Introduction

Eurostat, CEDEFOP, and other EU agencies have been looking into using the ISCO classification to link job–worker characteristics like skill importance and level ratings to European jobs. O*NET is a reliable source of occupational data for the European labour market because of its strong theoretical and empirical basis and the similarities between the European and US economic systems. Drawing on O*NET necessitates mapping from one classification to another since the O*NET database classifies jobs differently than the EU. This is accomplished via a “concordance” (also known as a “crosswalk” or “correspondence table”). We propose the a publicly accessible software for methodologically transparent concordances, by constructing an R-package that we hope will greatly reduce the expenses and time required to perform an approximate mapping between the two categorization systems.

Understanding the variations in how jobs are organized in the US and the EU is necessary to realize the necessity for a more granular concordance. While the SOC and the ISCO have similar goals, there are significant distinctions in the organization and information included in occupational profiles. The US SOC has 867 6-digit classifications for statistics reporting (e.g., number of people employed or average wage). The EU ISCO, on the other hand, employs the ISCO 4-digit categorization, which has 436 unit groups, and adds an extra layer of information of approximately 3000 occupational distinctions. Both systems group occupations according to a four-level hierarchy, with ISCO adding an extra degree of detail.

Crosswalks

This package introduces an alogithm that conducts approximate matching between the ISCO and SOC classifications using concordances provided by the Institute for Structural Research and Faculty of Economics, University of Warsaw. The crosswalks offer a complete step-by-step mapping of O*NET data to ISCO88 and ISCO-08 coding using an expanded version of SOC-00 and SOC-10 coding. We propose a mapping method based on the aforementioned research that converts measurements to the smallest possible unit of the target taxonomy, and then performs an aggregation/estimate to the appropriate degree of detail.

“In case of raw-count data the sum by occupation group is used, else for composite indicators the mean value is used by each occupational group of the target hierarchical level.”

library(iscoCrosswalks)

kable(foundation_skills[seq(1 , nrow(foundation_skills), by = 5), ])

data.table::setnames(foundation_skills,
                     c("preferredLabel", "Value"),
                     c("job", "value"))

soc_foundation_skills <- isco_soc_crosswalk(foundation_skills,
                                            brkd_cols = "Skill",
                                            isco_lvl = 1,
                                            soc_lvl = "soc_1",
                                            indicator = TRUE)

soc_foundation_skills[, Occupations := gsub(" Occupations", "", soc_label)]
soc_foundation_skills[, Skill := gsub(" skills", "", Skill)]
dat <- soc_foundation_skills[order(Skill, -value)][, head(.SD, 6), by = "Skill"]
kable(dat)