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. ÐResearchers and practitioners often use rapid rural appraisal (RRA) methods to secure a representative, accurate, and cost-eective 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 oers new validation methods for RRA eorts, 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 eorts are suggested. Ó 2000 Elsevier Science Ltd. All rights reserved.
Key words Ðrapid 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 dierent forms of capital, in the economic life of rural peoples (Barham, Coo-mes, & Takasaki, 1999; Bebbington, 1999;
Dercon, 1998; Reardon & Vosti, 1995; Turner, 1999). Dierences in the holding of land and other assetsÐeven dierences that appear small to outside observersÐcan profoundly in¯uence local natural resource use and human welfare outcomes. Typically, however, national house-hold surveys and censuses focus on income and expenditures rather than on assets, giving scant
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*We wish to thank our ®eld teamÐCarlos Rengifo, Doris Diaz and Jaime SalazarÐfor their advice, enthu-siasm and Herculean eort that made this project possible. Special thanks are owed also to theribere~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
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 (FAS-ID), 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 theFonds pour la Formation de Chercheurs et l'Aideala Recherche. Any errors of interpretation are solely the responsibility of the authors. Final revision accepted: 28 March 2000.
attention to nonland wealth forms, especially what Bebbington and others call social, natu-ral, 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 dierences among rural populations in an eective 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 incorpo-rate 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 misinfor-mation, disinformisinfor-mation, or recall problems in individual interviews. 1RRA validation eorts, 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 facili-tator also secures disaggregated wealth (rank-ing) 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, research-ers 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 dierences in wealth as well as in the composition of household wealth portfolios (e.g., who owns how much of which forms of capital). When-ever speci®c subgroups are targeted by research or development programsÐsuch as the ``poor-est of the poor'' who are of the great``poor-est 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 eorts, 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 meth-ods. Indeed, it seems imperative that research-ers 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, aord 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 imme-diately, it enabled us to move past the perva-sive, 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 criti-cally, the approach also allowed us to stratify our sample based on holdings of two dierent 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 ®sh-ing, hunt®sh-ing, forest product gather®sh-ing, and other forms of extraction.2 For other researchers, even where these particular attri-butes of the possession-based approach may not be of direct relevance, our approach can provide complementary information to total wealth rankings.
can data on individual wealth holdings be used to construct valid wealth distributions, espe-cially if the list of wealth items or categories is not comprehensive? This paper examines these concerns comparing information from our RRA eorts 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 Peru-vian 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 addi-tion to proposing and testing the validity of this approach, this paper makes a broader contri-bution by oering new methods for validating RRA rankings and identifying sources of error. These error analysis methods could be applied to other RRA techniques that gather informa-tion 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 vali-dation tests. In Section 3, the RRA valivali-dation 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 envi-ronments and other rural areas.
2. BACKGROUND (a) Research setting
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.
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, Vasquez, Ghersi, Soini, & Pinedo, 1991; Rodrõguez, Rodrõguez, & Vasquez, 1995). Over 170 communities are found in and around the PSNR, comprised largely of mestizo peasants (known locally as riberen~os) who make their living from ¯ood-plain 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 gath-ering become more productive during the high-water 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 peas-ants 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) nonpro-ductive capital (i.e., consumer durables, shops, houses owned elsewhere); and (iv) livestock (i.e., poultry, pigs, cattle, and water bualo) (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 dierent 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 ubiqui-tous.
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-eective 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 dier signi®cantly across villages. Indeed, based on income shares in dierent activities, the sample villages can be readily grouped into agricul-tural, 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 eorts were viewed as likely to have both signi®cant and unintended eects on local resource use activi-ties 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 n300according to a strati®ed sampling strategy designed to over-samplewealthierhouseholds 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 atten-dant environmental implications. The strati®-cation was based on the RRA wealth ranking eort 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 work-ing with a small group of long-term village
residents n3±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 commu-nity, 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 infor-mation 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
(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 capi-tal'' 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 dierences 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 respon-dents 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 impli-cations 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 compo-nentsÐvariance and bias (Judge, Hill, Griths, Lutkepohl, & Lee, 1988, p. 72). In this context, variance errors arise when respondents make attribution errors for reasons that are inde-pendent of household characteristics, whereas bias errors arise when respondents make attri-bution errors based on other information about
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 ``observ-ability'' 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 indi-vidual 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 infer-ences 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 aect RRA accuracy.
(a) Validation tests
typically employ correlation analyses between wealth rankings in the RRA and the survey stages. Here, we de®neaccuracy 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 ecacy of our strati®cation eort.
Three other validation methods for RRA evaluation are proposed (Table 1). One is an accuracy rate of possessionmeasure, 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 dierence between a householdÕs wealth rank in the RRA stage and in the survey stage, i.e.,
jRRRA;i±Rs;ij; the larger the dierence 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 magni-tude ofwealth valueerrors 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 rank-ings 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 ``®ne-tuning'' 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
Table 1. Four validation methods for RRA wealth possession identi®cation and ranking methods
Possession Ranking Accuracy tests Accuracy rate of possession
[0±100%]
Accuracy rate of wealth ranking
[0±100%] Error estimation Possession error
P jPRRAÿPsj
Ranking error R jRRRAÿRsj
wherePj1 if possessed;0 otherwise
[0no error; 1error]
whereRjis ranking
Weighted ranking(value)error V jVs Rs ÿVs RRRAj
whereVs(Rj) is the survey value that
corresponds to ranking Potential explanatory variables for regressions
Variable Description
Size of village Total population
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 oers three other village-level variables that are likely to aect 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) thepredominant economic activityof 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 alter-natively 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 there-fore used instead). The potential eects of asset sharing or rental arrangements on possession errors are captured by comparing the regres-sion results for the dierent 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 aect 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 dicult to observe, the RRA exercise might contain more errors among these ``high'' status house-holds because respondents will tend consis-tently 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 vari-ables could be associated with lower or higher
possession errors and rankings. For this reason we proer 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®ca-tion, 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 aects the accuracy of the overall rankings.
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 dierent (e.g., single unit price on all capital items, adjusted for size, in the RRA stage versus the respondentsÕ esti-mate 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:
Ve;i jVs;iÿVs;i RRRA;ij; 1
where Vs;i is the value of householdiÕs capital wealth in the survey, and Vs;i(RRRA;i) is the hypothetical capital value of householdi, 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-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.
4. VALIDATION RESULTS (a) Accuracy rate of possessions RRA respondents eectively 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 assetgroups and thus count an ``error'' on any item within the group as an ``error'' for the
Table 2. Descriptive statistics of regression variables (n282)
Unit Mean S.D. Min. Max. Possession rates (%)
Possession errors
Boat, engine, and chainsaw 0no error, 1error 0.19 0.39 0 1 18 Canoe 0no error, 1error 0.15 0.36 0 1 88 Large ®shing net 0no error, 1error 0.18 0.39 0 1 25 Shotgun 0no error, 1error 0.13 0.34 0 1 23 Consumer durables 0no error, 1error 0.38 0.49 0 1 57 Shop asset and other house 0no error, 1error 0.22 0.41 0 1 17 Upland 0no error, 1error 0.09 0.28 0 1 21 High Levee 0no error, 1error 0.41 0.49 0 1 51 Mud¯at 0no error, 1error 0.30 0.46 0 1 41
Ranking errors
Matched capital in ranka Rank 8.8 10.1 0 56
All capital in ranka Rank 9.5 11.3 0 63
Matched landb Rank 1.2 1.2 0 6
Value errors
Matched capital in valuec;d S/. 588 1015 0 6955
All capital in valuec;d S/. 1037 2487 0 17305
Independent variables
Number of adults Person 3.6 2.2 1 13 Household social status 0no, 1yes 0.2 0.4 0 1
Total land ha 3.4 3.8 0 19
Total nonland assetsd S/. 2383 7776 0 80550 a
Absolute value of ranking errors.
b
Absolute value of ranking errors, where values of 6 and 7 are combined to one category due to very few obser-vations of 7.
cAbsolute value of capital value errors. d
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
R20:51, 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 dierence 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 insucient observations). The village and asset variables tested with respect to possession errors include: village size(as measured by population);village dummies; and theaverage possession rateof 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 prob-lems: 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 assetsof the household.
Results of the Probit analyses are reported in Table 3 for each of the eight asset groups.
Table 3. Probit estimates of possession errors in RRA (n282)a
Boat, engine, and chain saw
Canoe Large ®shing net
Shotgun Consumer durables
Shop asset and other house
High levee Mud ¯atb
Constant )2.26 (3.0) 16.7 (0.7) )2.46 (5.6) )2.33 (3.7) )2.75 (1.6) )1.20 (2.8) )0.63 (1.8) )1.73 (4.3)
No. adults 0.03 (0.6) )0.12 (2.1) 0.08 (2.1) 0.05 (1.1) 0.11 (3.1) 0.02 (0.4) )0.01 (0.2) 0.12 (3.1)
Community leadership role (0no, 1yes)
0.33 (1.3) )0.35 (1.2) )0.01 (0.0) )0.12 (0.5) 0.32 (1.5) 0.50 (2.0) )0.03 (0.1) )0.38 (1.5)
Total land (ha) )0.11 (1.5) )0.14 (1.8) )0.03 (0.5) 0.21 (2.6) 0.09 (1.4) )0.09 (1.4) )0.02 (0.3) 0.12 (1.5)
Total land2 0.01 (2.0) 0.01 (1.2) 0.002 (0.5)
)0.01 (2.2) )0.01 (1.5) 0.01 (1.3) )0.003 (0.5) )0.01 (1.7)
Total nonland assets (104S/.) 1.92 (5.2) 0.60 (1.7) 0.39 (1.0) 0.47 (1.1) 0.83 (2.2) 3.29 (5.4) 1.15 (1.8) 0.88 (1.9) Total nonland assets2
)0.20 (3.7) )0.06 (1.2) )0.04 (0.8) )0.09 (1.0) )0.10 (1.9) )0.35 (3.0) )0.38 (1.3) )0.17 (1.9)
Possession rate (%) 6.11 (0.7) )30.7 (0.6) 8.82 (3.2) 5.17 (0.9) 2.28 (0.4) 11.9 (1.4)
Possession rate2
)4.87 (0.2) 13.0 (0.4) )9.77 (2.8) )8.90 (0.8) )0.93 (0.2) 29.9 (1.4)
Village size (103)
)0.14 (0.3) )0.67 (1.4) )0.41 (1.0) )0.13 (0.3) 1.25 (3.6) 1.35 (1.7)
Village dummy 1 1.78 (3.8) 0.20 (0.4)
Village dummy 2 1.16 (2.7)
Village dummy 3 0.31 (0.8) 0.73 (1.7)
Village dummy 4 0.44 (1.3) 0.49 (1.3)
Village dummy 5 0.06 (0.2) 1.23 (3.1)
Village dummy 6 0.23 (0.6) 0.46 (1.0)
Village dummy 7 )0.07 (0.2) )0.43 (0.7)
LR test v2
d:f: 66.7 (9) 28.5 (9) 24.1 (9) 19.4 (9) 48.8 (9) 75.6 (9) 54.9 (13) 54.2 (12)
a
Absolute values of asymptotict-ratios are in brackets.
bAll 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%.
WORLD
DEVELO
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 coecient 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 sion error (recall the very high average posses-sion 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 aord more consu-mer durables, and to be capable of providing the labor necessary to cultivate more commer-cially-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 respon-dents may consider richer households to be more likely to own these more expensive and/or consumption-oriented assets, and may have diculties 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 arrange-ment). 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 posses-sion error regresposses-sions. The asset possesposses-sion rate coecient was only signi®cant for large ®shing nets, which might be explained by the propen-sity 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
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 33 grids. A correct observa-tion 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 rank-ings 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 dierences in wealth are actually quite small (one minor asset may be all that separates them). By contrast, only four house-holds 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 eort 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.
(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 dierence 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 heteroske-dasticity, 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 coecients found to be signi®cantly rela-ted to ranking errors at the 5% con®dence level are nonland assets and village dummies (espe-cially 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
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 vari-ables 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)15provides 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 coecients 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), respec-tively, suggesting that the land ranking errors eectively 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 coecients 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 strat-i®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
Table 4. Estimates of ranking errors in RRAa(n282)
Matched capital GLS
All capital GLS
Matched landed/ Ordered probitb
Constant 4.03 (3.9) 4.29 (3.7)
No. adults )0.01 (0.0) )0.19 (1.1) 0.10 (2.9)
Community leadership role (0no, 1yes) )0.58 (0.8) )1.39 (1.8) )0.13 (0.6)
Total land (ha) 0.37 (1.5) 0.48 (1.7) 0.18 (2.7)
Total landb
)0.02 (1.9) )0.03 (1.6) )0.01 (2.0)
Total nonland assets (104S/.)
)4.60 (4.1) )2.58 (1.4) 0.71 (2.7)
Total nonland assetsb 0.58 (2.5) 0.18 (0.7)
)0.13 (2.3)
Village dummy 1 0.18 (0.2) 0.94 (0.7) 0.69 (1.8) Village dummy 2 2.27 (2.0) 2.03 (1.6) 0.91 (2.5)
Village dummy 3 0.76 (0.5) 0.91 (0.6) 0.41 (1.2) Village dummy 4 10.2 (6.0) 11.7 (6.8) 0.07 (0.3)
Village dummy 5 9.13 (5.6) 10.2 (5.8) 0.86 (2.9)
Village dummy 6 0.32 (0.2) 0.70 (0.5) 0.88 (2.7)
Village dummy 7 )0.41 (0.3) )0.31 (0.3) 0.61 (1.7)
B±P test v2 13c 102 138
LR test v2 13 56
aAbsolute values of asymptotict-ratios are in brackets.
bThe table does not provide coecient estimates of six constants. c
Breuch±Pagan heteroskedasticity test for all explanatory variables.
*
Signi®cant at 5%.
distributions and more detailed portrayals of wealth portfolios in rural communities; (b) to present methods for validating RRA eorts, in general, and speci®cally for a possession-based approach to RRA wealth assessment; and, (c) to test the possession-based approach using data from our ongoing research on wealth and resource use among forest peasant households in eight riverine communities of the Peruvian Amazon. Here we conclude by summarizing our ®ndings and indicating ways of improving the application of RRA methods.
Our study suggests that a possession-based approach is a valid means of identifying asset holdings and ranking households, even in tropical rain forest environments, where households own diverse assets and participate in a wide range of agricultural and extractive activities. This claim is supported by our ®nd-ings that: (i) RRA errors in the possession accuracy rate of respondents were low (i.e., less than 20%) for groups of productive capital assets and only somewhat higher for groups of consumer durables and certain types of agri-cultural land; (ii) major errors in RRA wealth rankings, especially the under-ranking of households were quite uncommon (less than 6% of the ``wealthiest'' households in the study were ranked in the RRA stage to be among the poorest); (iii) the most common error in the rankings was between the bottom and middle groups, where actual wealth dierences tend to be quite small; (iv) these results held up in both the ``matched capital'' and ``all capital'' cases,
which means that a subset of wealth items, if well chosen, can suce; and, (v) RRA rankings placed households in the correct wealth group suciently well to ensure that our eorts to stratify included enough of the households of interest (i.e., wealthiest) to permit further analysis.
To assess the sources of attribution error in RRA methods, we proposed and applied four validation methods. Particular attention was given to identifying the sources of variance and bias errors in possession identi®cation and wealth rankings using error regression methods that could readily be applied to future RRA validation and accuracy tests. Our validation eorts also yielded ®ndings that may be used to re®ne RRA exercises aimed at identifying possessions and creating wealth rankings. Possession errors were found to be higher: for assets owned by about one-half of the village respondents (whereas assets held by very few or almost all households had less possession error); for consumer durables in larger villages; for assets held by large households where probability of possession is likely to rise with household size; and, for shops and houses owned outside of the village by households with high social status or relatively high capital wealth. Ranking errors in ``matched capital'' wealth were found to be higher for the capital poor (for whom a single possession of relatively low value could in¯uence greatly their rank). Errors in land wealth rankings were highest for larger households and for richer households in
Table 5. GLS estimates of capital value errors in RRA (n282)a
Matched capital All capital
Constant )6.8 (0.1) 97 (0.5)
No. adults 9.0 (0.8) )14 (0.6)
Community leadership role (0no, 1yes) 194 (1.9) 364 (1.3) Total land (ha) 21 (0.9) 67 (1.1) Total land2
)1.0 (0.5) )4.4 (0.8)
Total nonland assets (104S/.) 1393 (4.5) 3775 (6.5)
Total nonland assets2
)174 (3.2) )436 (3.5)
Village dummy 1 41 (0.4) )124 (0.5)
Village dummy 2 612 (3.2) 550 (2.4)
Village dummy 3 109 (1.0) )214 (1.0)
Village dummy 4 248 (2.0) 192 (0.9)
Village dummy 5 295 (2.4) 111 (0.5)
Village dummy 6 16 (0.1) )184 (1.0)
Village dummy 7 107 (0.9) )109 (0.5)
B±P v2 13b 339 634
aAbsolute values of asymptotict-ratios are in brackets.
bBreuch±Pagan heteroskedasticity test for all explanatory variables. *
Signi®cant at 5%.
land holdings and/or nonland asset holdings. Overall then, household level factors aect RRA wealth rankings dierently for distinct asset types: demographic status matters for land; nonland wealth status aects the accuracy of rankings for land and capital in opposite ways; and social status seems to matter neither for land nor capital.
The possession-based approach to wealth can be improved by re®ning the ex ante design of RRA exercises. First, the initial wealth list can be modi®ed with the help of respondents to ensure that omissions are avoided and ambi-guities (over, for example, land types) are resolved. SecondÐalthough this was not explicitly testedÐwe learned that respondentsÕ advice on standard units of measure for asset items also helps to sharpen the eventual wealth ranking construction. Third, initial discussion with participants about asset possession rates will enable the RRA facilitator to develop more focused follow-up questions on those assets with moderate possession rates, thereby reducing associated attribution errors.
While the RRA exercise is underway, it would be useful for the facilitator to be particularly sensitive to any consistent tendency among respondents to rely on household features in drawing inferences about the wealth portfolios of others; such tendencies can give rise to bias. In our study, household size, leadership experience, and overall wealth were all associated with possession errors. These status-related errors can be reduced by identi-fying during the initial stage of the RRA exer-cise, for example, those households that are large in size or whose members are (or were) village leaders, and then probing with the
respondents regarding their certainty about the actual holdings of certain possessions. Where uncertainty is revealed, some limited follow-up with speci®c households in the community may be warranted, especially if the possessions are high-valued assets that might make a signi®cant dierence in the overall rankings. Similarly, some households will emerge during the RRA exercise as among the wealthiest in the village. For them, less observable items, such as consumer durables, or houses in other places, might be discussed with respondents to explore the basis for their attribution choices or resolved via spot checks with the speci®c households. Such measures would reduce possession and value errors and raise the accuracy of overall wealth rankings.
In conclusion, a possession-based RRA approach to wealth assessment and ranking appears to be a highly ecient means of gath-ering crucial economic information at low costÐeven in environments where peasants engage in diverse livelihood activities and hold varied asset portfolios. The value of RRA methods will continue to grow as gains are made on two fronts: strengthening the exercises based on further validation testing under diverse conditions; and, probing the role of wealth and other household factors in shaping key economic and environmental outcomes. Such gains may be simultaneous and self-rein-forcing, as synergy develops through the combination of improved RRA methods with more nuanced analyses of household dieren-tiation and resource use. Together, these advances promise a broader and deeper understanding of the diversity of economic life among rural peoples in the developing world.
NOTES
1. To the best of our knowledge, only one study, reported in Chambers (1994a), has done the necessary follow-up eort to identify discrepancies between RRA and survey rankings; this study found that most errors occurred on the survey side of the undertaking. 2. Where labor, land, and capital markets are likely to be imperfect or ``missing,'' household wealth holdings can be a good predictor of household participation and intensity of involvement in certain types of resource use activities or practices that may aect development and conservation outcomes (Coomes, Barham, & Takasaki, 2000; Takasakiet al., forthcoming; Takasaki, Barham, & Coomes, 2000).
3. Commercial rice cropping is dominant in agricul-tural villages, ®shing for multispecies food ®sh in ®shing villages, and mixed activities including forest product gathering and/or other aquatic resource extrac-tion such as ornamental ®sh and turtle egg gathering in mixed villages (see Takasaki et al., forthcoming). Hunting is only important for a few households in certain villages. Although poultry are very common, livestock plays a minimal role in wealth holdings except for small herds of cattle and/or bualo held by few owners.
villages than in smaller ones. Six of the eight villages in the study had less than 55 households. The other two had 80 and 129 villagers. RRA assessment may have been improved if it had been done in neighborhoods or multiple times in these larger villages. Below, we test the importance of village size as a source of error and ®nd that it is by no means a pervasive source of error in the possession and ranking eorts.
5. Minor assets, such as axes, shovels, and machetes, that are both ubiquitous and of limited valuable were omitted from both surveys.
6. It was not reasonable to ask these respondents to estimate the size of the holdings of other households of multiple parcels that are dispersed around the village. Moreover, in the absence of a land market (land is transferred primarily by gift or inheritance), it is very dicult to obtain reasonable monetary estimates of the value of land holdings. Thus, no uniform aggregated wealth measure combining capital and land was created from the data.
7. Three groups were de®ned as follows: top groupÐ households in the top 10% of either capital holdings or land holdings; bottom groupÐhouseholds in the bottom 30% of capital holdings and with little or no land; and middle groupÐall households not in either top or bottom group, where the range of little land in terms of aggregate land index was de®ned in each village so that it re¯ects land index distribution patterns.
8. We chose to use householdsÕsubjective valuations of capital wealthÐinstead of a single uniform valuation across all households for three reasons. First, respon-dents can incorporate into their valuation the current working condition of the asset, and this is especially important given the rapid depreciation that often occurs in this tropical riverine environment. Second, the alter-native of deriving a set of values according to each assetÕs vintage, physical condition, and secondary market would be prohibitively high in cost and fraught with measurement errors of its own. Third, subjective valuation best re¯ects the householdÕs perception of its wealth holdings, which given our studyÕs broader interest in the role of wealth portfolios in shaping activity choice, is a crucial measure. This error source could be examined for its eects by comparing the outcomes of this RRA wealth ranking with survey data using the same unit prices used in the RRA stage. Such an exercise, however, would essentially reduce to examining the implications of possession errors for wealth rank-ings. As we examine possession errors explicitly here, this additional comparison (using uniform unit prices for
physical capital across the two stages) is not pursued in this paper.
9. Thresholds were targeted to correspond to the ``categories'' de®ned in the RRA stage, with zero and small land holdings collapsed into one, near median land holdings as the ``a fair amount'' category, and substantially larger than average holdings being the ``a lot'' category. This method is more conservative than the proportion rule used for the capital rankings, because it allows for variation both across and within groups.
10. Four wealth groups are de®ned as follows: ``boat, engine and chain saw''Ðincludes boat, outboard and other motors, and chain saw; ``large ®shing net''Ð lampara seine, beach seine, large-mesh gill net, and ornamental ®shing net; ``consumer durables''Ðradio, stereo, sewing machine, and clock; ``shop asset and other house''Ðshop, generator, refrigerator, petrol lamp, and other house. The other wealth group not included in the possession analysis but incorporated in the ranking analysis is ``small ®shing net''Ðgill net, and cast net.
11. This outcome was surprising to us, but would also be easy to remedy by making sure that the two were distinguished for respondents by their suitability for speci®c crops (rice and cowpea, respectively).
12. Twenty percent of the households in the sample had someone who ®ts this description.
13. High-levee land is used primarily for subsistence crop production, whereas mud ¯ats are used almost exclusively for commercial rice production. Labor demands on subsistence crops are much lower, and thus the presence of more adults in a household is probably not associated with greater use of high-levee land among RRA respondents.
14. Upon closer examination of these problematic pairings, we ®nd that three of the four cases were households that had arrived in the community very recently or were found in the two larger villages, where observability problems may be greater.
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