Income Interpolation from Categories Using a Percentile-Constrained Inverse-CDF Approach
It is often the case that surveys of persons and households collect income data along with other demographic and socioeconomic questions. When income level is not the primary focus of the survey, it may be used in domain estimation or as a covariate in multivariable analyses. In these instances, it is common practice for income to be collected in a categorical form, with nonstandard category boundaries that vary from one survey to another. Though these categories may be appropriate for their originally-intended purposes, they often are not ideal for analyses not considered when the survey instrument was developed (e.g., for determining a household’s percent of the federal poverty level). This paper describes a method for estimating a continuous income measure based on observed categorical responses with arbitrary category boundaries. The authors present this method in general terms and provide validation results both from simulation and comparison with federal benchmark surveys.
Couzens, G. L., Berzofsky, M., & Peterson, K. (2016). Income Interpolation from Categories Using a Percentile-Constrained Inverse-CDF Approach. Survey Practice, 9(5).