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arXiv:2604.24336 · detector doi_compliance · incontrovertible · 2026-05-19 22:14:47.240229+00:00

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A. Gasparini. “comorbidity: An R package for computing comorbidity scores”. Journal of Open Source Software3.23 (2018), 648.DOI:10.21105/joss.00648.URL: https://doi.org/10.21105/joss.00648. 27 A Supplementary Figures and Tables A.1 Supplementary Figures Figure A.1: Standard deviation in wages from alternative wage models Measures of dispersion in actual and residualized wages for full-time workers, using the sample de- scribed in “Worker Ability and Firm Quality Measurement” section. Residual wages are obtained by sequentially adjusting worker-level wages for the covariates listed in the legend using linear models. The Mincer specification adjusts for education indicators and a cubic age profile. All models are esti- mated separately by calendar year. 95% confidence intervals. i Figure A.2: Density of EA-PGI by highest education level and track The density plot uses full analysis sample with non-missing education track information (N=50, 940). The blue lines correspond to secondary, and the red - to tertiary degree level. Solid lines correspond to academic, and the dashed lines - to vocational education tracks. ii Figure A.3: Average annual income of tertiary-educated individuals by EA-PGI level, over time The figure uses a subset of tertiary-educated individuals (N=32,364). The lines correspond to ten deciles of the EA-PGI distribution. Average income estimated from a regression of annual income on EA-PGI fully interacted with indicators measuring years since graduation and 

Evidence payload

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