The 2010 Standard Occupational Classification (SOC) and the International Standard Classification of Occupations (ISCO-08) are compared. To make the crosswalk more straightforward and hence more useful, the notion of parsimony was applied. This means that while a task completed in the SOC may appear in numerous ISCOs (or vice versa), the match in some of these instances is just coincidental and adds unneeded complexity. This function allows mapping of data from the top 3 ISCO levels to the 4 SOC groups.
Usage
isco_soc_crosswalk(
data,
isco_lvl = 3,
soc_lvl = "soc_2",
brkd_cols = NULL,
indicator = FALSE
)Arguments
- data,
data.table with mandatory columns
jobandvalue- isco_lvl,
numeric between 1 and 3
- soc_lvl,
character taking values from
soc_1tosoc_4- brkd_cols,
character vector with col names of stratification variables
- indicator,
Boolean indicating if data describe an indicator. If
TRUEthe mean value is computed, otherwise the sum by each breakdown group.
References
Hardy W, Keister R, Lewandowski P (2018). “Educational upgrading, structural change and the task composition of jobs in Europe.” Economics of Transition, 26(2), 201--231.
Examples
library(iscoCrosswalks)
library(data.table)
#from ISCO level 3 group to soc_1 occupations
path <- system.file("extdata", "isco_3_brkdwn_example.csv",
package = "iscoCrosswalks")
dat <- fread(path)
isco_soc_crosswalk(dat,
isco_lvl = 3,
soc_lvl = "soc_1",
brkd_cols = "gender")
#> soc10 soc_label gender
#> 1: 350000 Food Preparation and Serving Related Occupations Male
#> 2: 350000 Food Preparation and Serving Related Occupations Female
#> 3: 410000 Sales and Related Occupations Female
#> 4: 430000 Office and Administrative Support Occupations Male
#> 5: 410000 Sales and Related Occupations Male
#> 6: 150000 Computer and Mathematical Occupations Male
#> 7: 430000 Office and Administrative Support Occupations Female
#> 8: 150000 Computer and Mathematical Occupations Female
#> 9: 170000 Architecture and Engineering Occupations Female
#> 10: 170000 Architecture and Engineering Occupations Male
#> 11: 110000 Management Occupations Female
#> 12: 110000 Management Occupations Male
#> 13: 230000 Legal Occupations Male
#> 14: 310000 Healthcare Support Occupations Male
#> 15: 230000 Legal Occupations Female
#> 16: 310000 Healthcare Support Occupations Female
#> 17: 190000 Life, Physical, and Social Science Occupations Female
#> 18: 190000 Life, Physical, and Social Science Occupations Male
#> 19: 330000 Protective Service Occupations Female
#> 20: 330000 Protective Service Occupations Male
#> 21: 270000 Arts, Design, Entertainment, Sports, and Media Occupations Female
#> 22: 470000 Construction and Extraction Occupations Female
#> 23: 530000 Transportation and Material Moving Occupations Female
#> 24: 270000 Arts, Design, Entertainment, Sports, and Media Occupations Male
#> 25: 470000 Construction and Extraction Occupations Male
#> 26: 530000 Transportation and Material Moving Occupations Male
#> soc10 soc_label gender
#> value
#> 1: 42.000000
#> 2: 40.000000
#> 3: 18.750000
#> 4: 16.666667
#> 5: 15.750000
#> 6: 14.000000
#> 7: 13.333333
#> 8: 10.000000
#> 9: 8.125000
#> 10: 6.500000
#> 11: 6.250000
#> 12: 5.250000
#> 13: 4.166667
#> 14: 4.166667
#> 15: 3.333333
#> 16: 3.333333
#> 17: 3.125000
#> 18: 2.500000
#> 19: 1.875000
#> 20: 1.500000
#> 21: 0.625000
#> 22: 0.625000
#> 23: 0.625000
#> 24: 0.500000
#> 25: 0.500000
#> 26: 0.500000
#> value