Occupational Classification in the North Atlantic Population Project

Datos Bibliográficos

ID11253141
AutoresEvan Roberts (0000-0001-5621-4823, University of Minnesota, autor de correspondencia), Matthew Woollard (0000-0002-1077-4312, University of Essex), Chad Ronnander (University of Minnesota System), Lisa Dillon (0000-0003-1597-4839, Université de Montréal), Lisa Y Dillon, Gunnar Thorvaldsen (0000-0002-9462-7589, Norwegian Centre for Research Data)
Año2003
Volumen36
Número2
Páginas89-96
Fecha de publicación2003-01-01
Peer ReviewedSí
Open AccessNo
TipoARTICLE
RevistaHistorical Methods A Journal of Quantitative and Interdisciplinary History (JOURNAL)
Identificadores de la revistaISSN: 0161-5440 • E-ISSN: 1940-1906
EditorialInforma UK Limited (PUBLISHER • GB)
DOI10.1080/01615440309601218
OpenAlexW2000858996
IdiomaEN
Citas recibidas15
Referencias citadas4

The North Atlantic Population Project (NAPP) is a complete-count data set of late-nineteenth-century censuses from Canada, Great Britain, Iceland, Norway, and the United States. One of the project's most challenging tasks is the coding and classification of 2 million distinct responses to occupational questions. Using the Historical International Standard Classification of Occupations (HISCO) as the basis for their classification scheme, the authors have adapted it to address particular problems applicable to the NAPP occupational data—the inconsistent specification of tasks, industry, and employment status by census respondents; variation among the NAPP countries in the level of occupational detail provided; and spatial and temporal variation in the language used to describe occupations. Compared with HISCO's classification scheme, the NAPP system reduces the overall number of codes, introduces new codes, and retains more detail from vaguely specified occupations. Key Words: censusHistorical International Standard Classification of Occupations (HISCO)microdataNorth Atlantic Population Project (NAPP)occupations

Census · Classification scheme · Coding (social sciences · Geography · Information retrieval · Population · Sociology · Statistics · Variation (astronomy · Birth, Development, and Health · Census and Population Estimation · Computer Science · Demography · Mathematics