Tsukuba Economics Working Papers
No. 2011-003

Do chiefly systems discourage schooling?
by
Yoshito TakasakiMay 2011

UNIVERSITY OF TSUKUBA
Department of Economics
1-1-1 Tennodai
Tsukuba, Ibaraki 305-8571
JAPAN

Do chiefly systems discourage schooling?
a

Yoshito Takasaki
University of Tsukuba

May 4, 2011aCorresponding author. Graduate School of Humanities and Social Sciences, Universityof Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8571 Japan, Tel./fax: +81 29 853
6280. E-mail address: takasaki@sk.tsukuba.ac.jp.

1

Do chiefly systems discourage schooling?
Abstract
An indigenous chiefly system can shape a country’s economic growth and inequality
through institutional development in its colonial history. This paper addresses this thesis
by using original household survey data in rural Fiji, which contain unique information
about traditional chiefly status, and Fijian coups as a natural experiment. It demonstrates
that chiefly labor networks in non-farm occupations that originated from the Britishcolonial policy persistently affected Fijians’ schooling. Chiefly networks were effective
for employment among male Fijians before and after 1970 independence, until the firstcoup occurred in 1987; then, their schooling strongly adjusted to structural changes inlabor market. Those outside the chiefly network – the majority of Fijians – have always
been discouraged from making education investments, because of low returns in the
network-driven labor market. Without being directly constrained by this chiefly
institution, Indians and Female Fijians outperformed male Fijians in higher education.
Keywords: Chiefly system; Colonial policy; Labor network; Schooling; Fiji.
JEL classification: O15; O17.

2
1. Introduction
Recent literature has shown that historical circumstances can persistently affecteconomic performance through their effect on institutional development (Acemoglu et al.,
2001; Acemoglu et al., 2005; Sokoloff and Engerman, 2000). Although various
institutions, such as property rights, law, power, and class, have received much attention(e.g., Banerjee and Iyer, 2005; Goldstein and Udry, 2009; Huillery, 2009; La Porta et al.,
1998; Pandey, 2010), the mechanisms underlying such persistence are less understood(Acemoglu and Robinson, 2008 develop a theory; see also Nunn, 2007). Munshi andRosenzweig (2006) shed light on gender-cum-caste labor networks as one suchmechanism in 1990s’ Bombay: It made schooling responses to increased returns to
nontraditional white-collar occupation distinct in the gender sphere – girls’ enrollments in
English schools increased more than boys’. Using original household survey data in ruralFiji, this paper examines how the indigenous chiefly system interacted with Britishcolonial policy to persistently affect Fijians’ schooling.
Fiji’s ethnic division is well known: Fijians have significantly lagged behindIndians in commerce, the professions, and higher education since the 1950s (e.g., Norton,
1977; Tavola, 1992; White, 2001). 1 Despite affirmative-education policies for Fijians
following independence in 1970, the ethnic gap in secondary and tertiary education has
never narrowed. Indians’ economic and social mobility led to the country’s ethnic tension
and, in particular, four coups since the late 1980s. At the same time, after independence,
female Fijians caught up with and then surpassed male Fijians in secondary education.

1

As is common in Fiji, in this paper, Fijians means native Fijians and Indians means
Indo-Fijians.

3This paper sheds new light on Fijian labor networks as a potential mechanism thatdrove the country’s distinct education paths. The British colonial government establishednative administration based on the indigenous chiefly system; as a result, “the native (or,as it was known later, Fijian) administration became an important additional source ofpower and patronage for the chiefs, and employment for many commoners” (Ghai, 1987,
p10). That is, colonialism legitimatized historical inequalities in the hierarchical chiefly
system of Fijian society. Many politicians and highly ranked government officials havecome from chiefly families (for example, the current grand chief in my study area is aCabinet Minister). I argue that chiefly labor network for commoners’ employment in thenative administration sustained and expanded to broad labor networks in non-farm
occupations – from urban to rural – in the country, thereby persistently affecting theirschooling after independence. Those outside chiefly networks – the majority of Fijians –have been discouraged from making education investments over time, because of low
returns in the network-driven labor market. The paper shows evidence for the influenceof chiefly labor networks on secondary schooling in rural areas, suggesting its broad
discouraging effects on higher education in the country.
Chiefly labor networks are distinct from low-caste labor networks (Munshi andRosenzweig, 2006). In Bombay, labor networks in traditional occupations with low
returns that do not require higher education constrain schooling among those in the
network, because of the sanction imposed on those who exit the network. In Fiji, labornetworks in formal occupations with high returns that require higher education constrain
schooling among those outside the network. Though both types of networks have thepotential to involve dynamic inefficiency, the latter may be more relevant in poor

4
countries than the former. In both cases, as labor networks historically exist among males,
female schooling has been less constrained. In Fiji, this contrast extends to the ethnic
sphere: Although Indian labor networks could also play a major role, they were notformed according to hereditary chiefly status.
In the literature, a labor network is captured by a network link (e.g., whether anindividual has a connection with the job through kinship) or size (e.g., the number ofworkers in the same group, such as caste) (e.g., Munshi, 2003; Munshi and Rosenzweig,2006). Such information, which my data lack, is not sufficient to capture a chiefly
network. Crucial information about the chiefly status of the individual with whom
network is linked or of the group based on which network is formed is needed. These two
types of networks overlap with each other when the former individual-based network isformed within the group, and in Fiji, the latter group-based network can be formed byeither a village or clan (a kin group within the village, as defined below). Takasaki (2011)
finds that in Fijians’ household private transfers, both within- and across-village networks
are important and mainly formed among households that belong to the same clan. My
survey stratified Fijian villages by chiefly-village status (defined below) and households
in each village by clan. Thus, the data contain rich variations in chiefly-group status; instandard household surveys, by contrast, traditional elite status is often unobservable toresearchers, and even if it is observable, there are too few elite groups to make astatistical analysis possible.
Lack of network data precludes me from directly identifying chiefly labornetworks’ positive influences on schooling. My empirical strategy is to see whether
schooling responses to chiefly-group status correspond to employment responses over

5time, by using the coups as a natural experiment. First, I examine whether a group-basedlabor network is formed according to chiefly-group status, if such a network exists. The
chiefly-village network was effective for employment among male Fijians before andafter 1970 independence, until the first coup occurred in 1987; with village factors,
including network, fully controlled for, chiefly-clan network was always ineffective.
Next, I show that male Fijians’ schooling strongly adjusted to the structural change in thelabor market: After the network effect on employment vanished, those in the network
augmented education investment for better employment prospects. This pattern is foundin chiefly villages, but not in clans, for secondary-school completion (which is a passport
to employment), but not secondary enrollment. This strong correspondence of
employment and schooling is shown to be qualitatively robust.
The rest of the paper is organized as follows. Section 2 offers a description of
Fijian education history. Section 3 describes the Fijian chiefly system and the data.
Section 4 provides descriptive statistics on income, employment, and schooling. Section
5 develops empirical models to test the hypotheses discussed above. Section 6 reports
estimation results, the robustness of which is discussed in Section 7. The last section
concludes.
2. Fijian education history
2.1. Colonialism – 1874-1970
In 1874, Fiji became a British colony, and a native administration was established
for the indigenous chiefly system. Fiji’s formal education had begun in Methodist
mission schools (in 1835), and the British colonial policy was in principle against giving
too much education to natives. Commoners’ education was limited to primary schooling6with a practical focus – vocational, agricultural, and technical for boys and hygiene andcrafts for girls; in contrast, chiefs were educated to secondary and tertiary levels overseas.
This discriminatory policy was strongly seconded by administrative chiefs: “Fijians ofchiefly descent coveted a selective formal education as an avenue of privilege, a vehicle
to differentiate themselves from commoners, and a means to consolidate their status”(White, 2001, p260). 2
Between 1879 and 1916, the colonial government brought over 60,000 indentured
laborers from India for sugar plantation. 3 Under the paternalistic colonial policy
protecting Fijians, Indians experienced much lower economic and social conditions; inparticular, there existed a large gap in primary education between the two races. In 1916,
to expand schools with limited supervision and cost, the colonial government launched
the grant-in-aid system – providing government financial support to primary schools thatmet prescribed standards (by 1931, most mission schools were handed over to local
committees). Indians, who were virtually prevented from owning land and thus strove foreducation to attain mobility, took advantage of this policy; in contrast, Fijians with land
and ascribed social status were less likely to seek social mobility through education, and2

The most influential chief in the colonial era, Oxford-educated Ratu Sir Lala Sukuna,consistently called for “education with a local bias” for Fijians (Scarr, 1982): “What isrequired is a scheme of education that will fit the Fijian child to become a good citizen of
his own country which needs above all…farmers, mechanics, boat-builders, men skilled
in indigenous handicrafts, girls with a practical knowledge of housecraft: home-cleaning,cooking, washing, sewing, nursing.”
3
In 1921 Fijians and Indians accounted for 54% and 39%, respectively, of the total
population (Gillion, 1977). In 1920s Fijians suffered heavy losses of life from introduced
diseases, and in the mid-1940s Indians slightly outnumbered Fijians in the population.
The population share was reversed after the 1987 coups, because of Indians’ outmigration.

7
their responses were much weaker (Tavola, 1992). As a result, Indian primary schools
quickly expanded, closing the ethnic gap in primary education.
The two races followed distinctly different paths of secondary education. On one
hand, Fijians did not demand academic education, because they placed great faith in theirchiefs in the indigenous chiefly system and the native administration to represent theirinterests; on the other hand, Indians sought after academic secondary education foreveryone (Tavola, 1992). In 1937, the colonial government accepted providing funds forthe establishment of secondary schools only for Indians. Restrictions on rural Fijians’
migration to urban areas (from 1920s to mid-1960s) – which the colonial administration
and administrative chiefs considered to be a social disruption – also restricted their accessto secondary schools; such restrictions were not imposed on Indians (White, 2001). 4
Although there was almost no ethnic disparity in secondary-school rolls in the mid-1940s,
Indians’ rolls became double those of Fijians in 1955; after the grant-in-aid system was
extended to secondary schools in 1956, the gap widened even more. In the 1960s,
secondary-school enrollments quickly increased among both races, with a persistentethnic gap. 5
During the late-colonial period, government intervention in education increased,
and in 1947 the Ten Year Plan, the first comprehensive plan for education, wasdeveloped. From the mid-1940s, selected commoners started to be sent away for tertiary
4

Indians’ strengthened political presence underlay their progress: Beginning in 1929 theLegislative Council, the body of nominees that advised the governor, consisted of six
elected European seats, six elected Indians, and six nominated Fijians. In the mid-1940s,
the Legislative Council tightened migration restrictions on Fijians.
5
Bertocchi and Spagat (2004) theoretically study how the evolution of an education
system founded on a hierarchical differentiation between vocational and general interacts
with economic growth and inequality. Fiji is a unique example where such educational
differentiation tightly matched ethnicity.

8
education overseas, and by the early 1960s, some commoners enjoyed a successful
economic position and its accompanying status. The chiefs’ influence remained powerful,
however: “The high chiefs formed a dominant status group that guided the social andpolitical values of upwardly mobile commoners” (Norton, 1977).
2.2. Post-independence – 1970-1987
At independence in 1970, chiefly governance was maintained and Fijians’
political and economic status was ensured. 6 By then, education had proved to be a
passport for Indians to enter commerce and the professions. Realizing the instrumental
value of education for economic and social mobility, Fijians were concerned about theirinferior position (Tavola, 1992). Although the Fijian government adopted various
affirmative policies (e.g., school construction, scholarships) and secondary schooling
continued to improve among both races, the ethnic gap in secondary (and tertiary)
education and economic status never narrowed. Within both races, girls’ secondaryschool enrollments were almost nonexistent in the mid-1940s and much lower than boys’
before 1970 independence; then, girls’ enrollments quickly increased and surpassed boys’by 1981. The gender gap has persisted since that time.
In 1968 the University of the South Pacific was established with a main goal of
training secondary teachers; in the 1980s Fiji’s education policy shifted its focus fromquantity expansion to quality improvement and from teacher training to curriculum
revision (teaching opportunities were saturated by 1987) (Tavola, 1992).
2.3. Coups – 1987-2006

6

Under the 1970 Fijian constitution, parliament seats are allocated among ethnic groups(22 Fijians, 22 Indians, and 8 of other races), a large proportion of Senate members arenominated by the Great Council of Chiefs, and the land and rights of Fijians are protected.

9
The 1987 elections resulted in a coalition backed largely by Indians and sincethen, Fiji’s democratic rule has been interrupted by four coups – two in 1987, one in 2000,
and another in 2006 – aiming essentially to maintain Fijians’ political dominance overIndians. Major economic reforms for outward-looking liberalization were implemented
mainly after the 1987 coups (Elek et al., 1993). It is probable that associated structuralchanges occurred in the labor market (there is no systematic study of the Fijian labormarket).
3. Fijian chiefly system and data
3.1. Chiefly system
Chiefdom is "an autonomous political unit comprising a number of villages orcommunities under the permanent control of a paramount chief" (Cameiro, 1981, p45).
Fijian chiefdom, vanua, consists of three hierarchical subunits, yavusa-mataqali-tokatoka,
as illustrated in Figure 1 (Ravuvu, 1983). Each unit is a subset of its higher-order unit
(e.g., yavusa 1 consists of mataqali 1 and 2). Roughly matching an old district (tikina) in
the administrative unit, vanua ranges over several villages (koro). A village consists of
one or a few yavusa, which includes several lower-order units, mataqali, and thentokatoka. The chiefly system is of central importance not only for rural Fijians’ local
governance and ritual, but also for their livelihoods (Turner, 1992); in particular, land iscommunally owned by mataqali (about 83% of the country’s total land is communal andcannot be sold by law), and customary rights for coastal fishing are held by vanua or
several yavusa.
Each vanua has a paramount chief, and some yavusa and mataqali, but not
tokatoka, have sub-chiefs. There are also traditional leaders other than chiefs/sub-chiefs,

10
and village chiefs can be served by chiefs, sub-chiefs, or non-chief leaders. Accordingly,
Fijian villages are categorized as either a chiefly village with a vanua chief, or other(village 1 vs. 2), and mataqali (henceforth called clan) is either a chiefly clan to which thevanua chief in the chiefly village belongs, or other (mataqali 1 vs. 2). 7 In principle, there
is one chiefly village in each vanua (or tikina), and there is one chiefly clan in eachchiefly village. The chiefly status normally is ascribed and inherited through the maleline; chiefly-village status and chiefly-clan status are fixed across generations.
3.2. Data
In July-September 2005, I conducted a household survey in Cakaudrove Province,
which is located mainly on Vanua Levu Island and Taveuni Island, the second- and thirdlargest 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. Cakaudrove is home to many leading politicians and has been one of Fiji’s mostinfluential provinces.
The province has 134 Fijian villages in 16 districts (tikina). In each district,villages were stratified by chiefly-village status; 13 chiefly villages were sampled andanother 30 villages with distinct environmental and economic conditions wereintentionally chosen. 8 In each village, households were stratified by tokatoka (the7

I focus on the chiefly status of mataqali. Many villages have only one yavusa (i.e.,
yavusa defines the village) and the tokatoka to which the vanua chief belongs is too
uncommon for statistical analysis, as shown below.
8
Among 16 districts, 2 are headed by the same vanua chief and another 2 are headed by
the other same vanua chief; thus, there are 14 chiefly villages. One chiefly village was
not sampled because of a political concern. In each of three districts where there are morethan one vanua, only one village with a main vauna chief is treated as a chiefly village;that is, chiefly-village status is defined at the tikina level, not at the vanua level; using

11
smallest subunit) and a combination of individual leadership status discussed above andmajor asset holdings (e.g., shops) (all tokatoka are sampled); in each stratum, households
were randomly sampled (50% of the population in each stratum, on average). As such,chiefly villages and households with chiefs/sub-chiefs/non-chief leaders are oversampled.
Overall, the survey covered 906 households, collecting information about demographics,
education, assets, production, income, shocks, and transfers.
The 43 villages in the sample cover 20 vanua, 52 yavusa, 145 mataqali, and 238
tokatoka in total, and 13 yavusa, 12 mataqali, and 12 tokatoka have vanua chiefs; 9 34%
of households reside in chiefly villages and 14% belong to chiefly clans. Almost all
adults (age 20 or above) are commoners. There are 23 chiefs/sub-chiefs (less than 1%),
most of whom are sub-chiefs; many vanua chiefs reside in cities.
The survey did not cover individuals who migrated to urban areas, and in thesample, tertiary education was very uncommon even among young adults, indicating
their potentially significant urban migration in search of tertiary education, further
training, and better employment. The analysis in the remaining sections focuses on
employment and secondary education among those who stayed in the rural study area.
4. Income, employment, and education
4.1. Income and employment
On average, households in the sample earned an annual income of F$2,281 per
capita in 2005 (1 Fiji dollar = US$.60) (see Table 1). Farming and fishing, respectively,

chiefly-village status defined at the vanua level (there are 15 such chiefly villages) does
not alter the regression results presented below.
9
In one chiefly village, none of the chiefly mataqali (and tokatoka) resides in the village.

12
accounted for 62% and 11% of total earned income; 10 16% of households had permanent
non-farm employment, accounting for 10% of income (occupations are discussed shortly).
Households are divided into three cohorts by the age of household head – cohort 1(age 50 or above), 2 (35-49), and 3 (20-34). With no delay or repetition in primaryschooling, household heads in cohort 1 began secondary schooling, if any, during thelate-colonial period (when secondary-school enrollments started to increasesignificantly); those in cohort 2 did so between 1970 independence and the 1987 coups(when major construction of secondary schools occurred in rural areas); and those incohort 3 did so mostly between the 1987 and 2000 coups (when education policy focusedon quality improvement). Permanent employment was more common and important in
the livelihoods of the young cohort than for those of the old (8% of income share incohort 1 vs. 22% in cohort 3).
This change in earning patterns augmented income inequality among households.
According to the gini decomposition by source (Lerman and Yitzhaki, 1985; Stark et al.,1986), the contribution of permanent wage income (with the largest source gini of .92) tototal inequality was much higher for the young cohort than for the old (8% in cohort 1 vs.
28% in cohort 3); qualitatively the same across-cohort comparison holds for the ginielasticity to permanent wage income (almost 0 in cohort 1 vs. .058 in cohort 3). 11 Total

10

Almost all households employ traditional farming practices, using no mechanizedequipment or animal traction to produce taro, cassava, coconut, and kava plants; mosthouseholds also engage in artisanal fishing, using lines and hooks, simple spear guns, or
rudimentary nets.
11
This means that a 10% increase in permanent wage income augments inequality in total
income by about .6%. The converse holds true for the across-cohort comparison of thegini elasticity to crop income (with the smallest source gini of .62).

13
earned income of households with permanent employment was higher than those without;in particular, in cohort 3, the former was twice the latter.
Marriage across different clans (mataqali) in the village or in other villages iscommon. In the sample, 73% of 2,334 working adults (age 20-65) – 55% of females and
90% of males – are in their original clans (see Table 2); that is, marriage migration ismostly in the female domain. Among adults in both all and original clans, a small
proportion (6-7%) have permanent employment; while the across-cohort difference in
employment is not large, the gender gap is considerable (9% for males vs. 4% forfemales), reflecting the weak female labor market in the region.
Occupational distribution is distinct in the gender sphere: Although about one halfof male employments are office work/skilled labor, followed by unskilled labor, tourism
and teaching are also important for females (office work/skilled labor count for over 60%
of male permanent wage income). Although data about employers are lacking, anecdotal
evidence suggests that public-sector employment is common, especially among males.
4.2. Education
Fiji’s current education system consists of eight-year primary (Class 1-8; Class 1
begins at age 6) and four-year secondary (Form 3-6) (in 1997 primary education became
compulsory). 12 Almost all primary and secondary schools in the country are private
schools managed by community committees (76% primary schools and 42% secondary
schools) and religious organizations (Ministry of Education, 2010); most schools

12

Following independence, the Fijian government sought to introduce a new system of
six-year primary/four-year secondary/two-year college, but most schools did not make ashift to this new system, and many junior-secondary schools expanded to full secondary
schools by adding Forms 5-6. In the paper, Forms 1 and 2 in this alternative system(which are uncommon in the sample) are treated as Classes 7 and 8.

14(including those in the study area) are ethnically separated. Standardized examinations
are held at the end of Class 8, Form 4, and Form 6 (Tavola, 1992). Successful completionof Form 6 is a passport to employment, tertiary education, and further training.
Education attainments – measured by four levels (primary incomplete or below,
primary complete, secondary incomplete, and secondary complete or above) – greatly
improved across cohorts among working adults in all clans in the sample, as shown in
Figure 2 (the results for those in their original clans are very similar). Although males’
education level was higher than that for females in cohort 1, females surpassed males incohort 2, and a large gender gap in secondary-school completion emerged in cohort 3(under 30% for males vs. over 40% for females). Qualitatively the same pattern isobserved across the country (Ministry of Finance and National Planning, 2004).
Compared to cohort 3, secondary enrollment among youths (age 14-19)
significantly improved: Almost 70% of those at the age of 16 are at secondary schoolwith a small gender gap, as shown in Figure 3, where those at the age of 18 and 19 whocompleted secondary school are also included. At the same time, late enrollment iscommon among boys (57% of those age 15 are at secondary school), indicating
delay/repetition in primary school. The gender gap augments at the age of 17-18 (31% forboys vs. 53% for girls), indicating that repetition and dropouts are more common among
boys than girls.
5. Econometric specification
5.1. Chiefly labor network

15
I conjecture that adults’ employment is determined not only by their secondaryschool completion, but also by whether or not they belong to a chiefly labor network. I
start with the following reduced-form employment equations for working adults:qi

Dhv  ¦K a sia  ĳ; ijgv  D  eijgv ,(1-1)

Ehg  ¦K a sia  ĳ; ijg  V  eijg ,

(2-1)

a 1

qi

a 1

where i, j, g, and v stand for individual, household, clan, and village, respectively; qi is adummy for employment; hv and hg are dummies for chiefly-village and chiefly-clan status,
respectively; 13 sia is a dummy for own highest education level a (1: primary complete, 2:
secondary incomplete, 3: secondary complete or above); Xijgv and Xijg are vectors of other
determinants (detailed below); D and V are district and village dummies, respectively;
and eijgv and eijg are error terms. Equations (1-1) and (2-1) are estimated by a linear
probability model (probit estimates are very similar to OLS results).
In equation (1-1), district dummies capture all district-level factors, including
historical ones. Since there is only one chiefly village in each district, district dummies
fully control for any potential chiefly-village network that can be formed within thechiefly village or across chiefly villages (the model cannot distinguish between thesetwo). Positive Į means that access to such a village network – as a villager – contributes
to employment. Although this does not prove the existence of a labor network, it
indicates that if a labor network exists, it is formed according to chiefly-village status (i.e.,a chiefly-village labor network). In equation (2-1), village dummies capture all village
13

Chiefs/sub-chiefs are too uncommon (only 12 working adults) to estimate potential
effects of individual/household chiefly status. The analysis focusing on commoners
generates almost the same results as those presented below.

16factors and interpreting chiefly-clan network is analogous. These models do not capture
chiefly networks formed by individuals in non-chiefly villages/clans based on theirindividual connections. Hypotheses to be tested are:Hypothesis 1: Adults’ employment is positively affected by their secondaryschool completion, but not lower education level; i.e., Ș1 Ș2 = 0, Ș3 > 0.
Hypothesis 2: Adults’ employment is more likely in chiefly villages and/or clans;i.e., Į > 0 and/or ȕ > 0.
Three potential estimation problems need to receive special attention. First, with alack of historical information regarding adults’ employment, their current employment
status (at the time of interviews) is used as a proxy. Section 7 discusses this proxy’ssystematic errors. Second, since information about the chiefly status of the originalvillage/clan for adults not in their original clans is lacking, the analysis focuses on adults
in their original clans; this is also true in adults’ schooling models developed shortly. If
marriage migration is systematically correlated with chiefly status, then selection bias is aconcern for females, but not for males, most of whom are in their original clans. Third,
education can be correlated with unobserved factors that determine employment, such as
ability. My focus is not to identify impacts of education on employment, but to show
their heterogeneity across education levels (the current data lack identifying instrumental
variables for education). I estimate the employment models with and without own
education to see how distinct the results of the remaining variables – especially chiefly
status – are; the same approach is taken to address the potential endogeneity of parents’
education in the schooling models.
5.2. Chiefly labor networks and schooling17
My next conjecture is that chiefly labor networks positively affect secondary
completion, which is instrumental for employment (hypothesis 1). As chiefly status
captures any related factors that determine schooling, its estimated effect on schooling is
not necessarily qualitatively the same as the network effect. In particular, even if chiefly
labor networks positively affect schooling, chiefly status can appear to have no influence.
Still, network effects on schooling should change in response to those on employment asfollows. How labor networks affect schooling depends on the balance of their effects on
employment and post-employment, the latter of which mean that labor networks benefit
members through salary, promotion, tenure, and so forth, after they get hired. 14 On one
hand, the post-employment effect encourages secondary-school completion among
network members for better employment prospects; those outside the network are
discouraged. On the other hand, the employment effect makes secondary education lessimportant in the labor market, discouraging network members’ schooling. The net
network effect on schooling is generally ambiguous.
Now suppose that network effects on employment decrease, because of structuralchange in the labor market, and post-employment effects do not decrease, as accumulated
network capital sustains them. Then, net network effects on schooling unambiguously
increase; in the extreme case where the network effect on employment vanishes, the net
effect on schooling is unambiguously positive. I assume that the change in the effects on
employment/schooling of chiefly labor networks is qualitatively the same as the change
in those of chiefly status. Then, I can test the correspondence of the effects of chiefly
status on employment and schooling.
14

Extending the employment models to wage to examine the post-employment effect isinfeasible with the current data, in which employment is relatively uncommon.

18
To capture the change in the effects of chiefly status on employment acrosscohorts, equation (1-1) is extended to:qi¦D h c  ¦K s  ĳ;
k

k
v i

k 1

a a
i

ijgv

 D  eijgv ,

(1-2)

a 1

where cik is a dummy for cohort k (1: age 50-65, 2: age 35-49, 3: age 20-34). The
corresponding equations for secondary schooling among working adults and youths(cohort 4: age 14-19) are:sil

¦D h c  ĳ X
l
k
k v i

l

l
ijgv

l
 D  eijgv(l = 1, 2),(3-1,2)k 1

3
3
si3 D 3 hv  ĳ3 X ijgv
 D  eijgv
,

(3-3)

where s1i, s2i, and s3i,, respectively, are dummies for adults’ secondary-school completion,
adults’ secondary enrollment (i.e., secondary incomplete or above), and youths’
secondary enrollment (including completion); Xlijgv and X3ijgv are vectors of other
determinants (detailed below); and elijgv and e3ijgv are error terms. Extending equation (21) and developing corresponding schooling models are analogous. All equations are
estimated by a linear probability model.
For the sake of clarity, I hypothesize that chiefly-village network effects on
employment decreased in cohort 3, i.e., after the 1987 coups. In Section 7, I show that thedecreasing network effects on employment are crucial to qualitatively identify theincreasing effects on schooling. That is, I effectively use structural change in the labormarket after the coups as a natural experiment.
Hypothesis 3: If the effects of chiefly status on employment decrease acrosscohorts, those on secondary-school completion, but not enrollment, increase; i.e.,Į1 = Į2 > Į3 and Į11 = Į12 < Į13.

19
If the network effects on post-employment also significantly decrease, the effects ofchiefly status on schooling may not increase; if hypothesis 3 holds, that is not the case.
My last conjecture is that chiefly labor networks exist among males, but not
females. Unfortunately, female employment in the current data is too uncommon to test
hypotheses 1-3 for females separately. Only female schooling can be separately analyzed;
though interpreting the female results with potential selection bias requires caution.
5.3. Covariates
Standard covariates are used for adults’ employment: individual’s age and adummy for household head; household demographics (age of household head, household
size, proportions of children (<15) and elderly (>65)); clan size and assets (number ofhouseholds in the clan, in the population, and total clan land, which is fixed), which canbe correlated with chiefly-clan status; village size and market access (number ofhouseholds in the village and time distance to the closest local market), which can becorrelated with chiefly-village status (in models with district dummies). District dummies
control for district-level labor-market conditions. Village/clan size captures the size of thepotential labor network among village/clan members – either a chiefly or non-chiefly one.
Current household assets, which are determined by the history of income and thus
employment, are not included. Adding parents’ education as a proxy for historicalhousehold wealth does not alter the remaining results.
Standard covariates are used for youths’ secondary schooling: individual’s age
and sibling size (numbers of elder/younger sisters/brothers); mother’s/father’s educationlevel, household demographics and assets (age of household head, household size, land,and non-land assets), which capture household permanent income; clan size and assets;

20
and village size and school access (physical distance to primary and secondary
schools). 15 Household income is not included, because it is determined by household
decisions on the labor supply, including youths’. Village/clan size captures potential
externality and network effects: In the larger villages/clans, the greater number ofchildren who enroll in/complete secondary school may encourage schooling among
younger children, and parents (in remote villages) may have better networks forchildren’s co-residence with relatives out of the village. In contrast, the current data lackhistorical determinants of adults’ education attainment. I can control only for individual’sage, parents’ education (also partly capturing historical household wealth), clan assets(fixed), and proxies for historical school access (defined below). Potential omitted
variable bias is discussed in detail in Section 7. The descriptive statistics of all covariates
are reported in Table A1.
6. Estimation results
Estimation results for employment, secondary-school completion, and enrollment(adults and youths), respectively, are reported in Tables 3, 4, and 5, where the estimatedcoefficients of chiefly status, own education, and gender are shown (results of othercontrols are discussed in appendix). Panels A1/A2 and B1/B2 in Tables 3-5, as well as
panels C and D in Table 5, report results of the models with district dummies and thosewith village dummies, respectively; 16 panels A1 and B1 show results of the constrainedmodels ignoring across-cohort heterogeneity in the effects of chiefly status (standard

15

Access to primary school can influence delay and repetition in primary school, thereby
affecting secondary-school enrollment. Among adults (especially old adults), access toprimary school can also alter primary completion (it is almost uniform among youths).
16
The models with village dummies can be applied only to villages with intra-village
variations in dependent variables; otherwise, village dummies perfectly predict them.

21
errors are clustered by village and clan, respectively), and panels A2 and B2 show thoseof the unconstrained models. In each table, columns are organized by the sample ofinterest, and the first and second columns of each set show results without and withown/parents’ education as controls; 17 almost all results are qualitatively the same
between these two, suggesting that the potential endogeneity of education is unlikely tocause significant bias. I discuss results for males first and then females.
6.1. MalesFirst, only secondary-school completion positively influences adults’ employment
in a statistically significant manner (16% marginal effect); i.e., hypothesis 1 holds. This is
so across cohorts, as shown by an extended analysis using the dummy for secondaryschool completion interacted with cohort dummies (cohorts 1 and 2 combined). Second,
chiefly-village status, but not chiefly-clan status, 18 positively affects adults’ employment
in cohorts 1 and 2 (8-9% marginal effect), but not in cohort 3. Ignoring across-cohort
heterogeneity in the constrained model (1-1) fails to show that hypothesis 2 holds forchiefly-village status in cohorts 1 and 2 only. Third, although chiefly-village status does
not affect adults’ secondary education – both completion and enrollment – in cohorts 1
and 2, 19 it has strong positive influences on secondary completion in cohort 3; such

17

Information about parents’ education for adults is available only for parents who are in
the same household as the one to which each adult currently belongs or for parents ofhousehold heads and their spouses (the survey asked separate questions about the latterparents). The numbers of observations for the adults’ schooling models significantly
decrease with parents’ education added, especially for females.
18
Chiefly-clan status negatively influences employment in cohort 1; when a dummy for
employment in skilled labor (excluding unskilled/other labor in Table 2) is used as adependent variable, however, this effect loses statistically significance.
19
Because adults’ secondary completion and female adults’ secondary enrolment incohort 1 are uncommon, cohorts 1 and 2 are combined in interaction terms, i.e., Į11 = Į1222effects are not evident in secondary enrollment in cohort 3 (the estimated effects onenrollment are about half of those on completion, suggesting that the former mostly
capture the latter), as well as cohort 4 (youths). In contrast, secondary schooling for both
adults and youths are always neutral to chiefly-clan status. Hence, the estimation resultsof employment and schooling strongly correspond to each other in terms of timing,
education level, and group type, i.e., hypothesis 3 strongly holds.
These results suggest the following. First, a chiefly labor network is formed
among those in the chiefly village, with no significant difference between chiefly andnon-chiefly clans. 20 As it is natural that a labor network was first formed among membersin the same chiefly clan with close kin connections, this indicates that the network
expanded to non-chiefly clans in the chiefly village over time; the chiefly-clan network
did not necessarily become less important. Second, the chiefly-village labor network
drove employment, and network capital was accumulated accordingly not only in thecolonial era, but also after independence; after the 1987 coups, this network effect on
employment vanished. Third, schooling corresponded to this structural change in labormarket: Those in the chiefly labor network, which sustained, augmented their education
investment, seeking its post-employment benefits, which did not decrease much. As such,the chiefly-village labor network positively affected male secondary education over time.
6.2. Females

is assumed, in equation (3-1) for both males and females and equation (3-2) for females.
This is not a problem in testing hypothesis 3 because Į1 = Į2 is found in equation (1-2).
20
When sub-chiefly clan status (lower status than chiefly clan) is added as an additional
control in the models with village dummies, its estimated impacts are always
nonsignificant in the employment and schooling models. This buttresses the significanceof the village-level network.

23
Although a gender gap in secondary schooling is strong among both adults(especially in secondary completion) and youths, gender does not differentiate
employment, probably because male employment is also uncommon in the sample.
Chiefly status alters female adults’ secondary schooling in qualitatively the same way as
male adults’: Chiefly-village status has positive effects on secondary completion incohort 3 only (the estimated marginal effects are smaller than those for males, andaccordingly those for all adults are in between males and females). Available evidence
for a chiefly labor network for females is weak: 1) female employment is very
uncommon across cohorts, especially in cohort 3 (only 2%, Table 2); 2) the estimatedcoefficients of chiefly-village status on employment for all adults in cohorts 1 and 2 aresmaller and less statistically significant (in cohort 1) than those for males; and 3) themarginal effects for all adults decrease across cohorts in a much less sharp manner thanthose for males. 21
Why does it appear that females in chiefly villages started to complete secondary
school more commonly than those in other villages? Does this reflect only a recentchange in distinct patterns of their marriage migration between chiefly and non-chiefly
villages (i.e., selection bias)? Did females start to seek post-employment benefits of achiefly labor network, as males did, for example? More research is needed in otherlocales where female employment is common (e.g., urban areas and tourism) by
combining complete information of adults’ original clans.
7. Robustness21

It is possible that some adults (especially females) not in their original clans got theircurrent employment after they moved to the current village. I estimate the employment
equations for all adults (and males) including those not in their original clans, finding
similar results. This serves as some counterevidence against potential selection bias.

24
7.1. Systematic measurement errors in employment status
The estimated positive effects of chiefly status on current employment are those
not on employment per se, but on the combination of historical employment and tenure.
The estimated employment effects of chiefly-village status in cohorts 1 and 2 can bebiased upward; in contrast, its nonsignificant effects in cohort 3, as well as those ofchiefly-clan status across cohorts, are robust to such potential bias (such bias is minor for
young adults anyway). This means that the network effects on employment did notactually decrease across cohorts, only if the estimated positive effects of chiefly-village
status capture post-employment effects only. This should be very unlikely. Also, althoughsuch an upward bias is expected to be greater in cohort 1 than cohort 2, the estimatedmarginal effects of chiefly-village status are almost the same in between them. Therefore,
the decreasing effects of chiefly-village status on employment are qualitatively robust.
7.2. Omitted variable bias in schooling models
Chiefly-village/-clan status might be correlated with unobserved factors that
determine schooling. This is especially a concern in the adults’ schooling models withvery limited controls. As discussed shortly, although historical school access, school
quality, and historical household wealth are potential sources of upward bias in theestimated impacts of chiefly status on schooling, such bias could be significant mainly incohorts 1 and 2, not cohort 3. Then, if chiefly status picks up such unobserved effects, itcauses a decrease in the estimated effects across cohorts. Hence, the increasing effects on
secondary-school completion – in correspondence to the decreasing effects on
employment (hypothesis 3) – are qualitatively robust to such omitted variable bias.

25
Qualitative gender comparison in the schooling models is robust to any potential bias
caused by unobserved village and clan factors.
Historical school access
Distinct from the models with village dummies that fully control for historicalschool conditions, school construction, especially the expansion of secondary schools
after independence (at the time of cohort 2’s schooling), could be positively correlatedwith chiefly-village status in the models with district dummies (equations 3-1 and 3-2).
This is because under the grant-in-aid system, the government might treat community
committees associated with chiefly villages with stronger political connections better thanother villages (Banerjee et al., 2009 review empirical research on collective action andpublic goods; e.g., Banerjee and Somanathan, 2007). As only one Fijian secondary school
is available in each district, if any, district dummies control for historical availability of asecondary school in the district; though information of historical school access within the
district is lacking. I use distance to current schools – primary and secondary – interactedwith cohort dummies as proxies for historical school access. Results of the remaining
variables are very similar to each other, both with and without the proxies. It is noted that
not only are models with school access omitted, but also models with these proxies could
involve upward bias in the estimated impacts of chiefly-village status, because errors in
the proxies are smaller in magnitude in chiefly villages than in other villages (if theformer’s access improved faster); though the appendix offers counterevidence against
such systematic errors. Measurement errors in the proxies must be much larger forsecondary school than primary school with a much longer history; the older the cohorts,
the greater are the measurement errors, of course.

26
School quality
School quality also could be positively correlated with chiefly-village status.
Although the current data lack information about school quality, district dummies fully
control for the current and historical quality of secondary schools (including boarding
facilities). At the same time, however, district dummies partially control for the quality ofprimary schools, because some districts have/had more than one primary school. If thequality of primary schools uncontrolled by district dummies influences secondary
schooling through primary-school completion and academic performance, it could cause
upward bias in the estimated impacts of chiefly-village status on secondary schooling forboth adults and youths. This potential bias is likely to be the largest among older adults incohort 1, many of whom did not complete primary education. 22
Historical household wealthIn the adults’ schooling models, unobserved historical household wealth not
captured by parents’ education and clan assets might be positively correlated withchiefly-village/-clan status; then, if household wealth positively affects schooling, theestimated impacts of chiefly status are biased upward. As parents’ education became amore important determinant of income over time (Hypothesis 1 and Table 1), unobserved
welfare effects, if any, should be greater among older adults in cohort 1 than among
younger adults. Indeed, estimated welfare effects are not so strong among youths,
especially boys, as reported in the appendix.
Historical village and clan size

22

If the recent quality improvement of primary schools is systematically differentiated bychiefly-village status, the potential bias through distinct academic performance could beconsiderable in cohorts 3 and 4.

27
In the adults’ schooling models, unobserved historical village/clan size positively
correlated with chiefly village/clan status could cause upward bias, because of theexternality/network effects discussed above. In particular, if chiefly villages recently
augmented in size more rapidly than other villages, and both chiefly and non-chiefly
clans in the chiefly village augmented in the same way, this could lead to an increase insecondary schooling in chiefly villages, but not in chiefly clans; that is, chiefly-village
status could pick up network effects not related to a chiefly network. This potential bias isunlikely to be a concern for the following reasons. First, such demographic patterns areunlikely to be caused by female marriage migration, because there is no significant
difference in chiefly-village/-clan status of females in their original clans and others
across cohorts. Second, urban migration could be more common in chiefly villages/clans
than others, if chiefly network drives urban employment and tertiary education. Third,
explaining why externality/network effects matter for secondary completion, but notenrollment is not straightforward; the appendix offers counterevidence among youths. 23
8. Conclusion
Using original household survey data in rural Fiji, which contain uniqueinformation about chiefly-group status, and Fijian coups as a natural experiment, thispaper showed evidence that chiefly labor networks in non-farm occupations thatoriginated from the British colonial policy has persistently affected Fijians’ schooling.
Most male Fijians outside the chiefly network have been discouraged from making
education investments over time. As non-farm employment plays an increasing role in23

I also repeated the analysis using current village size interacted with cohort dummies asproxies for historical size, finding very similar results for chiefly status; these proxies
could contain systematic errors which are generally unsigned (cf. historical school
access).

28
earnings, income inequality has augmented among Fijians. Without being directly
constrained by this chiefly institution, Indians and Female Fijians outperformed maleFijians in higher education. As such, an indigenous chiefly system shaped the country’s
economic growth and inequality through institutional developments in its colonial history.
Appendix
This appendix discusses estimation results of other covariates (results not shown,
unless otherwise noted). First, significant results in the adults’ employment models arelimited to positive and negative effects of household-head status and distance to local
market (the latter indicates major employment opportunities in towns).
Second, the estimated coefficients of parents’ education in the schooling models
for adults and youths are reported in Table A2 (for adults, parents’ secondary incomplete
and complete or above are combined to secondary incomplete or above, because
secondary completion is uncommon among them). Reflecting the rapid progress offemale schooling, the higher the education level, the more important is mother’s
secondary education compared to father’s. Specifically, although only father’s secondary
education positively affects both male and female adults’ primary-school completion or
above (results not shown), father’s and mother’s secondary education, respectively,
influence male and female adults’ secondary enrollment, and only mother’s matters forboth adults’ secondary completion, with greater effects on females than males. In contrast,
mother’s secondary completion matters for boys’ secondary enrollment, but not girls’.
Third, consistent with general education development, secondary schooling – bothcompletion and enrollment – is more common among younger adults (both males andfemales). Distance to secondary school (proxies) negatively affects secondary-school

29
enrollment, but not completion, among female adults, but not male, in cohorts 1 and 2,
but not cohort 3. Hence, secondary enrollment was constrained by school access, andrural-school expansion after independence significantly helped female schooling. This
does not mean that school access did not matter for young adults, rather, that their school
access affecting enrollment is captured by district dummies (for secondary completion
this is the case over time). This also serves as counterevidence against significant
systematic errors in the proxies for historical school access.
Fourth, the following results are found for youths’ secondary schooling. While
girls’ schooling is constrained by non-land assets (positive impacts), but not land,secondary schooling is strived for by boys with a smaller prospect for land accumulation(total clan land, not household assets, has negative effects). Girls with a greater numberof younger sisters are less likely to enroll (presumably to help care them), and boys inlarger clans are more likely to enroll (consistent with the externality/network effects). As
found among young adults, access to secondary school not captured by district dummies
is not a constraint. At the same time, girls’ secondary schooling is negatively affected bydistance to primary school (similar patterns are found for male adults’ secondary
enrollment). This is probably because school access (within the district) affects
delay/repetition in primary school.
Acknowledgments
I wish to thank my field team – Jonati Torocake, Viliame Manavure, Viliame
Lomaloma, and 16 enumerators – for their advice, enthusiasm, and exceptional efforts onbehalf of this project. Special thanks are owed to the Fijians of the region who sowillingly participated in the survey. The Cakaudrove Provincial Office in Fiji offered30
valuable institutional support for this project. This paper has benefited significantly fromthe comments and suggestions of Yutaka Arimoto, Takashi Kurosaki, Chiaki Moriguchi,
and seminar participants at Hitotsubashi University. This research has been madepossible through support provided by the Sumitomo Foundation, the Japan Society for
the Promotion of Science, and the Ministry of Education, Culture, Sports, Science andTechnology in Japan. Any errors of interpretation are solely the author’s responsibility.
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34
Table 1. Employment, income, and inequality decomposition by income
source by cohort.
A. Employment and income.

All

Age of household head
Cohort 1:
Cohort 2: Cohort 3:
age 50 orage 35-49 age 20-34
above

Permanent wage labor
0.16
0.13
0.17
0.22
employment (0/1)
Total earned income per capita (F$)
All
2281
2337
2083
2727
(2647)(2951)(2110)(2825)
Without permanent wage
2113
2178
1991
2235
labor employment(2575)(2952)(2123)(1981)
With permanent wage
3168
3359
2521
4408
labor employment(2847)(2758)(2006)(4339)
No. observations
903
455
349
99
B. Inequality decomposition by income source.
IncomeSourceGini cor- Inequality
Elasticity
share
Gini
relation
share
All:
Crop
0.62
0.62
0.91
0.67
0.047
Fishing
0.11
0.72
0.66
0.10
-0.011
Handicraft
0.08
0.70
0.42
0.05
-0.037
Permanent wage labor
0.10
0.92
0.60
0.11
0.004
Other
0.08
0.81
0.62
0.08
-0.004
Total
0.53
Cohort 1:
Crop
0.63
0.66
0.94
0.69
0.055
Fishing
0.11
0.74
0.69
0.10
-0.010Handicraft
0.10
0.71
0.47
0.06
-0.041
Permanent wage labor
0.08
0.93
0.62
0.08
0.001
Other
0.09
0.84
0.63
0.08
-0.005Total
0.57
Cohort 2:
Crop
0.63
0.58
0.90
0.68
0.051
Fishing
0.11
0.69
0.61
0.10
-0.014
Handicraft
0.07
0.66
0.40
0.04
-0.032
Permanent wage labor
0.10
0.90
0.50
0.09
-0.006
Other
0.09
0.77
0.63
0.09
0.000
Total
0.48
Cohort 3:
Crop
0.54
0.58
0.79
0.53
-0.006
Fishing
0.12
0.71
0.64
0.12
-0.004
Handicraft
0.05
0.71
0.26
0.02
-0.033
Permanent wage labor
0.22
0.88
0.67
0.28
0.058
Other
0.06
0.72
0.47
0.05
-0.017Total
0.47
Note: Standard deviations are in parentheses.

35
Table 2. Employment and education attainment of working adults.
Original clans
All
clans

All

Male

Female

0.06
0.05
0.08
0.06

0.07
0.05
0.09
0.07

0.09
0.06
0.11
0.09

0.04
0.04
0.04
0.02

41%
9%
13%
3%
26%
8%

42%
11%
9%
3%
30%
5%

47%
7%
5%
3%
32%
5%

25%
25%
25%
0%
21%
4%

Employment
Permanent wage labor (0/1)
Cohort 1: age 50-65
Cohort 2: age 35-49
Cohort 3: age 20-34
Occupational distribution (unweighted)
Office work/skilled labor
Tourism
Teacher
Military
Unskilled labor
Other
Occupational distribution (weighted by earnings)
Office work/skilled labor
Tourism
Teacher
Military
Unskilled labor
Other
Education attainment
Primary incomplete or below (0/1)
Primary complete (0/1)
Secondary incomplete (0/1)
Secondary complete or above (0/1)

50%
7%
15%
3%
17%
8%

55%
8%
9%
3%
19%
6%

61%
6%
6%
3%
19%
6%

28%
18%
28%
0%
21%
5%

0.17
0.29
0.34
0.20

0.17
0.29
0.33
0.20

0.18
0.31
0.34
0.17

0.17
0.26
0.31
0.26

No. observations
Cohort 1: age 50-65
Cohort 2: age 35-49
Cohort 3: age 20-34

2334
23%
36%
41%

1712
22%
34%
43%

1093
23%
37%
41%

619
22%
30%
48%

36
Table 3. Permanent employment of working adults in original clans.
All
Male
No
Yes
No
Yes
(1)
(2)
(3)
(4)
A1. Models with district dummies - chiefly-village status.
Chiefly village (0/1)
0.042
0.038
0.060
0.052
(0.026)(0.027)(0.037)(0.038)R-squared
0.109
0.120
0.103
0.116
No. observations
1617
1591
1019
1009
A2. Models with district dummies - chiefly-village status by cohort.
Chiefly village × cohort 1
0.042
0.040
0.087 **
0.080 *
(0.028)(0.028)(0.042)(0.042)Chiefly village × cohort 2
0.065 **
0.065 **
0.091 *** 0.088 **(0.026)(0.026)(0.035)(0.035)
Chiefly village × cohort 3
0.023
0.015
0.012
0.001
(0.022)(0.022)(0.030)(0.029)B1. Models with village dummies - chiefly-clan status.
Chiefly clan (0/1)-0.021-0.017-0.068-0.071(0.049)(0.047)(0.061)(0.063)
Female (0/1)
0.014
0.001
(0.018)(0.017)
Primary complete (0/1)
0.029
0.053
(0.025)(0.037)
Secondary incomplete (0/1)
0.032
0.057
(0.026)(0.038)
Secondary complete or above
0.123 ***
0.159 ***
(0/1)
(0.034)(0.047)R-squared
0.149
0.166
0.161
0.182
No. observations
1071
1049
699
689
B2. Models with village dummies - chiefly-clan status by cohort.
Chiefly clan × cohort 1-0.127 ** -0.127 **-0.149 ** -0.159 **(0.052)(0.053)(0.075)(0.077)
Chiefly clan × cohort 2
0.087
0.092
0.028
0.032
(0.077)(0.075)(0.096)(0.095)
Chiefly clan × cohort 3-0.035-0.030-0.091-0.098(0.053)(0.052)(0.067)(0.065)
Education*10% significance, **5% significance, ***1% significance.
Note: OLS estimates are shown. Robust standard errors are in parentheses.
Standard errors in panels A1 and B1 are clustered by village and clan,
respectively. Other controls not shown here are age, household head dummy, ageof household head, household size, proportion of children (<15), proportion ofelderly (>65), total clan land (log), clan size (log), distance to local market (log),
village size (log), and constant. Female dummy is also included in columns (1)and (2) of panels A1, A2, and B2 and education variables are also included incolumns (2) and (4) of panels A1, A2, and B2.

37
Table 4. Secondary-school completion of working adults in original clans.
All
Male
No
Yes
No
Yes
(1)
(2)
(3)
(4)
A1. Models with district dummies - chiefly-village status.
Chiefly village (0/1)
0.052 ** 0.086 ***0.064 *** 0.094 ***(0.021)(0.028)(0.019)(0.027)R-squared
0.194
0.194
0.076
0.122
No. observations
1596
972
1012
648
A2. Models with district dummies - chiefly-village status by cohort.
Chiefly village × cohorts 1&2-0.018-0.004-0.0100.003(0.022)(0.031)(0.028)(0.036)
Chiefly village × cohort 3
0.147 *** 0.205 ***0.180 *** 0.239 ***(0.038)(0.048)(0.049)(0.062)B1. Models with village dummies - chiefly-clan status.
Chiefly clan (0/1)-0.002-0.0360.023
0.005(0.043)(0.050)(0.048)(0.064)
Female (0/1)
0.076 *** 0.088 ***(0.021)(0.029)R-squared
0.130
0.189
0.086
0.129
No. observations
1485
932
939
590
B2. Models with village dummies - chiefly-clan status by cohort.
Chiefly clan × cohorts 1&2-0.032-0.057-0.030-0.035(0.046)(0.053)(0.056)(0.066)
Chiefly clan × cohort 3
0.038-0.0070.100
0.060(0.070)(0.083)(0.087)(0.103)
Parents' education

No(5)

Female
Yes
(6)

0.030
(0.035)
0.206
584

0.059
(0.047)0.313
319-0.039(0.038)0.108 *
(0.063)-0.044(0.058)0.155 **
(0.075)-0.021(0.064)-0.092(0.072)

0.240
516

0.363
300

0.003
(0.074)-0.052(0.120)-0.030(0.092)-0.167(0.141)*10% significance, **5% significance, ***1% significance.
Note: OLS estimates are shown. Robust standard errors are in parentheses. Standard errors in panels
A1 and B1 are clustered by village and clan, respectively. Other controls not shown here are age, totalclan land (log), and constant. Distance to primary and secondary schools (interacted with cohort
dummies) (log) are also included in panel A1 and A2. Female dummy is also included in columns (1)and (2) of panels A1, A2, and B2 and parents' education variables are also included in columns (2), (4),and (6) of all panels.

38
Table 5. Secondary-school enrollment of working adults in original clans and youths.
Male/BoyAll
Female/Girl
No
Yes
No
Yes
No
Yes
(1)
(2)
(3)
(4)
(5)
(6)
A1. Adults' models with district dummies - chiefly-village status.
Chiefly village (0/1)
0.047 *
0.062 *
0.052 *
0.051
0.038
0.103 *
(0.027)(0.031)(0.028)(0.032)(0.036)(0.051)R-squared
0.194
0.194
0.170
0.189
0.250
0.237
No. observations
1596
972
1012
648
584
319
A2. Adults' models with district dummies - chiefly-village status by cohort.
Chiefly village × cohort 1-0.034
0.001
0.009
0.036
(0.045)(0.064)(0.058)(0.075)0.065 *
0.065
0.016
0.017
0.067
0.120
Chiefly village × cohort 2a(0.039)(0.053)(0.050)(0.066)(0.052)(0.078)Chiefly village × cohort 3
0.075 **
0.091 **
0.110 **
0.093
0.007
0.087
(0.036)(0.044)(0.047)(0.057)(0.055)(0.071)B1. Adults' models with village dummies - chiefly-clan status.
Chiefly clan (0/1)
0.024
0.014
0.066
0.082
-0.035-0.135 *(0.050)(0.046)-0.050-0.055(0.079)-0.080Female (0/1)
0.055 **
0.048
(0.022)(0.030)R-squared
0.193
0.203
0.164
0.191
0.275
0.284
No. observations
1485
947
966
638
512
295
B2. Adults' models with village dummies - chiefly-clan status by cohort.
Chiefly clan × cohort 1-0.059-0.047-0.0060.042(0.077)(0.092)(0.103)(0.116)
0.103
0.075
0.079
0.067
0.037
-0.057
Chiefly clan × cohort 2a(0.071)(0.094)(0.090)(0.121)(0.093)(0.128)Chiefly clan × cohort 3
0.019
0.015
0.104
0.120
-0.126-0.230 *(0.059)(0.072)(0.071)(0.084)(0.104)(0.135)
C. Youths' models with district dummies - chiefly-village status.
Chiefly village (0/1)
0.023
0.066
0.029
0.085
0.031
0.064
(0.049)(0.057)(0.080)(0.114)(0.068)(0.075)R-squared
0.120
0.120
0.111
0.113
0.187
0.226
No. observations
506
389
258
201
226
171
D. Youths' models with village dummies - chiefly-clan status.
Chiefly clan (0/1)
0.042
0.005
0.123
0.066
-0.086-0.132(0.120)(0.143)(0.125)(0.182)(0.183)(0.225)
Girl (0/1)
0.119 **
0.122 **
(0.048)(0.057)R-squared
0.181
0.187
0.233
0.275
0.237
0.249
No. observations
480
365
235
173
194
132
*10% significance, **5% significance, ***1% significance.
a
Cohorts 1 and 2 combined in columns (5) and (6).
Note: OLS estimates are shown. Robust standard errors are in parentheses. Standard errors in panels
A1/C and B1/D are clustered by village and clan, respectively. Other controls in panels A1, A2, B1, andB2 not shown here are age, total clan land (log), and constant. Distance to primary and secondary
schools (interacted with cohort dummies) (log) are also included in panel A1 and A2 and female dummyis also included in columns (1) and (2) of panels A1, A2, and B2. Other controls in panels C and D notshown here are age, no. of elder sisters, younger sisters, elder brothers, and younger sisters, age ofhousehold head, household size, land (m2, log), non-land assets (log), total clan land (acre, log), clan
size (log), and constant. Distance to primary and secondary schools (log) and village size (log) are also
included in panel C and girl dummy is also included in columns (1) and (2) of panel C. Parents' education
variables are also included in columns (2), (4), and (6) of all panels.
Parents' education

39
Figure 1. Fijian chiefdom structure.
(District 1)
Vanua 1
(Village 1)(Village 2)
Yavusa 1

Mataqali 1

Tokatoka 1

Household 1

Yavusa 2

Mataqali 2

Tokatoka 2Household 2Note: Chiefly village and chiefly clan (mataqali) are bolded.

40

0

.2

Proportion
.4
.6

.8

1

Figure 2. Education attainment of working adults in all clans by sex and cohort.

Age 50-65

35-49

20-34

male
primary incomplete or below
secondary incomplete

50-65

35-49

20-34

femaleprimary complete
secondary complete or above

41
Figure 3. Secondary-school enrollment or completion of youths by sex and age.
0.70

0.65

0.60

Proportion

0.55

0.50

0.45

0.40

0.35

0.30
14

15

16

17
Age

All

Male

Female

18

19

42

Table A1. Descriptive statistics of explanatory variables.
A. Households.

Chiefly status
Chiefly village (0/1)
Chiefly clan (0/1)
Household characteristics
Age of household head
Household size
Proportion of children (<15)
Proportion of elderly (>65)
Land (acre)Non-land assets (F$)
Clan characteristics
No. households in the clan
Village characteristics
No. households in the village
Distance to primary school (km)Distance to secondary school (km)Distance to local market (min)B. Working adults (age 20-65) and youths (age 14-19).
Adults in original clans
Adultsin allAll
Male Female
clans
0.34
0.14
51.4 (14.6)
5.5 (2.7)
0.32 (0.24)
0.09 (0.21)
2.9 (4.8)
1844 (4455)
21.0 (14.3)
53.3 (21.3)
1.5 (2.8)
19.2 (28.2)
81.4 (68.7)

No. observations
903
Note: Standard deviations are in parentheses.

Female (0/1)
AgeHousehold head (0/1)
No. of elder brothers
No. of younger brothers
No. of elder sisters
No. of younger sisters
No. observations
Mother's education
Primary incomplete or below (0/1)
Primary complete (0/1)
Secondary incomplete (0/1)
Secondary complete or above (0/1)
Father's education
Primary incomplete or below (0/1)
Primary complete (0/1)
Secondary incomplete (0/1)
Secondary complete or above (0/1)
No. observations

0.48
38.8
(12.8)
0.31

0.36
38.3
(13.0)
0.39

0.00
38.9
(12.7)
0.57

1.00
37.3
(13.5)
0.07

Youths
0.46
16.4
(1.7)

2334

1712

1093

619

0.25 (0.51)
1.23 (1.26)
0.37 (0.67)
1.51 (1.37)
545

0.63
0.25
0.09
0.03

0.61
0.26
0.10
0.03

0.63
0.26
0.09
0.03

0.58
0.28
0.12
0.03

0.17
0.36
0.41
0.06

0.62
0.26
0.07
0.05
1543

0.60
0.28
0.07
0.05
1146

0.61
0.28
0.07
0.05
763

0.59
0.28
0.09
0.04
383

0.19
0.40
0.31
0.10
430

43
Table A2. Effects of parents' education on schooling of working adults in originalclans and youths unreported in Tables 4 and 5.

(1)

Male
/Boy
(2)

Female/Girl(3)0.089 **
(0.040)0.043(0.044)0.208 ***
(0.060)0.099(0.065)0.104 *
(0.053)0.008(0.053)0.136 *
(0.078)0.081(0.085)0.104(0.077)0.066(0.083)0.379 ***
(0.108)0.118(0.100)0.018(0.045)0.053(0.047)0.027(0.055)0.123 **
(0.060)0.016(0.054)0.042(0.050)-0.015(0.073)0.149 **
(0.065)0.042(0.087)0.049(0.085)0.153 *
(0.088)0.062(0.110)0.100(0.077)0.076(0.075)0.144 *
(0.078)0.077(0.079)0.183(0.138)0.010(0.131)

0.023(0.151)-0.082(0.170)0.119(0.133)0.039(0.171)0.359 *
(0.200)-0.073(0.226)0.157(0.184)0.113(0.176)0.210(0.190)0.148(0.176)0.013(0.265)0.080(0.221)All
A. Adults' secondary-school completion
Mother's primary complete (0/1)
Father's primary complete (0/1)
Mother's secondary incomplete or above (0/1)
Father's secondary incomplete or above (0/1)
B. Adults' secondary-school enrollment
Mother's primary complete (0/1)
Father's primary complete (0/1)
Mother's secondary incomplete or above (0/1)
Father's secondary incomplete or above (0/1)
C. Youths' secondary-school enrollment
Mother's primary complete (0/1)
Father's primary complete (0/1)
Mother's secondary incomplete (0/1)
Father's secondary incomplete (0/1)
Mother's secondary complete or above (0/1)
Father's secondary complete or above (0/1)*10% significance, **5% significance, ***1% significance.
Note: Columns (1)-(3) of panels A, B, and C, respectively, match columns (2), (4), and (6)in panel B1 of Table 4, panel B1 of Table 5, and panel D of Table 5.