README.md

In Guidowe/occupationcross: Package for making crosswalks among different Occupational codes

occupationcross

Description

Occupationcross is designed to facilitate the application of crosswalks between occupational classifiers from different parts of the world.

The main function of this package is reclassify_to_isco08(). Basically, this function takes as an imput a database containing a variable associated with a national occupational classifier and performs a reclassification to International Standard Classification of Occupations 08 developed by International Labour Organization.

In addition, the package also has dataframes specifying the available classifications and crosswalks, as well as the tables used to make the crosswalks.

• available_classifications
• available_crosswalks
• crosstable_sinco2011_isco08
• crosstable_cno2001_isco08

How to cite this package

You can cite this package mentioning it as "'occupationcross' R package" (Weksler y Lastra, 2022)".

To include it in references:

"Guido Weksler & Facundo Lastra (2022). occupationcross: Package for making crosswalks among different occupational codes. R package version https://doi.org/10.5281/zenodo.7025097"

bib entry:

@Manual{Weksler2022,
    title = {occupationcross: Package for making crosswalks among different occupational codes},
    author = {Guido Weksler & Facundo Lastra},
    year = {2022},
    doi = {10.5281/zenodo.702509},
    url = {https://guidowe.github.io/occupationcross/},
  }

Instalation

Install the development version of occupationcross from GitHub with:

# install.packages("devtools")
devtools::install_github("Guidowe/occupationcross")

Example

This is a basic example of how this package works :

Let´s load first occupationcross, and also tidyverse

library(occupationcross)
library(tidyverse)

The objects available_classifications and available_crosswalks show respectively which are the classifications collected and which crosswalks can be obtained applying the functions contained in this package.

occupationcross::available_classifications
#>   classification                                 classification_fullname
#> 1         ISCO08 International Standard Classification of Occupations 08
#> 2         ISCO88 International Standard Classification of Occupations 88
#> 3      SINCO2011   Sistema Nacional de Clasificación de Ocupaciones 2011
#> 4        CNO2001               Clasificador Nacional de Ocupaciones 2001
#> 5        CNO2017               Clasificador Nacional de Ocupaciones 2017
#> 6     Census2010                 2010 Census Occupational Classification
#> 7        SOC2010               2010 Standard Occupational Classification
#>         country
#> 1 International
#> 2 International
#> 3        Mexico
#> 4     Argentina
#> 5     Argentina
#> 6 United States
#> 7 United States

occupationcross::available_crosswalks
#> # A tibble: 8 x 3
#>   from          to     detail                                                   
#>   <chr>         <chr>  <chr>                                                    
#> 1 SINCO2011     ISCO08 complete crosswalk                                       
#> 2 Census2010    ISCO08 complete crosswalk                                       
#> 3 CNO2001       ISCO08 crosswalk only available to isco digits 1 and 2          
#> 4 CNO2017       ISCO08 crosswalk only available to isco digits 1 and 2          
#> 5 ISCO88        ISCO08 complete crosswalk                                       
#> 6 ISCO88_3digi~ ISCO08 crosswalk designed for databases with ISCO88 containing ~
#> 7 ISCO08        ISCO88 complete crosswalk                                       
#> 8 Census2010    SOC20~ complete crosswalk

Let´s use a sample database from a Mexico´s household survey (Encuesta Nacional de Ocupación y Empleo) already embedded in this package. This database contains a variable named “p3” corresponding to SINCO 2011 (Sistema Nacional de Clasificación de Ocupaciones - 2011) occupational codes

toy_base_mexico
#> # A tibble: 200 x 8
#>      sex t_loc clase2  tue1 pos_ocu   per   fac    p3
#>    <dbl> <dbl>  <dbl> <dbl>   <dbl> <dbl> <dbl> <dbl>
#>  1     1     1      1     4       2   119   435  7121
#>  2     2     1      1     1       1   119   542  5116
#>  3     2     1      1     3       1   119   173  9611
#>  4     2     3      0     0       0   119   411    NA
#>  5     1     1      3     0       0   119   224    NA
#>  6     2     1      0     0       0   119   104    NA
#>  7     2     1      0     0       0   119   314    NA
#>  8     2     1      4     0       0   119   104    NA
#>  9     1     4      1     1       3   119   450  6111
#> 10     2     4      1     3       3   119   293  4111
#> # ... with 190 more rows

Applying the reclassify_to_isco08() function we can obtain a reclassification of each case of our database into International Standard Classification of Occupations - 08 (ISCO-08) codes. - The classif_origin is used to specify which classification is used in the original database. - The add_major_groups parameter allows you to add a new variable identifying ISCO-08 major group. - The add_skill parameter allows you to add a new variable identifying skill level of each occupation according to ISCO-08 classification of major groups. - The code_titles parameter allows you to get the occupation names both from the origin classification and isco 08 classification

crossed_base <- reclassify_to_isco08(base = toy_base_mexico,
                                     variable = p3,
                                     classif_origin = "SINCO2011",
                                     add_major_groups = T,
                                     add_skill = T,
                                     code_titles = T)


crossed_base %>% 
  select(p3,ISCO.08,major_group)
#> # A tibble: 200 x 3
#>       p3 ISCO.08 major_group                                          
#>    <dbl> <chr>   <fct>                                                
#>  1  7121 7112    7. Craft and Related Trades Workers                  
#>  2  5116 5131    5. Services and Sales Workers                        
#>  3  9611 9111    9. Elementary Occupations                            
#>  4    NA 0000    <NA>                                                 
#>  5    NA 0000    <NA>                                                 
#>  6    NA 0000    <NA>                                                 
#>  7    NA 0000    <NA>                                                 
#>  8    NA 0000    <NA>                                                 
#>  9  6111 6111    6. Skilled Agricultural, Forestry and Fishery Workers
#> 10  4111 5221    5. Services and Sales Workers                        
#> # ... with 190 more rows

Guidowe/occupationcross documentation built on Oct. 4, 2023, 12:09 p.m.