Optimization models for degrouping population data

Abstract

In certain countries population data are available in grouped form only, usually as quinquennial age groups plus a large open-ended range for the elderly. However, official statistics call for data by individual age since many statistical operations, such as the calculation of demographic indicators, require the use of ungrouped population data. In this paper a number of mathematical models are proposed which, starting from population data given in age groups, enable these ranges to be degrouped into age-specific population values without leaving a fractional part. Unlike other existing procedures for disaggregating demographic data, ours makes it possible to process several years' data simultaneously in a coherent way, and provides accurate results longitudinally as well as transversally. This procedure is also shown to be helpful in dealing with degrouped population data affected by noise, such as those affected by the age-heaping phenomenon.

Can online political targeting be rendered transparent? Prospects for campaign oversight using the Facebook Ad Library

“…When conducting statistical analysis care must be taken so that relationships detected at the aggregate level are not taken to imply relationships at the individual level (Pollet et al, 2015). While statistical techniques can be applied to estimate individual from grouped data, these techniques may not be applicable to online platforms where we don't have publicly available dis-aggregated statistics about the user population (Bermúdez, & Blanquero 2016). These features make inference to national populations from social media observations methodologically challenging (Wang et al, 2019).…”

The short-term mortality fluctuation data series, monitoring mortality shocks across time and space

“…0-17, 18-64, 65+). Despite a number of methods for ungrouping aggregated vital statistics [13][14][15][16] , none of them was designed for the specific case of weekly death counts with elevated mortality due to pandemics. In order to minimize a possible model-related bias and the associated misinterpretation of the data, we decided to focus only on broad age groups.…”