Tsukuba Economics Working Papers
No. 2010-004	
 Groups, Networks, and Hierarchy in Household Private Transfers:
Evidence from Fijiby
Yoshito Takasaki	 March 2010

UNIVERSITY OF TSUKUBA
Department of Economics
1-1-1 Tennodai
Tsukuba, Ibaraki 305-8571
JAPANGroups, Networks, and Hierarchy in Household Private Transfers:
Evidence from Fiji

Yoshito Takasaki
University of Tsukuba

Contact Information:
Yoshito Takasaki
Graduate School of Humanities and Social Sciences
University of Tsukuba
1-1-1 Tennodai, Tsukuba
Ibaraki 305-8571 Japan
Tel./fax: +81 29 853 6280
E-mail address: takasaki@sk.tsukuba.ac.jp

March 25, 2010

Acknowledgments
I wish to thank my field team – Jonati Torocake, Viliame Manavure, Viliame Lomaloma,
and 16 enumerators – for their advice, enthusiasm, and exceptional efforts on behalf of
this project. Special thanks are owed also to the Fijians of the region who so willingly
participated in the survey. This research has been made possible through support
provided by the Sumitomo Foundation, the Japan Society for the Promotion of Science,and the Ministry of Education, Culture, Sports, Science and Technology in Japan. Anyerrors of interpretation are solely the author’s responsibility.

1

Groups, Networks, and Hierarchy in Household Private Transfers:
Evidence from Fiji

Abstract
Although economists have extensively studied private transfers exchanged amonghouseholds within a network, those exchanged directly with groups to which thehousehold belongs – such as ritual gifts, communal work, and church donations – havereceived very limited attention. Using original household survey data gathered in ruralFiji, this paper shows that extant studies on across-household private transfers areincomplete for two reasons. First, group-based transfers are much greater than networkbased transfers because of significant contributions to groups for their provision of local
public goods. Second, group-based transfers significantly influence network-basedtransfers through the social hierarchy: A comparison of various groups (e.g., kin andchurch groups) and social ranks (e.g., gender, disability, elite kin, and religious elite)
indicates that network-based transfers adjust to hierarchy bias in group-based transfers
among fixed members depending on the physical and social connections of groups andnetworks.

2
1. Introduction
Roles of communities, groups, and networks in development are a central theme
that development economists often address (Barrett, 2005). Private transfers are one of
the most direct forms of social interaction. Although economists have extensively studied
private transfers exchanged among households within a network (see, for example, Cox
and Fafchamps, 2008 for review), those exchanged directly with groups to which thehousehold belongs – such as ritual gifts, communal work, and church donations – havereceived very limited attention. I argue that extant studies on across-household transfers
are incomplete for two reasons. First, the latter group-based transfers can be much greaterthan the former network-based transfers, because significant contributions are made togroups for their provision of local public goods, such as social activities and village
upkeep. Second, these two forms of transfers can be tightly linked with each other. In
particular, group-based transfers may significantly influence network-based transfers
through the social hierarchy: As members’ social ranks (e.g., elite status) affect groupbased transfers, individual households may adjust network-based transfers in a way tocounteract this hierarchy bias. This is because although group membership is often
exogenously determined by kinship, heredity, and eligibility, household transfer networks
are endogenously formed by individual households.
This paper generates empirical evidence for these conjectures. In particular, thecomparison of various groups and social ranks discussed shortly indicates that theadjustment to hierarchy depends on the physical and social connections of groups andnetworks. The paper echoes the work of Cox and Fafchamps (2008), who call for
broadening the study of determinants of private transfers beyond income, which has been

3
a dominant focus of extant works that explore transfer motives and crowding-out; while
they emphasize demographics, I focus on groups and hierarchy.
The empirical analysis exploits original household survey data I gathered in thefollowing two major village subgroups in rural Fiji. First, in Fiji and other Pacific islandstates, kinship underlies gift exchange not only among households through networks, butalso between each household and the kin group to which it belongs, for securing social
status and showing commitment to the group (Malinowski, 1922; Mauss, 1967). Indeed,following Malinowski’s (1922) seminal work on gifts and reciprocity in Melanesia, mostrelated anthropological studies have been conducted in the Pacific region (Hann, 2006).
Second, development agents are increasingly recognizing community groups – such as
women’s groups and school groups – as essential local partners for implementing
community-based development projects (Heyer et al., 2002; Mansuri and Rao, 2004;
World Bank, 2002). Bernard et al. (2008) assess the existence of village organizations,
their performance, and members’ participation in benefits in Senegal and Burkina Faso;
Okten and Osili (2004) analyze household contributions to community organizations inIndonesia with a focus on ethnic diversity within the organizations; and Imai and Eklund(2008) examine the roles of women’s community-based organizations in child health inPapua New Guinea. Christianity also underlies Fijian society, and church donations arequite significant. Religious networks may be as important as kin networks in household
private transfers.
Hierarchy in Fijian society is determined by various ranks. Turner (1992, p. 291)
highlights the roles of age, kin elite, and gender:
Hierarchy is defined here as the ranking of the elements of a whole (society) in
relation to the whole. In this sense, the elements that are ranked are social

4categories or positions defined in terms of age, seniority of descent, and gender,
and the whole in relation to which they are ranked is a social system grounded inritual. Elder is superior to junior, chief to commoner, and male to female. But
while age, rank, and gender differences entail relations of superiority/inferiority
among persons, they also create interdependence. . . . These relations of inequality
and interdependence (which do not preclude conflict) are expressed andreproduced in the practice of everyday life.
Community-group leaders who play major roles in development, social, and/or
humanitarian activities are also likely to hold a high rank, as religious leaders do.
Disability may signify a low rank, as the disabled are often considered to be particularly
disadvantaged in developing countries with limited public safety nets, though research on
disability among the poor is scant (Yeo and Moore, 2003). A unique feature of the Fijian
data is that because households in each village are stratified by their kin group and elite
status, direct measures of elite status are available. In standard household surveys, incontrast, elite status is often unobservable to researchers, and even if it is observable,
there are too few elites to make a statistical analysis possible.
Two other features of the Fijian data are to be noted. First, distinct from many
extant studies, transfer measures capture not only cash and inkind (e.g., food, handicrafts),
but also labor time. This is crucial for group-based transfers, which often containsignificant labor-time contributions to groups (e.g. communal work). Second, the paper
focuses on domestic private transfers. This is simply because, distinct from extant studies
in the Pacific region (Bertram, 1986; World Bank, 2006), overseas remittances are almost
nonexistent in my study area in Fiji’s remote islands.
The remainder of the paper proceeds as follows. Section 2 offers a description of
the data and groups. Section 3 compares group- and network-based transfers, showingthat their main difference is contributions to groups for their provision of local public

5goods. Section 4 explores the group-network connection in a descriptive manner. Section
5 discusses the econometric specification and hierarchy measures to test the hypothesized
link of group- and network-based transfers. Estimation results are reported in Section 6.
The last section concludes.
2. Data and groups
In 2005 I conducted surveys among native Fijian households in Cakaudrove
Province (Fiji is divided almost evenly between native Fijians and Indo-Fijians). The
province is mainly located on Vanua Levu Island and Taveuni Island, the second- andthird-largest islands in the country, which significantly lag behind the largest island, Viti
Levu, where the state capital, two international airports, and most tourism businesses are
situated. The province has 134 villages in 16 districts. In each district, I purposefully
chose 43 villages to cover distinct environmental, economic, and social conditions. Whilethe data represent neither the province nor the nation, the villages in the sample well
capture various types of villages in Fiji’s underdeveloped islands. In each village,
households were stratified by the smallest kin group unit (defined shortly), as well as by
the combination of leadership status (defined in Section 5) and major asset holdings (e.g.,
shops), and households were randomly sampled in each stratum (50% on average).
Overall, the survey covered 906 households.
Kin groups
Each native Fijian belongs to a lineage of the vanua-yavusa-mataqali-tokatoka
hierarchy: Vanua consists of several yavusa; yavusa consists of several mataqali; andmataqali consists of several tokatoka (Ravuvu, 1983). While vanua ranges over several
villages, yavusa is mostly formed within the village; in another words, village formation

6
is largely based on yavusa. The dominant symbol of Fijian culture is kava (a beverageinfused from the root of a pepper plant, Piper methysticum), and kava rituals frequently
involve exchanges of ceremonial goods, such as food, mats, and bark cloth (Turner,
1987). Many ritual activities, such as funerals and weddings, are organized by mataqali
and yavusa; vanua occasionally hold large traditional meetings. Kin groups also underlie
household income-generating activities: Land is communally owned by mataqali (about
83% of the country’s total land is communal), and customary rights for coastal fishing areheld by vanua or several yavusa. The sample covers 20 vanua, 53 yavusa, 146 mataqali,
and 234 tokatoka; an average village in the sample consists of 1.2 yavusa, 3.4 mataqali,
and 5.6 tokatoka; and on average, each yavusa, mataqali, and tokatoka consists of 40, 14,
and 8 households, respectively (see Table 1).
Community groups
Church, women’s, school, and youth groups are four major types of community
groups, and their distributions and memberships are distinct from each other. A churchgroup formed for each church, which often covers more than one village, is available inall villages – 3.3 church groups per village on average – and almost all households are
members (church membership is largely based on heredity). Village church groups havestrong ties with larger groups in the same sect. In contrast, if there are any non-religious
community groups in a village at all, there is usually just one for each type. Membershipin non-religious community groups is based on eligibility – gender, child schooling, andage –, and accordingly, fewer individuals belong to them than to a church. Whilemembership is almost uniform among the eligible for school groups, such is not the casefor women’s and youth groups. The school group formed for each school can cover more

7than one village, while women’s and youth groups are village based. The data lackinformation about the number of school groups in the village and the group-level
membership of community groups; in particular, there is no information to indicate theparticular church group to which each individual belongs.
3. Differences between group- and network-based transfers
My first argument is that group-based transfers are greater than network-basedtransfers, mainly because of household contributions to groups for their provision of local
public goods, such as social activities and village upkeep. Because the Fijian data do notallow me to directly tell which contributions are actually used for public goods provision,
I offer indirect, descriptive evidence supporting this conjecture.
Group-based transfers
Respondents were asked about the transfers – in the form of cash, inkind, andlabor time, separately – they contributed to and received directly from each kin andcommunity group discussed in the last section, as well as the village, in the past year.
These group-based transfer data are unbalanced in coverage between receipt and giving:
While the transfers that the household offers to groups contain all the resources thehousehold contributes, those it receives from the group capture only partial benefits,
excluding those of local public goods that the group provides. Directly measuring such
benefits is very difficult, because they often include unobservable social and cultural
benefits and can be heterogeneous across households.
This imbalance in the data is reflected in aggregate transfer patterns: While almost
all households make contributions to some groups, 42% of households receive transfersfrom at least one group; the mean amount of transfers in any form given to all groups

8
combined is more than six times those received from them (see panel A of Table 2). On
the net, an average household makes cash-inkind contributions to groups by F$1,619(=1,934–315), or 9% of its pre-transfer earned income (F$1 = US$.60). 1 An average
household also contributes 47 man-days of labor time, which is equivalent to 5% of its
labor-time endowment (the mean number of working adults is 3.1 and annual labor-timeendowment per working adult is assumed to be 300 days), or 7% of its pre-transfer
income (labor time is monetized based on men’s daily wage in each village, the mean ofwhich is F$16).
Since transfers made for within-group and across-group reallocations of resourcesin the village are roughly balanced, a major part of household contributions to groups
should be used for their provision of local public goods. 2 This is supported by two piecesof evidence. First, the importance of labor-time transfers relative to cash-inkind is muchgreater in giving than in receipt. Second, the most common reason reported byrespondents (in both receipt and giving) is ritual – a category containing significant
public goods components. Table 3 shows proportions of reasons reported by respondents
for each transfer (proportions are calculated using transfer amounts as weights, and if
there is more than one reason for the same transfer, equal weights are assigned amongthem).
Disaggregate transfer patterns are also consistent with the significance ofhousehold contributions for public goods. While transfers received from kin groups andcontributed to community groups are the most common and largest, the importance oflabor-time transfers relative to cash-inkind is the greatest for the village (over 60%)
(results not shown). When only members of each community group are considered,9almost all make contributions, and church donations and school contributions are large.
Correspondingly, general contributions – another category containing a significant public
goods components – to community groups as well as the village are as common as ritual;in contrast, ritual dominates kin group transfers (almost two thirds in both receipt andgiving) (results not shown). 3 Members receive almost no transfers from non-religious
community groups, which presumably concentrate on the provision of public goods.
Network-based transfers
Respondents were also asked about each major transfer received from and given
to other households in the past year. 4 These standard across-household transfer data arebalanced in coverage between receipt and giving. While transfers received from otherhouseholds are about twice as much as those received from groups, those given to otherhouseholds are only about one fifth of those contributed to groups. The following patterns
in contrast to group-based transfers are consistent with my conjecture that public-goods
provision can be mainly distinguished between group- and network-based transfers.
Participation in cash-inkind transfers – both receipt and giving – are almost uniform; themean amount of cash-inkind transfers is about four times that of monetized labor-timetransfers; and an average household is a net recipient of cash-inkind transfers, but notlabor-time transfers (panel B of Table 2). The most common reason isconsumption/expenditure, a category that captures public goods components less thanritual and general contributions (Table 3).
4. Connection between groups and networks
My second argument is that group- and network-based transfers are tightly linked
with each other. This section offers descriptive evidence for the connection between

10groups and networks. Respondents were asked about the characteristics of householdswith which transfers were made. An average household exchanged transfers with 3.6
households in its network, approximately 80%, 70%, and 80% of whom are in the samevillage, the same tokatoka (closest kin), and the same church group, respectively. It isthus clear that physical and social proximities are major determinants of network
formation (as found by Fafchamps and Gubert, 2007 in the Philippines). An importantnew finding is that social distance is determined not only by kinship, but also by religion.
Kin and religious connections are distinct from each other. On the one hand, withthe hierarchical structure of kin groups in the village, how kinship matters in group- andnetwork-based transfers depends on the closeness of kin relationships: The tokatoka
network is more important than the tokatoka group, and the comparison is the opposite
for mataqali, yavusa, and vanua. In particular, transfers received from tokatoka are almost
negligible, and household contributions to tokatoka are half of transfers given to otherhouseholds in the same tokatoka; at the same time, transfers received from otherhouseholds in the same tokatoka are over two times those from all kin groups (Table 2).
It appears that the difference between the tokatoka group and the tokatoka network isblurred, as one third of households in the same tokatoka in the village are also part of thetransfer network. 5 In contrast, households belonging to the same kin group higher than
tokatoka are relatively uncommon in the network, and transfers exchanged directly withsuch kin groups are large (transfers contributed to all kin groups are twice as much as
those given to other households in the same tokatoka).
On the other hand, there is no hierarchy among church groups in the village, andboth church groups and church networks are important for giving and receipt,

11
respectively: Household contributions to its church group are 1.5 times as much astransfers given to other households in the same church group; transfers received fromother households in the same church group are 10 times those received directly from thechurch group.
In- and out-of-village networks
While group-based transfers are mostly made within the village, transfers
exchanged with households in other villages or in another city, but not in another country,
are more significant than suggested from relatively uncommon out-of-village networks.
First of all, while within-village transfers are much more common and larger than acrossvillage transfers, transfers (mostly cash-inkind) received from households outside thevillage are twice as common and four times larger than those given (they are actuallysimilar to cash-inkind transfers received from all groups in the village); in contrast,
within-village transfers are balanced between receipt and giving in both participation andamount (Table 2). This means that the net transfers received from other households at theaggregate level found above mostly come from out-of-village networks. An average
household received 38% of transfers from 20% of households in its out-of-village
network, i.e., the mean net across-village transfer received per partner household is about
2.5 times that of the within-village counterpart ((38%/20%)/(62%/80%)). There is nosuch difference in transfer amount per partner household between transfers made within
and outside the same tokatoka or church group.
Out-of-village networks are mainly formed by close kin and religious
relationships. Consistent with the comparison of within- and across-village transfers, anaverage household is a net recipient in its tokatoka network as well as in its out-of-kin

12
network (consisting of households belonging to other vanua outside the village); in
contrast, transfers in its mataqali/yavusa/vanua network are balanced between receipt andgiving. An average household is a net recipient both in and out of the church network.
5. Hierarchy and transfer linkage - econometric specification
I argue that network-based transfers counteract the hierarchy bias in group-basedtransfers. The last section showed that these two are linked through the physical andsocial connections of groups and networks and that transfers received through out-ofvillage networks are also considerable. I thus conjecture the following. First, theadjustment in network-based transfers to the hierarchy bias in group-based transfers isstronger outside the village than in the village, because out-of-village networks are lesstightly connected with in-village groups than in-village networks are. Second, theadjustment to social ranks that are not formed by groups is stronger than those formed bykin and church groups, because groups and networks are socially connected by kinshipand religion.
Identifying how group-based transfers biased by social ranks alter network-basedtransfers is very difficult, because these two are simultaneously determined. Instead I test
the following hypotheses consistent with my conjectures.
Hypothesis 1: Network- and group-based transfers are affected by social ranks in
opposite ways, and this is more strongly so in out-of-village networks than invillage networks.
Hypothesis 2: Network-based transfers are more strongly affected by social ranks
that are not formed by groups than those formed by groups.

13
These hypotheses can be straightforwardly tested by estimating the effects of various
social ranks on network- and group-based transfers in a comparable manner. Thoughadjustments in network-based transfers can occur in either network formation or transfer
exchange in the network, or both, distinguishing between these two is beyond the scopeof this paper. I first discuss the econometric specification and then detail social ranks.
Econometric specification
I employ the following standard transfer equation:yi = α + βX i + γZ i + V + ei ,(1)where yi is household i’s net transfers received from other households, gross transfers
received from groups, or gross transfers given to groups; Xi is household social rank, as
defined below; Zi is other household characteristics, such as shock, demographic factors,
asset holdings, and public transfers; V is village dummies, which control for all village
factors, such as location, village size, and inequality; and ei is an error term.
Two clarifications are needed. First, earned income is not controlled for inequation (1). For net transfers received from other households, this is the only difference
from many extant works in the literature. My goal is to identify the impact of social ranks,
not that of earned income. To the extent that permanent income that can be correlatedwith social ranks is controlled for by demographic factors and asset holdings, omitted
earned income does not cause bias. In contrast, earned income is endogenous as adeterminant of household private transfers, because, in anticipation of private transfers, it
may adjust earning efforts (i.e., decisions of earning and transfers are madesimultaneously), and any unobservable factors that are correlated with earned income,such as skills, may also influence its transfer decisions. Controlling for income

14
endogeneity is infeasible with these data, which lack valid instrumental variables (cf.
Jensen, 2004; Juarez, 2009; Kazianga, 2006).
Second, the same transfer equation (1) is used for group-based transfers. My goal
is not to dissect group mechanisms determining transfers, but to show how householdsocial ranks alter transfer outcomes. For estimations comparable with network-basedtransfers, equation (1) can be used for group-based transfers with the following caveats.
Distinct from across-household transfers, transfers received from and given to groups
need to be separately estimated, because their decisions are made by different agents andwhat they cover is unbalanced in the data. It is important to control for group factors,
such as group size and inequality, because they can be correlated with social ranks
formed in the group. When aggregate transfers made with multiple groups in the village
are considered, however, it is not straightforward to construct aggregate group-levelmeasures, though village dummies still control for the combination of group factors
common in the village.
When participation in transfers is almost uniform, I employ Ordinary LeastSquares (OLS) to estimate equation (1); otherwise, I first estimate the determinants ofparticipation using probit for the entire sample and then those of the amount among
participants using OLS. This is a two-part, or hurdle, model commonly used in previous
works on private and public transfers (e.g., Dercon and Krishnan, 2005; Jayne et al.,2002); a tobit model with a restrictive assumption that coefficients are the same between
the participation and amount equations yields qualitatively similar results. An alternative
sample selection model is infeasible with these data, which lack the identifying
instruments required to credibly estimate the selection equation.

15
Hierarchy measures
I compare the following eight social ranks. Age, gender, and disability are ranks
not formed by groups. They are measured by the age and gender of household head andthe disability status of any household member (in the sample, the mean age is 51 years,
9% of households are headed by females, and 21% of households have at least one
disabled member, according to respondents’ subjective assessments).
Group-based ranks are categorized into two: kin and non-kin elite status. In Fiji,
traditional permanent leadership positions determine individual and kin group status.
Highly ranked vanua chiefs and yavusa/mataqali chiefs assume traditional duties acrossvillages and within the village, respectively (there is no takatoka chief). Yavusa andmataqali chiefs, some of whom are also a vanua chief, are available only in highly ranked
yavusa and mataqali (chief’s kin) (27% of households belong to chief’s mataqali, see
Table 1). Kin leaders, including chiefs, play a major role in the kin group’s decisionmaking and negotiations among groups in the village (3%, 11%, and 18% of households
have yavusa, mataqali, and tokatoka leaders, respectively). 6 Village chiefs are shared bysome kin leaders and are not necessarily yavusa/mataqali chiefs. As such, kin elite statusis measured by two dummies: chief’s kin defined by mataqali and kin leaders of anygroup. Using chief’s kin defined by tokatoka yields very similar results (recall thatyavusa is a group very close to the village).
Another important village position is the gatekeeper (turaga ni koro), who
handles most matters in connection with the local government (receiving information andmaterials from the government and non-governmental organizations, distributing them tovillagers, and coordinating village meetings). Gatekeepers and community group leaders

16
are neither permanent nor directly related to kinship. Approximately 3% of households
have village leaders (village chiefs and gatekeepers combined), church leaders, women’s
leaders, and youth leaders, respectively (information about school leaders is lacking)(Table 1). Non-kin elite status is measured by three dummies: church leader, women’s
leader, and other non-kin leader. The last combines gatekeeper and youth leader(capturing these two separately is infeasible, as there are a limited number of them).
A comparison of age, gender, and disability reveals which ranks matter most, anda similar comparison can be made among different group-based elite ranks. I also analyze
kin groups, village, and community groups separately, thus capturing potential acrossgroup linkage through the social hierarchy – elite status formed by one group may also
affect other groups. These analyses can lead to important policy implications by showing
who is most disadvantaged and who is most influential.
My focus is on comparing hierarchy biases, not explaining each one. The
theoretical prediction of the impact of each rank is ambiguous. Even if households with alow status (e.g., female head) are shown to receive greater private transfers, which motive
operates it is unknown: It may be altruism helping the disadvantaged or a result of
exchanges with more “services” offered by them (Cox, 1987). Similarly, households witha high status (e.g., church leader) may appear to be favored because of the exchange
motive, cultural norms that prioritize them, or a result of misappropriation; householdswith another high status (e.g., kin leader) may instead appear to be disfavored as a resultof the exchange motive or reputation building.
6. Hierarchy and transfer linkage – estimation results

17
Estimation results of the eight social rank variables are reported in Table 4 –estimated marginal effects at means for the probit in panel A and estimated coefficientsfor the OLS in panel B. 7 Participation is almost uniform for gross transfers given to allgroups and community groups and net transfers received from all other households andthose in the same village (the corresponding columns in panel A are thus blank);
participation in other group-based transfers is not uniform (Table 2). The dummy for
women’s leader is excluded in the amount equations for transfers received from groups,
because of the limited number of women’s leaders among recipients (when women’s
leaders are combined with other non-kin leaders, almost the same results are obtained).
These are results for cash-inkind and monetized labor-time transfers combined; separate
results for each are also discussed when they exhibit important distinct patterns. I discuss
results for the group-network linkage hypothesized in the last section and the comparisonof age, gender, and disability first, and then those of the across-group linkage.
Group-network linkage
Age, gender, and disability strongly affect group-based transfers. Households withan old head receive a larger amount from and contribute a smaller amount to groups,
especially kin groups; female-headed households are more likely to be recipients oftransfers from all three groups, are less likely to contribute to kin groups, and contribute a
smaller amount to the village; and in contrast, households with a disabled member areless likely to participate in transfers with kin groups and the village (both receipt andgiving), and they receive a smaller amount from the village. Hence, kin groups and thevillage treat females and the disabled in opposite ways in their reallocation of resources
within the group. The disabled are strongly disfavored. While females are favored in all18groups in the village, the elderly are favored in kin groups only weakly (the marginal
effect of age is F$6.6 per year). Corresponding to these hierarchy biases, households withan old head and a disabled member rely more on their out-of-village networks. Observethat while households with an old head, a female head, and a disabled member receive
greater net transfers from other households (column 9), when only within-villagetransfers are considered (column 10), the estimated coefficient of age becomes very small
and loses statistical significance, that of gender does not change, and that of disability
halves. That is, network-based transfers respond to age, gender, and disability outside thevillage, within the village, and both, respectively. These findings are consistent withhypothesis 1.
While kin leaders contribute more to kin groups (but are not large recipients) andchurch leaders are more likely to be recipients of transfers and receive a greater amountfrom community groups (but are not large donors), neither of them affects network-basedtransfers. Combined with the findings on age, gender, and disability, these results
strongly support hypothesis 2. Qualitatively the same results about church leaders are
obtained from estimating equation (1) for church groups separately: Compared to nonleaders, church leaders are 55% more likely to receive transfers and receive a greater
amount by about F$260 – over six times the mean – from the church group (results notshown); with a lack of group-level membership information, however, no group factors
are controlled for in this disaggregated analysis. Consistent with hypothesis 2, other nonkin leaders that are not strongly associated with networks do not affect any transfers.
A puzzling result of women’s leaders is that while they do not influencecommunity group transfers, they do receive smaller transfers (cash-inkind) from other19households in the same village. The former result holds when women’s groups are
examined separately among group members (results not shown). These results requirecaution. They might be biased if women’s leadership is correlated with unobservableskills and entrepreneurship, in particular, for handicraft making for both gifts and selling(another consistent result is obtained below). The nonsignificant result of group-basedtransfers may be the result of selection bias caused by endogenous participation in
women’s groups (participation is not uniform among the eligible as discussed above, anda sample selection model is infeasible for the same reason given above). Earlier findings
on religious elite are unlikely to be contaminated by the potential endogeneity ofwomen’s leadership though, because dropping the latter variable hardly alters the resultsof the remaining variables. 8
Across-group linkage
Elite kin and religious elite are the most influential across groups as follows. First,
while kin group status (chief’s kin) does not affect kin group transfers, households inchief’s kin, but not kin leaders, are less likely to contribute (especially labor time) to thevillage. This may be because communal labor in the village is arranged mainly among
kin groups. Next, while households of chief’s kin receive a large amount from churchgroups, church leaders receive a smaller amount (cash-inkind) from and contribute alarger amount to kin groups; in contrast, church leaders are more likely to receive
transfers from the village. These patterns may be because church membership relatively
matches kinship in the village (proving this is infeasible with a lack of group-level
membership data). Lastly, women’s leaders contribute more labor time to the village.

20This may be because women’s group members – both leaders and non-leaders – play asignificant role in village activities (e.g., handicrafts, festive meals).
7. Conclusion
Using original household survey data gathered in rural Fiji, this paper provided
evidence that (1) group-based transfers – both cash-inkind and labor time – are muchgreater than network-based transfers, mainly because of significant contributions togroups for their provision of local public goods, and (2) group-based transfers influence
network-based transfers depending on the physical and social connections of groups andnetworks. In particular, network-based transfers, especially those outside the village,
counteract the hierarchy bias in group-based transfers caused by social ranks not formedby groups – age, gender, and disability –, but not kin and religious-elite status, because
the main networks are kin and religious ones. In the village, the disabled are the mostdisadvantaged; elite kin and religious elite are the most influential.
These findings lead to the following research and policy implications. First, extant
studies focusing on across-household transfers are incomplete. Are Pacific islands – themain field in which anthropologists study gifts and reciprocity – exceptional in thesignificance they place on groups? More research on group-based transfers in otherlocales is needed. Second, in lineage-based societies in the Pacific, social hierarchy
strongly shapes people’s interactions and is likely to affect community-based
development, with existing and newly created village organizations serving as local
partners. Platteau and Abraham (2002) argue that community-based programs in SubSaharan Africa are captured by local elites, because cultural norms restrict non-elites’
access to information and emphasize consensual decision-making; Bardhan and21
Mookherjee (2000) offer a theory of elite capture. Policymakers need to pay attention tosocial ranks locally formed not only by kinship, but also by religion. Third, as the private
redistributive mechanism for the disabled is very weak, strong public support for andmore research on disability among the poor are greatly needed.
References
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Levels, American Economic Review, 90(2), pp. 135-39.
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Groups, and Networks. (London and New York: Routledge, Taylor and Francis).
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Senegal and Burkina Faso, World Development, 36(11), pp. 2188-2204.
Bertram, G. (1986) Sustainable development in Pacific microeconomies, World
Development, 14(7), pp. 809-822.
Cox, D. (1987) Motives for Private Income Transfers, Journal of Political Economy,
95(3), pp. 508-46.
Cox, D., M. Fafchamps (2008) Extended family and kinship networks: economic insights
and evolutionary directions, in: T. P. Schultz, J. A. Strauss (Eds) Handbook of
Development Economics (Amsterdam: Elsevier).
Dercon, S., P. Krishnan (2005) Food Aid and Informal Insurance, in: S. Dercon (Ed)
Insurance Against Poverty (Oxford and New York: Oxford University Press).
Fafchamps, M., F. Gubert (2007) The formation of risk sharing networks, Journal of
Development Economics, 83(2), pp. 326-350.

22
Hann, C. (2006) The gift and reciprocity: perspectives from economic anthropology, in:
S.-C. Kolm, J. M. Ythier (Eds) Handbook of The Economics of Giving, Altruism
and Reciprocity (Amsterdam: Elsevier).
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Studies, 36(2), pp. 209-233.
Jayne, T. S., J. Strauss, T. Yamano, D. Molla (2002) Targeting of Food Aid in Rural
Ethiopia: Chronic Need or Inertia?, Journal of Development Economics, 68(2), pp.
247-88.
Jensen, R. T. (2004) Do Private Transfers 'Displace' the Benefits of Public Transfers?
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Juarez, L. (2009) Crowding out of private support to the elderly: evidence from ademogrant in Mexico, Journal of Public Economics, 93454-463.
Kazianga, H. (2006) Motives for Household Private Transfers in Burkina Faso, Journalof Development Economics, 79(1), pp. 73-117.
Malinowski, B. (1922) Argonauts of the Western Pacific: An Account of Native
Enterprise and Adventure in the Archipelagoes of Melanesian New Guinea(London: Routledge).
Mansuri, G., V. Rao (2004) Community-Based and -Driven Development: A Critical
Review, World Bank Research Observer, 19(1), pp. 1-39.

23
Mauss, M. (1967) The Gift: Forms and Functions of Exchange in Archaic Societies (NewYork: W. W. Norton).
Okten, C., U. O. Osili (2004) Contributions in Heterogeneous Communities: Evidence
from Indonesia, Journal of Population Economics, 17(4), pp. 603-26.
Platteau, J.-P., A. Abraham (2002) Participatory Development in the Presence of
Endogenous Community Imperfections, Journal of Development Studies, 39(2),pp. 104-36.
Ravuvu, A. (1983) Vaka I Taukei: The Fijian Way of Life (Suva: Institute of PacificStudies).
Turner, J. W. (1987) Blesses to give and receive: ceremonial exchange in Fiji, Ethnology,
26(3), pp. 209-219.
Turner, J. W. (1992) Ritual, habitus, and hierarchy in Fiji, Ethnology, 31(4), pp. 291-302.
World Bank (2002) Reaching the poor: a renewed strategy for rural development(Washington, D.C.: World Bank).
World Bank (2006) at Home & Away: Expanding Job Opportunities for Pacific Islanders
through Labour Mobility (Washington, D.C.: World Bank).
Yeo, R., K. Moore (2003) Including disabled people in poverty reduction work: "nothing
about us, without us", World Development, 31(3), pp. 571-590.
Notes
1

The mean earned income among sample households is F$10,374, or F$1,897 per capita.

Almost all households employ traditional farming practices, using no mechanizedequipment or animal traction and limited purchased inputs to produce taro, cassava,
coconut, and kava plants. Most households engage in subsistence fishing, using lines and24hooks, simple spear guns, or rudimentary nets, and more commercially oriented
fishermen use boats with engines, along with more valuable nets. Farming and fishing,respectively, count for 62% and 12% of household earned income. One third ofhouseholds receive public transfers – mostly pensions – and the mean amount is only
2.5% of earned income.
2

Two exceptions are noted. First, contributions from members are not the only

disposable resources for groups: Some kin groups earn incomes through land lease,logging concessions, and fishing licensing, and some non-kin groups (especially village)
receive public transfers. Second, within-village reallocation and local public-goods
provision are not the only ways for groups to use their resources: Kin and church groups
with a hierarchical connection with larger groups outside the village make contributions
to them (on the net). In contrast, across-group transfers in the village (see note 3 below)
are balanced.
3

Transfers received and given are balanced in yavusa; in contrast, transfers given tomataqali and tokatoka are 8-9 times the amount of transfers received from them. This
indicates significant across-group transfers in the village along the kin hierarchy: Yavusa
receives transfers from its subgroups.
4

The survey also asked about informal loans. Informal loans are much smaller than gifts,

and when informal loans are added to private transfers, results are almost the same aswhat are presented here.
5

The analysis focuses on the kin groups to which households currently belong. Marriage

across different kin groups is common. If the kin groups to which individuals used to25

belong prior to marriage are considered, transfer networks further concentrate on
tokatoka.
6

The sample contains a very small number of vanua chiefs in an incomplete manner,because many vanua chiefs live in cities. All vanua chiefs in the sample share yavusa
chiefs.
7

Other household characteristics controlled for that are not shown include: sickness ofany household member (capturing transitory income), household size of three age groups(younger than 15, between 15 and 65, and older than 65), secondary education of adultmembers, land holdings, and public transfers received (controlling for permanent income).
Although demographics strongly affect household private transfers – households withmore working adults receive smaller transfers from other households and contribute morelabor time to the village and community groups, group- and network-based transfers areneutral to transitory and permanent incomes (as an exception, households with educatedadults receive smaller amounts from other households in the same village). These weak
results buttress the central roles played by social ranks in Fijians’ private transfers.
8

Omitted group factors are unlikely to be a major source of potential bias, because

running the same regression for women’s groups in selected villages where there is only
one group yields very similar results. Note that in this subsample analysis, village
dummies fully control for group factors. This approach is infeasible for church groups,
because most villages contain more than one such group.

26
Table 1. Group characteristics.
Population in sample villages
Group mean
Village meanof no.
Total no.
of no. groups
members
pergroupsper village
group
Vanua
20
1.0 (0.2)
96 (55)
Yavusa53
1.2 (0.6)
14 (11)
Mataqali146
3.4 (2.0)
14 (11)
Tokatoka234
5.6 (3.2)8 (7)Village43
1.0 (0.0)45 (21)
Church group142
3.3 (2.2)
Women's group47
1.1 (0.6)
School group
Youth group44
1.0 (0.5)
Note: Standard deviations are in parentheses.

Proportion in sample
households (n=900)
Group
MemberLeaders with kin
ship
chief
100%
100%
100%
100%
100%
98%
52%
60%
15%

3.2%
11%
18%
2.8%
3.1%
2.8%
2.6%

56%
27%
18%
-

27
Table 2. Participation in and amounts of household private transfers.
Transfers received
Partici- Mean amounts(F$)
pationTransfers given
Partici- Mean amounts(F$)
pationA. Group-based transfers
Total
Cash-inkind
Labor-time (man-day)

42%
42%
25%

315 (644)
257 (591)
3.9 (12)

99%
99%
79%

1934 (2413)
1221 (1634)
51 (75)

Group type:
Kin groups
VillageCommunity groups

35%
22%
18%

188 (433)
67 (191)
61 (238)

86%
88%
97%

604 (990)
384 (483)
959 (1512)

Kin groups:
Vanua
YavusaMataqali
T okatoka

16%
34%
11%
9%

34 (120)109 (287)
25 (105)
19 (93)

43%
40%
74%
50%

113 (272)111 (254)
232 (377)
153 (299)

Community groups among group members:
Church16%
W omen11%
School2%
Youth1%

41 (136)
22 (81)4 (43)
3 (26)

97%
94%
97%
87%

480 (788)
306 (407)
466 (898)
298 (389)

B. Network-based transfers
Total
Cash-inkind
Labor-time (man-day)

94%
94%
32%

606 (686)
481 (570)
8.7 (19)

88%
89%
33%

420 (491)
303 (354)
8.2 (18)

Location:
Same village
Other village or city

89%
37%

375 (426)
231 (509)86%17%363 (432)
56 (192)

Kin group:Same tokatoka
Other tokatoka and same mataqali
Other mataqali and same yavusa
Other yavusa and same vanua
Other vanua

82%
14%
29%
7%
14%

417 (570)
33 (121)68 (167)
17 (98)73 (250)

77%
14%
28%
7%
6%

303 (409)
30 (104)64 (176)
12 (76)17 (104)

Community group:Same church group
Not same church group

79%
29%

413 (531)
186 (521)74%23%

317 (412)
102 (328)1

1

Proportions of members are shown in Table 1.
Note: Standard deviations are in parentheses. Number of observations vary.

28
Table 3. Reasons for household private transfers.
Group-based transfers
Network-based transfers(n=887)
Received
Given
Received
Given
Consumption & expenditure
19%
16%
50%
52%
Ritual
57%
40%
22%
18%
Medical expense
4%
1%
4%
4%
Production & investment
1%
5%
4%
3%
General
17%
22%
16%
19%
Other
2%
7%
3%
4%
Note: These are proportions of reasons weighted by transfer amounts. If there is more than
one reason for the same transfer, equal weights are assigned among them.

29
Table 4. Effects of social ranks on household private transfers.

Gross transfers received from groups

Gross transfers given to groups

Net transfers
received fromhouseholds

All

Kin

VillageCommunity

All

Kin

VillageCommunity

All

Withinvillage

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

0.003 *
0.001
(0.002)(0.001)0.001(0.001)0.000(0.001)0.122 *
0.156 ***
(0.077)(0.067)-0.092 * -0.066(0.060)(0.056)

Panel A. Probit for participation - marginal effects at means.
Head age

0.002(0.002)

Female head

0.220 *** 0.148 *(0.074)(0.082)Disability-0.051(0.058)-0.135 ** -0.083 *0.028(0.058)(0.044)(0.042)-0.109 ** -0.087 **(0.052)(0.048)Chief's kin

0.020(0.055)0.018(0.054)0.006(0.044)0.012(0.040)-0.033(0.051)-0.092 **(0.049)Kin leader-0.016(0.057)-0.007(0.060)-0.047(0.042)-0.004(0.037)-0.002(0.039)-0.005(0.036)

Church leader

0.273 ** 0.089(0.112)(0.127)0.345 *** 0.479 ***(0.120)(0.113)-0.056(0.097)0.031(0.061)

Women's leader-0.172(0.095)-0.090(0.105)-0.138(0.056)-0.041(0.065)0.076(0.055)0.036(0.071)Other non-kin
leader

-0.087(0.089)-0.092(0.092)-0.034(0.070)-0.063(0.048)-0.017(0.070)0.012(0.058)Log likelihood-420.1-351.6-315.1-313.2-260.6-258.5Chi sq. (p value)

0.000

0.000

0.000

0.000

0.000

0.001

Pseudo R sq.

0.236

0.263

0.278

0.230

0.195

0.135

809

714

755

823

684

707

6.6 **
(2.9)

3.5
(2.6)

1.4
(3.0)

-11.7
(7.6)

-7.3 *
(4.3)

-1.7
(2.4)

-6.4
(4.7)

3.9 **
(1.9)

1.0
(0.9)

No. obs.

Panel B. OLS for amounts.
Head age
7.2 *
(3.7)

0.002
(0.002)

Female head

-145.5(161.2)-164.0(110.9)62.8(81.8)-199.0(181.4)-138.5(223.5)-121.4(151.1)-80.7 *
(46.3)-91.6(146.8)134.8 *
(74.3)

132.6 ***
(49.0)Disability

0.9(224.4)-97.3(124.9)-168.7 ** 382.1(79.2)(302.4)120.3(219.0)123.3(142.2)12.5(48.8)

16.0(125.8)134.4 **
(63.9)

58.7 **(27.2)

Chief's kin

139.4(133.2)56.8(94.0)

11.6(64.0)

277.3(185.0)33.5(135.8)112.9(87.3)-40.6(42.1)

20.4(83.0)-43.5(45.5)-3.4(28.3)

Kin leader

6.3(151.8)-69.7(97.3)

88.2(63.2)

34.5(128.7)100.5(238.9)287.0 *
(157.5)

20.6(53.9)-105.2(121.2)-19.1(57.1)

4.0(24.5)

Church leader

331.0(251.5)-322.8 *** 117.3(119.4)(93.3)

415.9 ***
36.2
(141.9)(375.3)286.6 *
(163.0)-89.6(69.4)-11.0(256.2)102.3(147.6)-48.3(45.2)

234.0(537.8)-12.8(277.9)406.6 *** 143.6(131.1)(347.2)-156.8 *(89.9)-209.4 ***(74.6)-98.3(287.6)-95.4(112.3)29.4(87.4)-86.4(73.4)-37.6(42.0)

Women's leader
Other non-kin
leader

13.3(157.6)-41.0(112.3)44.8(71.4)-43.1(154.9)-106.0(190.3)F (p value)

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

R squared

0.207

0.263

0.324

0.335

0.220

0.319

0.172

0.225

0.173

0.157

No. obs.

332

274

195

150

867

560

601

883

880

895

Note: *10% significance, **5% significance, ***1% significance. Marginal effects at means in probit estimates with standard errors inparentheses are shown in panel A. OLS estimates with robust standard errors in parentheses are shown in panel B: columns (1)-(4),(6), and (7) are for households conditional on participation, and columns (5), (8)-(10) are for the whole sample. Other controls which
are not shown here are other household characteristics discussed in the text, village dummies, and constant.