how geographically concentrated is poverty in fiji?

How geographically concentrated is poverty in Fiji?apv_1485 205..217

Laura Pabon,* Nithin Umapathi* and Epeli Waqavonovono†*Department of Social Protection for East Asia and Pacific, World Bank, Washington, DC, USA.

Email: [email protected] (L. Pabon); [email protected] (N. Umapathi)†Household Surveys Unit, Fiji Bureau of Statistics, Suva, Fiji.

Email: [email protected]

Abstract: In this paper, we present highly disaggregated estimates of expenditure-based poverty inFiji using data from the 2007 national census and 2008–2009 Household Income and ExpenditureSurvey. Predicted poverty is estimated at provincial and tikina levels. Poverty in Fiji is marked byconsiderable spatial heterogeneity that cannot be gauged by the division level household surveyestimates revealing pockets of poverty even within relatively well-off regions. Predicted poverty ishighest in Cakaudrove province in Northern Division. Most strikingly, we find that 50% of all thepoor in Fiji are concentrated in just 6 out of 85 tikinas, namely Suva, Labasa, Ba, Naitasiri, Vuda andNadi. This finding has important implications for efficiency of targeted poverty alleviation pro-grammes. We also focus on squatter settlements for which poverty headcount estimates using theHousehold Income and Expenditure Survey are not feasible. We find these settlements have rates ofpoverty headcount ratio that range from 38–55% depending on the Division the squatter settlementis located in; this range is significantly higher compared with the average urban poverty estimated at26% and raises important social policy issues for addressing urban poverty.

Keywords: Fiji, national census, poverty mapping, poverty measurement

Introduction

The most recent national household survey-based estimates of poverty in Fiji are based onincome (Narsey et al., 2010). This paper aims tocomplement and go further than those estimatesby developing expenditure poverty estimatesand presenting the spatial dispersion of povertyin Fiji at a more local scale. As a general rule,poverty estimates using household surveys aretypically not designed to estimate poverty inci-dence at low geographic levels. In the case ofFiji, this rarely goes lower than at the scale ofrural or urban administrative divisions. Wepresent provincial and tikina1 level povertyusing the small area estimation method pro-posed by Elbers et al. (2003) utilising the FijiHousehold Income and Expenditure Survey(HIES 2008/2009) and the national census of2007. In the first stage, we estimated a model ofhousehold consumption using the HIES. Thevariables used in the model are restricted tothose that are available in both the survey and

the census; the data sources are carefully com-pared to ensure this is the case. In the secondstage, the estimated parameters are applied tothe census data. This provides an estimate ofconsumption per capita for every household2 inthe census that is used along the poverty line toestimate poverty measures at various levels ofaggregation. The estimates are then merged witha map to facilitate presentation and visualanalysis of poverty patterns.

Poverty maps provide a powerful visualdepiction of poverty pockets that can help toensure that anti-poverty programmes reach thepoor. Knowing the geographical distributionof the poor across the country helps to ensurethat anti-poverty programmes reach the poorthrough improved targeting of social pro-grammes. Elbers et al. (2007) showed that geo-graphic targeting in the case of Cambodia basedon poverty maps significantly reduced the costof reducing poverty by about one-third the costof a uniform transfer. Maps can be informativefor the planning process at a subnational level

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Asia Pacific Viewpoint, Vol. 53, No. 2, August 2012ISSN 1360-7456, pp205–217

© 2012 Victoria University of Wellington doi: 10.1111/j.1467-8373.2012.01485.x

where maps may assist in regional planningefforts. Countries can also use small area esti-mation (poverty maps) to analyse existing pro-grammes or resource allocation and assess theireffectiveness. For example, the poverty map canbe overlaid with the administrative informationon governmental aid flows across Fiji to assessthe extent of efficiency in targeting aid to theneediest areas. Another key application ofpoverty maps is in determining the funding for-mulas that will cause interventions to varyacross areas depending on the level of povertyand other indicators. For example, in Kenya, theallocation formula used in the ConstituencyDevelopment Fund has been revised so that25% of the allocations are based on the inci-dence of poverty. In Bulgaria, the poverty mapsaccount for one of five formal criteria used inallocating social infrastructure projects amongmunicipalities (Bedi et al., 2007). Such povertymaps have been estimated in other countries,but the only poverty map estimated in thePacific was for rural Papua New Guinea in 2004(Allen et al., 2005; Gibson et al., 2005). There isa growing role for state-provided social safetynets in the Pacific, and regional budgetary allo-cations are often not determined by regionalneeds. Poverty mapping can provide an effec-tive tool for improving impact by informing aidallocation.

The official income poverty rates based on2008/2009 HIES for Fiji are 19% and 43%in urban and rural areas, respectively (Narseyet al., 2010). Instead of relying on income-based poverty, we estimated expenditure-basedwelfare and compared it with a cost-of-basic-needs poverty line, the resulting poverty esti-mates are 26% and 44% in urban and rural areas,respectively, indicating a higher expenditure-based poverty rate in urban areas. Both incomeand expenditure estimates show a similarchange over time since the previous round ofHIES (in 2002/2003). Although the levels ofexpenditure-based poverty are higher, regionaland other poverty profile rankings betweenincome and expenditure-based poverty accordvery well.

Our small area estimates of poverty indicatorsrevealed pockets of poverty even within rela-tively well off divisions. Poverty incidence ishighest (above 50%) in the provinces of Ra,Cakaudrove and Macuata. An overwhelming

majority of the poor resides in Ba, which is alsothe most populous province of the country.Most strikingly, we find that almost 50% of allthe poor are concentrated in just 6 out of 86tikinas, namely Suva, Labasa, Ba, Naitasiri,Vuda and Nadi. This finding has considerableimplications for efficiency of targeted povertyalleviation programmes, and especially relevantgiven that the Fiji government has stated povertyreduction as a major developmental objective.The following section briefly explains the dataused for the new consumption-based povertyestimates and mapping. The third section brieflyexplains the new expenditure-based povertymeasures. The fourth section reviews the meth-odology, and finally, the fifth section presentsthe spatial dimension of poverty using nationalcensus and HIES to estimate poverty at provinceand tikina (i.e. district) levels.

Data

The small area prediction relies on the popu-lation census conducted during 2007. Thequestionnaire has two parts: a dwelling ques-tionnaire and an individual questionnaire. Thecountry was divided into 1602 enumerationareas (EAs), and data were collected on 175 246households composed of 837 271 people. Theimputation model is based on the HIES 2008/2009 that includes 3570 households of which1662 are urban and 1911 are rural. Both theHIES and the national census collected informa-tion on household characteristics including:employment status, education level, housingand fixed assets owned by the household. Thekey difference is that the HIES collected detailedexpenditure information on over 2000 items.These goods span several categories, namelyfood (purchased and self-produced), personalcare and hygiene, clothing, education, health,services, transportation, housing and durablegoods purchases. The food information is col-lected from a two-week diary; other expendi-tures have a recall period of four weeks. Theconsumption aggregate constructed from theHIES follows standard practices described inDeaton and Zaidi (2002).

Administratively, Fiji is divided into 4 divi-sions, 15 provinces and 86 tikinas. The survey isdesigned to be representative at the level ofstrata (division-rural-urban level). This means

L. Pabon et al.

© 2012 Victoria University of Wellington206

that the survey is not able to guarantee consis-tent poverty estimates at lower levels of aggre-gation (such as the province or tikina).

During the preparation stage, after carefulcross checks of both the census and the HIES,we identified variables common to bothdatasets. We compared the mean values ofthese variables in Table 1; of these, there were40 variables from the household section, 6related to characteristics of heads-of-householdand the last 9 were on household demographiccharacteristics.

We also constructed tikina level data that wasmerged with the household level data. Forselected household level variables from 2007census, we derived EA level mean values, whichwere merged with the HIES at the EA level.3 Forexample, we constructed an EA level ratio ofhouseholds that use kerosene for cooking, aratio of households with walls made of concreteand other variables described in the tablescontaining the consumption model output.Note that these variables are comparable byconstruction and therefore not presented inTable 1.

The EA level variables were complementedby additional location-specific information thatwas obtained from the hotel census and the EAcharacteristics collected during the census. Weadded number of hotel beds and number ofpeople employed by hotels in each EA. We alsoincluded variables that capture the nine arealevel socioeconomic classes: high, middle, low,squatters settlements, housing authority lands,mixed class, urban village, institutional andindustrial areas.

Expenditure-based poverty line and estimates

In this paper, we rely on a newly constructedwelfare indicator and a new poverty line basedon reported expenditures. It is widely agreedthat there are several advantages to usingexpenditures instead of income in low incomesettings where subsistence sector plays asignificant role generating family resources. Theconsumption aggregate includes all food expen-ditures and self-produced food valued atmarket prices. Consumption of non-food itemsincludes expenditures on personal care andhygiene items, clothing, utilities, transportationand other non-food items. The consumption

aggregate excludes expenditures on durablegoods and hospitalisation. Rent paid is countedas housing expenditures and counted as con-sumption. However, the housing rental marketis not well developed in Fiji especially in ruralareas. Therefore, rent is imputed for homeowners based on a hedonic regression andincluded in the consumption aggregate. Theconsumption aggregate is adjusted for variationin the prices of food across rural and urbanlocations. The prices are calculated usingreported quantities and total value of purchasedgoods in the HIES 2008/2009. The constructedindices reflect cost of consumption basket rela-tive to the national median prices, and esti-mated to be 0.96 in urban areas and 1.04 inrural areas. The poverty line is based on the costof basic needs for adequate nutrition at 2100calories per capita per day. The cost of the foodbundle is fixed across Fiji – there is only onefood poverty line. However, we set distinctpoverty lines for rural and urban areas by allow-ing different non-food requirements acrossthese areas as reflected by much higher share ofnon-food expenditure among urban house-holds. The share of food used is 41% and 53%in urban and rural areas, respectively. Theresulting poverty lines are $1830 in rural areasand $2349 in urban areas. In comparison, theofficial (income based) poverty lines in the sameyear are $2140 in rural and 2420 in urbanareas. The national poverty results are reviewedin Table 2.

Estimation methodology

The small area estimation method developed byElbers et al. (2002, 2003) allows householdsurvey data to be combined with a census datathrough an income or expenditure model.

We follow the standard setup and first esti-mate a household expenditure model using theHIES:

ln ,y x c ch( ) = ′ + +β η ε (1)

where ln(y) denotes the logarithmic per capitaexpenditure, x the vector of explanatory vari-ables, b the vector of regression coefficients, hc

the EA-specific random effect and ech thehousehold-specific random effect. The subscript

Poverty mapping in Fiji

© 2012 Victoria University of Wellington 207

Table 1. Summary statistics of common household variables for the survey and census

Variable Survey Census Type ofvariable(HIES 2008) 2007

Urban 0.506 0.510 BinaryHave car 0.190 0.266 BinaryHave carrier/track 0.038 0.059 BinaryHave outboard motor 0.023 0.032 BinaryHave brush cutter 0.212 0.250 BinaryHave refrigerator 0.620 0.613 BinaryHave washing machine 0.464 0.471 BinaryHave gas/electric stove 0.563 0.560 BinaryHave radio 0.873 0.817 BinaryHave telephone 0.414 0.383 BinaryHave computer 0.174 0.170 BinaryHave video/TV 0.751 0.730 BinaryMaterial of the walls

Concrete, brick or cement 0.396 0.393 BinaryWood 0.272 0.241 BinaryTin or corrugated iron 0.302 0.337 BinaryTraditional bure 0.017 0.018 BinaryMakeshift or improvised materials 0.004 0.006 BinaryOther materials 0.009 0.005 Binary

Household size 4.649 4.699 ContinuousNumber of rooms 3.329 3.716 BinarySource of light

Electricity 0.866 0.786 BinaryBenzene lamp 0.005 0.024 BinaryKerosene lamp 0.124 0.180 BinaryOther source of light 0.006 0.010 Binary

Tenure of living quarterOwn living quarter 0.729 0.740 BinaryRent house from private landlord 0.149 0.142 BinaryRent house from public rental board 0.006 0.016 BinaryOccupy government or institutional housing 0.040 0.037 BinaryHouse occupy by leave of employer 0.013 0.015 BinaryCaretaker 0.017 0.018 BinaryOther tenure 0.046 0.032 Binary

Cooking fuelWood 0.475 0.421 BinaryKerosene 0.231 0.260 BinaryLPG (Fiji gas) 0.283 0.280 BinaryElectricity 0.011 0.035 Binary

Type of toiletFlush toilet for exclusive use 0.750 0.703 BinaryFlush toilet shared with other households 0.129 0.121 BinaryWater sealed for exclusive use 0.024 0.043 BinaryPit latrine 0.095 0.126 BinaryOther toilet facilities 0.002 0.001 Binary

Household head is Fijian 0.541 0.511 BinaryAge of household head 47.840 46.580 ContinuousEducation of household head

Household head with no education 0.030 0.000 BinaryHousehold head with primary 0.150 0.190 BinaryHousehold head with secondary 0.633 0.633 BinaryHousehold head with tertiary 0.184 0.177 Binary

Proportion of women in the household 0.495 0.490 ContinuousProportion of household members employed 0.223 0.081 ContinuousProportion of household members with no education 0.123 0.083 ContinuousProportion of household members with lower education 0.206 0.243 ContinuousProportion of household members with secondary education 0.536 0.530 ContinuousProportion of household members with post-secondary education 0.033 0.021 ContinuousProportion of household members with diploma education 0.077 0.092 ContinuousProportion of household members with higher education 0.021 0.032 ContinuousDependency ratio 0.421 0.479 ContinuousNumber of observations 3573 172 158

Note: The dependency ratio is equal to the number of individuals aged below 14 divided by the number of adults aged above15 years.Source: Calculations based on HIES 2008/2009 and Census 2007.

L. Pabon et al.


ch refers to household h living in enumerationarea c. The explanatory variables x must beavailable in both census and survey. Thehousehold-specific errors are assumed to beindependent from each other, and independentfrom the ea error.

Once all the parameters of interest have beenidentified, the dependent variable is imputedinto the census:

ln ,�� y x c ch( ) = ′ + +β η ε (2)

where ��β , ��ηc and ��εch denote the estimates for b,hc and ech.

For accurate estimation of the standard errors,we use 100 repeated Monte-Carlo simulations.In each round, a simulated regression coeffi-cient ��β t is drawn (from its estimated distribu-tion), where t denotes the t-th round ofsimulation. Further, ��ηc

t and ��εcht are drawn from

their estimated distributions, resulting in a simu-lated idiosyncratic and household error for eachhousehold in the census. Each round of simula-tion yields a new estimate of poverty indicators.By taking the average and standard deviationover the t different simulated values of bothpoverty headcount ratio and gap, we obtainboth the point estimate and the correspondingstandard errors.

A separate expenditure model was estimatedfor each of the divisions in the HIES, namelycentral, eastern, western and northern divisions.Specifically, we regressed a log of adult equiva-lent expenditure on a set of manually selectedhousehold and area characteristics. The selec-tion of variables was guided by a priori consid-erations about determinants of poverty, extent ofregression fit and significance of explanatoryvariables. Thus, different divisions have differentsets of explanatory variables in the expendituremodels. Overall, to avoid overfitting, we tended

to models that are both relatively small androbust. Using the HIES-based parameter esti-mates, consumption was estimated for house-holds in the census given the observedhousehold characteristics. Table 3 presents theregressions4 of the logarithm of per adultequivalent expenditure. It is found that all esti-mates of the model parameters make economicsense (have expected signs). For example,households of large size are more likely to havelower per adult equivalent expenditure thanhousehold of small size. As expected, assets arepositively correlated with expenditures. House-holds who have more working members ormembers with higher education tend to havehigher expenditure. Finally the R-squaredvalues are quite encouraging with the rangefrom 0.5 to 0.6.

For each of the divisions, province and tikina,the poverty headcount ratio and contribution ofthe poor were estimated.5 The small area esti-mates showed acceptable margins of errordown to the tikina level.

Poverty estimates

Division and province level estimates

We can estimate poverty at the level of a divi-sion and strata from the HIES. We can triangu-late the results between the poverty map resultsfor each division or strata and results from theHIES. Table 4 shows that they are reasonablyclose at divisional level of aggregation which isnot surprising given that the census and thesurvey occurred around the same time. It isreassuring that these estimates, which can beestimated from both census and the HIES, arenot statistically different. We will not be able todo this comparison beyond the division or stratalevel estimates.

The poorest region is the northern divisionwith a poverty rate around 53%. The centraldivision is characterised by the lowest levels ofpoverty; about 24% of the population livesbelow poverty line. Although the western divi-sion is not the poorest, it is the biggest contribu-tor in terms of the number of poor as 44% of allpoor live in this division. Similarly, despitebeing the least poor division, the central divi-sion accounts for almost a third of all the poor inthe country. The eastern division has the lowest

Table 2. National poverty headcount ratios

Expenditure-based poverty(%)

Income-basedpoverty (%)(FIBOS 2010)

Rural population 44 43Urban population 26.2 19National 35.2 31



Tabl

e3.

Log

ofad

ult

equi

vale

ntex

pend

iture

regr

essi

ons

Dep

ende

ntva

riab

les

Cen

tral

East

ern

Nor

ther

nW

este

rnEs

timat

eSt

anda

rder

ror

Estim

ate

Stan

dard

erro

rEs

timat

eSt

anda

rder

ror

Estim

ate

Stan

dard

erro

r

Hou

seho

ldva

riab

les

Urb

an0.

173*

**0.

034

NA

NA

NA

NA

NA

NA

Hav

eca

r0.

254*

**0.

031

NA

NA

0.28

6***

0.07

6N

AN

AH

ave

brus

hcu

tter

0.07

8***

0.03

NA

NA

NA

NA

NA

NA

Hav

eco

mpu

ter

0.22

8***

0.03

4N

AN

A0.

149

0.10

20.

158*

**0.

037

Hav

evi

deo/

TV0.

165*

**0.

036

NA

NA

NA

NA

0.11

5***

0.03

8H

ave

refr

iger

ator

NA

NA

0.21

6***

0.06

80.

102*

0.05

8N

AN

AH

ave

carr

ier/

trac

kN

AN

AN

AN

A0.

334*

**0.

108

NA

NA

Hav

ew

ashi

ngm

achi

neN

AN

AN

AN

A0.

128*

*0.

058

0.08

2***

0.03

Hav

ete

leph

one

NA

NA

NA

NA

0.17

3***

0.05

NA

NA

Hav

ega

s/el

ectr

icst

ove

NA

NA

0.10

4*0.

055

0.13

3***

0.04

60.

157*

**0.

029

Hou

seho

ldsi

ze-0

.209

***

0.01

5-0

.169

***

0.02

8-0

.229

***

0.03

-0.2

62**

*0.

016

Hou

seho

ldsi

zesq

uare

d0.

008*

**0.

001

0.00

6***

0.00

20.

009*

**0.

002

0.01

1***

0.00

1M

ater

ial

ofw

all

isco

ncre

te,

bric

kor

cem

ent

0.12

3***

0.02

5N

AN

AN

AN

A0.

075*

**0.

028

Sour

ceof

light

isel

ectr

icity

0.12

3**

0.05

1N

AN

AN

AN

A0.

079*

0.04

4So

urce

oflig

htis

kero

sene

lam

pN

AN

AN

AN

A-0

.153

***

0.05

NA

NA

Flus

hto

ilet

for

excl

usiv

eus

eN

AN

A0.

101*

0.05

8N

AN

AN

AN

AC

ooki

ngfu

elis

woo

d-0

.056

0.03

6N

AN

A-0

.105

0.06

6-0

.094

***

0.02

9O

wn

livin

gqu

arte

r0.

115*

**0.

027

NA

NA

NA

NA

NA

NA

Ren

tho

use

from

priv

ate

land

lord

NA

NA

NA

NA

-0.3

17**

*0.

093

NA

NA

Occ

upy

gove

rnm

ent

orin

stitu

tiona

lho

usin

g0.

286*

**0.

073

NA

NA

NA

NA

NA

NA

Num

ber

ofro

oms

0.06

2***

0.00

80.

048*

**0.

018

0.04

8***

0.01

50.

095*

**0.

01A

geof

hous

ehol

dhe

adN

AN

A-0

.004

**0.

002

-0.0

04**

0.00

2-0

.002

***

0.00

1H

ouse

hold

head

isem

ploy

edN

AN

A0.

159*

0.09

3N

AN

AN

AN

AH

ouse

hold

head

isFi

jian

0.08

8***

0.02

5N

AN

A0.

214*

**0.

049

0.17

7***

0.02

8H

ouse

hold

head

with

seco

ndar

yed

ucat

ion

0.09

0***

0.03

4N

AN

AN

AN

AN

AN

AH

ouse

hold

head

with

tert

iary

educ

atio

n0.

178*

**0.

044

0.44

5***

0.11

7N

AN

A0.

186*

**0.

039

Prop

ortio

nof

hous

ehol

dm

embe

rsw

ithdi

plom

aed

ucat

ion

0.33

1***

0.06

60.

318*

0.19

30.

426*

*0.

173

NA

NA

Prop

ortio

nof

hous

ehol

dm

embe

rsw

ithhi

gher

educ

atio

n0.

541*

**0.

108

NA

NA

0.69

2**

0.27

50.

472*

**0.

178

Dep

ende

ncy

ratio

0.12

1***

0.01

9N

AN

A0.

084*

*0.

042

NA

NA

Rew

a0.

104*

**0.

024

NA

NA

NA

NA

NA

NA

Bua

NA

NA

NA

NA

0.15

7***

0.05

6N

AN

AEn

umer

atio

nar

ea(E

A)

vari

able

s(c

ensu

sde

rived

)Pr

opor

tion

ofho

useh

olds

inth

een

umer

atio

nar

eaw

ithke

rose

ne-0

.445

***

0.05

2N

AN

AN

AN

AN

AN

AW

ithbr

ush

cutte

rN

AN

AN

AN

AN

AN

A0.

395*

**0.

108

With

roof

tank

asth

em

ain

sour

ceof

wat

erN

AN

AN

AN

AN

AN

A0.

174*

*0.

077

With

wal

lsm

ade

ofco

ncre

teN

AN

AN

AN

AN

AN

A0.

189*

**0.

064

Ave

rage

hous

ehol

dsi

zein

the

enum

erat

ion

area

NA

NA

-0.1

29**

*0.

045

NA

NA

NA

NA

Num

ber

ofbe

dsin

hote

lsin

the

EAN

AN

A0.

010*

*0.

005

NA

NA

0.00

3***

0.00

1Ty

peof

urba

nEA

NA

NA

NA

NA

NA

NA

NA

NA

Hig

hcl

ass

NA

NA

NA

NA

NA

NA

0.26

5***

0.06

1M

iddl

ecl

ass

NA

NA

NA

NA

NA

NA

0.14

1**

0.05

9Lo

wcl

ass

NA

NA

NA

NA

-0.1

36**

0.06

50.

133*

0.07

3Sq

uatte

r0.

097*

*0.

047

NA

NA

NA

NA

0.17

7**

0.07

3H

ousi

ngau

thor

ity0.

115*

**0.

038

NA

NA

NA

NA

NA

NA

Mix

edcl

ass

NA

NA

-0.1

59*

0.08

4N

AN

AN

AN

AV

illag

e(u

rban

)N

AN

AN

AN

AN

AN

A0.

196*

**0.

056

Oth

ercl

ass

NA

NA

NA

NA

NA

NA

0.14

0***

0.04

7_c

ons

7.97

7***

0.07

58.

876*

**0.

294

8.34

5***

0.14

67.

914*

**0.

087

Num

ber

ofob

serv

atio

ns1,

427

330

570

1,14

0A

djus

ted

R2

0.63

30.

500

0.50

00.

619

Not

e:**

*P<

0.01

,**

P<

0.05

,*P

<0.

1.So

urce

:C

alcu

latio

nsba

sed

onH

IES

2008

/200

9an

dC

ensu

s20

07.

L. Pabon et al.


contribution to the number of poor due to itssmall population size.

Table 5 presents the poverty estimates foreach stratum (by division and rural/urbanstatus). Once again the census-based estimatesare very close to the HIES estimates. The esti-mates of provincial poverty are presented inTable 6. For instance, in the central division,where the overall poverty rate is 24%, there areprovinces with substantially higher poverty,such as Tailevu (30%) and Namosi (32%). Thetikina level estimates will inform whether thereare more disaggregated pockets of poverty.

Poverty incidence is highest (above 50%) inthe provinces of Ra, Cakaudrove and Macuata.This can be seen in Figure 1, which shows amap with the poverty estimates at the provincelevel. The provinces of Nadroga/Navosa andBua also report high poverty headcount ratesbetween 40% and 50%. The same way thatpoverty rates vary across provinces, poor peopleappear to be concentrated in some specificareas. An overwhelming majority of the poorresides in Ba (see Fig. 2), which is also the mostpopulous province of the country.

Tikina estimates

Generally, to improve targeting resources forpoverty reduction, it is very useful to haveprecise poverty estimates at lowest levels ofaggregation as possible. While estimates at theEA level will be unreliable, due to the smallnumber of households in each cluster, estimatesof tikina poverty can be obtained with anacceptable level of precision. Figure 3 presentsthe map with the estimates of poverty for alltikina. The exact poverty estimates along withthe standard errors is included in SupportingInformation Table S1.

We find that poverty in Fiji is marked byconsiderable spatial heterogeneity that can notbe gauged by the division level HIES estimates.The poverty rate in Oinafa tikina in 2007(6.3%, see Supporting Information Table S1for exact figures) was less than a tenth ofNakorotubu tikina (76%). Figure 3 presents amap of poverty headcount ratios at the tikinalevel and illustrates some interesting geo-graphical characteristics of poverty incidence.First, even within better off divisions such as

Table 4. Division poverty rates compared across HIES and Census

Division Poverty incidence Number of poorHIES 2008 Census 2007 HIES 2008 Census 2007

Central 0.234 (0.018) 0.240 (0.011) 75 812 78 294Eastern 0.330 (0.047) 0.301 (0.053) 14 559 11 254Northern 0.535 (0.026) 0.523 (0.012) 75 377 68 222Western 0.397 (0.024) 0.395 (0.018) 121 190 123 789

Note: Standard errors are shown in brackets.Source: Calculations based on HIES 2008/2009 and Census 2007.

Table 5. Strata poverty rates compared across HIES and Census

Poverty incidence Poverty gapCensus 2007 HIES 2008 Census 2007 HIES 2008

Estimate Standarderror




Central/eastern urban 0.22 0.01 0.21 0.02 0.05 0.00 0.05 0.01Central rural 0.30 0.02 0.33 0.03 0.07 0.01 0.07 0.01Eastern rural 0.29 0.06 0.31 0.05 0.07 0.02 0.08 0.01Northern urban 0.50 0.02 0.52 0.05 0.18 0.01 0.18 0.03Northern rural 0.53 0.01 0.54 0.03 0.19 0.01 0.18 0.02Western urban 0.33 0.02 0.30 0.04 0.09 0.01 0.08 0.01Western rural 0.44 0.02 0.47 0.03 0.13 0.01 0.14 0.01

Note: Standard errors are shown in brackets. Calculations based on HIES 2008/2009 and Census 2007.



the western or central divisions, there arepockets of very high poverty incidence.Second, the highest poverty rates are found inthe remote in-land areas of Viti Levu.6 Thenorthern division, which corresponds to the

island of Vanua Levu, is quite homogenouswith very high rates of poverty incidenceacross the division.

High headcount ratios do not always indicatethat there is a large population of poor people.

Table 6. Province level poverty rate and gap based on national census

Region Province Poverty incidence Poverty gap Number of poor

Western Ba 0.37 (0.02) 0.10 (0.01) 83 579Northern Bua 0.47 (0.03) 0.16 (0.02) 6566Northern Cakaudrove 0.55 (0.01) 0.20 (0.01) 26 470Eastern Kadavu 0.26 (0.05) 0.07 (0.02) 2468Eastern Lau 0.31 (0.07) 0.08 (0.03) 3215Eastern Lomaiviti 0.34 (0.06) 0.09 (0.03) 5272Northern Macuata 0.51 (0.01) 0.18 (0.01) 35 181Western Nadroga/Navosa 0.42 (0.02) 0.12 (0.01) 23 054Central Naitasiri 0.25 (0.01) 0.06 (0.00) 38 665Central Namosi 0.32 (0.04) 0.08 (0.02) 2131Western Ra 0.56 (0.03) 0.19 (0.02) 17 157Central Rewa 0.17 (0.01) 0.04 (0.00) 16 530Central Serua 0.26 (0.03) 0.06 (0.01) 4619Central Tailevu 0.30 (0.02) 0.07 (0.01) 16 368Rotuma Rotuma 0.15 (0.09) 0.03 (0.02) 298

Note: Standard errors are shown in brackets.

Figure 1. Poverty headcount ratio at the province level

L. Pabon et al.


This is the case even in high poverty incidencetikinas where absolute numbers of poor willdepend on the area’s total population. Figure 4illustrates this clearly. Thus, for example, eventhough the headcount ratios in central divisiontikinas are relatively low, the population of poorpeople in these and in particular Suva andNaitasiri are very high relative to other parts ofFiji. One of the most striking results is thatalmost 50% of all the poor come from just 6 outof 86 tikinas, namely Ba, Nadi, Vuda, Labasa,Suva and Naitasiri. In the cases of Suva, Nadiand Nitasiri, this is despite having poverty ratesthat are lower than the national average.

Poverty in squatter settlements and typeof area

Each urban EA in the survey and the census iscategorised into ‘area classes’ by Fiji IslandsBureau of Statistics (FIBOS) based on theirsocioeconomic well-being. There is no formaldescription of these classifications. They arebroadly ranked according to the well-being clas-

sification or type of area defined by the nationalstatistical office in the following order, fromricher to lower income areas: high class, EAswith commercial/ industrial core, middle class,low class, housing authority, urban villages,squatter settlement7. Despite lack of formal defi-nition of the classes, it is useful to focus onsquatter settlements as households residingin these areas may be of particular interest.Rural areas do not have a similar subclas-sification. Table 7 presents the estimates ofpoverty for each of these area types.

There are only 150 households in the HIESfrom the squatter settlements: too few to reli-ably estimate poverty. The poverty map in con-trast to the HIES enables analysis of poverty forsquatter settlements. According to the census,4% of Fijian households are located in squattersettlements. The poverty rates for squattersettlements are among highest across all thedivisions. In the central division, the best offareas (high class) have the lowest poverty ratesaveraging at 7%. The squatter settlementsaverage a rate of 38%. In the eastern division,

Figure 2. Distribution of the poor at province level as a proportion of total poor



there were no areas designated as high class oras squatter settlements. In the poorest division,northern, even the high class areas have regis-tered a poverty rate of 35%, and the squattersettlements have poverty rates comparable withrural areas in the northern division, around53%. Finally, in the western division, the squat-ter settlements have a poverty rate of 47% thatis slightly higher than rural poverty. To sum-marise, poverty rates are relatively low amonghigh, industrial and institutional class areas.Poverty rates are highest among householdsliving in rural, urban villages, squatter settle-ment and low class areas. Other classes fall inbetween.

Conclusions

In this paper we present disaggregated povertymaps for Fiji by combining HIES 2008–2009with the national census of 2007 through a con-sumption model. We presented provincial andtikina level poverty and find spatial heterogene-ity that cannot be gauged by the division levelhousehold survey estimates revealing pockets of

poverty even within relatively well-off regions.The results can facilitate regional targeting ofdevelopmental aid. One of the most strikingresults is that very few tikinas contributetowards 50% of all poor population as definedby consumption poverty, indicating high con-centration poverty clusters. Urban squattersettlements are of big concern given recentmigration from rural areas, and we estimate thefirst household expenditure based poverty ratesfor squatter settlements using the technique ofpoverty mapping and find poverty headcountratios ranging between 0.38 and 0.55 depend-ing on the division the squatter settlement islocated in. Relatively, urban squatter settle-ments have about twice the average urbanpoverty rate and closer to poverty rates found inrural areas.

Acknowledgements

Pabon and Umapathi gratefully acknowledgethe financial support from AusAid. We thankFiji Bureau of Statistics staff Serevi Baledro-kadroka, Toga Raikoti, Adrian Rajalingam, Epeli

Figure 3. Poverty headcount ratio at the tikina level

L. Pabon et al.


Waqavonovono andTevitaVakalalabure for theircontinuous engagement and support during thedata preparation and analysis, and Sasun Tsiru-nyan who provided input for construction of theexpenditure-based welfare aggregate and thepoverty line. Oleksiy Ivaschenko and Roy vander Weide provided helpful discussion, andSergiy Redyakin responded with troubleshootingassistance for mapping the spatial analysis, manythanks.

These are the views of the authors and do notreflect those of the World Bank, its ExecutiveDirectors or the countries they represent.

Notes

1 Fiji is divided administratively into four divisions, whichare subdivided into 14 provinces. Several villagescombine to form a tikina, two or more of which com-prise a province, similar to a district.

2 Simulation methods are used to introduce random dis-turbance term for each household because the modeldoes not predict consumption perfectly.

3 One of the important concerns in the second stageregression modelling exercise is the question of whetherthe econometric model is able to capture intra-cluster(EA) correlation across households in welfare. It is pos-sible, for example, that within a specific cluster, house-holds are all typically less well off, or better off, thansimilar looking households in other clusters. This couldbe due to cluster-level factors such as whether or not landis irrigated in that EA, whether households in particularEAs have access to certain public goods and infrastruc-ture and so on. While many of the cluster-level charac-teristics of interest may not be readily observed in thecensus and survey data that are available for analysis, itmay be possible to proxy these factors by including anumber of such derived cluster-level variables in theregression model.

4 Using generalised least squares regression.5 The parameter estimates and the predicted residual

were drawn 100 times, providing the expected valueof consumption for each household in the censuspopulation.

6 Viti Levu is the largest island (in terms of population sizeand territory) where the capital city of Suva (in the east)is located.

7 Communication with Chief Statistician of FIBOS’sHousehold Surveys Unit.

Figure 4. Distribution of the poor at tikina level as a proportion of total poor



References

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Elbers, C., T. Fujii, P. Lanjouw, B. Ozler and W. Yin (2007)Poverty alleviation through geographic targeting: Howmuch does disaggregation help? Journal of Develop-ment Economics 83(1): 198–213.

Table 7. Census-based poverty estimates for each class category in the urban areas

Division Class Poverty incidence Poverty gapEstimate Standard error Estimate Standard error

Central Rural 0.30 0.02 0.07 0.01High class area 0.07 0.02 0.01 0.00Middle class 0.14 0.01 0.03 0.00Low class 0.35 0.05 0.09 0.02Housing authority 0.17 0.03 0.03 0.01Industrial 0.08 0.02 0.02 0.00Institutional 0.11 0.03 0.02 0.01Squatter 0.38 0.04 0.11 0.02Urban village 0.30 0.04 0.07 0.01Mixed 0.23 0.02 0.05 0.01Other 0.34 0.03 0.08 0.01

Eastern Rural 0.29 0.06 0.07 0.02High class area NA NA NA NAMiddle class NA NA NA NALow class 0.56 0.27 0.18 0.14Housing authority NA NA NA NAIndustrial 0.14 0.11 0.03 0.03Institutional 0.18 0.16 0.05 0.06Squatter NA NA NA NAUrban village 0.51 0.25 0.16 0.11Mixed 0.37 0.18 0.12 0.08Other NA NA NA NA

Northern Rural 0.53 0.01 0.19 0.01High class area 0.35 0.04 0.12 0.02Middle class 0.43 0.04 0.14 0.02Low class 0.63 0.04 0.25 0.02Housing authority 0.42 0.04 0.14 0.02Industrial 0.44 0.05 0.15 0.03Institutional 0.54 0.09 0.23 0.06Squatter 0.55 0.05 0.20 0.03Urban village 0.62 0.06 0.23 0.03Mixed 0.47 0.02 0.16 0.01Other 0.51 0.05 0.18 0.03

Western Rural 0.44 0.02 0.13 0.01High class area 0.16 0.03 0.04 0.01Middle class 0.26 0.05 0.06 0.02Low class 0.51 0.08 0.16 0.04Housing authority 0.38 0.05 0.10 0.02Industrial 0.29 0.04 0.09 0.02Institutional 0.29 0.07 0.08 0.03Squatter 0.47 0.07 0.16 0.03Urban village 0.42 0.07 0.12 0.03Mixed 0.39 0.03 0.11 0.01Other 0.35 0.05 0.10 0.02

Rural poverty included for comparison.Source: Calculations based on HIES 2008/2009 and Census 2007.

L. Pabon et al.


Elbers, C., J. Lanjouw and P. Lanjouw (2002) Micro-Levelestimation of welfare. Policy Research Working PaperNo. WPS 2911. The World Bank.

Elbers, C., J. Lanjouw and P. Lanjouw (2003) Micro-levelestimation of poverty and inequality, Econometrica71(1): 355–364.

Gibson, J., G. Datt, B. Allen, V. Hwang, M. Bourke andD. Parajuli (2005) Mapping poverty in rural PapuaNew Guinea, Pacific Economic Bulletin 20(1): 27–43.

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in Fiji in 2008–09. Suva: Fiji Islands Bureau ofStatistics.

Supporting information

Additional Supporting Information may be found in theonline version of this article:

Table S1. Tikina level predicted poverty rate and gap.

Please note: Wiley-Blackwell are not responsible for thecontent or functionality of any supporting materials suppliedby the authors. Any queries (other than missing material)should be directed to the corresponding author for thearticle.



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