Age heaping patterns in Mosaic data

Abstract

This paper analyzes the extent and nature of age-misreporting in the Mosaic data, currently one of the largest historical census microdata infrastructures for continental Europe. We use demographic measures known as the age heaping indexes to explore regional, periodic and sex-specific patterns of age misreporting across 115 Mosaic regional datafiles, from Catalonia to Moscow, during Europe's demographic ancien r egime and thereafter. The paper's second significant contribution is the comparison of Mosaic-based results to those derived from two other big census data projects-IPUMS and NAPP. Beyond this exploratory data analysis, we also investigate possible sources of variation in age heaping across Mosaic data by examining how it relates to variability in socioeconomic, institutional, and environmental conditions. Overall, our systematic inquiry into quality of age reporting in Mosaic consolidates the project's potentially transformative role in comparative historical family demography and suggests some avenues for future research.

Age heaping among adults in Nigeria: evidence from the Nigeria Demographic and Health Surveys 2003–2013

“…This is in contrast to previous findings, which showed that men were reportedly less likely to heap their age compared to women (Dahiru & Dikko, 2013;Földvári et al, 2012;Manzel et al, 2012). Studies that examined sex differences in age heaping using the Mosaic data found mixed evidence but concluded that sex differences in heaping are smaller than might be expected from reading economic history and demographic literature (Földvári et al, 2012;Manzel et al, 2012;Szołtysek et al, 2018). Although the findings showed a contrasting result, the sex difference in age heaping in the NDHS is small.…”

“…Age, as a variable, represents a critical basis for demographic classification; thus, its misrepresentations or misreporting alter the accuracy of demographic estimates (Dorjee & Spoorenberg, 2016;Szołtysek, Poniat, & Gruber, 2018). The preference of certain digits, mostly ending in 5s and 0s, have been largely reported in censuses and surveys where age is inquired (A'Hearn et al, 2009;Dahiru & Dikko, 2013;Randall & Coast, 2016).…”

Two Stories, One Fate: Age-Heaping and Literacy in Spain, 1877-1930

“…Age, however, has often been misreported because of custom and tradition, poor numeracy skills, or a badly designed and executed data collection process 7 . Digit preference for numbers ending in 0 and 5, as well as an aversion to certain digits such as 4 and 13, has been extensively documented in economic and social history (A 'Hearn et al 2009;Crayen and Baten 2010a;Szoltysek et al 2017). In doing so, researchers have used a battery of tools to gauge age-heaping 8 .…”

“…2009; Crayen and Baten 2010a; Szoltysek et al . 2017). In doing so, researchers have used a battery of tools to gauge age-heaping 8 .…”

Automatic Estimation of Age Distributions from the First Ottoman Empire Population Register Series by Using Deep Learning

“…Although the Ottoman population registers convey minute and diverse detail on individuals such as names, family relations, occupations, physical appearance, and body height for the purposes of historical demography, age data and its distribution are the most telling and the most rewarding ones. The age structure of any population in any given time is a crucial marker of demographic transition [6,7]. Furthermore, age heaping, a directly related phenomenon to registered age distribution, has vast potential for historical demography studies with its connection to human capital formation [8].…”