A nationwide wealth score based on the 2000 Brazilian demographic census



Aluísio J D Barros; Cesar G Victora

Centro de Pesquisas Epidemiológicas. Universidade Federal de Pelotas. Pelotas, RS, Brasil





OBJECTIVE: To propose an asset based indicator of wealth for Brazil using variables present in the demographic census.
METHODS: The indicator, named IEN (Indicador Econômico Nacional/ National Wealth Score), was developed using 12 assets and the schooling of the household head, through principal component analysis. Data from the 2000 Brazilian Demographic sample was used for deriving the score and for the calculation of decile cut-off points.
RESULTS: The indicator, first component obtained from the analysis with the 13 variables, retained 38% of the total variability, and presented a Spearman correlation of 0,74 with total family income and of 0,67with per capita income. The necessary scores to calculate the indicator are presented, as well as reference distributions for the 27 states and their capitals, the five major regions as for the whole country. An example of use of indicator is presented.
CONCLUSIONS: Differently from other economic indicators, the Indicador Econômico Nacional has local reference distributions available, along with the national distribution. It is therefore possible to compare a study sample to the municipal, state or country distribution. The small number of variables allow investigators to calculate the Indicador Econômico Nacional in research studies where economic classification is of interest.

Keywords: Socio-economic survey. Censuses. Social class. Income. Poverty. Brazil.




Public health research has a tradition of investigating not only biological but also socio-economic determinants of illness. In Latin America investigations on the importance of socio-economic conditions in health status have been conducted for quite a long time.1 This approach demands some socio-economic indicator to classify the study individuals. Most commonly, schooling of the head of the household and family income have been used, despite all of the difficulties related to collecting good information on the latter, as clearly explained by Ferguson et al.3

The Marxist concept of social class has also been used with success, after an operational definition was proposed in the late eighties in Brazil.2,7 This method classifies people into six groups: under proletariat, typical and atypical proletariat, and petty, new petty and traditional bourgeoisie. Despite its theoretical appeal, it has proved difficult to use, mainly because it requires manual classification of families, as it was not feasible to transform the criteria into a programmable algorithm.

Another practical alternative is the construction of a wealth score based on household possessions. In Brazil, the first criterion for economic classification based on assets was proposed in 1970 by the Brazilian Advertisers Association (ABA). Four classes, labeled from A (richest) to D (poorest), were used in the classification that was based on eight assets, presence of domestic employees and the education level of the household head. This classification was modified in 1976, and a full revision was made in 1978. This time, six assets, domestic employees, and education were used to classify the population into five groups, labeled A to E.8 Other two revisions have been made so far. In 1996, a revision carried out by the National Association of Market Research Companies1 (ANEP), updated the classification using slightly different indicators, but maintaining the general idea. A last update was made in 2000 using data from a survey covering nine metropolitan regions. The previous criterion was kept, except for the inclusion of DVD players as an alternative to VCR.2 From the beginning, the methodology used aimed at creating a proxy for household income.

One difficulty with this criterion is that it is not feasible to disaggregate the source data into smaller geographical areas. One reason is that the data are not publicly available and such data has not been produced, the other is that the survey did not cover the entire country. Therefore, it is not possible to determine the specific distribution of the proposed score for more limited geographical areas. For instance, a study was carried out on households covered by the Family Health Program in the city of Porto Alegre (Rio Grande do Sul State capital, Brazil). In order to compare this population with the whole capital it was necessary to know the economic score distribution for city (which is completely different from the national distribution).

The use of data collected by the 2000 Brazilian Demographic Census (Brazilian Institute of Geography and Statistics - IBGE) could solve this specific problem and would also offer a general solution for Brazilian investigators, as a score derived from Census sample data could be calculated for every municipality of the country, as well as for larger geographical areas such as states and regions.

It was then decided to use the Census data on assets and household head education to extract a wealth score through principal components analysis, as proposed by Filmer & Pritchett.4 One of the goals was to keep the set of variables used limited in number so that they would be easy to collect in population surveys or epidemiological studies. National, regional, state and local wealth score distributions were generated to serve as a reference to position other study samples in terms of wealth in relation to the desired comparison group. The use of the proposed score makes it possible to compare a given sample against the wealth distribution of several different geographical levels, as well as estimating the proportion of the poor (or of the rich) they include.



The 2000 Brazilian Demographic Census collected a limited number of variables from every household in the country. A more detailed questionnaire was applied in a sample of households, chosen randomly in each municipality through systematic sampling within each census tract. The sampling fraction was 10% in municipalities with an estimated population greater than 15,000 people and 20% in the other towns. A total of 5,304,711 households were included in the sample, resulting in a average sampling fraction of 11.7%. The final weights were calculated by IBGE using a calibration technique in relation to a set of variables for which the population totals are known (obtained from the Census universe). Details of the Census sample methodology are described at IBGE homepage.3

Using more than 5 million households to estimate a wealth score is not only impractical but also unnecessary. The available data was re-sampled using a fraction of 10%. This was done by state, using the Stata command sample,9 and varying the pseudo-random number generator seed for each state according to the time (hh:mm:ss) the procedure started. A fixed sampling fraction was chosen so that the original weights calculated by IBGE could be used simply multiplying them by 10 if population totals were needed.

The score was developed for urban areas only. Rural areas are fairly different from urban areas in terms of infrastructure and way of life, what would justify separate scores. As most research is done for urban areas, this setting was selected for this exercise. A similar score for rural areas may well be developed in the future, if needed. The total study sub-sample was, then, 418,032 households, with the largest state contributing 104,348 households and the smallest 690.

Principal components analysis (PCA) was the method of choice for several reasons. There is no need for the variables used with PCA to be of any particular type or have any specific distribution. Its main objective, to summarize the variance of a set of variables, can be achieved with any type of data.6 It precludes data on income or consumption, which are difficult to obtain and frequently are of questionable quality.3 Finally, the score derived is not arbitrary - the first component yielded by PCA captures the greatest possible amount of the data variability with a single linear combination. PCA has already been evaluated4 and used for this purpose in many situations, such as in the "Country Reports on Health, Nutrition, Population, and Poverty" series published by the World Bank.4

PCA can be performed with the covariance or the correlation matrices of the selected variables, the latter option being equivalent to using standardized variables. The results are not the same or a simple function of each other. The difference in results will be most striking when the variances of the variables used are very different. This happens, for example, when the variables are measured in different scales. In such cases, variables with large variances will dominate the first principal component.5 In the present case, most of the variables are binary, indicating the presence of an asset, some of them are counts (e.g., the number of TV sets), and one is categorical (level of education). The use of standardized variables (or the correlation matrix) helps to minimize the considerable differences in variance that are observed in this case. When this strategy is used, there is little difference between using the original variables or a set of indicators for the polytomic variables. Correlation coefficients between the scores generated by the two alternatives were typically greater than 95%.

In order to obtain a valid wealth score it was important to work with a reasonable number of variables. On the other hand, an excessive number of assets could make the score impractical for use in small scale studies. Twelve variables related to household assets and size were selected, and the education level of the household head from the 2000 Brazilian Demographic Census. The variables used and how they were coded are shown in Table 1. The upper cut-off points for count variables were chosen based on their distribution, leaving at least 5% of the households in the last groups.

In summary, the principal components analysis was performed using the covariance matrix of 13 variables, and using the sample weights calculated and provided with the data by IBGE. The coefficients were calculated by rounding the expression loading/s.d.x100 to the nearest integer, and the individual scores were obtained through the expression where ci is the coefficient and vi the coded value of the ith variable. This strategy produced a score that is shifted from the standard PCA score by a fixed amount where is the mean of vi, with the practical advantage that all scores are positive.

After the wealth score was derived, deciles for the country, geographic regions, states and state capitals were calculated. The entire Census sample was used to calculate the deciles for the state capitals, while the study sub-sample was used for the other levels. All analyses were performed with Stata 8.9



The first principal component was extracted based on the 13 variables presented in Table 1, with the corresponding numeric codes. The results obtained are summarized in the same table, where the variable loadings, standard deviations and the final score coefficients (loading / std. deviation x 100 rounded to the nearest integer) are presented. The first component retained 38% of the total data variability, while the second component had only 9%.

For the national sample, the minimum value for the score was 20, the maximum 1,086, the mean 412 and the median 358. The frequency distribution was skewed to the right, though less asymmetrical than income distributions use to be (Figure 1). Box plots showing the distribution of per capita household income for each population quintile of the wealth score are also shown in Figure 1. There is evident increase in per capita income mean and median values as well as in its dispersion.



Correlations (Pearson) were calculated between the wealth score and total household income and per capita income with values of 0.40 and 0.38, respectively. The correlations with the logarithms of income were considerably higher: 0.76 for log total income, and 0.68 for log per capita income. Spearman rank correlations were also calculated: 0.74 with total household income, 0.67 with per capita income, and 0.75 and 0.68 with their respective logarithms. Due to the sample size (408,976 households), all p-values were virtually zero.

Decile cut-off points for the for the whole country, the five geographic regions and 26 states plus the Federal District (calculated using the study sub-sample) are shown in Table 2. The state and regional differences are obvious from the table. The Federal District (DF), where, the federal capital Brasília is located, presented the highest median score (484). São Paulo (SP), Brazil most industrialized state, ranked second, with a median score of 463, followed by Santa Catarina (SC), Rio de Janeiro (RJ) and Rio Grande do Sul (RS), all located in the South or Southeast. On the other extreme, the poorest states were Piauí (PI), Alagoas (AL), Tocantins (TO) and Maranhão (MA), located in the Northeast and North of the country. Their median scores ranged from 258 to 218, respectively. The differences are so striking that the median for Maranhão is lower than the first decile cut-off point for São Paulo.

The score deciles for the 26 state capitals plus the federal capital (using the whole Census sample) are shown in Table 3. People living in the state capitals are evidently better off than the whole state population. It is also clear that the richest states do not make the richest capitals. Among the capitals, Florianópolis (SC), ranks first, followed by Porto Alegre (RS) and Curitiba (PR). The city of São Paulo is only fifth in terms of median score. Again, on the low end of the rank are capitals located in the North and Northeast. Palmas (TO), Rio Branco (AC) and São Luís (MA) are the three presenting the lowest median scores.

As a practical application exercise of the score, a sample of approximately 3,000 individuals drawn from areas covered by the Family Health Program in the city of Porto Alegre, (RS) was used. The score was calculated and compared to the distributions for Porto Alegre and Brazil. If the sample was similar to the city population, it would be observed a histogram showing five bars close to 20%. Instead, Figure 2 (left histogram) shows that the study sample is concentrated towards the lower reference quintiles, meaning that the population covered by the Family Health Program in Porto Alegre comes from a much poorer group. Almost 40% of the sample fall below the first quintile cut-off point. On the other extreme, less than 5% of the sample are within the range of the fifth reference quintile.



A different picture emerges when the sample is compared to the score distribution of the whole country (Figure 2, right). The sample is now concentrated in the 3rd and 4th reference quintiles. That is, this population compared to the Brazilian reference is no longer concentrated on poor side, but more towards the middle of the distribution.



The availability of census data on income and on household assets provided a unique opportunity for developing a wealth score based on a large sample and with national, regional and local representativeness. Unlike other wealth scores currently available, which only have a national reference distribution, the score can be contrasted to different levels of regional aggregation, as shown in the example.

The number of variables to compose the score was kept manageable for small scale surveys and epidemiological studies. The 13 variables that compose the score are straightforward to collect and to code. Other possible variables available in the Census sample data set, such as type of construction materials or coverage by public services were not included. The main reason was that they did not add significantly to the score (results not shown). Also, it was wanted to keep the number of variables small and some of these additional variables are difficult to collect. Specifically, ownership of the house was not included because, contrary to the expected, ownership was more frequent among the poor, while renting was more common among the better-off. It is also a tricky variable to collect, as many peculiar situations are frequent; for example, the poor often own the shack but not the plot where it is located.

The set of variables selected produced a valid wealth indicator. This is suggested by the behavior of household income across the quintiles of the score. Also, the percentage of the total variability explained by the first PCA component was higher than the 26% obtained by Filmer et al,4 and the Spearman correlation was higher than those found by Ferguson et al3 for a PCA score and permanent income: 0.49 for Pakistan (38 indicator variables), 0.68 for Greece (24 variables) and 0.73 for Peru (28 variables). Using the example data from Porto Alegre, the distribution of household income showed the expected increase in the median values by score quintile. Unfortunately, other health related outcomes were not available in this dataset so that it could further validate the score - stunting would have been a nice option.

Higher correlation coefficients were found between the score and total household income, suggesting that it is a better proxy for this measure than for per capita income. It is not surprising since the score is not adjusted for the number of people in the household. As a consequence, larger households tend to have a higher score, given there are more people to contribute in economic terms. However, simply dividing the score by the number of people is not a sensible option since economies of scale will not be taken into account. It is obvious that the number of TV sets or cars will not increase steadily with the number of people living in a household. Alternatives to correct this score for household size are currently being explored.

In conclusion, it is presented a valid wealth score based on good quality and easily accessible data that can be used for the economic classification of sub-samples from the Census data or for new studies that include information on the 13 variables used. Given the availability of reference distributions for several geographical aggregation levels, it is not only possible to use this score to do a within sample economic classification, but also to compare the sample to the municipality, the state or the country distributions. This score was named IEN, acronym for "Indicador Econômico Nacional", or National Economic Indicator, in English.



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Correspondence to
Aluísio J D Barros
Centro de Pesquisas Epidemiológicas - UFPel
Caixa Postal 464
96001-970 Pelotas, RS, Brasil
E-mail: abarros@epidemio-ufpel.org.br

Received on 1/12/2004. Approved on 7/4/2005.
Supported by the World Bank through the Reach the Poor Program (Grant n. 7122804).



Presented at the VI Brazilian Congress of Epidemiology, Recife, June 2004, and at the V European Conference on Health Economics, London, September 2004.
1 Available in URL: http://www.anep.org.br [24 nov 2004]
2 Available in URL: http://www.anep.org.br/codigosguias/CCEB.pdf [24 nov 2004]
3 Available in URL: http://www.ibge.gov.br/censo/text_amostragem.shtm
4 Available in URL: http://www.worldbank.org/poverty/health/data/index.htm)

Faculdade de Saúde Pública da Universidade de São Paulo São Paulo - SP - Brazil
E-mail: revsp@org.usp.br