after 1861 and later received a spousefor this child. This restriction reduces our sample from 2,246,140 possible pairs ofhouseholds (2120 * 2119/2) to 1,582,405. Among these 1,582,405 pairs of households,
282 pairs (0.018%) are identified as having established a new marriage relationshipbetween 1861 and 1870. 5
It is possible, because of the high mortality rate for people beyond the age of
55 in the period we are studying (see for example Tsuya and Kurosu 2004), thatelderly people were not considered as a candidate for a new marriage6 To control forpossible impacts such consideration may have on our analysis, we have created asecond dataset that restrict attention to the pairs of households in which there are atleast one non-married individuals of different sexes who are below the age of 55 in
the year 1860.
This additional restriction reduces our sample to 1,345,890. Among these
1,345,890 pairs of households, 268 pairs (0.020%) are identified as having
established a new marriage relationship between 1861 and 1870. We report below theresults from analyses based on both datasets.

5

If we do not restrict our sample, then among 2,246,140 possible pairs of households,
370 pairs (0.016%) are identified as having created a new marriage relationship.
6
Although remarriage did take place after this age, the probability of remarriage
became extremely small.
13

Existing research suggests that various household characteristics may affectthe possibility of two households in establishing a new marriage relationship. For
example, hypotheses related to socio-economic homogamy versus heterogamy, or, atvillage level, endogamy versus exogamy. To capture these effects, we include in theset of independent variables, the levels as well as similarities of socio-economic status
of households, such as their landholdings and yaku-zuke (being a village official), andthe geographical characteristics of the villages where a pair of households belonged
such as being in the same village or the distance between the two villages if a pair ofhouseholds belonged to two different villages.
Since it is possible for a pair of households in which there are many nonmarried individuals to establish a marriage relationship with a higher probability, we
have controlled for number and the average ages for non-married males and females
in a pair of households in year 1860 as well.
One technical issue to be noted is that since the unit of observation is a pair ofhousehold, we do not include the individual household characteristics as our control
variables. Instead we define a variable such as average landholdings of two
households or total numbers and average ages of non-married males and females in
the pair of households. 7
It is also possible for the existing links among households to have an effect on
forming another new marriage relationship as suggested by the previous studies on
kinship organizations in pre-industrial Japan. In order to examine such effects, we
7This is a rather technical issue but one can construct a dataset that contains
observation for the pair of household twice. For example, one can treat observation
for pair 1-2 and pair 2-1 to be different and include individual household
characteristics in the control variables. But even if we do so, when we try to estimate
the likelihood of a pair of households to engage in a new marriage relationship, it is
not possible to separately identify the effect of characteristics of each of the twohouseholds, instead what we obtain are the average effects of characteristics of two
households.
14

have included independent variables that are created based on network of householdin 1860. The network of household in 1860 is created by deleting the links that are
identified by individual movements between 1861 and 1870 from the network ofhousehold in 1870 we have discussed in section 3 above.
It should be noted that because of the possible death of some individuals
between 1861 and 1870, we do not have the complete information of the individuals
who were alive in 1860. And as a result, there may be several links that existed but
are missing from our 1860 network. Such missing links can have a large effect on the
global measures of network such as the shortest distance (Kossinets 2006); wetherefore confine ourselves to locally defined measures in defining our independent
variables. In particular, we have constructed dummy variables, d1 and d2, that takevalue one if a pair of households are at distance one and two in the 1860 network, andzero otherwise.
In addition to characteristics of households, we investigate the effect of village
characteristics. In particular, as suggested by previous research on geography ofmarriage market (e.g. Segawa 2006; Kawaguchi 1984), we would like to investigate
the effect of distance between two villages.8 But because the exact distances between
two villages are not obtainable, we approximate them as follows. First, from the
Nihon Rekishi Chimei Taikei (2002), we identify the contemporary districts
corresponding to the 35 villages. We assume the center of each village to be in theproximity of the geographical center of the corresponding contemporary districts, andthe distance between two centers to be the distance between two villages.
Table 2 summarizes the description of the variables, and summary statistics ofthem are presented in Table 3.
8

Ideally, we would like to have geographical distance between all the pair ofhouseholds. But this is simply not possible to obtain.
15

[Table 2 and Table 3 around here.]

5. Results
Table 4 presents the result of our regression analyses. We employ the logit model, as
our dependent variable, to form a new marriage relationship, is either zero or one.
Since we include variables that are defined for pairs of villages while the unit ofobservations is a pair of households, we have adjusted the standard error for theclustering effect.9[Table 4 about here]

There are four regression results presented in Table 4 depending on the sample
and the included control variables. Model 1 and Model 2 are based on the sample
without below age of 55 restrictions, while Model 3 and Model 4 are based on thesample with the restrictions. Model 1 and Model 3 include two village level variables,
the average and the difference of total yield of two villages (totYield and
difVilYields) in the set of control variables, while Model 2 and Model 4 do not.
We are interested in testing homogamy versus heterogamy. This can be seenfrom the estimated coefficients of the similarities of households. In particular, thesimilarity of socio-economic status measured by the difference in the landholdings(difWeath) and whether pair of the households both act as village officials(bothByaku). The latter is found to be positive and statistically significant in all themodels. The difference in the landholding is found to have a negative and significant
9

Because the unit of analysis is pairs of households, inclusion of village levelvariables means that these village level variables are repeated for many observations.
Since such repeated observations reduce the standard errors of the estimates, one
needs to correct for them.
16effect in all the four regressions. Therefore, it was indeed the case that householdswith similar socio-economic status were engaging in marriage relationships. This
result supports the tendency for socio-economic homogamy. In addition, the average
landholdings of the pair of households are found to have a positive and significant
effect on the formation of new marriage relationships.
The estimated coefficients of the same village dummy are positive andstatistically significant in all the specification. This result is consistent with theprevious researches that have shown the tendency for endogamy. In addition, thedistances between villages where the pair of households reside are found to havesignificant negative effect. This finding is consistent with previous findings that mostmarriage relationships were established in close geographical area, mainly within thesame village, or among villages within the distance of daily activities. While thedifference in the total yields of two villages is not statistically significant, the total
yield in the two villages is found to have a negative and significant effect (in Model 1
and Model 3).
Our results present a strong evidence of marriages within kinship in 19th
century Tama. Namely, pairs of households that are directly connected via marriage
or adoption are more likely to establish a new marriage relationship than those thatare not related. The estimated coefficient of d1 (i.e., households directly connected
via marriage/adoption) is positive and significant in all the four regressions reported
in Table 4. In addition, two households that share a common kin, that is, those with
d2=1, are found to be more likely to establish a new marriage relationship than thosethat are not related. The estimated result shows that two households that are directly
connected are 0.5% more likely, while those with network distance two are 0.1%
more likely, to establish a new relationship than those with network distance 3 or17above. The weak tendency for triadic closure, i.e., those pair of households in network
distance 2 is more likely to form a new marriage relationship, partly explain therelatively high clustering coefficient found in 1870 marriage network discussed in
section 3. Of course, it should be noted that, as shown by the size of the largest
connected component being close to a half of all the households in the data, there are
marriage relationships between two households that are in the network distance threeor further.
The estimated coefficients of d1 being positive and significant in all themodels means that, there are cases in which a pair of household established multiple
marriage relationships within the period we study. As an interesting example of sucha pair of households, we have found a pair of households that has established fourmarriage relationships between 1853 and 1870. Let us discuss this case in detail to
illustrate marriages within a kinship. These two households are from the villages ofHorinouchi (Household A headed by Takesuke) and Bessho (Household B headed by
Chuzaemon). The sizes of landholding in both households were larger than the
average---household A had around 22.8 koku while household B had 9.0 koku. Whilethe head of household B was the head of the village, the head of household A was not.

[Figure 1 around here]

The four recorded marriage relationships are as follows: (1) Around 1853
(judging from the age of the first surviving child): “Iku”, an older sister of“Takesuke” from household A, married to the head of household B “Chuzaemon”. (2)In March 1860, a daughter of Takesuke married to a son of Chuzaemon and Iku. (3)In November 1860, Chuzaemon’s sister married to Takesuke (second marriage for18

Takesuke). (4) In November 1870, a daughter of Chuzaemon and Iku married to a sonof Takesuke (and his previous wife). These marriages are summarized in Figure 1.
The second and the fourth marriage relationships between these twohouseholds are what anthropologists call “cross-cousin marriage” where a son and adaughter of a set of siblings get married. It has been noted that more extensive, stable,and permanent trans-generation ties among allies can be developed by a widely
instituted system of cross-cousin marriage (see, for example, Levi-Strauss 1969).

5. Summary and Conclusion
This paper studies the formation of marriage relationships between twohouseholds in 19th century, Tama, Japan. Previous studies on marriage market or
partner selection in the Japanese past had at least two limitations. One, since most
longitudinal historical data, that would allow us to examine detailed marriage-related
exchanges over generations, come from one village, the study of marriage relationshiptended to be confined to the alliance among households within one village. Two, since
many anthropological frameworks on marital customs, which provide us with many
important variations in the past, are based on collections of oral histories from prewar Japan, it is difficult to specify time and space where the customs apply to. By
using the household registers in Tama 1870 and applying the method of social
network analysis, we have tried to go beyond these limitations.
In particular, one needs to study not only the relationships that have been
formed but also those that did not materialize in order to study which types ofhouseholds tend to form marriage relationships between themselves. For this aim, we
have constructed a pool of potential pairs of household and studied which pair19

actually established a tie. The pool of potential matching would allow us to go beyond
existing conceptual frameworks and examine what are the driving factors ofhouseholds to ally.
Our empirical results show that there was a tendency for socio-economic
homogamy and endogamy (both within kinship as well as village) in the mid 19th
century Tama, Japan.
Applying the method of social network analysis as well as statistical analysis
to the rich historical data provide us with a very rich research possibility. The resultsof our study offer a new insight to the understanding of marriage and kinshiporganizations in early modern Japan. It will be of great interest to apply the same
methodology to study formation of marriage networks of other regions of Japan orother countries (for example, using the Korean data explored in Kim, 2009) toinvestigate the effect of natural and economic environments on formation of suchnetworks.
Other potential area of interest includes investigating the nature of marriage
relationships from the perspective of risk sharing in the face of negative shocks. Thereis a large literature in the field of development economics on the risk sharing amonghouseholds (see, for example, Fafchamp 2008, for a survey). The use of historical
data covering over 100 years including years of famines (such as one studied in Tsuya
and Kurosu 2000 and 2004) would be of great interest. We will leave this as a future
research.

Acknowledgement
We are grateful to Katsuo Kogure for his excellent assistance in identifying
geographical location of 35 villages. We thank Akira Hayami for making the original
household registration data available, Yoshihiko Ono for his technical help with the
management of the original data, and late Sahoko Toyoda for data entry.

20

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22

Figure 1: An example of marriage within kinship.

23

Table 1: Properties of the kinship network in 1870, Tama
Size of network
2120
Number of linked nodes
1455
Size of the largest connected components (LCC)
1010
Number of connected components
140
Average degree among linked nodes
2.09
Max degree
9
Average Path Length in the LCC
12.67(for a random network)(9.38)
Clustering Coefficient
0.029(for a random network)(0.0014)24

25

Table 3: Summary statistics of the variables
Variable
LijNo.ofObs.

Mean
Std.Dev. Min
1507494
0.0002
0.013
0
(1277799)
(0.0002) (0.014)
(0)
numMale
1507494
3.08
1.523
1
(1277799)
(3.02)
(1.51)
(1)
numFemale
1507494
2.14
1.187
1
(1277799)
(2.14)
(1.157)
1
ageMale
1340733
18.49
10.90
1
(1277799)
(15.52)
(8.59)
1
ageFemale
1503894
14.05
12.55
1
(1277799)
(11.03)
(8.44)
1
aveLand
1331588
5.10
5.642
0.0
(1122414)
(5.36)
(5.799)
(0.0)
difLand
1331588
6.14
9.541
0.0
(1122414)
(6.32)
(9.761)
(0.0)
bothByaku
1507494
0.0027
0.052
0
(1277799)
(0.0032) (0.056)
(0)
d1
1507494
0.0005
0.023
0
(1277799)
(0.0006) (0.024)
(0)
d2
1507494
0.0006
0.024
0
(1277799)
(0.0007) (0.026)
(0)
sameVil
1507494
0.0359
0.186
0
(1277799)
(0.0360) (0.186)
(0)
distVil
1507494
4.65
2.479
0.0
(1277799)
(4.66)
(2.478)
(0.0)
totYield
1507494
292.08
137.160.0
(1277799)
(290.81) (137.34)
(0.0)
difVilYield
1507494
196.25
191.990.0(1277799)
(196.26) (192.40)
(0.0)
For the sample restricted to people below 55 in 1860 are in the parentheses

Max
1
(1)
15
(15)
11
11
75
54
78
54
109.7
(109.7)
133.1
(133.1)
1
(1)
1
(1)
1
(1)
1
(1)
12.8
(12.8)
868.49
(868.49)
868.49
(868.49)

26

Table 4: Results of Logistic Regression
Variables
numMale
numFemale
ageMale
ageFemale
aveWealth
difWealthDependentVariableLij
All
OnlyBelow55in1860
Model1
Model2
Model3
Model4
0.113***
0.124***
0.138***
0.149***
(0.035)
(0.035)
(0.037)
(0.037)
-0.051
-0.054
-0.036
-0.031
(0.058)
(0.057)
(0.062)
(0.062)
0.003
0.002
0.015**
0.015**
(0.006)
(0.006)
(0.007)
(0.007)
0.011**
0.086*
0.017**
0.015**
(0.005)
(0.005)
(0.007)
(0.007)
0.076***
0.071***
0.075***
0.069***
(0.010)
(0.010)
(0.011)
(0.011)
-0.068***-0.065***-0.066***-0.032***(0.013)(0.014)(0.013)(0.014)
1.197**
1.300**
1.016*
1.100*
(0.565)
(0.560)
(0.607)
(0.608)
3.810***
3.845***
3.866***
3.896***
(0.271)
(0.265)
(0.269)
(0.265)
[0.004]
[0.005]
[0.005]
[0.005]
2.586***
2.552***
2.647***
2.610***
(0.474)
(0.473)
(0.477)
(0.476)
[0.001]
[0.001]
[0.001]
[0.001]
1.287***
1.066***
1.324***
1.109***
(0.249)
(0.243)
(0.259)
(0.249)
-0.290***
-0.308*** -0.278*** -0.295***
(0.060)
(0.060)
(0.062)
(0.061)
-0.0017*** -0.0017*** (0.0005)
(0.0006)

0.0002
0.0002
(0.0006)
(0.0006)
-7.843***
-8.208*** -8.225*** -8.612***
(0.322)
(0.296)
(0.341)
(0.332)
1176522
1176522
1122414
1122414
0.125
0.122
0.124
0.121
-1905.66
-1912.12
-1815.24
-1820.89

bothByaku

d1


d2


SameVil
DistVil
totYield
difVilYieldsConstantN
PseudoR^2
Log-likelihoodStandarderrorsareintheparentheses.
Marginaleffectsareinthesquarebrackets.
***Significantat1%level.
** Significantat5%level.* Significantat10%level.

27