Socioeconomic Status Measurement with Discrete Proxy Variables

By Stanislav Kolenikov, G. Ángeles. 2009

844 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research, Sociology and Political Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Management Science and Operations Research

In Sociology and Political Science

Citations in the corpus

Assets, Shocks, and Poverty Traps in Rural Mozambique (2012, methodology): “2 For instance, Naschold (2005) for Pakistan, Naschold (2009) and Dercon and Outes (2009) for India, Quisumbing and Baulch (2009) for Bangladesh, Lokshin and Ravallion (2002) for Hungary and Russia, Jalan and Ravallion (2004) for rural China and Antman and McKenzie (2007) for urban Mexico.”
Approaches and Alternatives to the Wealth Index to Measure Socioeconomic Status Using Survey Data: A Critical Interpretive Synthesis (2019, methodology): “As a response to the primary statistical vulnerability levelled against the DHS wealth index—its inappropriate application to non-continuous variables—an improved polychoric PCA was proposed by Kolenikov and Angeles (2009).”
Social Discrimination in the Corporate Elite (2013, methodology): “In the primary analyses, we combined the indicators into a single index using principal components analysis, a data reduction technique that is appropriate for combining causal indicators (Kolenikov and Angeles, 2009; Bollen and Bauldry, 2011).”
Measuring inequality of opportunity with imperfect data: the case of Turkey (2010, background): “In those cases it is statistically preferable to treat those variables explicitly as ordinal in the analysis (Kolenikov and Angeles, 2009).”
Financial literacy and its consequences: Evidence from Russia during the financial crisis (2013, methodology): “For each question, we create a binary variable to identify the correct response and perform PCA analysis based on polychoric correlations, following the method developed to adapt PCA to ordinal data by Kolenikov and Angeles (2004, 2009).”
Widening participation in higher education: analysis using linked administrative data (2008, methodology): “We are aware of the problems of adopting principal components analysis with dichotomous variables (Kolenikov and Angeles, 2009); however, we are using only one binary variable (eligibility for FSMs)—the remainder are continuous—which should minimize the problem.”
On Weighting the Components of the Human Development Index: A Statistical Justification (2011, methodology): “On the use of PCA to generate an income proxy, see also Kolenikov and Angeles (2009).”
A Generalized Index of Fractionalization (2011, neutral): “To extract principal components, we employ a polychoric correlation matrix to take into account the fact that some of our variables are categorical (see for example, Kolenikov and Angeles 2009).”