In trying to ascertain how to evaluate socioeconomic status (SES) among women and children in Kenya, we might consider an asset-based measure, such as an asset index. While using variables such as occupation, education, or income are commonly used to measure SES in high-income countries (HIC), such options may not accurately capture SES in low- and middle-income countries (LMIC), and one alternative that is commonly used is an asset index.
Historically, this concept was originally information taken from the Demographic and Health Surveys, which included information on durable asset ownership (e.g. cars, refrigerators and televisions), housing characteristics (e.g. material of dwelling floor and roof and main cooking fuel), and access to basic services (e.g. electricity supply, source of drinking water and sanitation facilities). Staff at DHS and the World Bank formed a composite index of some of these variables to measure SES, which has since been validated multiple times since the seminal work in India by Filmer and Pritchett.
The asset index provides a simple, quick, and replicable method for determining SES. In addition, some would argue that asset indices offer more stable measures of SES in LMIC than measures like consumption expenditure, since they vary less in response to fluctuations in income and expenditure.
Although asset-based measures are widely used in LMIC, such as Kenya, they are not perfect. Asset indices measure SES at the household level, not the individual level. Thus, if we were using an asset-based index for childhood mortality, we would be measuring the assets of the household. In addition, there can be challenges in determining asset quality. For example, we might think that the SES of a household with a new car should be ranked differently than a household with a nonfunctioning car that has been on cinder blocks for 5 years, but without more information, both households would be categorized as having a car. Some surveys try to gather information on quality in the form of resale value of assets, but that introduces additional practical challenges in data collection.
In addition, some have argued that asset indices have an urban bias, since they are based on assets that capture social stratification better in urban versus rural settings. For example, urban households are far more likely to live in a home constructed from modern rather than traditional materials as compared with rural households, but rural households may have more access to land and livestock that are not captured in the asset index. This can potentially skew the data, because either a large proportion of households in urban areas are assigned the same high score or a large proportion of rural households are assigned the same low score. To address this potential bias, some researchers have proposed separate asset indices for urban versus rural areas. Still, the ability of an asset index to differentiate between different levels of SES is likely to vary both between countries and over time within a country, due to variations or changes in the availability of some assets. Therefore, different indices may be required in different countries and within a country over time, which is potentially problematic for research aiming to compare inequalities across countries or examine changes in inequalities over time.
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