Directory UMM :Data Elmu:jurnal:UVW:World Development:Vol28.Issue11.Nov2000:

World Development Vol. 28, No. 11, pp. 1961±1977, 2000
Ó 2000 Elsevier Science Ltd. All rights reserved
Printed in Great Britain
0305-750X/00/$ - see front matter

www.elsevier.com/locate/worlddev

PII: S0305-750X(00)00062-0

Rapid Rural Appraisal in Humid Tropical Forests: An
Asset Possession-Based Approach and Validation
Methods for Wealth Assessment Among Forest
Peasant Households
YOSHITO TAKASAKI, BRADFORD L. BARHAM
University of Wisconsin, Madison, USA
and
OLIVER T. COOMES *
McGill University, Montreal, Quebec, Canada
Summary. Ð Researchers and practitioners often use rapid rural appraisal (RRA) methods to
secure a representative, accurate, and cost-e€ective portrayal of wealth rankings among rural
populations. This paper proposes an asset possession-based approach to developing RRA wealth

rankings and portrayals of wealth holdings and portfolios. It also o€ers new validation methods for
RRA e€orts, especially for identifying sources of error in wealth attribution and rankings. The
approach and validation methods are examined using data gathered from forest peasant
households in the Pacaya-Samiria National Reserve in the Peruvian Amazon. Ways of improving
future RRA e€orts are suggested. Ó 2000 Elsevier Science Ltd. All rights reserved.
Key words Ð rapid rural appraisal, peasants, wealth, validation, humid tropical forests

1. INTRODUCTION
Increasingly, researchers and practitioners
working on conservation and development
issues are recognizing the instrumental role of
wealth, as di€erent forms of capital, in the
economic life of rural peoples (Barham, Coomes, & Takasaki, 1999; Bebbington, 1999;

* We

wish to thank our ®eld teamÐCarlos Rengifo,
Doris Diaz and Jaime SalazarÐfor their advice, enthusiasm and Herculean e€ort that made this project
possible. Special thanks are owed also to the ribere~
nos

of the region who so willingly participated in the long
interviews on repeat visits to their homes. This paper
bene®ted signi®cantly from the suggestions of two
anonymous referees, Michael Carter, and seminar
participants at the University of Wisconsin. It was also
made possible by the tireless data cleaning work of
Nathalie Gons and Xiaogan Li. Finally, we gratefully
acknowledge the generous ®nancial support of this

Dercon, 1998; Reardon & Vosti, 1995; Turner,
1999). Di€erences in the holding of land and
other assetsÐeven di€erences that appear small
to outside observersÐcan profoundly in¯uence
local natural resource use and human welfare
outcomes. Typically, however, national household surveys and censuses focus on income and
expenditures rather than on assets, giving scant
project by the following organizations: The Nature
Conservancy, Ford Foundation, AVINA/North-South
Center of the University of Miami, Foundation for
Advanced Studies on International Development (FASID), The Institute for the Study of World Politics, The

Mellon Foundation, The Graduate School of the
University of Wisconsin±Madison, McGill University,
the Social Sciences and Humanities Research Council of
Canada and the Fonds pour la Formation de Chercheurs
et l'Aide a la Recherche. Any errors of interpretation are
solely the responsibility of the authors. Final revision
accepted: 28 March 2000.

1961

1962

WORLD DEVELOPMENT

attention to nonland wealth forms, especially
what Bebbington and others call social, natural, and cultural capitals. In the absence of
household wealth data, rapid rural appraisal
(RRA) and participatory rural appraisal (PRA)
methods often are used to capture wealth
di€erences among rural populations in an

e€ective and low-cost manner.
RRA wealth assessments typically entail ®eld
workers asking a small group of respondents to
develop an ordinal ranking of households in a
village by total wealth, or across a subset of
wealth measures, such as land, equipment, and
livestock (Chambers, 1994a; Grandin, 1988).
The total wealth-ranking approach is often
favored because: (a) respondents can incorporate a wider range of wealth measures than
``outsiders'' might include and value them with
local ``weights'' they deem most appropriate;
and, (b) it is viewed as more accurate than
survey-based wealth measures because of the
well-known survey biases related to misinformation, disinformation, or recall problems in
individual interviews. 1 RRA validation e€orts,
to date, comprise mostly correlation studies
that compare RRA and survey data on physical
wealth forms (livestock, land) (Adams, Evans,
Mohammed, & Farnsworth, 1997; Chambers,
1994a). As a group, validation methods for

total wealth ranking exercises are inherently
limited, because speci®c sources of error in
rankings cannot be identi®ed unless the facilitator also secures disaggregated wealth (ranking) data from RRA respondents. Still, drawing
on the extant validation tests of wealth ranking
methods, Chambers (1994a) and Adams et al.
(1997) judge that such methods do perform
satisfactorily.
Based on our experience in the Peruvian
Amazon using RRA methods, this paper
explores two challenges to the further evolution
of RRA wealth assessment methods. Under
certain circumstances, as in our case, researchers may wish to deploy RRA techniques not
only to discern which households are wealthier
than others within communities, but also to
know more about the magnitude of di€erences
in wealth as well as in the composition of
household wealth portfolios (e.g., who owns
how much of which forms of capital). Whenever speci®c subgroups are targeted by research
or development programsÐsuch as the ``poorest of the poor'' who are of the greatest need, or
the ``better o€'' among the poor who may be

using natural resources more rapaciously (or
indeed more sustainably)Ðthen researchers

and practitioners may require RRA methods
for more ®ne-tuned strati®cation e€orts, using
total wealth measures and/or certain wealth
items. Methods that enable such discernment
by ®eld workers would be a welcome addition
to the RRA ``tool kit.''
A second challenge facing RRA users is to
deepen their study of the accuracy and the
sources of errors associated with RRA methods. Indeed, it seems imperative that researchers not only probe the validity of RRA
methods but also devise ways to identify
potential sources of error and possible steps to
minimize errors. In this paper, we seek to
contribute on both fronts by proposing and
evaluating a ``possession-based'' method for
the assessment of rural household wealth, one
that may serve as another tool in the suite of
RRA methods. Our approach asks respondents

to identify whether each household holds
speci®c wealth items, and then uses these
itemized household lists to construct wealth
rankings. These same lists, in turn, a€ord the
researcher the opportunity to assess the validity
of the method and to identify possible sources
of error among respondents.
In the riverine communities of the Peruvian
Amazon, we found that a possession-based
approach was especially helpful. Most immediately, it enabled us to move past the pervasive, local peasant ethos that ``we are all poor,''
which made the use of a total wealth-ranking
method problematic as a starting point for
respondents. By contrast, RRA respondents
were readily able to share their knowledge of
assets held by other households. More critically, the approach also allowed us to stratify
our sample based on holdings of two di€erent
types of wealthÐland holdings (of speci®c
types) and other physical capital assetsÐthat
we hypothesized are in¯uential in shaping
household activity choices between agriculture

and a range of other activities, including ®shing, hunting, forest product gathering, and
other forms of extraction. 2 For other
researchers, even where these particular attributes of the possession-based approach may
not be of direct relevance, our approach can
provide complementary information to total
wealth rankings.
Just how well a possession-based approach
actually works is a vital question, one that we
address at three levels: (i) can RRA respondents accurately identify the wealth holdings of
other households?; (ii) what are the likely
sources of error in their responses?; and, (iii)

RAPID RURAL APPRAISAL

can data on individual wealth holdings be used
to construct valid wealth distributions, especially if the list of wealth items or categories is
not comprehensive? This paper examines these
concerns comparing information from our
RRA e€orts in the Peruvian Amazon with data
from a subsequent household survey of wealth

holdings. Particular attention was given in our
study to tangible wealth holdings and the link
to resource use behavior. Data gathered later
on intangible capital, including educational
levels, social networks, and urban remittance
¯ows, were not explicitly incorporated into our
wealth validation tests.
The humid tropical rain forests of the Peruvian Amazon provide a formidable testing
ground for validating a possession-based
approach to household wealth assessment.
Because forest peasant households participate
in a wide variety of economic activities, their
physical asset holdings (i.e., land and capital
items) can be quite varied. If a possession-based
RRA approach works well in this settingÐas
an accurate means of identifying a wide range
of physical wealth holdings, ranking the wealth
of households, and capturing the narrow tail(s)
of the wealth distributionÐthen its reliability in
other rural contexts seems probable. In addition to proposing and testing the validity of this

approach, this paper makes a broader contribution by o€ering new methods for validating
RRA rankings and identifying sources of error.
These error analysis methods could be applied
to other RRA techniques that gather information about speci®c wealth or other household
features.
In the next section, we describe the study
area and population, general asset types found
in the region, the selection of study villages and
households, and the data sets used in the validation tests. In Section 3, the RRA validation
methods are set forth. Section 4 discusses the
validation results. Section 5 concludes with the
lessons learned about RRA validation and use
among peasants in tropical rain forest environments and other rural areas.
2. BACKGROUND

1963

Extending over two million hectares of
lowland, dominated by swamp and ¯ood forest,
this area is one of the worldÕs richest regions of

biological diversity (Bayley, V
asquez, Ghersi,
Soini, & Pinedo, 1991; Rodrõguez, Rodrõguez,
& V
asquez, 1995). Over 170 communities are
found in and around the PSNR, comprised
largely of mestizo peasants (known locally as
ribere~
nos) who make their living from ¯oodplain agriculture, ®shing, hunting, and forest
product gathering (Coomes, Barham, & Craig,
1996). Livelihood practices are adapted closely
to the annual ¯ood cycle; in general, ¯oodplain
agriculture and ®shing are most productive
during low water, whereas hunting and gathering become more productive during the highwater period when access to forests improves
and agricultural options are limited by the
paucity of high land.
(b) Household asset holding in the PSNR
The physical wealth holdings of forest peasants in the PSNR can be grouped into four
types of tangible assets: (i) agricultural land; (ii)
productive capital (i.e., ®shing nets, shotguns,
chain saws, boats, motors, etc.); (iii) nonproductive capital (i.e., consumer durables, shops,
houses owned elsewhere); and (iv) livestock
(i.e., poultry, pigs, cattle, and water bu€alo)
(Takasaki, Barham, & Coomes, forthcoming).
Land is held by usufruct (i.e., with no titles),
privately used, and transferred principally
along kin lines; markets for land are extremely
thin in this land abundant region of Peru,
which makes economic valuation problematic.
At least ®ve distinct types of agricultural land
are identi®able: upland, high levee, low levee or
backslope, mud ¯at, and sand bar or beach
(Denevan, 1984; Hiraoka, 1985). Upland is
never ¯ooded, high levee is ¯ooded in some
years, and low levee is ¯ooded almost every
year. Mud ¯ats and sand bar emerge only
during the low-water period, and both their
area and soil conditions can change radically
year-to-year. Access to di€erent types of land
varies across the PSNR: few villages have
access to upland; some have no mud¯ats or
sandbars; and only low-levee land is ubiquitous.

(a) Research setting
(c) Selection of villages and households
The Pacaya-Samiria National Reserve
(PSNR) is situated in northeastern Peru,
between the Mara~
non and Ucayali Rivers,
some 110 km southwest of the city of Iquitos.

Our detailed study of household wealth
accumulation and resource use was undertaken
in eight PSNR villages over 16 months in 1996±

1964

WORLD DEVELOPMENT

97 (Barham et al., 1999; Takasaki et al.,
forthcoming). Village size ranged from 35 to
129 households, and the villages selected lie at
varying distances from Iquitos, the main urban
center and market of northeastern Peru. Village
selection was purposive, rather than random, in
order to capture in a cost-e€ective manner the
regional diversity in environmental conditions
and resource use activity. Although both
subsistence agriculture (mainly manioc and
plantain) and ®shing (the primary protein
source along the ¯oodplain) are common in all
eight villages, market-oriented activities di€er
signi®cantly across villages. Indeed, based on
income shares in di€erent activities, the sample
villages can be readily grouped into agricultural, mixed, or ®shing communities. 3 In
addition, particular care was taken to avoid
communities targeted by nongovernmental
organizations (NGOs) for conservation and
development initiatives. These e€orts were
viewed as likely to have both signi®cant and
unintended e€ects on local resource use activities that would have hampered the objectives of
examining the role of wealth and other social
and environmental factors in shaping resource
use decisions.
Households were chosen …n ˆ 300† according
to a strati®ed sampling strategy designed to
over-sample wealthier households who, by their
relatively small numbers, were likely to have
been overlooked by random sampling. We
sought to include wealthier as well as poor
households to ensure that our sample would
re¯ect the full range in local resource use
activity mix and technologies, with the attendant environmental implications. The strati®cation was based on the RRA wealth ranking
e€ort under examination in this paper, and is
further explained below.
(d) Data sets used for RRA validation tests
Data sets on household wealth holdings were
gathered in the ®rst two stages of the study, ®rst
from a group of local respondents using RRA
methods and then from sample households
selected according to the RRA ranking results.
The ensuing validity analysis compares the
possession and wealth rankings for those
households for which we have observations at
both stages.
(i) RRA ranking data
The RRA rankings were obtained by working with a small group of long-term village

residents …n ˆ 3±4†, who were asked by a
facilitator to identify, based on their current
knowledge, the physical capital and land
holdings of each household in the village. 4 A
standard, regionally-speci®c checklist of major
capital assets was used to guide the assessment
of nonland assets, with the facilitator asking
the group, for each household in the community, whether each asset on the list was owned
(and for certain types of assetsÐthe size or
type). 5 For land holdings, the facilitator
conducted a repeated sort of cards, each
bearing the name of a head of household in
the community, to determine the amount of
land held by each household along a crude
ordinal index (ranging from ``none'' to ``a
little'' to ``a fair amount'' to ``a lot''). This was
done for the three major land types (upland,
high levee, and mud¯ats). 6
Based on the resulting possession data for
physical capital and the ordinal measures of
land holdings, two aggregate measures of
wealth were constructed to rank households.
Total capital value was calculated for each
household by combining respondentsÕ information on possession and size or type
measures with a unit price for each item,
which was obtained from markets in Iquitos
where these items are often purchased. In
addition, an aggregate land index score was
developed for each household by summing the
ordinal index measure (0±3) for each land
type, or 0±9 for the three combined. Using
these two wealth measures, households in each
village were then strati®ed into three groupsÐ
top, middle, and bottomÐof which 100, 40,
and 50% of households, respectively, were
included into the household sample for further
study. 7
(ii) Household survey data
In the household survey, structured questionnaires were administered to each sample
household which focused on their capital and
land holdings. Survey respondents were asked
systematically about their possession of all of
the physical assets and land types included in
the RRA wealth ranking exercise, plus others
that had not been included in the RRA stage.
They were also asked to report on number and
size of plots for each land type, thereby allowing the construction of an estimate of total land
holdings of each type by household. Finally,
households were requested to appraise the
current market value of each of their capital
assets.

RAPID RURAL APPRAISAL

(iii) Comparing the RRA and household survey
data
For capital and land holdings included in
both stages, the possession-based approach
allows an explicit statistical analysis of the
sources of error in possession identi®cation by
RRA respondents. It also allows a comparison
of the wealth rankings of the RRA stage with a
``matched capital'' outcome from the survey
stage, where the wealth rankings are compared
using only those asset items included in both
stages. Another comparison could then be
made between the RRA stage and an ``all
capital'' ranking from the survey data. Along
with the ``matched capital'' case, the ``all capital'' case allows an evaluation of the potential
ranking error introduced in the RRA stage by
only asking about a subset of possible wealth
items. Thus, if the ``matched capital'' and ``all
capital'' rankings yield similar outcomes in
terms of ranking accuracy, then this result
implies that little accuracy is lost in ranking by
using a more compact wealth item list in the
RRA exercise. We should note also that our
decision to use respondentsÕ valuations on
physical capital wealth items in the survey stage
and uniform price measures in the RRA stage
gives rise to another potentially interesting
basis for di€erences in wealth values and
rankings across the two stages. 8
3. VALIDATION METHODS
The validity of RRA wealth rankings and
possession identi®cation depends on whether
respondents are prone to attribution errors. By
attribution error, we mean that RRA respondents may not know or may misperceive the
wealth holdings of households in the village,
and thus credit households with holdings they
do not possess or fail to recognize assets that
are indeed held. A set of validation methods is
proposed here to assess the sources and implications of attribution errors that may arise in
RRA exercises.
A commonly used measure in statistics of the
performance of an estimator is its mean square
error, which is divided into two error componentsÐvariance and bias (Judge, Hill, Griths,
L
utkepohl, & Lee, 1988, p. 72). In this context,
variance errors arise when respondents make
attribution errors for reasons that are independent of household characteristics, whereas
bias errors arise when respondents make attribution errors based on other information about

1965

a household that leads them to incorrect
inferences about their holdings. Underlying
both variance and bias errors in RRA wealth
exercises are the basic problems of observation
that respondents face when trying to correctly
ascribe asset ownership or wealth ranking to
neighbors.
Certain assets are more prone to ``observability'' problems than othersÐe.g., consumer
durables stored in the home, compared to
®shing nets that are taken daily to and from the
river and then hung out to dry in front of the
house. Some assets may be shared frequently
with other households, thereby making it dicult for RRA respondents to discern which
households actually own the item. Other assets
are very commonly (or uncommonly) held
within the population, which may in¯uence the
probability that RRA respondents correctly
assess ownership compared to those assets that
are held by, say, half of the population.
Respondents in more populous villages are
more likely to face observability problems,
simply because they will need to possess asset
ownership knowledge across more households.
Any such asset or village feature that limits
observability, but does so independent of individual household characteristics, potentially
can give rise to variance errors.
Bias errors arise when RRA respondents,
facing observability problems, employ known
household characteristics to guide their inferences about household asset holdings. For
example, if respondents rely upon a given
householdÕs social position (as wealth holdings,
household size, etc.) as a guide in deciding
whether a speci®c household holds a certain
asset, then these various ``status'' indicators can
give rise to systematic bias. In sum, our error
analyses use such status measures as well as
asset and village characteristics to discern the
extent to which bias and variance errors a€ect
RRA accuracy.
(a) Validation tests
Four validation tests are proposed in Table 1,
one set for possession rankings, the other for
wealth rankings. The ®rst row presents accuracy tests for comparing the consistency of
RRA and survey data, while the second row
proposes regression models for identifying
sources of possession and ranking error. Previous analyses of RRA validity (Chambers,
1994b) that examined the accuracy rate of
wealth rankings (the upper right box in Table 1)

1966

WORLD DEVELOPMENT
Table 1. Four validation methods for RRA wealth possession identi®cation and ranking methods
Possession

Ranking

Accuracy rate of possession
[0±100%]

Accuracy rate of wealth ranking
[0±100%]

Possession error
P ˆ jPRRA ÿ Ps j
where Pj ˆ 1 if possessed; ˆ 0 otherwise
[0 ˆ no error; 1 ˆ error]

Ranking error
R ˆ jRRRA ÿ Rs j
where Rj is ranking

Accuracy tests
Error estimation

Weighted ranking (value) error
V ˆ jVs …Rs † ÿ Vs …RRRA †j
where Vs (Rj ) is the survey value that
corresponds to ranking
Potential explanatory variables for regressions
Variable
Size of village
Average possession rates
Average household wealth
Main economic activity
Asset sharing arrangements
Wealth
Community leadership
Number of adults

Description
Total population
Percent of households owning each asset item/group
Mean value of wealth per household
Activity generating highest mean share of income
Frequency of loans or asset-sharing arrangements among households
Household wealth holdings (land or capital assets)
Household member past or present in leadership role in community
Number of adults in family

typically employ correlation analyses between
wealth rankings in the RRA and the survey
stages. Here, we de®ne accuracy rate of wealth
rankings as the proportion of households that
falls into the same wealth group (bottom,
middle, or top) in both the RRA and survey
stages. This measure provides a more explicit
evaluation of the ecacy of our strati®cation
e€ort.
Three other validation methods for RRA
evaluation are proposed (Table 1). One is an
accuracy rate of possession measure, which like
the accuracy rate of wealth ranking measure,
compares the RRA observations on individual
asset holdings to the survey data for each
household. In the possession error regression
model, the dependent variable is de®ned by a
bivariate measure which takes the value zero (0)
when the RRA and survey results on individual
asset holdings correspond and the value one (1)
when they do not. It is estimated, in our speci®c
case, using a Probit model and a set of
explanatory factors similar to those listed in
Table 1 (see Peters, 1988 for similar error
analysis).
In addition to methods for asset possession
validation, two complementary wealth ranking
error regressions are proposed (Table 1). The
ranking error regression is de®ned as the
di€erence between a householdÕs wealth rank in
the RRA stage and in the survey stage, i.e.,
jRRRA;i ±Rs;i j; the larger the di€erence in this

value, the greater the ranking error. The
regression speci®cation itself depends on
whether the ranking data are continuous, as in
the physical capital data, or indexed, as in the
land data. In the former case, generalized least
squares (GLS) methods are used to control for
heteroskedasticity, whereas in the latter, an
ordered probit speci®cation is employed to
examine the magnitude of the gap in the land
rankings between the two stages.
The other wealth ranking error method
identi®es those factors that shape the magnitude of wealth value errors for households when
the physical capital data for the RRA exercise
and survey are compared. Wealth value errors
are of interest because large discrepancies could
arise between the two stages, even if the rankings prove to be relatively accurate. Where
wealth rankings are inaccurate, examination of
the wealth value errors may reveal whether
systematic factors give rise to such errors
which, ultimately, might be avoided by ``®netuning'' RRA methods. Here, the continuous
nature of the wealth value error makes a GLS
speci®cation possible.
(b) Explanatory factors of wealth possession
and ranking errors
A list of explanatory variables to be used in
the statistical analyses of error sources are
presented in the bottom half of Table 1. As

RAPID RURAL APPRAISAL

discussed above, observability problems among
respondents can give rise to variance or bias. In
addition to size of village and the average
possession rates which were discussed above,
Table 1 o€ers three other village-level variables
that are likely to a€ect the variance of errors: (i)
average household wealth within the village
(hypothesis: observability problems are greater
in richer communities than they are in poorer
communities, because the average household
may hold more assets and a broader range of
assets); (ii) the predominant economic activity of
the village (hypothesis: participation in similar
economic activities may provide households
with better knowledge of activity-speci®c asset
holdings, e.g., residents in ®shing-oriented
villages will be more aware of each otherÕs
®shing assets than one another landÕs holdings);
and, (iii) asset sharing/rental arrangements
(hypothesis: asset sharing may reduce or alternatively increase transparency about asset
ownership, depending on the extent of sharing).
In the regressions presented below, the ®rst two
village measures are captured by village dummy
variables (except where other village variables
are collinear with the dummies and are therefore used instead). The potential e€ects of asset
sharing or rental arrangements on possession
errors are captured by comparing the regression results for the di€erent asset groups.
The explanatory variables in Table 1 include
three household status variables (i.e., wealth,
community leadership, and number of adults)
that can give rise to bias in RRA responses.
Speci®cally, how these features and inherent
observability problems may a€ect responses
and the resulting error structure is less evident
for household than village-level features. For
example, household status may be in¯uential,
with high-status households (e.g., larger
households) assumed by observers to be more
likely to hold certain types of assets than
others. For an asset that is held only by a
moderate number of households but is dicult
to observe, the RRA exercise might contain
more errors among these ``high'' status households because respondents will tend consistently to infer that certain households own the
asset because of their high status. If the same
asset is very commonly held, however, then an
inference drawn from knowledge of the
householdÕs status could serve to improve the
accuracy of attribution in the RRA exercise, if
in fact high status households are indeed more
likely to possess the asset. Thus, status variables could be associated with lower or higher

1967

possession errors and rankings. For this reason
we pro€er no speci®c hypotheses for household
level variables, beyond the expectation that
some or all may be in¯uential.
(c) Validation tests with the PSNR data
Analyzing the accuracy and possession error
sources using individual wealth items data is a
rather straightforward undertaking, because
both stages have comparable data on asset
possession. It should be noted, though, that
some capital items have been grouped together
in the possession analysis to avoid data
censoring problems.
Speci®cally how wealth-ranking data are
compared for accuracy and error sources across
the two data sets merits further explanation.
Recall that the RRA wealth rankings data were
divided into three tiers in order to stratify the
sample. To test the accuracy of this strati®cation, the household survey data on capital were
also divided into three tiers to arrive at a similar
proportion of respondents in each category as
obtained from the RRA stage. The same
procedure was followed for the ``matched
capital'' and ``all capital'' comparisons. These
distinct measures allow us to identify whether
the omission of wealth items in the RRA stage
signi®cantly a€ects the accuracy of the overall
rankings.
For land holdings, the task of comparison is
more complex. In the RRA stage, only three
types of land were considered (upland, high
levee, and mud¯at), with four possible index
values (``none,'' ``a little,'' ``a fair amount,'' and
``a lot''). More explicit measures, such as land
area, were not used because it is more dicult
for respondents to observe ``area'' in a landabundant environment when other households
have multiple, dispersed parcels, than it might
be in other rural locales. In the survey,
however, land holding measures were
constructed with each household for the ®ve
land types by reported plot size (by dimensions). Thus, constructing a consistent landranking comparison meant transforming the
survey data on the matched types of land
holdings into a land index. To do so, we
constructed for each village a continuous
ranking for each of the three land types, identi®ed thresholds in the rankings, and then
divided the distribution in a similar manner to
the RRA stage. 9 Once the index measures were
consistent, we then compared the RRA and

1968

WORLD DEVELOPMENT

survey data in the same manner as for capital
holdings.
The other challenge for comparison lies in
the capital values of the RRA and the survey
stages. Direct comparison is infeasible given
that the quantities and price estimates used in
the two stages are quite di€erent (e.g., single
unit price on all capital items, adjusted for size,
in the RRA stage versus the respondentsÕ estimate of the current market valuation of the
capital item obtained in the household survey).
To remedy this, the value errors (Ve ) which
represent the dependent variable in the last set
of regressions (lower right of Table 1) are
constructed as follows:

tion of the value error. In other words, the
second term in Eqn. (1) provides a wealth
value, based on the RRA rank and the actual
wealth level of a comparably ranked household
in the survey. If the RRA and survey ranking
are the same for a given household, Ve will
be zero. But, if the household is ranked higher
in the RRA than in the survey, then
Vs;i …RRRA;i † > Vs;i , and vice versa.

Ve;i ˆ jVs;i ÿ Vs;i …RRRA;i †j;

RRA respondents e€ectively identi®ed the
wealth possessions of other households in their
village. As reported in Table 2, respondents had
accuracy rates of over 80% for productive
capital assets and 78% for shop asset and other
house holdings. Indeed, some of the scores are
especially strong given that they are determined
for asset groups and thus count an ``error'' on
any item within the group as an ``error'' for the

…1†

where Vs;i is the value of household iÕs capital
wealth in the survey, and Vs;i (RRRA;i ) is the
hypothetical capital value of household i, which
is de®ned as the survey wealth value of the
household that corresponds to its rank in the
RRA ranking. Thus, only the wealth values
from the survey data are used in the construc-

4. VALIDATION RESULTS
(a) Accuracy rate of possessions

Table 2. Descriptive statistics of regression variables (n ˆ 282)
Unit
Possession errors
Boat, engine, and chainsaw
Canoe
Large ®shing net
Shotgun
Consumer durables
Shop asset and other house
Upland
High Levee
Mud¯at
Ranking errors
Matched capital in ranka
All capital in ranka
Matched landb
Value errors
Matched capital in valuec;d
All capital in valuec;d
Independent variables
Number of adults
Household social status
Total land
Total nonland assetsd
a

0 ˆ no
0 ˆ no
0 ˆ no
0 ˆ no
0 ˆ no
0 ˆ no
0 ˆ no
0 ˆ no
0 ˆ no

error,
error,
error,
error,
error,
error,
error,
error,
error,

Mean

1 ˆ error
1 ˆ error
1 ˆ error
1 ˆ error
1 ˆ error
1 ˆ error
1 ˆ error
1 ˆ error
1 ˆ error

Rank
Rank
Rank
S/.
S/.
Person
0 ˆ no, 1 ˆ yes
ha
S/.

0.19
0.15
0.18
0.13
0.38
0.22
0.09
0.41
0.30
8.8
9.5
1.2

S.D.

0.39
0.36
0.39
0.34
0.49
0.41
0.28
0.49
0.46
10.1
11.3
1.2

Min.

Max.

0
0
0
0
0
0
0
0
0

1
1
1
1
1
1
1
1
1

0
0
0

56
63
6

588
1037

1015
2487

0
0

6955
17305

3.6
0.2
3.4
2383

2.2
0.4
3.8
7776

1
0
0
0

13
1
19
80550

Possession
rates (%)
18
88
25
23
57
17
21
51
41

Absolute value of ranking errors.
Absolute value of ranking errors, where values of 6 and 7 are combined to one category due to very few observations of 7.
c
Absolute value of capital value errors.
d
Capital values and total nonland assets are in 1996 Peruvian Sol (US$1 ˆ S/. 2.6).
b

RAPID RURAL APPRAISAL

1969

Figure 1. Relationship between possession rate and accuracy rate for capital assets and land holdings.

entire group. 10 At 62%, consumer durables
had the lowest accuracy rate for reasons
discussed below, whereas accuracy rates for
land holdings vary considerably across land
types, with the highest score for upland (91%)
and the lowest for high levee (59%).
Also reported in Table 2 are the average
possession rates by asset groups. Among these
assets, only canoes are widely held in the
sample. Several asset groups have low average
possession rates, ranging from 17% to 25% of
households. Average possession rates for
consumer durables and two land types (high
levee and mud¯at) fall near 50% of households.
Data on accuracy and average possession
rates for capital and land asset groups are
mapped in Figure 1. As expected, possession
errors,
especially
of
physical
capital
…R2 ˆ 0:51†, appear to be related quadratically
to the average possession rate in a village. In
other words, identi®cation errors are lowest for
high and low possession rates, and greatest for
assets held by 35±65% of the households (e.g.,
consumer durables).
The lower accuracy rates for two land
typesÐhigh levee and mud ¯atÐwere noted
especially in three of the villages (as seen in
Table 3). For high-levee land, it would appear
that some confusion arose among respondents
about the distinction between ``high'' and
``low'' levees, as the di€erence depends on
whether the land is ¯ooded ``occasionally'' or
``regularly.'' Similarly, in one of the villages,
there seems to have been considerable confu-

sion in distinguishing between mud¯at and
sandbar holdings. 11
(b) Possession error estimation
Sources of error in possession identi®cation
were assessed for eight of the nine asset groups
discussed in Table 2 using a bivariate Probit
analysis (upland was omitted due to insucient
observations). The village and asset variables
tested with respect to possession errors include:
village size (as measured by population); village
dummies; and the average possession rate of the
asset in the village. In the nonland asset groups,
village size and average possession rates were
used to identify sources of error associated with
two meaningful sources of observability problems: the number of households, and the
frequency of asset possession. For the land
types, village dummies were used to capture the
very low accuracy rates for land in the three
villages discussed above. The household status
variables included in the error regressions
comprise: number of adults (family members
greater than or equal to 15 years old residing in
the village); community leadership role (a
dummy variable denotes if a household
member is currently or has been a village
leader, such as lieutenant governor, municipal
agent, or president of a community association
in recent years); 12 and total land and total
nonland assets of the household.
Results of the Probit analyses are reported in
Table 3 for each of the eight asset groups.

1970

Table 3. Probit estimates of possession errors in RRA (n ˆ 282)a

a

Canoe

Large ®shing
net

Shotgun

Consumer
durables

Shop asset
and other
house

)2.26 (3.0)
0.03 (0.6)
0.33 (1.3)

16.7 (0.7)
)0.12 (2.1)
)0.35 (1.2)

)2.46 (5.6)
0.08 (2.1)
)0.01 (0.0)

)2.33 (3.7)
0.05 (1.1)
)0.12 (0.5)

)2.75 (1.6)
0.11 (3.1)
0.32 (1.5)

)1.20 (2.8)
0.02 (0.4)
0.50 (2.0)

)0.63 (1.8)
)0.01 (0.2)
)0.03 (0.1)

)1.73 (4.3)
0.12 (3.1)
)0.38 (1.5)

)0.03 (0.5)
0.002 (0.5)
0.39 (1.0)
)0.04 (0.8)
8.82 (3.2)
)9.77 (2.8)
)0.41 (1.0)

0.21 (2.6)
)0.01 (2.2)
0.47 (1.1)
)0.09 (1.0)
5.17 (0.9)
)8.90 (0.8)
)0.13 (0.3)

0.09 (1.4)
)0.01 (1.5)
0.83 (2.2)
)0.10 (1.9)
2.28 (0.4)
)0.93 (0.2)
1.25 (3.6)

)0.09 (1.4)
0.01 (1.3)
3.29 (5.4)
)0.35 (3.0)
11.9 (1.4)
29.9 (1.4)
1.35 (1.7)

)0.02 (0.3)
)0.003 (0.5)
1.15 (1.8)
)0.38 (1.3)

0.12
)0.01
0.88
)0.17

)0.11 (1.5)
0.01 (2.0)
1.92 (5.2)
)0.20 (3.7)
6.11 (0.7)
)4.87 (0.2)
)0.14 (0.3)

66.7 (9)

)0.14 (1.8)
0.01 (1.2)
0.60 (1.7)
)0.06 (1.2)
)30.7 (0.6)
13.0 (0.4)
)0.67 (1.4)

28.5 (9)

24.1 (9)

19.4 (9)

48.8 (9)

Absolute values of asymptotic t-ratios are in brackets.
All 29 observations in village 2, where no mud ¯at exist and correction rate of mud ¯at was 100%, are not included.
*
Signi®cant at 5%.
**
Signi®cant at 1%.
b

75.6 (9)

High levee

1.78 (3.8)
1.16 (2.7)
0.31 (0.8)
0.44 (1.3)
0.06 (0.2)
0.23 (0.6)
)0.07 (0.2)
54.9 (13)

(1.5)
(1.7)
(1.9)
(1.9)

0.20 (0.4)
0.73 (1.7)
0.49 (1.3)
1.23 (3.1)
0.46 (1.0)
)0.43 (0.7)
54.2 (12)

WORLD DEVELOPMENT

Constant
No. adults
Community leadership role
(0 ˆ no, 1 ˆ yes)
Total land (ha)
Total land2
Total nonland assets (104 S/.)
Total nonland assets2
Possession rate (%)
Possession rate2
Village size (103 )
Village dummy 1
Village dummy 2
Village dummy 3
Village dummy 4
Village dummy 5
Village dummy 6
Village dummy 7
LR test …v2…d:f:† †

Mud ¯atb

Boat, engine,
and chain saw

RAPID RURAL APPRAISAL

Possession rate errors of the RRA exercise are
explained primarily by certain features related
to household status, speci®cally the number of
adults and total nonland asset holdings, which
are signi®cant in about half of the eight
possession error regressions. Only one of the
coecient estimates, the number of adults in
the canoe error regression, has a negative and
signi®cant sign; i.e., it is the only source of
``bias'' that tends to reduce the rate of possession error (recall the very high average possession rate for canoes). At the same time, the
number of adults was a positive source of bias
for large ®shing nets, consumer durables, and
mud ¯at holdings. We explain these positive
bias results as follows: RRA respondents
appear to view households with more adults as
being more likely to own more ®shing nets, to
be richer and thus able to a€ord more consumer durables, and to be capable of providing
the labor necessary to cultivate more commercially-oriented land, i.e., mud ¯ats. 13
Total nonland assets are positively related to
possession error for three asset types: boat,
engine, and chain saw; shop assets and other
house; and, consumer durables. RRA respondents may consider richer households to be
more likely to own these more expensive and/or
consumption-oriented assets, and may have
diculties both in directly observing the latter
two and in specifying who actually owns the
boats or engines (often used by wealthier
households to transport their products to
markets under a rental or sharing arrangement). Each of the other two ``status''
measures, community leadership and total land
holdings, are only signi®cant sources of error
for a single asset type.
Village level factors and asset possession
rates are signi®cant in very few of the possession error regressions. The asset possession rate
coecient was only signi®cant for large ®shing
nets, which might be explained by the propensity for sharing arrangements to emerge in
villages with a high possession rate of large
nets. Village population was only signi®cant as
an explanatory factor in the possession error
regression for consumer durables, which seems
logical given the greater likelihood that RRA
respondents may not know which assets are
held in the homes of households in a larger
community. Finally, village dummy variables
were among the few signi®cant variables in the
land possession regressions. Overall, those
factors related directly to variance (i.e., village
and asset possession rates) play a weak role in

1971

explaining possession errors for nonland assets
but a stronger role for land in the three villages
where land types were confused by RRA
respondents.
(c) Accuracy rate of wealth ranking and
strati®cation
Rankings for capital and land wealth in the
RRA and survey stages are compared in Figure
2 in a series of 3  3 grids. A correct observation arises where a household falls in the same
stratum in both the RRA and survey stages,
and these are seen in the diagonal that runs
across the table grids from ``southwest'' to
``northeast.'' A random guess would be correct
one-third of the time. All of the wealth rankings are much greater than one-third, ranging
from a high of 69% in the ``matched capital''
case to a low of 56% in the ``matched land''
case. More important for our purposes, most of
the errors in the two capital rankings occurred
because households were misplaced between
the middle and bottom categories, where the
underlying di€erences in wealth are actually
quite small (one minor asset may be all that
separates them). By contrast, only four households in the ``matched capital'' case and ®ve in
the ``all capital'' case (less than 2% of the entire
sample) were identi®ed in the bottom tier in the
RRA stage but proved to be in the top tier
from the survey data. Thus, our e€ort to
oversample the wealthier households appears
to have been quite successful; accuracy rates
are high and we sampled all households in the
top wealth group.
As expected, the matched land results
proved to be less accurate than the capital
items because of the ordinal manner in which
the land categories were constructed. Nonetheless, even here a relatively small number of
large errors occurred with households in the
bottom of the RRA stage who later were
found by the survey data to be in the top
group. Finally, in terms of strati®cation by
total wealth (i.e., combined capital and land
rankings), the highest success was found in the
``top'' category, with a score of 70% correct;
only four of 74 households in the bottom
wealth category according to the RRA data
proved by the survey to be in the top group. 14
Overall, these ranking error observations
strongly support the validity of the possessionbased approach to wealth rankings as a basis
for strati®cation.

1972

WORLD DEVELOPMENT

Figure 2. Frequency comparison of strati®cation between RRA and survey data. (Note: Accuracy rates of wealth
ranking are in brackets, n ˆ 282.)

(d) Ranking error estimation
Two sets of regression analyses are considered
here: one explores the sources of ranking errors,
the other examines value errors in capital wealth.
Ranking errors are de®ned as the absolute
di€erence between the rank of the household in
the RRA and the survey stages (i.e., only for
households observed in both stages). Using this
measure, we expected that villages with more
households would have larger ranking errors.
Estimates of the factors that explain ranking
errors are obtained for ``matched capital'' and
``all capital'' using GLS to adjust for heteroskedasticity, and for ``matched land'' using an
ordered Probit analysis. The same village
dummy and household status variables used in
the possession error analysis are employed in our
analysis of ranking error estimation.

Regression results on ranking error for
``matched capital'' and ``all capital'' show
limited evidence of bias (Table 4). Indeed, the
only coecients found to be signi®cantly related to ranking errors at the 5% con®dence level
are nonland assets and village dummies (especially those for the two largest villages, i.e., 4
and 5). The number of adults, which was a
signi®cant source of bias in the possession error
analysis, was not found to be signi®cant in
terms of ranking errors. Nonetheless, the coef®cient estimates on the total nonland asset
terms in the matched capital regression suggest
that ranking errors are smaller for households
with larger nonland asset holdings. At the
lower end of the distribution, ranking is highly
sensitive to ``small errors''Ðbeing wrong on a
single nonland asset can change their ranking
substantially. Although the statistical signi®-

RAPID RURAL APPRAISAL

1973

Table 4. Estimates of ranking errors in RRAa (n ˆ 282)

Constant
No. adults
Community leadership role (0 ˆ no, 1 ˆ yes)
Total land (ha)
Total landb
Total nonland assets (104 S/.)
Total nonland assetsb
Village dummy 1
Village dummy 2
Village dummy 3
Village dummy 4
Village dummy 5
Village dummy 6
Village dummy 7
B±P test …v2…13† †c
LR test …v2…13† †

Matched capital
GLS

All capital
GLS

4.03 (3.9)
)0.01 (0.0)
)0.58 (0.8)
0.37 (1.5)
)0.02 (1.9)
)4.60 (4.1)
0.58 (2.5)
0.18 (0.2)
2.27 (2.0)
0.76 (0.5)
10.2 (6.0)
9.13 (5.6)
0.32 (0.2)
)0.41 (0.3)
102

4.29 (3.7)
)0.19 (1.1)
)1.39 (1.8)
0.48 (1.7)
)0.03 (1.6)
)2.58 (1.4)
0.18 (0.7)
0.94 (0.7)
2.03 (1.6)
0.91 (0.6)
11.7 (6.8)
10.2 (5.8)
0.70 (0.5)
)0.31 (0.3)
138

Matched landed/
Ordered probitb
0.10 (2.9)
)0.13 (0.6)
0.18 (2.7)
)0.01 (2.0)
0.71 (2.7)
)0.13 (2.3)
0.69 (1.8)
0.91 (2.5)
0.41 (1.2)
0.07 (0.3)
0.86 (2.9)
0.88 (2.7)
0.61 (1.7)
56

a

Absolute values of asymptotic t-ratios are in brackets.
The table does not provide coecient estimates of six constants.
c
Breuch±Pagan heteroskedasticity test for all explanatory variables.
*
Signi®cant at 5%.
**
Signi®cant at 1%.
b

cance of this result does not hold up in the ``all
capital'' regression model, the signs of the
estimates are the same. Overall, only the variables for total nonland assets and village size
prove to have signi®cant explanatory power in
both the possession and wealth ranking error
analyses. As such, further attention to these
sources of error in the possession analysis may
help to improve the accuracy of the overall
wealth rankings.
In the case of ``matched land,'' an aggregate
land index (0±6) 15 provides richer information
for regression tests than could be used in the
possession error analysis. Not surprisingly,
somewhat higher levels of signi®cance are found
on the coecients for certain explanatory
factors. Again, we ®nd a positive relationship
between the number of adults and land ranking
error, and a concave quadratic relationship
between total land as well as total nonland
assets and land ranking errors. The maximum
errors for those two measures occur at about 10
ha. and S/. 28,000 (about $US10,700), respectively, suggesting that the land ranking errors
e€ectively increase with wealth holdings far into
the narrow tail of the wealth distribution. This
®nding also suggests that most of the land
ranking error is likely to be found in assessments
of relatively better-o€ households.
The ®nal set of regression analyses examines
the value of errors in the ``matched capital'' and

``all capital'' measures (Table 5). Similar results
are found across these two regression models,
although village dummies contribute signi®cantly to explaining value errors only in the
case of matched capital. In both models, only
the coecients on total nonland assets are
signi®cant at the 5% con®dence level. Their
concave shape and the very high value of
wealth at which the curve reaches its in¯ection
point suggests that value errors increase with
wealth and more so in the ``all capital'' case
than with ``matched capital''. Although such
value errors may not be problematic for strati®cation purposes, if the sampling strategy
incorporates a high proportion (as in our case)
of the wealthiest households, there could be a
problem if RRA methods are used solely to
estimate household wealth, with no follow-up
surveying of the better-o€ households. Our
error analysis suggests that closer study would
be required to determine accurately the relative
rankings and actual holdings of households in
the upper tier (i.e., top 10±20%) of the wealth
spectrum.
5. CONCLUSIONS
The three goals of this paper were: (a)
to propose a ``possession-based'' RRA method
for creating more ®nely strati®ed wealth

1974

WORLD DEVELOPMENT
Table 5. GLS estimates of capital value errors in RRA (n ˆ 282)a
Matched capital

Constant
No. adults
Community leadership role (0 ˆ no, 1 ˆ yes)
Total land (ha)
Total land2
Total nonland assets (104 S/.)
Total nonland assets2
Village dummy 1
Village dummy 2
Village dummy 3
Village dum