Open Access

Assessing household wealth in health studies in developing countries: a comparison of participatory wealth ranking and survey techniques from rural South Africa

  • James R Hargreaves1, 2Email author,
  • Linda A Morison1,
  • John SS Gear2,
  • Julia C Kim1, 2,
  • Mzamani B Makhubele2,
  • John DH Porter1,
  • Charlotte Watts1 and
  • Paul M Pronyk1, 2
Emerging Themes in Epidemiology20074:4

DOI: 10.1186/1742-7622-4-4

Received: 15 December 2006

Accepted: 01 June 2007

Published: 01 June 2007

Abstract

Background

Accurate tools for assessing household wealth are essential for many health studies in developing countries. Household survey and participatory wealth ranking (PWR) are two approaches to generate data for this purpose.

Methods

A household survey and PWR were conducted among eight villages in rural South Africa. We developed three indicators of household wealth using the data. One indicator used PWR data only, one used principal components analysis to combine data from the survey, while the final indicator used survey data combined in a manner informed by the PWR. We assessed internal consistency of the indices and assessed their level of agreement in ranking household wealth.

Results

Food security, asset ownership, housing quality and employment were important indicators of household wealth. PWR, consisting of three independent rankings of 9671 households, showed a high level of internal consistency (intraclass correlation coefficient 0.81, 95% CI 0.79–0.82). Data on 1429 households were available from all three techniques. There was moderate agreement in ranking households into wealth tertiles between the two indicators based on survey data (spearman rho = 0.69, kappa = 0.43), but only limited agreement between these techniques and the PWR data (spearman rho = 0.38 and 0.31, kappa = 0.20 and 0.17).

Conclusion

Both PWR and household survey can provide a rapid assessment of household wealth. Each technique had strengths and weaknesses. Reasons for differences might include data inaccuracies or limitations in the methods by which information was weighted. Alternatively, the techniques may measure different things. More research is needed to increase the validity of measures of socioeconomic position used in health studies in developing countries.

Background

Research into the socioeconomic determinants of health requires accurate tools for assessing socioeconomic position. While in developed countries pre-existing data are often available, these resources rarely exist in developing countries and original data must be collected [1]. Economists generally regard detailed data on household income and/or expenditure as the gold-standard measure of current socioeconomic position. However, health researchers rarely have the resources or expertise necessary to conduct such assessments. Furthermore, total wealth, reflecting the balance between income and expenditure over a longer period, may be a more appropriate marker of socioeconomic position when health outcomes are considered. Consequently, rapid techniques for assessing household wealth are needed.

A variety of proxy measures of socioeconomic position have been developed. These have included shortened income or expenditure questionnaires, and measures of housing quality, education or nutritional status [1] Recently, researchers have used statistical techniques to combine multiple socioeconomic variables, usually including at least data on housing and ownership of fixed assets, into a measure of household wealth. The aggregation of such data can be achieved through a simple count, weighting of variables based on local consultation, or through the application of statistical procedures such as principal components analysis (PCA) [27]. However, there is no consensus on what variables should be included in such analyses [8]. Furthermore, there remains limited evidence on the association between asset indices and more established measures of wealth or socioeconomic position [9, 10].

An alternative technique is to use participatory wealth ranking (PWR), in which community members rank the wealth of households in their community. This approach is widely used in development practice [11], but rarely used in health studies. PWR can generate useful statistics and provide valid information on relative wealth [1216].

We conducted a household survey and PWR in rural South Africa. We constructed three indicators of household wealth, using the data from each of the two techniques separately and also by combining them. We assessed internal validity where this was possible, assessed agreement between the results of the techniques in their ranking of household wealth, and investigated the reasons for any differences.

Analysis

Methods

Setting

The study was conducted in eight rural villages of Limpopo Province, South Africa. The province is among the most deprived in the country, with nearly 50% of the population under 15 years old, unemployment in excess of 40%, and high levels of labour migration [1720]. The data come from the baseline evaluations of a cluster randomised trial [21].

Data collection

Participatory Wealth Ranking (PWR)

PWR was conducted in the local language by specialised facilitators from a local development NGO (Small Enterprise Foundation, Tzaneen). Data were recorded on pre-designed data collection forms [22, 23].

Community members residing in the same village section, most often women from poor households, drew a map of their residential area and listed the households on cards. Following this, groups of 4–6 residents were asked to characterise households that were "very poor", "poor, but a bit better off", and "doing OK". The proceedings of this discussion were captured by the facilitator in the form of "general statements". Households were then ranked from the poorest to the wealthiest according to these definitions and piles of households of comparable wealth generated. Participants were then asked to describe the characteristics of the households in each ranking pile ("pile statements"). Neither the number of wealth ranks nor the number of households in each rank was determined in advance, although at least four separate piles had to be generated during the process.

The ranking process was then repeated twice more with different groups of four to six community members, so that statements were collected and each household ranked on three separate occasions.

Household survey

A random sample of approximately 200 dwellings from each village (total N = 1640) was visited at least three times where necessary to collect data. Interviews were conducted in the local language. Interviewers received extensive training and data entry was validated through data cleaning procedures. Questionnaires captured salient aspects of socioeconomic well-being including household members' education and employment status, details of the dwelling construction, ownership of a small number of assets, details of the most important household incomes and information on food security.

Generating indicators of household wealth

Three approaches were used to generate a measure of relative household wealth. The first used data only from the participatory wealth ranking; the second used data from the household survey, but with their selection and weighting informed by PWR; the third used only data from the survey, employing principal components analysis (PCA) to determine the weights.

Method 1: an index of household wealth from PWR

Details of the scoring method used are provided in detail elsewhere [24]. Briefly, within each of the three ranking processes, piles of households were assigned a score such that the wealthiest pile received a score of 100 and the poorest pile a score of 0. Scores for the remaining piles were calculated as Score for pile n = 100*((N-n)/(N-1)), where n was the pile number and N was the total number of ranking piles.

Coded pile statements made in relation to the piles generated were assigned the numeric score allocated to the pile. An average pile statement score was calculated as the mean of the pile scores to which that statement was associated, covering the full PWR process in all eight villages (Table 1). A wealth index was calculated for each household as the mean of the pile statement scores of all the pile statements made in relation to the piles into which each household was ranked.
Table 1

Pile statement scores and frequency of statements made during participatory wealth ranking in rural South Africa, in descending order of pile statement score

Pile statements

Theme

Statement

General statements

    

No. of times said

Pile statement score

No. of times said

  

Very poor

Poor but a bit better off

Doing OK

0.0

22

Family and household

Orphanhood/no parents

24

  

0.4

39

Food

Beg for food

33

  

1.1

85

Begging

Begging

49

  

3.1

134

Food

No food

137

  

3.2

41

Housing

Not got shelter

33

  

3.7

58

Employment

No one is working

34

  

5.2

101

Schooling

Doesn't go to school

39

  

5.6

73

Clothing

No clothes/do not have clothes

73

  

5.7

199

Employment

Not got job(s)/unemployed

113

  

5.8

22

Food

Sleep without food

17

  

6.4

82

Money

Don't have/earn money/income

49

  

8.3

100

Schooling

Unable to/can't afford to go to school

66

  

9.0

67

Housing

Not got housing

65

  

9.6

37

Schooling

Cannot afford/does not pay school fee

18

  

11.9

23

Clothing

Tattered/torn/poor clothes

20

  

14.4

76

Housing

Shacks

18

  

15.0

51

Housing

No proper housing/shelter

18

  

22.7

64

Housing

Bad/poor housing

19

  

24.0

175

Employment

Farms

 

80

 

28.4

145

Self employment

Selling fruits and vegetables

 

39

 

28.5

71

Food

Mealy meal only

 

37

 

28.7

99

Employment

Domestic work

 

45

 

29.9

60

Pensions

Pension and many responsibilities

 

25

 

34.7

26

Food

At least have food

 

19

 

35.1

28

Food

Little food

 

33

 

35.7

47

Self employment

Self employed

 

17

 

38.2

55

Clothing

Second-hand clothes

 

21

 

39.4

70

Money

Little money/income/earn less

 

29

 

39.8

44

Housing

Small/little housing

 

26

 

40.4

25

Schooling

Attains Matric/std 10/grade 12

 

17

 

44.4

70

Pensions

Receiving pension

 

16

 

61.4

79

Schooling

Able to/affords to go to school

 

29

 

65.4

28

Employment

Got jobs/employed

 

24

18

71.0

32

Clothing

Good clothes

  

58

78.6

26

Clothing

Children have good clothes

  

30

80.8

134

Self employment

Taxis

  

41

83.1

104

Cars

Have/drive cars

  

50

84.7

101

Employment

Government

  

26

84.8

162

Schooling

Attains university/tertiary

  

52

86.4

97

Employment

Both husband and wife employed

  

18

87.9

163

Housing

Big house

  

96

90.1

123

Schooling

Private/expensive

  

76

90.4

73

Housing

Beautiful/attractive housing

  

42

93.8

65

Self employment

Has a business

  

47

95.5

74

Self employment

Shop owners

  

32

95.6

142

Cars

Have/drive expensive/flashy cars

  

102

95.7

47

Housing

Tiled housing

  

21

Method 2 : an index of household wealth from household survey data informed by PWR
Survey data were used to generate an indicator of household wealth, using PWR to inform which factors to use and how to weight the data. Where data were available on aspects of household wealth relating to each of the 10 commonest themes identified in PWR, this was used to inform the calculation of the index of household wealth (Table 2). Broadly, where relevant PWR pile statements identified "very poor" households, the most related survey item was given a score of -2, and where relevant statements identified households "doing OK" the associated survey item was scored 2. A sliding scale for intermediate situations was developed where this was possible. For school attendance, scoring was stratified on the basis of age. On the basis of this scoring system, each household could receive a maximum score of 9 (wealthiest) and a minimum score of -10 (poorest).
Table 2

Statement scores for poverty statements from PWR and scores for indicators collected in the survey data to create household index of wealth

Theme(s)

Relevant statements (score)

Relevant data from survey

Score applied to survey data

Employment, Self employment, Pensions, Money

Shop owners (95.5)

More than one household member has a salaried job

2

 

Has a business (93.8)

Either one household member has a salaried job, or three or more have a pension or other work

1

 

Both husband and wife employed (86.4)

No household members have a salaried job, but two has a pension or other work

0

 

Government (84.7)

No household members have a salaried job, but one has a pension or other work

-1

 

Taxis (80.8)

No household members have a salaried job, pension or other work

-2

 

Got job/employed (65.4)

  
 

Receiving pension (44.4)

  
 

Self employed (35.7)

  
 

Pension and many responsibilities (29.9)

  
 

Domestic work (28.7)

  
 

Selling fruits and vegetables (28.4)

  
 

Farms (24.0)

  
 

Don't have/earn money/income (6.4)

  
 

Not got job(s)/unemployed (5.7)

  
 

No one is working (3.7)

  

Schooling

Private/expensive (90.1)

If there are 20–25 year olds, if any attending or already achieved technikon or university

2

 

Attains university/tertiary (84.8)

If there are 14–19 year olds and all are in school

1

 

Able to/affords to go to school (61.4)

If there are 7–13 year olds and all are in school OR If there are 14–19 year olds and any are not attending school OR If no 7–25 year olds in household

0

 

Attains matric (40.4)

If there are 7–13 year olds and any are not attending school

-2

 

Can not afford/doesn't pay school fees (9.6)

  
 

Unable to/can't afford to go to school (8.3)

  
 

Doesn't go to school (5.2)

  
  

Overall score; if there were young people from more than one age group in the household the average of the three scores was used

 

Housing

Tiled housing (95.7)

Face bricks

2

 

Beautiful/attractive housing (90.4)

Block bricks with cement covering

1

 

Big house (87.9)

Mud bricks, or block bricks without cement covering

0

 

Small/little housing (39.8)

Tin or mud and sticks

-2

 

Bad/poor housing (22.7)

  
 

No proper housing/shelter (15.0)

  
 

Shacks (14.4)

  
 

Mud housing (13.3)

  
 

Not got housing (9.0)

  
 

Not got shelter (3.2)

  

Food, begging

Little food (35.1)

Food insecurity score 2–3

1

 

At least have food (34.7)

Food insecurity score 4

0

 

Mealy meal only (28.5)

Food insecurity score 5–6

-1

 

Sleep without food (5.8)

Food insecurity score 7–8

-2

 

No food (3.1)

  
 

Begging (1.1)

  
 

Beg for food (0.4)

  
  

Sum of two questions about the frequency of poor food security during the last month* pre-scored as Never (1), Once only (2), A few times (3), Often (4).

 

Cars

Have/drive expensive/flashy cars (95.6)

Have/drive cars (83.1)

Own any cars

2

Family and Household

Widows 1.8, n = 15^

Orphanhood/no parents (0.0)

Female Headed Household AND/OR

Household consists only of children/young people

-2

* The two questions were During the last month how often a) have most of the family had a meal that consisted of pap alone, bread alone or worse, and b) have you or any of your own children gone without food or had a reduced amount to eat for a single day because of a shortage of food?

^ This statements is not listed in Table 1 because it was made less than 15 times in one stage, but was the second most common single statement about family and household made during the PWR process

Method 3 : an index of household wealth from household survey data with weightings assigned by PCA
Fourteen variables capturing salient aspects of household wealth, decided upon a priori following literature review and piloting in the local area, were made available for entry into the PCA. Items included were not limited to durable assets [5] (Table 3). Asset values were derived from the survey data by multiplying the number of owned assets that were new (less than 2 years), relatively new (2–6 years), or old (>6 years) by estimations of the value of those assets, which came from a small sub-study. Other variables were drawn from the questionnaire. Non-continuous variables were coded even-spaced ordinally.
Table 3

Distribution of indicators of household wealth from survey data

Indicator

Variables considered for PCA

Mean (SD), Range

Groupings

N

%

N

    

Estimated value of selected non-livestock assets per person a (Quintiles)

1548 (3211), 0–76664

0 ZAR

415

28.2

  

1–131 ZAR

173

11.8

  

132–348.5 ZAR

293

19.9

  

350–1100 ZAR

295

20.1

  

> 1100 ZAR

295

20.1

Estimated value of selected livestock assets per person a (Quintiles)

873 (1809), 0–28160

0 ZAR

468

31.6

  

1–220 ZAR

120

8.1

  

220–1115 ZAR

300

20.3

  

1115–2440 ZAR

296

20.0

  

> 2440 ZAR

297

20.1

Land tenure b

0.3 (0.5), 0 (no) – 1(yes)

No

1070

72.3

  

Yes

410

27.7

Quality of house wall material a

3.9 (1.5), 0 (poorest) – 6 (best)

Poor

807

54.5

  

Good

675

45.5

Quality of toilet facility

1.8 (0.4), 1 (no facility) – 3 (modern)

No facility

272

18.4

  

Basic

1195

80.7

  

Modern

14

1.0

Household Electricity b

0.7 (0.5), 0 (no) – 1(yes)

No

468

31.6

  

Yes

1012

68.4

Accessibility of water supply b

1.7 (0.5), 1(low) – 3 (good)

Low

489

33.1

  

Medium

929

62.9

  

Good

60

4.1

Density of household living conditions a

0.9 (0.8), 0.1–8 rooms per person

<= 1 rm per person

1127

76.2

  

>1 rm per person

352

23.8

Proportion of household members receiving a regular income a

0.2 (0.2), 0–1

0

292

19.7

  

Less than 25%

560

37.8

  

25–49%

408

27.5

  

50% or more

222

15.0

Educational level of household head a

3.0 (1.7), 1 (illiterate)-8 (university)

No schooling

562

38.0

  

Attended primary

546

36.7

  

Attended secondary or more

372

25.1

Percentage of household members working age adults b

0.6 (0.2), 0–1

50% or less

558

37.9

  

>50%

915

62.1

Gender of household head

0.6 (0.5), 0 (female) – 1(male)

Female

587

39.6

  

Male

894

60.4

Second most important household income b

0.6 (0.5), 0 (Non-financial)-1(financial)

Non-Financial

561

37.9

  

Financial

921

62.1

Regularity of household having a meal consisting of mielie meal alone, bread alone or worse

2.3 (1.2), 1 (Often)-4 (Never)

Often

525

35.5

  

A few times

413

27.9

  

Once only

136

9.2

  

Never

407

27.5

Car ownership c

-

No

1200

81.0

 

-

Yes

281

19.0

Schooling (7–13 yrs) c

-

Any not attending

35

3.5

 

-

All attending

958

96.5

Schooling(14–19 yrs) c

-

Any not attending

177

19.2

 

-

All attending

747

80.8

Schooling (20–25 yrs) c

-

All not achieved college or techikon

692

90.6

 

-

Any achieved college or technikon

72

9.4

a denotes variables grouped for presentation in table, but where an ordered or continuous variable was used in the PCA analysis.

b denotes variables considered for inclusion in the principal components analysis but not included in the final analysis

c denotes variables not considered for inclusion in the principal components analysis

Non-livestock assets comprised cars, televisions, hi-fis, fridges, bicycles, cellphones. Livestock assets were cows, goats, chickens.

Low accessibility of water supply was defined as those collecting rain or stream water, medium level access was through a borehole or tap in the village, while those with high quality access were those with a tap in the plot of the dwelling.

ZAR = South African Rand

Two factors not associated in the expected direction with the value of selected non-livestock assets per person (screening variable) in a χ2-test (p < 0.05) were not included in the PCA (percentage of household members of working adult age and land tenure). The remaining factors were included. PCA transforms a set of correlated variables into a set of uncorrelated 'components'. When variables hold information about some underlying concept, PCA can produce the best single composite variable among all possible linear functions of the original variables [10]. The component explaining the greatest proportion of the total variance is called the first principal component. This weights the data in proportion to how well each variable is correlated with the others and was used as the indicator of household wealth.

A number of analyses were run. Factors with component loadings less than 0.2 on the first principal component were excluded (household electricity supply, quality of water supply and the nature of the second most important ranked household income). Nine factors were included in the final analysis in which the first principal component explained 22.7% of the variance of the factors in the model. The greatest weight was given to the density of household living conditions (scoring coefficient = 0.44), with the value of non-livestock assets (0.42) and the food security indicator (0.39) also being important. The lowest weighting was given to the proportion of individuals receiving an income (0.23). A wealth index was calculated for households where data were available on all variables. This component was normally distributed and had a mean of 0 and a standard deviation of 1.

Statistical analysis of consistency and agreement

For the PWR method only, the intra-cluster correlation coefficient, a measure of internal consistency, was first calculated from a random-effects ANOVA to describe the level of agreement in rankings of wealth between each of the three rankings made for each household [25].

Secondly, the association of each index with the individual survey indicators was estimated. Households were divided into wealth-rank tertiles on the basis of each of the methods. The association between these tertiles of wealth and each specific indicator of wealth from the survey was assessed using a χ2-test.

Finally, the three techniques were compared in their ranking of household wealth. The agreement of each technique placing households into wealth tertiles was estimated with a kappa coefficient. Spearman rank correlation coefficients were also calculated. While correlation coefficients measure the level of predictability of one variable on the basis of another, they do not directly assess agreement; thus a correlation coefficient of 1 will be measured if all values of one variable are twice that of another, though these clearly do not agree.

Results

The wealth ranking process identified 9824 dwellings in 79 village sections in the eight villages, and wealth ranking data were available for 9671 of these (98.4%). Some 3556 general statements were coded describing the general properties of households seen as "very poor" (1240), "poor, but a bit better off" (1097) or "doing OK" (1216). A further 8856 pile statements were coded, describing the characteristics of the households included in each of the piles assembled by the wealth ranking process. Some 47 statements were made more than 15 times in both stages of the process (Table 1), with all but one of the statements ("Got jobs/employed") being mentioned exclusively in relation to a single wealth category. Successful interviews were completed with 1482/1640 (90.4%) households.

Distribution and determinants of wealth

Households judged "very poor" by PWR participants were struggling to feed themselves and to clothe or educate their children, with little access to jobs or housing (Table 1). Households deemed "poor, but a bit better off" had access to low paid jobs and exhibited a basic ability to meet food and educational needs. Finally, households that were "doing OK" had access to good food, drove cars and had big or attractive housing. Some members were employed in high-return and/or high-stability activities.

Survey data (Table 3) suggested modern assets were widely distributed, though 28.2% of households reported owning none of the listed assets. Livestock assets were common. Dwellings were built of simple materials. Some 18.4% of households had no access to a toilet. Electricity supply was determined largely by village, with two villages remaining largely unelectrified. Water accessibility was generally low. Some 19.7% of households had no adults receiving a regular income, while many households were headed by an individual with no education (38.0%). Some 35.5% of households often had a meal consisting only of basic foodstuffs. Cars were owned by 19.0% of households. School attendance was high for young children but lower at later ages.

Internal consistency of PWR

The single-measure intra-class correlation coefficient from a random effects two-way ANOVA on the three rankings of household wealth, over 9671 households, was 0.81 (95%CI0.79–0.82) denoting a high level of agreement.

Association between wealth indices and different dimensions of wealth

Data on individual socioeconomic variables were significantly correlated (p < 0.01) with each of the wealth indices in most cases (Table 4). Land tenure was least strongly associated with the PCA measure (p = 0.026). Household electrification was not strongly associated with the measure of household wealth generated by either of the methods that used the survey data, although it was associated with the PWR index (p = 0.002). Water accessibility was least strongly associated with the PWR index (p = 0.028). Finally, the proportion of adults who were of productive age (14–60 years) was not strongly associated with household wealth as estimated by any of the techniques.
Table 4

The association between household wealth rank tertiles and survey indicators of socioeconomic status

 

Method 1 : PWR

Method 2 :Survey + PWR

Method 3 : Survey only

 

χ 2

P

χ 2

P

χ 2

P

Estimated value of selected non-livestock assets per person

114.9

<0.001

432.5

<0.001

445.2

<0.001

Estimated value of selected livestock assets per person

31.8

<0.001

54.4

<0.001

133.2

<0.001

Land tenure

11.7

0.003

13.0

0.002

7.3

0.026

Quality of house wall material

73.7

<0.001

219.5

<0.001

258.8

<0.001

Quality of toilet facility

38.5

<0.001

67.6

<0.001

275.5

<0.001

Household Electricity

12.6

0.002

3.1

0.21

6.3

0.044

Accessibility of water supply

10.9

0.028

23.6

<0.001

19.5

0.001

Density of household living conditions

18.5

<0.001

12.4

0.002

317.9

<0.001

Proportion of household members receiving a regular income

101.4

<0.001

188.2

<0.001

92.5

<0.001

Educational level of household head

28.6

<0.001

98.8

<0.001

155.0

<0.001

Percentage of household members working age adults

7.4

0.02

9.2

0.01

4.3

0.114

Gender of household head

64.3

<0.001

456.9

<0.001

210.6

<0.001

Second most important household income

17.2

<0.001

53.9

<0.001

11.5

0.003

Regularity of household having a meal consisting of mielie meal alone, bread alone or worse

46.4

<0.001

470.0

<0.001

539.4

<0.001

Car ownership

82.8

<0.001

354.8

<0.001

232.1

<0.001

School attendance score

23.4

0.009

83.6

<0.001

48.9

<0.001

N's for each association vary from 1442–1468 dependent on missing data.

Agreement between the indices

The survey data methods were quite strongly correlated (Spearman rho = 0.69, p < 0.001, n = 1442), and there was a reasonable degree of agreement in their placing of households into wealth-rank tertiles (Kappa = 0.43).

The PWR wealth index was significantly, but weakly, correlated with both the index combining PWR and survey information (Spearman rho = 0.38, p < 0.001, n = 1443) and the PCA-based method (Spearman rho = 0.31, p < 0.001, n = 1451). The levels of agreement in placing households into wealth tertiles were low (kappa statistics of 0.20 and 0.17 respectively).

Discussion

We constructed three indices of household wealth using data from a household survey and participatory wealth ranking. PWR and the survey identified similar dimensions of socioeconomic well-being as important. The two indices developed from survey data showed a reasonable level of agreement in ranking households into wealth tertiles. However, there was limited agreement between the survey-data based indices and the index based only on information from PWR. Methodological differences meant that it was not surprising that the methods differed in their results, though the magnitude of the differences noted was surprising.

The three approaches differed in at least two dimensions. The first dimension was whether information was provided by household members (as for both of the techniques using survey data), or by other community members (for the PWR only approach). The second dimension was whether community views were used to weight the importance of different aspects of wealth (as for the approaches that used PWR data), or whether external statistical rules were used (as in the PCA method). Nevertheless, there were striking similarities in the associations seen between the three wealth indices and each of the survey variables collected. The strongest associations between individual variables and the PWR wealth index were seen for variables associated at a significance level of p < 0.001 with both survey indices, while weaker associations also generally mapped across all three indices. The only exceptions to this were with the variables on household electrification and water supply.

Despite these similarities, the PWR index showed relatively low agreement with the survey-based measures, even when themes from the PWR were used to inform the selection and weighting of data. Two potential reasons for the lack of agreement are; firstly, each may have suffered from inaccurate data collection or weighting; secondly, the techniques may measure different things.

The survey attempted to maximise accurate reporting through collecting data on objective indicators, fieldworker training and stressing the importance of honesty to participants. Nevertheless, reporting biases may have occurred [26]. PWR partially accounts for this, since information is acquired from neighbours and is triangulated. However, households may conceal information from their neighbours. PWR may therefore best measure conspicuous consumption. PWR participants might also misreport household wealth. However, the high level of internal consistency for the household wealth ranks obtained from three separate groups of PWR participants provided some evidence against this. This finding differs from a previous report of low reliability for group-informant food-security ratings [27]. However, reasons for the low reliability reported by those authors were addressed in this study since trained facilitators worked with a homogenous group of PWR participants at all rankings and emphasised local definitions of poverty, participation and consensus. However, PWR may provide invalid results, but high levels of internal consistency, if participants, who were mostly poor women, ascribe a greater weight to certain dimensions of poverty (for example, being widowed) than would other groups in society.

Survey data included information on employment, educational status and asset-ownership of migrants, since temporary migrants are important contributors to the rural economy in South Africa [2830]. However, no information was available on levels of income remittance. PWR participants may be poorly informed about the wealth of migrants or their levels of remittance. However, PWR participants may also have had a more nuanced understanding of the role of migrants in generating household wealth than it was possible to capture from the survey data.

Each method might have weighted the importance of different aspects of household wealth differently. PCA assigns weights to variables according to mathematical rules, while wealth ranking participants assess households in ways that are complex and non-transparent. Our approach to PCA incorporated different facets of wealth, as in previous applications, [5] and drew out the common underlying correlation between them. However, the first principal component explained only 22.7% of the total variance, suggesting that factors included were not well correlated. The index where PWR was used to inform the selection and weighting of survey data has intuitive appeal. However, it was not possible to directly map PWR statements to survey data, and the weighting system applied to the data was somewhat arbitrary. While combining data on multiple dimensions of socioeconomic well-being should provide a more stable marker than individual variables on their own, the selection of variables for inclusion in such analyses requires further study, as does the widespread practice of including ordered categorical and binary variables in PCA.

Finally, there was also room for differences in interpretation in PWR. Wealth ranking was conducted in Sepedi, applying a translation of the question, "What are the characteristics of a very poor household?" to start the ranking process. Many characteristics identified by PWR participants resonated with the survey data. Nevertheless, the way in which PWR participants judge household wealth was inevitably unclear. One possibility is that PWR participants may have ranked households more directly on their current level of welfare than the survey based methods.

In our comparison of three approaches to assessing household wealth, the method by which data were collected was more important than the method by which variables were selected or weighted in determining agreement between the rankings. None of the techniques was precise in defining what aspects of wealth they wished to measure, so ultimately the indices may have measured different things. Survey data on individual variables may be most appropriate when comparison is needed between different settings or time-periods. PCA is a useful tool for the reduction of multiple indicator data, yet in this application did not agree with household wealth ranking ascribed by community members. PWR allowed a measure of wealth to be generated for about 200 households in a given geographical area over a two-day period by a skilled practitioner. Although the use of this technique will require epidemiologists to attain new skills, PWR may represent a rapid, useful and internally valid tool for health researchers in situations where locally-grounded data on household wealth are required.

Declarations

Acknowledgements

The study received financial support from AngloAmerican Chairman's Fund Educational Trust, AngloPlatinum, Department for International Development (UK), The Ford Foundation, The Henry J. Kaiser Family Foundation, HIVOS, South African Department of Health and Welfare, and the Swedish International Development Agency. Thanks also to Chris Martin, an intern with RADAR who coded many of the wealth ranking statements. James Hargreaves is supported by an ERC/MRC interdisciplinary fellowship.

Authors’ Affiliations

(1)
London School of Hygiene and Tropical Medicine
(2)
Rural AIDS and Development Action Research Programme

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