Annex 9. Measures of economic status applicable to health inequality monitoring

Direct measures of economic status include income and consumption (1). The Organisation for Economic Co-operation and Development (OECD) defines household income as “all receipts, whether monetary or in-kind (goods and services) that are received by the household or by individual members of the household at annual or more frequent intervals” (2). The components of household income cover income from employment; property income; goods or services produced within the household for the household; and transfers received (including cash, goods and services). In high-income countries, self-reported information on income may be available, but more robust measures of income are based on surveys collecting information at the individual level. When income is used as a measure of economic status, it does not account for variability in consumption over time by borrowing or depleting savings and assets.

Household consumption expenditure is the value of consumption goods and services used or paid for by a household to directly meet its needs – that is, people’s use of goods and services to meet their material wants and needs for food, shelter, social activity and so on. These goods and services may be obtained through the purchase of consumption goods and services in the market; the acquisition of consumption goods and services in the form of in-kind income from employment; services produced by the household for its own consumption; and in-kind transfers received from other households and from businesses.

Compared with income, consumption may be more consistent and can often be smoothed over time as people are able to borrow or use their savings and assets to sustain a more constant level of consumption. In contexts where households have multiple or changing sources of income, or where there are large informal sectors of the economy, consumption is considered a better measure of living standards than income.

There are limitations to the measurement of economic status using direct measures of income and consumption. Reliable data about income and consumption are difficult and expensive to collect. In economies with predominantly formal sectors, richer households may be less prone to disclose their total income and less likely to participate in income surveys due to fear of taxation. Questions about income and consumption may be sensitive, especially in poorer households – although questions about consumption are perceived to be less sensitive. Data about direct measures may be susceptible to measurement errors – for example, stemming from imperfect recall. In the case of income, non-monetary income, such as in-kind gifts, transfers or trading, which tends to be more prevalent in low- and middle-income country settings, may not be captured by direct measures. In economies with substantial agricultural sectors, income measures may not capture food grown for a household’s own consumption, and thus subsistence farmers may appear to have a better standard of living than urban dwellers on a similar low income. Problems may also arise regarding the calculation of income when it is transitory, irregular or received through informal employment, especially in economies with large informal sectors.

Proxy (indirect) measures are sometimes preferred to measure economic status because the collection of these data tends to be straightforward (1). Proxy measures of economic status, such as asset indices, summarize household wealth using data about assets, housing and access to services. Asset indices may take the form of simple asset indices, where equal weight is given to items on a list of assets. More complex approaches, such as principal component analysis, may also be used, which rely on statistical methods to determine the weights of items in the index (3). The data collected through multicountry household surveys such as the Demographic and Health Surveys (4) and the Multiple Indicator Cluster Surveys (5) permit the calculation of wealth indices, which are a standard part of their final reports and datasets (6, 7).

References

1. O’Donnell O, Van Doorslaer E, Wagstaff A, Lindelow M. Analyzing health equity using household survey data: a guide to techniques and their implementation. Washington, DC: World Bank; 2008 (https://openknowledge.worldbank.org/entities/publication/8c581d2b-ea86-56f4-8e9d-fbde5419bc2a, accessed 23 September 2024).

2. OECD framework for statistics on the distribution of household income, consumption and wealth. Paris: Organisation for Economic Co-operation and Development; 2013 (https://www.oecd-ilibrary.org/docserver/9789264194830-7-en.pdf, accessed 23 September 2024).

3. Filmer D, Pritchett LH. Estimating wealth effects without expenditure data – or tears: an application to educational enrollments in states of India. Demography. 2001;38(1):115–132. doi:10.1353/dem.2001.0003.

4. Demographic and Health Surveys Program. The DHS program. Rockville, MD: United States Agency for International Development (https://dhsprogram.com/, accessed 23 September 2024).

5. UNICEF MICS. New York: United Nations Children’s Fund (https://mics.unicef.org/, accessed 23 September 2024).

6. The DHS Program. Wealth index. Washington, DC: United States Agency for International Development (https://dhsprogram.com/topics/wealth-index/, accessed 23 September 2024).

7. Rutstein SO, Johnson K. The DHS wealth index. DHS Comparative Reports No.6. Calverton, MD: ORC Macro; 2004 (https://dhsprogram.com/pubs/pdf/CR6/CR6.pdf, accessed 23 September 2024).