Per cent who are women in different occupational groups in Sweden, feature importance

In a previous post, I analysed the feature importance for the per cent of engineers in Sweden who are women. I found that the size of the region is a feature that is significant for the per cent of engineers in Sweden who are women. In this post, I will analyse the feature importance of different occupational groups in Sweden. I will use an ensemble of linear models in my analysis.

Statistics Sweden use NUTS (Nomenclature des Unités Territoriales Statistiques), which is the EU’s hierarchical regional division, to specify the regions.

Please send suggestions for improvement of the analysis to ranalystatisticssweden@gmail.com.

First, define libraries and functions.

library (tidyverse)

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## v tibble  3.0.0     v dplyr   0.8.5
## v tidyr   1.0.2     v stringr 1.4.0
## v readr   1.3.1     v forcats 0.5.0

## -- Conflicts ----------------------------------------------------- tidyverse_conflicts() --
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library (broom)
library (car)

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library (caret)    

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library (recipes)  

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library (PerformanceAnalytics)

## Loading required package: xts

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library (ggpubr)

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library (ipred) 
library (iml)
library (SuperLearner)

## Loading required package: nnls

## Super Learner

## Version: 2.0-26

## Package created on 2019-10-27

library (scatterplot3d)

readfile <- function (file1){read_csv (file1, col_types = cols(), locale = readr::locale (encoding = "latin1"), na = c("..", "NA")) %>%
  gather (starts_with("19"), starts_with("20"), key = "year", value = groupsize) %>%
  drop_na() %>%
  mutate (year_n = parse_number (year))
}

perc_women <- function(x){  
  ifelse (length(x) == 2, x[2] / (x[1] + x[2]), NA)
} 

nuts <- read.csv("nuts.csv") %>%
  mutate(NUTS2_sh = substr(NUTS2, 3, 4))

nuts %>% 
  distinct (NUTS2_en) %>%
  knitr::kable(
    booktabs = TRUE,
    caption = 'Nomenclature des Unités Territoriales Statistiques (NUTS)')

Table 1: Nomenclature des Unités Territoriales Statistiques (NUTS)

NUTS2_en
SE11 Stockholm
SE12 East-Central Sweden
SE21 Småland and islands
SE22 South Sweden
SE23 West Sweden
SE31 North-Central Sweden
SE32 Central Norrland
SE33 Upper Norrland

SL.lm.caret <- function(..., method = "lm", tuneLength = 3, obsWeights = obsWeights, trControl = caret::trainControl(method = "cv", number = 10, verboseIter = FALSE)){
    suppressWarnings(SL.caret(..., obsWeights = obsWeights, method = method, tuneLength = tuneLength, trControl = trControl))
}

SL.lmStepAIC.caret <- function(..., method = "lmStepAIC", tuneLength = 3, obsWeights = obsWeights, trControl = caret::trainControl(method = "cv", number = 10, verboseIter = FALSE)){
    suppressWarnings(SL.caret(..., obsWeights = obsWeights, method = method, tuneLength = tuneLength, trControl = trControl))
}  

SL.bayesglm.caret <- function(..., method = "bayesglm", tuneLength = 3, obsWeights = obsWeights, trControl = caret::trainControl(method = "cv", number = 10, verboseIter = FALSE)){
    suppressWarnings(SL.caret(..., obsWeights = obsWeights, method = method, tuneLength = tuneLength, trControl = trControl))
}  

SL.rlm.caret <- function(..., method = "rlm", tuneLength = 3, obsWeights = obsWeights, trControl = caret::trainControl(method = "cv", number = 10, verboseIter = FALSE)){
    suppressWarnings(SL.caret(..., obsWeights = obsWeights, method = method, tuneLength = tuneLength, trControl = trControl))
}

The data tables are downloaded from Statistics Sweden. They are saved as a comma-delimited file without heading, UF0506A1.csv, http://www.statistikdatabasen.scb.se/pxweb/en/ssd/.

The tables:

UF0506A1_1.csv: Population 16-74 years of age by region, highest level of education, age and sex. Year 1985 - 2018 NUTS 2 level 2008- 10 year intervals (16-74)

000000CG_1: Average basic salary, monthly salary and women´s salary as a percentage of men´s salary by region, sector, occupational group (SSYK 2012) and sex. Year 2014 - 2018 Monthly salary All sectors.

000000CD_1.csv: Average basic salary, monthly salary and women´s salary as a percentage of men´s salary by region, sector, occupational group (SSYK 2012) and sex. Year 2014 - 2018 Number of employees All sectors.

The data is aggregated, the size of each group is in the column groupsize.

I have also included some calculated predictors from the original data.

perc_women: The percentage of women within each group defined by edulevel, region and year

perc_women_region: The percentage of women within each group defined by year and region

regioneduyears: The average number of education years per capita within each group defined by year and region

eduquotient: The quotient between regioneduyears for men and women

salaryquotient: The quotient between salary for men and women within each group defined by year and region

perc_women_eng_region: The percentage of women who are engineers within each group defined by year and region

numedulevel <- read.csv("edulevel_1.csv") 

numedulevel[, 2] <- data.frame(c(8, 9, 10, 12, 13, 15, 22, NA))

tb <- readfile("000000CG_1.csv") 
tb <- readfile("000000CD_1.csv") %>% 
  left_join(tb, by = c("region", "year", "sex", "sector","occuptional  (SSYK 2012)")) 

tb <- readfile("UF0506A1_1.csv") %>%  
  right_join(tb, by = c("region", "year", "sex")) %>%
  right_join(numedulevel, by = c("level of education" = "level.of.education")) %>%
  filter(!is.na(eduyears)) %>%  
  mutate(edulevel = `level of education`) %>%
  group_by(edulevel, region, year, sex, `occuptional  (SSYK 2012)`) %>%
  mutate(groupsize_all_ages = sum(groupsize)) %>%  
  group_by(edulevel, region, year, `occuptional  (SSYK 2012)`) %>% 
  mutate (perc_women = perc_women (groupsize_all_ages[1:2])) %>% 
  mutate (suming = sum(groupsize.x)) %>%
  mutate (salary = (groupsize.y[2] * groupsize.x[2] + groupsize.y[1] * groupsize.x[1])/(groupsize.x[2] + groupsize.x[1])) %>%
  group_by (sex, year, region, `occuptional  (SSYK 2012)`) %>%
  mutate(regioneduyears_sex = sum(groupsize * eduyears) / sum(groupsize)) %>%
  mutate(regiongroupsize = sum(groupsize)) %>% 
  mutate(suming_sex = groupsize.x) %>%
  group_by(region, year, `occuptional  (SSYK 2012)`) %>%
  mutate (sum_pop = sum(groupsize)) %>%
  mutate (regioneduyears = sum(groupsize * eduyears) / sum(groupsize)) %>%
  mutate (perc_women_region = perc_women (regiongroupsize[1:2])) %>% 
  mutate (eduquotient = regioneduyears_sex[2] / regioneduyears_sex[1]) %>% 
  mutate (salary_sex = groupsize.y) %>%
  mutate (salaryquotient = salary_sex[2] / salary_sex[1]) %>%   
  mutate (perc_women_eng_region = perc_women(suming_sex[1:2])) %>%  
  left_join(nuts %>% distinct (NUTS2_en, NUTS2_sh), by = c("region" = "NUTS2_en")) %>%
  drop_na()

summary(tb)

##     region              age            level of education     sex           
##  Length:29050       Length:29050       Length:29050       Length:29050      
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##      year             groupsize          year_n        sector         
##  Length:29050       Min.   :   405   Min.   :2014   Length:29050      
##  Class :character   1st Qu.: 25412   1st Qu.:2015   Class :character  
##  Mode  :character   Median : 61291   Median :2016   Mode  :character  
##                     Mean   : 71345   Mean   :2016                     
##                     3rd Qu.:113524   3rd Qu.:2017                     
##                     Max.   :271889   Max.   :2018                     
##  occuptional  (SSYK 2012)  groupsize.x       year_n.x     groupsize.y    
##  Length:29050             Min.   :  100   Min.   :2014   Min.   : 20200  
##  Class :character         1st Qu.:  490   1st Qu.:2015   1st Qu.: 28900  
##  Mode  :character         Median : 1300   Median :2016   Median : 33900  
##                           Mean   : 3258   Mean   :2016   Mean   : 37066  
##                           3rd Qu.: 3400   3rd Qu.:2017   3rd Qu.: 42100  
##                           Max.   :45000   Max.   :2018   Max.   :133600  
##     year_n.y       eduyears       edulevel         groupsize_all_ages
##  Min.   :2014   Min.   : 8.00   Length:29050       Min.   :   405    
##  1st Qu.:2015   1st Qu.: 9.00   Class :character   1st Qu.: 25412    
##  Median :2016   Median :12.00   Mode  :character   Median : 61291    
##  Mean   :2016   Mean   :12.71                      Mean   : 71345    
##  3rd Qu.:2017   3rd Qu.:15.00                      3rd Qu.:113524    
##  Max.   :2018   Max.   :22.00                      Max.   :271889    
##    perc_women         suming          salary       regioneduyears_sex
##  Min.   :0.3575   Min.   :  240   Min.   : 20661   Min.   :11.18     
##  1st Qu.:0.4343   1st Qu.: 1330   1st Qu.: 29046   1st Qu.:11.63     
##  Median :0.4655   Median : 3100   Median : 34041   Median :11.78     
##  Mean   :0.4775   Mean   : 6515   Mean   : 37105   Mean   :11.83     
##  3rd Qu.:0.5132   3rd Qu.: 7400   3rd Qu.: 42068   3rd Qu.:12.09     
##  Max.   :0.6423   Max.   :60000   Max.   :113976   Max.   :12.55     
##  regiongroupsize    suming_sex       sum_pop        regioneduyears 
##  Min.   :128262   Min.   :  100   Min.   : 262870   Min.   :11.39  
##  1st Qu.:292864   1st Qu.:  490   1st Qu.: 596546   1st Qu.:11.56  
##  Median :528643   Median : 1300   Median :1057419   Median :11.82  
##  Mean   :499413   Mean   : 3258   Mean   : 998826   Mean   :11.83  
##  3rd Qu.:708813   3rd Qu.: 3400   3rd Qu.:1417931   3rd Qu.:11.93  
##  Max.   :827940   Max.   :45000   Max.   :1655215   Max.   :12.41  
##  perc_women_region  eduquotient      salary_sex     salaryquotient  
##  Min.   :0.4831    Min.   :1.019   Min.   : 20200   Min.   :0.6423  
##  1st Qu.:0.4890    1st Qu.:1.027   1st Qu.: 28900   1st Qu.:0.9144  
##  Median :0.4937    Median :1.032   Median : 33900   Median :0.9556  
##  Mean   :0.4931    Mean   :1.033   Mean   : 37066   Mean   :0.9502  
##  3rd Qu.:0.4971    3rd Qu.:1.040   3rd Qu.: 42100   3rd Qu.:0.9941  
##  Max.   :0.5014    Max.   :1.047   Max.   :133600   Max.   :1.3090  
##  perc_women_eng_region   NUTS2_sh        
##  Min.   :0.01659       Length:29050      
##  1st Qu.:0.30876       Class :character  
##  Median :0.56000       Mode  :character  
##  Mean   :0.52565                         
##  3rd Qu.:0.72414                         
##  Max.   :0.94527

tbtemp <- ungroup(tb) %>% dplyr::select(salary, suming, year_n, sum_pop, regioneduyears, perc_women_region, salaryquotient, eduquotient, perc_women_eng_region, `occuptional  (SSYK 2012)`)

tb_unique <- unique(tbtemp)

I will use SuperLearner to train the ensemble consisting of four linear models without interactions. The four models are Linear Regression (lm), Linear Regression with Stepwise Selection (lmStepAIC), Bayesian Generalized Linear Model (bayesglm) and Robust Linear Model (rlm).

summary_table = vector()
cor_table = vector()
sp_table <- vector()
rmse_table <- vector()

for (i in unique(tb_unique$`occuptional  (SSYK 2012)`)){
  temp <- filter(tb_unique, `occuptional  (SSYK 2012)` == i)
  if (dim(temp)[1] > 20){
     temp_weights = temp$suming
     temp <- dplyr::select(temp, - c(`occuptional  (SSYK 2012)`, suming))
     blueprint <- recipe(perc_women_eng_region ~ ., data = temp) %>%
       step_integer(matches("Qual|Cond|QC|Qu")) %>%
       step_center(all_numeric(), -all_outcomes()) %>%
       step_scale(all_numeric(), -all_outcomes()) %>%
       step_dummy(all_nominal(), -all_outcomes(), one_hot = TRUE)
     prepare <- prep(blueprint, training = temp)
     temp <- bake(prepare, new_data = temp)
  
     invisible(capture.output(model <- SuperLearner(
        temp$perc_women_eng_region,
        data.frame(dplyr::select(temp, -c(perc_women_eng_region))),
        family = gaussian(),
        verbose = FALSE,
        obsWeights = temp_weights,
        SL.library = list("SL.lm.caret", "SL.lmStepAIC.caret", "SL.bayesglm.caret", "SL.rlm.caret"))))

     pred <- function(object, newdata){
       predict(model, newdata=newdata, onlySL = TRUE)$pred
     }  
    
     predictor <- Predictor$new(model, 
        data = dplyr::select(temp, -perc_women_eng_region), 
        y = temp$perc_women_eng_region,
        predict.fun = pred)   
   
     imp <- FeatureImp$new(predictor, loss = "mae", n.repetitions = 30)
    
     summary_table <- rbind(summary_table, mutate(tibble(.rows = 7), importance = imp$results$importance, feature = imp$results$feature, importance.05 = imp$results$importance.05, ssyk = i))
    
     cor_table <- rbind(cor_table, mutate(tibble(.rows = 7), feature = colnames(dplyr::select(temp, -c(perc_women_eng_region))), cor = cor(dplyr::select(temp, -c(perc_women_eng_region)), temp$perc_women_eng_region), ssyk = i))
    
     sp_table <- rbind(sp_table, mutate(tibble(.rows = 4), coef = model$coef, model = names(model$coef),  ssyk = i))
    
     prs <- postResample(pred = predict(model)$pred, obs = temp$perc_women_eng_region)
    
     rmse_table <- rbind(rmse_table, mutate(tibble(.rows = 1), RMSE = prs[1], Rsquared = prs[2], MAE = prs[3], ssyk = i))    
  }
}

## Registered S3 methods overwritten by 'lme4':
##   method                          from
##   cooks.distance.influence.merMod car 
##   influence.merMod                car 
##   dfbeta.influence.merMod         car 
##   dfbetas.influence.merMod        car

The table below shows the feature values for the different occupation groups and if there is a single important feature (diff1) or if there are two important features (diff2) for the occupational group. The Rsquared value shows if the model for the occupational group does have a good fit.

summary_table %>% 
  group_by(ssyk) %>% 
  group_by(ssyk) %>% 
  dplyr::mutate(diff1 = importance.05[1] / importance[2]) %>% 
  dplyr::mutate(diff2 = importance.05[2] / importance[3]) %>% 
  left_join(cor_table, by = c("ssyk", "feature")) %>% 
  left_join(sp_table %>% spread(model, coef), by=c("ssyk")) %>% 
  left_join(rmse_table, by=c("ssyk")) %>% 
  dplyr::select(ssyk, feature, importance, importance.05, diff1, diff2, Rsquared) %>%
  knitr::kable( 
     booktabs = TRUE,
     caption = 'Feature values for different occupation groups')

Table 2: Feature values for different occupation groups

ssyk	feature	importance	importance.05	diff1	diff2	Rsquared
123 Administration and planning managers	eduquotient	3.8170179	3.2624044	0.9992710	0.9746650	0.5296568
123 Administration and planning managers	sum_pop	3.2647844	2.6462447	0.9992710	0.9746650	0.5296568
123 Administration and planning managers	salary	2.7150299	2.3345443	0.9992710	0.9746650	0.5296568
123 Administration and planning managers	regioneduyears	2.5260824	2.1015060	0.9992710	0.9746650	0.5296568
123 Administration and planning managers	salaryquotient	1.2063518	1.0434914	0.9992710	0.9746650	0.5296568
123 Administration and planning managers	perc_women_region	1.1813452	1.0858792	0.9992710	0.9746650	0.5296568
123 Administration and planning managers	year_n	1.0740710	1.0155535	0.9992710	0.9746650	0.5296568
141 Primary and secondary schools and adult education managers	regioneduyears	2.7494050	2.2970027	1.3327022	0.9503406	0.6950328
141 Primary and secondary schools and adult education managers	year_n	1.7235678	1.4984603	1.3327022	0.9503406	0.6950328
141 Primary and secondary schools and adult education managers	salary	1.5767613	1.4698417	1.3327022	0.9503406	0.6950328
141 Primary and secondary schools and adult education managers	sum_pop	1.0000000	1.0000000	1.3327022	0.9503406	0.6950328
141 Primary and secondary schools and adult education managers	perc_women_region	1.0000000	1.0000000	1.3327022	0.9503406	0.6950328
141 Primary and secondary schools and adult education managers	salaryquotient	1.0000000	1.0000000	1.3327022	0.9503406	0.6950328
141 Primary and secondary schools and adult education managers	eduquotient	1.0000000	1.0000000	1.3327022	0.9503406	0.6950328
151 Health care managers	salaryquotient	2.4538279	1.9290583	0.8094247	0.9296316	0.5799653
151 Health care managers	regioneduyears	2.3832462	2.1362284	0.8094247	0.9296316	0.5799653
151 Health care managers	salary	2.2979301	1.9260029	0.8094247	0.9296316	0.5799653
151 Health care managers	eduquotient	2.2848362	1.7029592	0.8094247	0.9296316	0.5799653
151 Health care managers	year_n	1.5379944	1.3579666	0.8094247	0.9296316	0.5799653
151 Health care managers	sum_pop	1.3662380	1.1596424	0.8094247	0.9296316	0.5799653
151 Health care managers	perc_women_region	1.0388730	1.0045578	0.8094247	0.9296316	0.5799653
153 Elderly care managers	year_n	8.1559615	6.4775973	0.9123375	1.4669946	0.8177027
153 Elderly care managers	salary	7.1000012	5.9284122	0.9123375	1.4669946	0.8177027
153 Elderly care managers	perc_women_region	4.0411955	3.4263668	0.9123375	1.4669946	0.8177027
153 Elderly care managers	salaryquotient	1.5390920	1.2329029	0.9123375	1.4669946	0.8177027
153 Elderly care managers	sum_pop	1.0000000	1.0000000	0.9123375	1.4669946	0.8177027
153 Elderly care managers	regioneduyears	1.0000000	1.0000000	0.9123375	1.4669946	0.8177027
153 Elderly care managers	eduquotient	1.0000000	1.0000000	0.9123375	1.4669946	0.8177027
159 Other social services managers	sum_pop	3.0725550	2.5099215	0.8325266	0.9953769	0.7325679
159 Other social services managers	salary	3.0148243	2.6352584	0.8325266	0.9953769	0.7325679
159 Other social services managers	regioneduyears	2.6474981	2.2659272	0.8325266	0.9953769	0.7325679
159 Other social services managers	eduquotient	2.1941589	1.9031203	0.8325266	0.9953769	0.7325679
159 Other social services managers	year_n	1.7175697	1.4021163	0.8325266	0.9953769	0.7325679
159 Other social services managers	salaryquotient	1.5834901	1.3638281	0.8325266	0.9953769	0.7325679
159 Other social services managers	perc_women_region	1.0000000	1.0000000	0.8325266	0.9953769	0.7325679
211 Physicists and chemists	eduquotient	3.1793564	2.4217960	0.8168496	1.4762352	0.7777887
211 Physicists and chemists	perc_women_region	2.9648004	2.4885379	0.8168496	1.4762352	0.7777887
211 Physicists and chemists	year_n	1.6857326	1.4429244	0.8168496	1.4762352	0.7777887
211 Physicists and chemists	regioneduyears	1.6402704	1.3256462	0.8168496	1.4762352	0.7777887
211 Physicists and chemists	sum_pop	1.5988744	1.2158115	0.8168496	1.4762352	0.7777887
211 Physicists and chemists	salary	1.5727207	1.3608653	0.8168496	1.4762352	0.7777887
211 Physicists and chemists	salaryquotient	1.3311748	1.0615814	0.8168496	1.4762352	0.7777887
214 Engineering professionals	sum_pop	3.2085990	2.5852735	1.0619019	1.1085082	0.8501315
214 Engineering professionals	regioneduyears	2.4345690	2.0768022	1.0619019	1.1085082	0.8501315
214 Engineering professionals	eduquotient	1.8735109	1.5486497	1.0619019	1.1085082	0.8501315
214 Engineering professionals	salary	1.0000000	1.0000000	1.0619019	1.1085082	0.8501315
214 Engineering professionals	year_n	1.0000000	1.0000000	1.0619019	1.1085082	0.8501315
214 Engineering professionals	perc_women_region	1.0000000	1.0000000	1.0619019	1.1085082	0.8501315
214 Engineering professionals	salaryquotient	1.0000000	1.0000000	1.0619019	1.1085082	0.8501315
218 Specialists within environmental and health protection	year_n	1.1998968	1.0271753	0.9319265	1.0204712	0.2072889
218 Specialists within environmental and health protection	sum_pop	1.1022064	1.0204712	0.9319265	1.0204712	0.2072889
218 Specialists within environmental and health protection	salary	1.0000000	1.0000000	0.9319265	1.0204712	0.2072889
218 Specialists within environmental and health protection	regioneduyears	1.0000000	1.0000000	0.9319265	1.0204712	0.2072889
218 Specialists within environmental and health protection	perc_women_region	1.0000000	1.0000000	0.9319265	1.0204712	0.2072889
218 Specialists within environmental and health protection	salaryquotient	1.0000000	1.0000000	0.9319265	1.0204712	0.2072889
218 Specialists within environmental and health protection	eduquotient	1.0000000	1.0000000	0.9319265	1.0204712	0.2072889
221 Medical doctors	regioneduyears	3.2538623	2.7126722	1.6832188	1.0066594	0.7935628
221 Medical doctors	eduquotient	1.6115981	1.4986615	1.6832188	1.0066594	0.7935628
221 Medical doctors	perc_women_region	1.4887473	1.3049164	1.6832188	1.0066594	0.7935628
221 Medical doctors	sum_pop	1.0890646	1.0519492	1.6832188	1.0066594	0.7935628
221 Medical doctors	salaryquotient	1.0136252	0.9725422	1.6832188	1.0066594	0.7935628
221 Medical doctors	salary	1.0079123	0.9912346	1.6832188	1.0066594	0.7935628
221 Medical doctors	year_n	0.9691336	0.9264889	1.6832188	1.0066594	0.7935628
222 Nursing professionals	perc_women_region	1.3570281	1.2297860	1.1599907	0.9741302	0.2680452
222 Nursing professionals	salaryquotient	1.0601688	0.9943949	1.1599907	0.9741302	0.2680452
222 Nursing professionals	eduquotient	1.0208028	0.9745776	1.1599907	0.9741302	0.2680452
222 Nursing professionals	year_n	1.0058709	0.9866984	1.1599907	0.9741302	0.2680452
222 Nursing professionals	sum_pop	1.0015554	0.9908744	1.1599907	0.9741302	0.2680452
222 Nursing professionals	salary	0.9999898	0.9996937	1.1599907	0.9741302	0.2680452
222 Nursing professionals	regioneduyears	0.9991395	0.9967976	1.1599907	0.9741302	0.2680452
223 Nursing professionals (cont.)	perc_women_region	1.7410583	1.4227133	0.8555381	0.9923183	0.6124752
223 Nursing professionals (cont.)	eduquotient	1.6629455	1.4414067	0.8555381	0.9923183	0.6124752
223 Nursing professionals (cont.)	sum_pop	1.4525648	1.3870921	0.8555381	0.9923183	0.6124752
223 Nursing professionals (cont.)	salaryquotient	1.2526913	1.1340334	0.8555381	0.9923183	0.6124752
223 Nursing professionals (cont.)	year_n	1.1251733	1.0377315	0.8555381	0.9923183	0.6124752
223 Nursing professionals (cont.)	regioneduyears	1.0772452	0.9979456	0.8555381	0.9923183	0.6124752
223 Nursing professionals (cont.)	salary	1.0064257	0.9699734	0.8555381	0.9923183	0.6124752
227 Naprapaths, physiotherapists, occupational therapists	year_n	3.3148421	2.8516422	1.2860370	1.4787861	0.5424810
227 Naprapaths, physiotherapists, occupational therapists	salary	2.2173875	1.9400838	1.2860370	1.4787861	0.5424810
227 Naprapaths, physiotherapists, occupational therapists	eduquotient	1.3119435	1.1242123	1.2860370	1.4787861	0.5424810
227 Naprapaths, physiotherapists, occupational therapists	regioneduyears	1.3023137	1.1403014	1.2860370	1.4787861	0.5424810
227 Naprapaths, physiotherapists, occupational therapists	salaryquotient	1.0993687	0.9919804	1.2860370	1.4787861	0.5424810
227 Naprapaths, physiotherapists, occupational therapists	perc_women_region	1.0570268	0.9703933	1.2860370	1.4787861	0.5424810
227 Naprapaths, physiotherapists, occupational therapists	sum_pop	0.9923096	0.9587380	1.2860370	1.4787861	0.5424810
231 University and higher education teachers	perc_women_region	6.9035680	6.1896016	1.0668866	0.8353526	0.9357939
231 University and higher education teachers	year_n	5.8015553	4.8319783	1.0668866	0.8353526	0.9357939
231 University and higher education teachers	salary	5.7843576	4.9118836	1.0668866	0.8353526	0.9357939
231 University and higher education teachers	sum_pop	4.2107594	3.2672779	1.0668866	0.8353526	0.9357939
231 University and higher education teachers	eduquotient	3.6947346	3.1285856	1.0668866	0.8353526	0.9357939
231 University and higher education teachers	regioneduyears	2.8014376	2.4699874	1.0668866	0.8353526	0.9357939
231 University and higher education teachers	salaryquotient	1.7814310	1.4686631	1.0668866	0.8353526	0.9357939
232 Vocational education teachers	perc_women_region	5.8995689	4.5458286	0.8486146	0.9931816	0.9152722
232 Vocational education teachers	salary	5.3567644	4.4992796	0.8486146	0.9931816	0.9152722
232 Vocational education teachers	regioneduyears	4.5301682	4.0344389	0.8486146	0.9931816	0.9152722
232 Vocational education teachers	year_n	2.5787996	2.2283684	0.8486146	0.9931816	0.9152722
232 Vocational education teachers	eduquotient	1.9948566	1.7227708	0.8486146	0.9931816	0.9152722
232 Vocational education teachers	salaryquotient	1.7484881	1.4137498	0.8486146	0.9931816	0.9152722
232 Vocational education teachers	sum_pop	1.1795101	1.0464747	0.8486146	0.9931816	0.9152722
233 Secondary education teachers	year_n	1.7519346	1.5963138	0.9861296	0.8798435	0.2711955
233 Secondary education teachers	salary	1.6187667	1.3647524	0.9861296	0.8798435	0.2711955
233 Secondary education teachers	perc_women_region	1.5511308	1.3237125	0.9861296	0.8798435	0.2711955
233 Secondary education teachers	eduquotient	1.4901622	1.3515191	0.9861296	0.8798435	0.2711955
233 Secondary education teachers	regioneduyears	1.1340296	1.0608823	0.9861296	0.8798435	0.2711955
233 Secondary education teachers	sum_pop	1.1115054	1.0431384	0.9861296	0.8798435	0.2711955
233 Secondary education teachers	salaryquotient	1.0011883	0.9739857	0.9861296	0.8798435	0.2711955
234 Primary- and pre-school teachers	regioneduyears	2.6651473	2.3148568	1.0820339	0.9396348	0.7919968
234 Primary- and pre-school teachers	eduquotient	2.1393570	1.8980735	1.0820339	0.9396348	0.7919968
234 Primary- and pre-school teachers	sum_pop	2.0200119	1.7615651	1.0820339	0.9396348	0.7919968
234 Primary- and pre-school teachers	year_n	1.9879886	1.7570799	1.0820339	0.9396348	0.7919968
234 Primary- and pre-school teachers	salaryquotient	1.5711047	1.3697916	1.0820339	0.9396348	0.7919968
234 Primary- and pre-school teachers	salary	1.5376109	1.3834061	1.0820339	0.9396348	0.7919968
234 Primary- and pre-school teachers	perc_women_region	1.0541899	1.0163654	1.0820339	0.9396348	0.7919968
235 Teaching professionals not elsewhere classified	eduquotient	3.4752946	3.1173913	1.1572182	1.0071504	0.7038429
235 Teaching professionals not elsewhere classified	perc_women_region	2.6938664	2.2359223	1.1572182	1.0071504	0.7038429
235 Teaching professionals not elsewhere classified	year_n	2.2200482	1.9098982	1.1572182	1.0071504	0.7038429
235 Teaching professionals not elsewhere classified	salaryquotient	1.8916217	1.6391206	1.1572182	1.0071504	0.7038429
235 Teaching professionals not elsewhere classified	regioneduyears	1.3369762	1.1453482	1.1572182	1.0071504	0.7038429
235 Teaching professionals not elsewhere classified	sum_pop	1.0086567	0.9599072	1.1572182	1.0071504	0.7038429
235 Teaching professionals not elsewhere classified	salary	1.0045400	0.9954951	1.1572182	1.0071504	0.7038429
241 Accountants, financial analysts and fund managers	perc_women_region	2.7081423	2.2326348	0.8460985	1.0919083	0.7445476
241 Accountants, financial analysts and fund managers	eduquotient	2.6387410	2.1737040	0.8460985	1.0919083	0.7445476
241 Accountants, financial analysts and fund managers	year_n	1.9907387	1.5693998	0.8460985	1.0919083	0.7445476
241 Accountants, financial analysts and fund managers	salary	1.4932763	1.3223917	0.8460985	1.0919083	0.7445476
241 Accountants, financial analysts and fund managers	regioneduyears	1.3933309	1.2289757	0.8460985	1.0919083	0.7445476
241 Accountants, financial analysts and fund managers	salaryquotient	1.0962361	1.0154278	0.8460985	1.0919083	0.7445476
241 Accountants, financial analysts and fund managers	sum_pop	0.9995289	0.9973319	0.8460985	1.0919083	0.7445476
242 Organisation analysts, policy administrators and human resource specialists	salary	4.1453246	3.4636361	1.4988404	1.0425932	0.6524219
242 Organisation analysts, policy administrators and human resource specialists	perc_women_region	2.3108772	1.9737256	1.4988404	1.0425932	0.6524219
242 Organisation analysts, policy administrators and human resource specialists	year_n	1.8930927	1.6469206	1.4988404	1.0425932	0.6524219
242 Organisation analysts, policy administrators and human resource specialists	regioneduyears	1.8639424	1.6551601	1.4988404	1.0425932	0.6524219
242 Organisation analysts, policy administrators and human resource specialists	eduquotient	1.3041251	1.2098787	1.4988404	1.0425932	0.6524219
242 Organisation analysts, policy administrators and human resource specialists	sum_pop	1.0982455	1.0016594	1.4988404	1.0425932	0.6524219
242 Organisation analysts, policy administrators and human resource specialists	salaryquotient	1.0559543	0.9813767	1.4988404	1.0425932	0.6524219
243 Marketing and public relations professionals	sum_pop	4.9569245	3.9496983	1.1206542	0.9392641	0.6445752
243 Marketing and public relations professionals	regioneduyears	3.5244578	2.8349264	1.1206542	0.9392641	0.6445752
243 Marketing and public relations professionals	salary	3.0182422	2.2538137	1.1206542	0.9392641	0.6445752
243 Marketing and public relations professionals	eduquotient	2.6524352	1.9742342	1.1206542	0.9392641	0.6445752
243 Marketing and public relations professionals	year_n	1.7174528	1.4543276	1.1206542	0.9392641	0.6445752
243 Marketing and public relations professionals	salaryquotient	1.4552259	1.2338961	1.1206542	0.9392641	0.6445752
243 Marketing and public relations professionals	perc_women_region	1.3152657	1.1626737	1.1206542	0.9392641	0.6445752
251 ICT architects, systems analysts and test managers	perc_women_region	2.7358787	2.5568847	0.9528479	1.1920886	0.4818438
251 ICT architects, systems analysts and test managers	salary	2.6834131	2.1391217	0.9528479	1.1920886	0.4818438
251 ICT architects, systems analysts and test managers	year_n	1.7944318	1.5618951	0.9528479	1.1920886	0.4818438
251 ICT architects, systems analysts and test managers	eduquotient	1.0100090	1.0001357	0.9528479	1.1920886	0.4818438
251 ICT architects, systems analysts and test managers	sum_pop	1.0047852	1.0012175	0.9528479	1.1920886	0.4818438
251 ICT architects, systems analysts and test managers	salaryquotient	0.9996955	0.9963383	0.9528479	1.1920886	0.4818438
251 ICT architects, systems analysts and test managers	regioneduyears	0.9930233	0.9888326	0.9528479	1.1920886	0.4818438
261 Legal professionals	salary	4.7549424	3.8162165	0.8451578	1.1021776	0.7483456
261 Legal professionals	sum_pop	4.5153896	3.1719595	0.8451578	1.1021776	0.7483456
261 Legal professionals	perc_women_region	2.8779023	2.5745840	0.8451578	1.1021776	0.7483456
261 Legal professionals	year_n	2.6379752	2.2504586	0.8451578	1.1021776	0.7483456
261 Legal professionals	regioneduyears	2.5557708	2.1184918	0.8451578	1.1021776	0.7483456
261 Legal professionals	eduquotient	2.0561141	1.7014956	0.8451578	1.1021776	0.7483456
261 Legal professionals	salaryquotient	1.4548882	1.2819586	0.8451578	1.1021776	0.7483456
262 Museum curators and librarians and related professionals	sum_pop	3.3548098	2.4968169	0.7595766	0.8090532	0.7594220
262 Museum curators and librarians and related professionals	eduquotient	3.2871165	2.5885880	0.7595766	0.8090532	0.7594220
262 Museum curators and librarians and related professionals	perc_women_region	3.1995277	2.8162677	0.7595766	0.8090532	0.7594220
262 Museum curators and librarians and related professionals	salary	1.9883411	1.6716447	0.7595766	0.8090532	0.7594220
262 Museum curators and librarians and related professionals	regioneduyears	1.4723407	1.3110794	0.7595766	0.8090532	0.7594220
262 Museum curators and librarians and related professionals	year_n	1.1910891	1.1044098	0.7595766	0.8090532	0.7594220
262 Museum curators and librarians and related professionals	salaryquotient	1.0596886	0.9983240	0.7595766	0.8090532	0.7594220
266 Social work and counselling professionals	year_n	2.0699805	1.8890549	1.3195137	0.9204363	0.6423319
266 Social work and counselling professionals	regioneduyears	1.4316296	1.1948843	1.3195137	0.9204363	0.6423319
266 Social work and counselling professionals	perc_women_region	1.2981716	1.0993452	1.3195137	0.9204363	0.6423319
266 Social work and counselling professionals	sum_pop	1.2974744	1.1788171	1.3195137	0.9204363	0.6423319
266 Social work and counselling professionals	salaryquotient	1.0015134	0.9980390	1.3195137	0.9204363	0.6423319
266 Social work and counselling professionals	eduquotient	1.0004463	0.9980253	1.3195137	0.9204363	0.6423319
266 Social work and counselling professionals	salary	1.0002662	0.9921359	1.3195137	0.9204363	0.6423319
311 Physical and engineering science technicians	perc_women_region	2.8239037	2.3308712	1.2124932	1.1060730	0.6129610
311 Physical and engineering science technicians	year_n	1.9223788	1.6234583	1.2124932	1.1060730	0.6129610
311 Physical and engineering science technicians	salary	1.4677678	1.2914193	1.2124932	1.1060730	0.6129610
311 Physical and engineering science technicians	salaryquotient	1.0353719	0.9889612	1.2124932	1.1060730	0.6129610
311 Physical and engineering science technicians	eduquotient	1.0236570	0.9963231	1.2124932	1.1060730	0.6129610
311 Physical and engineering science technicians	sum_pop	1.0233421	0.9919135	1.2124932	1.1060730	0.6129610
311 Physical and engineering science technicians	regioneduyears	1.0116614	0.9855591	1.2124932	1.1060730	0.6129610
331 Financial and accounting associate professionals	eduquotient	3.7511968	3.2140254	1.3911100	0.9346430	0.6495278
331 Financial and accounting associate professionals	perc_women_region	2.3104035	1.9925122	1.3911100	0.9346430	0.6495278
331 Financial and accounting associate professionals	salary	2.1318431	1.8521291	1.3911100	0.9346430	0.6495278
331 Financial and accounting associate professionals	sum_pop	1.4214195	1.2916594	1.3911100	0.9346430	0.6495278
331 Financial and accounting associate professionals	salaryquotient	1.3864519	1.1803667	1.3911100	0.9346430	0.6495278
331 Financial and accounting associate professionals	regioneduyears	1.0725493	1.0231476	1.3911100	0.9346430	0.6495278
331 Financial and accounting associate professionals	year_n	1.0156559	0.9924611	1.3911100	0.9346430	0.6495278
332 Insurance advisers, sales and purchasing agents	perc_women_region	3.8010119	3.0275822	1.5742205	1.3483567	0.7642041
332 Insurance advisers, sales and purchasing agents	sum_pop	1.9232262	1.7322825	1.5742205	1.3483567	0.7642041
332 Insurance advisers, sales and purchasing agents	salaryquotient	1.2847360	1.1413503	1.5742205	1.3483567	0.7642041
332 Insurance advisers, sales and purchasing agents	year_n	1.2196519	1.0707646	1.5742205	1.3483567	0.7642041
332 Insurance advisers, sales and purchasing agents	salary	1.0000000	1.0000000	1.5742205	1.3483567	0.7642041
332 Insurance advisers, sales and purchasing agents	regioneduyears	1.0000000	1.0000000	1.5742205	1.3483567	0.7642041
332 Insurance advisers, sales and purchasing agents	eduquotient	1.0000000	1.0000000	1.5742205	1.3483567	0.7642041
333 Business services agents	regioneduyears	3.1945994	2.5604487	1.4159454	1.0419269	0.4301719
333 Business services agents	eduquotient	1.8082963	1.4856024	1.4159454	1.0419269	0.4301719
333 Business services agents	year_n	1.4258221	1.1847659	1.4159454	1.0419269	0.4301719
333 Business services agents	sum_pop	1.1998397	1.0205232	1.4159454	1.0419269	0.4301719
333 Business services agents	salaryquotient	1.0691040	1.0183061	1.4159454	1.0419269	0.4301719
333 Business services agents	perc_women_region	1.0376611	0.9739336	1.4159454	1.0419269	0.4301719
333 Business services agents	salary	1.0005005	0.9973068	1.4159454	1.0419269	0.4301719
335 Tax and related government associate professionals	eduquotient	4.1013441	3.4857810	1.2177057	1.1768387	0.7469849
335 Tax and related government associate professionals	sum_pop	2.8625809	2.6778006	1.2177057	1.1768387	0.7469849
335 Tax and related government associate professionals	perc_women_region	2.2754185	1.9606631	1.2177057	1.1768387	0.7469849
335 Tax and related government associate professionals	salary	1.0000000	1.0000000	1.2177057	1.1768387	0.7469849
335 Tax and related government associate professionals	year_n	1.0000000	1.0000000	1.2177057	1.1768387	0.7469849
335 Tax and related government associate professionals	regioneduyears	1.0000000	1.0000000	1.2177057	1.1768387	0.7469849
335 Tax and related government associate professionals	salaryquotient	1.0000000	1.0000000	1.2177057	1.1768387	0.7469849
336 Police officers	eduquotient	6.6149010	5.4101856	1.3781681	0.9403836	0.6720628
336 Police officers	sum_pop	3.9256356	3.1250520	1.3781681	0.9403836	0.6720628
336 Police officers	salary	3.3231673	2.8227248	1.3781681	0.9403836	0.6720628
336 Police officers	regioneduyears	3.2344097	2.8043712	1.3781681	0.9403836	0.6720628
336 Police officers	perc_women_region	2.0402484	1.7876511	1.3781681	0.9403836	0.6720628
336 Police officers	year_n	1.8543180	1.6559515	1.3781681	0.9403836	0.6720628
336 Police officers	salaryquotient	1.1808364	1.0825015	1.3781681	0.9403836	0.6720628
411 Office assistants and other secretaries	perc_women_region	2.1872367	1.7804951	0.8268219	0.9425405	0.5768773
411 Office assistants and other secretaries	sum_pop	2.1534202	1.8012277	0.8268219	0.9425405	0.5768773
411 Office assistants and other secretaries	salary	1.9110349	1.5828173	0.8268219	0.9425405	0.5768773
411 Office assistants and other secretaries	year_n	1.3145981	1.1258907	0.8268219	0.9425405	0.5768773
411 Office assistants and other secretaries	salaryquotient	1.1366153	1.0368470	0.8268219	0.9425405	0.5768773
411 Office assistants and other secretaries	regioneduyears	1.1011724	1.0330416	0.8268219	0.9425405	0.5768773
411 Office assistants and other secretaries	eduquotient	1.0150045	0.9988372	0.8268219	0.9425405	0.5768773
422 Client information clerks	sum_pop	2.2556210	2.0199751	1.1114007	1.2922028	0.5754468
422 Client information clerks	regioneduyears	1.8175038	1.6419553	1.1114007	1.2922028	0.5754468
422 Client information clerks	salaryquotient	1.2706638	1.1570272	1.1114007	1.2922028	0.5754468
422 Client information clerks	salary	1.0000000	1.0000000	1.1114007	1.2922028	0.5754468
422 Client information clerks	year_n	1.0000000	1.0000000	1.1114007	1.2922028	0.5754468
422 Client information clerks	perc_women_region	1.0000000	1.0000000	1.1114007	1.2922028	0.5754468
422 Client information clerks	eduquotient	1.0000000	1.0000000	1.1114007	1.2922028	0.5754468
532 Personal care workers in health services	regioneduyears	6.6360229	5.5777322	1.6115974	0.8774912	0.8998367
532 Personal care workers in health services	eduquotient	3.4609960	2.8222051	1.6115974	0.8774912	0.8998367
532 Personal care workers in health services	salary	3.2162205	2.7855818	1.6115974	0.8774912	0.8998367
532 Personal care workers in health services	year_n	2.1602674	1.8489474	1.6115974	0.8774912	0.8998367
532 Personal care workers in health services	sum_pop	1.2635892	1.1422356	1.6115974	0.8774912	0.8998367
532 Personal care workers in health services	salaryquotient	1.0352898	0.9386980	1.6115974	0.8774912	0.8998367
532 Personal care workers in health services	perc_women_region	0.9978554	0.9931738	1.6115974	0.8774912	0.8998367
533 Health care assistants	regioneduyears	5.8197354	4.7731043	1.3421333	1.1939419	0.9165128
533 Health care assistants	eduquotient	3.5563562	3.0151663	1.3421333	1.1939419	0.9165128
533 Health care assistants	year_n	2.5253877	2.3079860	1.3421333	1.1939419	0.9165128
533 Health care assistants	sum_pop	1.6773638	1.3046246	1.3421333	1.1939419	0.9165128
533 Health care assistants	salary	1.6134876	1.4965381	1.3421333	1.1939419	0.9165128
533 Health care assistants	perc_women_region	1.3948836	1.1740007	1.3421333	1.1939419	0.9165128
533 Health care assistants	salaryquotient	1.3923600	1.1347137	1.3421333	1.1939419	0.9165128
534 Attendants, personal assistants and related workers	salary	3.5389050	3.1870397	0.9663243	0.9694028	0.6206695
534 Attendants, personal assistants and related workers	year_n	3.2981057	2.6457771	0.9663243	0.9694028	0.6206695
534 Attendants, personal assistants and related workers	regioneduyears	2.7292856	2.3165318	0.9663243	0.9694028	0.6206695
534 Attendants, personal assistants and related workers	sum_pop	2.2153663	1.9380926	0.9663243	0.9694028	0.6206695
534 Attendants, personal assistants and related workers	eduquotient	2.1263593	1.8101511	0.9663243	0.9694028	0.6206695
534 Attendants, personal assistants and related workers	perc_women_region	1.5857830	1.4075435	0.9663243	0.9694028	0.6206695
534 Attendants, personal assistants and related workers	salaryquotient	1.0341103	0.9911844	0.9663243	0.9694028	0.6206695
541 Other surveillance and security workers	salary	4.7774908	4.1261786	1.0229660	0.8534003	0.6723747
541 Other surveillance and security workers	perc_women_region	4.0335443	3.0521977	1.0229660	0.8534003	0.6723747
541 Other surveillance and security workers	year_n	3.5765135	2.9675823	1.0229660	0.8534003	0.6723747
541 Other surveillance and security workers	eduquotient	2.0845999	1.6709589	1.0229660	0.8534003	0.6723747
541 Other surveillance and security workers	sum_pop	1.8146068	1.5619745	1.0229660	0.8534003	0.6723747
541 Other surveillance and security workers	regioneduyears	1.0411189	0.9862411	1.0229660	0.8534003	0.6723747
541 Other surveillance and security workers	salaryquotient	1.0341765	0.9505941	1.0229660	0.8534003	0.6723747
962 Newspaper distributors, janitors and other service workers	sum_pop	2.7464002	2.2523244	1.1644473	0.8815180	0.7418281
962 Newspaper distributors, janitors and other service workers	perc_women_region	1.9342434	1.6159634	1.1644473	0.8815180	0.7418281
962 Newspaper distributors, janitors and other service workers	regioneduyears	1.8331599	1.5608802	1.1644473	0.8815180	0.7418281
962 Newspaper distributors, janitors and other service workers	salary	1.6450591	1.3684304	1.1644473	0.8815180	0.7418281
962 Newspaper distributors, janitors and other service workers	eduquotient	1.2975906	1.1453299	1.1644473	0.8815180	0.7418281
962 Newspaper distributors, janitors and other service workers	salaryquotient	1.0911504	1.0023219	1.1644473	0.8815180	0.7418281
962 Newspaper distributors, janitors and other service workers	year_n	1.0074452	0.9471405	1.1644473	0.8815180	0.7418281
134 Architectural and engineering managers	salary	6.6628692	5.5853307	0.9346946	0.8201284	0.9151922
134 Architectural and engineering managers	eduquotient	5.9755676	4.7127798	0.9346946	0.8201284	0.9151922
134 Architectural and engineering managers	regioneduyears	5.7463923	4.7283129	0.9346946	0.8201284	0.9151922
134 Architectural and engineering managers	perc_women_region	2.1729423	1.7202449	0.9346946	0.8201284	0.9151922
134 Architectural and engineering managers	salaryquotient	1.7104284	1.4330406	0.9346946	0.8201284	0.9151922
134 Architectural and engineering managers	sum_pop	1.5720877	1.3353975	0.9346946	0.8201284	0.9151922
134 Architectural and engineering managers	year_n	1.3342337	1.0743796	0.9346946	0.8201284	0.9151922
321 Medical and pharmaceutical technicians	sum_pop	2.7792282	2.4396571	1.3557086	0.9362958	0.4082033
321 Medical and pharmaceutical technicians	salaryquotient	1.7995439	1.5614282	1.3557086	0.9362958	0.4082033
321 Medical and pharmaceutical technicians	regioneduyears	1.6676655	1.4757725	1.3557086	0.9362958	0.4082033
321 Medical and pharmaceutical technicians	salary	1.6228142	1.3713812	1.3557086	0.9362958	0.4082033
321 Medical and pharmaceutical technicians	perc_women_region	1.2073206	1.1181762	1.3557086	0.9362958	0.4082033
321 Medical and pharmaceutical technicians	year_n	1.1558014	1.0202584	1.3557086	0.9362958	0.4082033
321 Medical and pharmaceutical technicians	eduquotient	1.0268559	0.9480420	1.3557086	0.9362958	0.4082033
351 ICT operations and user support technicians	perc_women_region	2.3485720	1.9425995	0.9224562	1.3928600	0.2801627
351 ICT operations and user support technicians	sum_pop	2.1058989	1.8259434	0.9224562	1.3928600	0.2801627
351 ICT operations and user support technicians	regioneduyears	1.3109310	1.1486190	0.9224562	1.3928600	0.2801627
351 ICT operations and user support technicians	salaryquotient	1.1923807	1.0975135	0.9224562	1.3928600	0.2801627
351 ICT operations and user support technicians	eduquotient	1.1053954	0.9764972	0.9224562	1.3928600	0.2801627
351 ICT operations and user support technicians	year_n	1.0054393	0.9660905	0.9224562	1.3928600	0.2801627
351 ICT operations and user support technicians	salary	0.9995223	0.9843242	0.9224562	1.3928600	0.2801627
432 Stores and transport clerks	sum_pop	5.3245183	4.2675746	1.9035879	0.9786471	0.7755035
432 Stores and transport clerks	regioneduyears	2.2418584	1.8267779	1.9035879	0.9786471	0.7755035
432 Stores and transport clerks	perc_women_region	1.8666360	1.5362588	1.9035879	0.9786471	0.7755035
432 Stores and transport clerks	salaryquotient	1.6103759	1.3880391	1.9035879	0.9786471	0.7755035
432 Stores and transport clerks	eduquotient	1.4745504	1.2612910	1.9035879	0.9786471	0.7755035
432 Stores and transport clerks	year_n	1.1201795	0.9753529	1.9035879	0.9786471	0.7755035
432 Stores and transport clerks	salary	1.1088593	0.9673804	1.9035879	0.9786471	0.7755035
531 Child care workers and teachers aides	perc_women_region	2.2875316	1.9727938	0.8746626	1.0323371	0.4740983
531 Child care workers and teachers aides	year_n	2.2554913	1.9691707	0.8746626	1.0323371	0.4740983
531 Child care workers and teachers aides	salary	1.9074879	1.6568279	0.8746626	1.0323371	0.4740983
531 Child care workers and teachers aides	sum_pop	1.7777613	1.5135123	0.8746626	1.0323371	0.4740983
531 Child care workers and teachers aides	regioneduyears	1.6700858	1.5224323	0.8746626	1.0323371	0.4740983
531 Child care workers and teachers aides	eduquotient	1.6139928	1.4242386	0.8746626	1.0323371	0.4740983
531 Child care workers and teachers aides	salaryquotient	1.0492472	0.9970015	0.8746626	1.0323371	0.4740983
819 Process control technicians	eduquotient	2.0817719	1.7942830	1.1691890	0.8777738	0.4932349
819 Process control technicians	year_n	1.5346389	1.2918349	1.1691890	0.8777738	0.4932349
819 Process control technicians	salaryquotient	1.4717173	1.3207021	1.1691890	0.8777738	0.4932349
819 Process control technicians	perc_women_region	1.0716159	0.9923244	1.1691890	0.8777738	0.4932349
819 Process control technicians	regioneduyears	1.0646529	1.0173561	1.1691890	0.8777738	0.4932349
819 Process control technicians	salary	1.0537050	0.9954603	1.1691890	0.8777738	0.4932349
819 Process control technicians	sum_pop	0.9959705	0.9677651	1.1691890	0.8777738	0.4932349
821 Assemblers	regioneduyears	15.1386847	12.3171157	1.3260624	0.8326956	0.8026313
821 Assemblers	sum_pop	9.2884888	6.7454161	1.3260624	0.8326956	0.8026313
821 Assemblers	perc_women_region	8.1006984	6.2366514	1.3260624	0.8326956	0.8026313
821 Assemblers	year_n	5.1637791	4.0079990	1.3260624	0.8326956	0.8026313
821 Assemblers	salaryquotient	1.5702498	1.3980286	1.3260624	0.8326956	0.8026313
821 Assemblers	salary	1.3401459	1.1613060	1.3260624	0.8326956	0.8026313
821 Assemblers	eduquotient	1.2776958	1.0033735	1.3260624	0.8326956	0.8026313

The sum of the per cent that the model was used by the SuperLearner analysing the different occupational groups.

sp_table %>%
  ggplot (aes(coef, model)) +  
    geom_col ()  

The sum of the strongest feature for every occupational group.

summary_table %>% 
  arrange(desc(importance)) %>% 
  group_by(ssyk) %>% 
  slice(1) %>%
  ggplot (aes(importance, feature)) +  
    geom_col () 

Let’s see what we have found. First, check the occupation groups with a single feature that is significantly stronger than all other features. Linear models will not be suitable for all occupational groups implying that the model will not have a high R squared value.

A strong signal, the average number of education years in the region, Personal care workers in health services

temp <- filter(tb_unique, `occuptional  (SSYK 2012)` == "532 Personal care workers in health services")

model <- lm(perc_women_eng_region ~ regioneduyears, weights = suming, data = temp)

temp %>%
  ggplot () +  
    geom_jitter (mapping = aes(x = regioneduyears, y = perc_women_eng_region, colour = suming)) +
    geom_abline (slope = model$coefficients[2], intercept = model$coefficients[1])  +
    labs(
      x = "Education years",
      y = "Per cent of women in the occupation"
    )

summary(model)$adj.r.squared

## [1] 0.7732263

anova(model)

## Analysis of Variance Table
## 
## Response: perc_women_eng_region
##                Df  Sum Sq Mean Sq F value    Pr(>F)    
## regioneduyears  1 315.573 315.573  133.98 5.039e-14 ***
## Residuals      38  89.506   2.355                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

postResample(pred = predict(model), obs = temp$perc_women_eng_region)

##       RMSE   Rsquared        MAE 
## 0.01225219 0.69069055 0.01023249

A strong signal, the average number of education years in the region, Medical doctors

temp <- filter(tb_unique, `occuptional  (SSYK 2012)` == "221 Medical doctors")

model <- lm(perc_women_eng_region ~ regioneduyears, weights = suming, data = temp)

temp %>%
  ggplot () +  
    geom_jitter (mapping = aes(x = regioneduyears, y = perc_women_eng_region, colour = suming)) +
    geom_abline(slope = model$coefficients[2], intercept = model$coefficients[1]) +
    labs(
      x = "Education years",
      y = "Per cent of women in the occupation"
    )

summary(model)$adj.r.squared

## [1] 0.8057127

anova(model)

## Analysis of Variance Table
## 
## Response: perc_women_eng_region
##                Df  Sum Sq Mean Sq F value    Pr(>F)    
## regioneduyears  1 164.765 164.765  154.44 1.385e-14 ***
## Residuals      36  38.407   1.067                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

postResample(pred = predict(model), obs = temp$perc_women_eng_region)

##       RMSE   Rsquared        MAE 
## 0.01683530 0.72088034 0.01385548

A strong signal, the per cent women in the region, Insurance advisers, sales and purchasing agents

temp <- filter(tb_unique, `occuptional  (SSYK 2012)` == "332 Insurance advisers, sales and purchasing agents")

model <- lm(perc_women_eng_region ~ perc_women_region, weights = suming, data = temp)

temp %>%
  ggplot () +  
    geom_jitter (mapping = aes(x = perc_women_region, y = perc_women_eng_region, colour = suming)) +
    geom_abline(slope = model$coefficients[2], intercept = model$coefficients[1]) +
    labs(
      x = "Per cent of women in the region",
      y = "Per cent of women in the occupation"
    )

summary(model)$adj.r.squared

## [1] 0.6283407

anova(model)

## Analysis of Variance Table
## 
## Response: perc_women_eng_region
##                   Df Sum Sq Mean Sq F value    Pr(>F)    
## perc_women_region  1 529.66  529.66  56.791 1.395e-08 ***
## Residuals         32 298.45    9.33                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

postResample(pred = predict(model), obs = temp$perc_women_eng_region)

##       RMSE   Rsquared        MAE 
## 0.02935038 0.49206133 0.02250770

Two strong signals, population size in the region and the average number of education years in the region, Engineering professionals

temp <- filter(tb_unique, `occuptional  (SSYK 2012)` == "214 Engineering professionals")

s3d <- scatterplot3d(
  temp$sum_pop, 
  temp$regioneduyears, 
  temp$perc_women_eng_region,
  type = "h", 
  color = "blue", 
  xlab = "Population in region",
  ylab = "Education years",
  zlab = "Per cent of women in the occupation")

model <- lm(perc_women_eng_region ~ sum_pop + regioneduyears, weights = suming, data = temp)

s3d$plane3d(model)

summary(model)$adj.r.squared

## [1] 0.8121964

anova(model)

## Analysis of Variance Table
## 
## Response: perc_women_eng_region
##                Df  Sum Sq Mean Sq F value    Pr(>F)    
## sum_pop         1 255.902 255.902 144.321 5.673e-14 ***
## regioneduyears  1  31.373  31.373  17.693 0.0001712 ***
## Residuals      35  62.060   1.773                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

postResample(pred = predict(model), obs = temp$perc_women_eng_region)

##        RMSE    Rsquared         MAE 
## 0.012229213 0.835386966 0.009935413

Two strong signals, population size in the region and the per cent women in the region, Insurance advisers, sales and purchasing agents

temp <- filter(tb_unique, `occuptional  (SSYK 2012)` == "332 Insurance advisers, sales and purchasing agents")

s3d <- scatterplot3d(
  temp$sum_pop, 
  temp$perc_women_region, 
  temp$perc_women_eng_region,
  type = "h", 
  color = "blue", 
  xlab = "Population in region",
  ylab = "Per cent of women in the region",
  zlab = "Per cent of women in the occupation")

model <- lm(perc_women_eng_region ~ sum_pop + perc_women_region, weights = suming, data = temp)

s3d$plane3d(model)

summary(model)$adj.r.squared

## [1] 0.6525952

anova(model)

## Analysis of Variance Table
## 
## Response: perc_women_eng_region
##                   Df Sum Sq Mean Sq F value    Pr(>F)    
## sum_pop            1 263.40 263.403  30.214 5.168e-06 ***
## perc_women_region  1 294.45 294.455  33.776 2.099e-06 ***
## Residuals         31 270.25   8.718                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

postResample(pred = predict(model), obs = temp$perc_women_eng_region)

##       RMSE   Rsquared        MAE 
## 0.02638844 0.57325855 0.02034915

Two strong signals, year and the per cent women in the region, Physical and engineering science technicians

temp <- filter(tb_unique, `occuptional  (SSYK 2012)` == "311 Physical and engineering science technicians")

s3d <- scatterplot3d(
  temp$year_n, 
  temp$perc_women_region, 
  temp$perc_women_eng_region, 
  type = "h", 
  color = "blue", 
  xlab = "Year",
  ylab = "Per cent of women in the region",
  zlab = "Per cent of women in the occupation")

model <- lm(perc_women_eng_region ~ year_n + perc_women_region, weights = suming, data = temp)

s3d$plane3d(model)

summary(model)$adj.r.squared

## [1] 0.5373011

anova(model)

## Analysis of Variance Table
## 
## Response: perc_women_eng_region
##                   Df Sum Sq Mean Sq F value    Pr(>F)    
## year_n             1  32.63  32.630  7.6503  0.009621 ** 
## perc_women_region  1 134.39 134.393 31.5091 4.127e-06 ***
## Residuals         30 127.96   4.265                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

postResample(pred = predict(model), obs = temp$perc_women_eng_region)

##       RMSE   Rsquared        MAE 
## 0.01695193 0.59082239 0.01266243

Two strong signals, year and salary, Naprapaths, physiotherapists, occupational therapists

temp <- filter(tb_unique, `occuptional  (SSYK 2012)` == "227 Naprapaths, physiotherapists, occupational therapists")

s3d <- scatterplot3d(
  temp$year_n, 
  temp$salary, 
  temp$perc_women_eng_region, 
  type = "h", 
  color = "blue", 
  xlab = "Year",
  ylab = "Salary",
  zlab = "Per cent of women in the occupation")

model <- lm(perc_women_eng_region ~ year_n + salary, weights = suming, data = temp)

s3d$plane3d(model)

summary(model)$adj.r.squared

## [1] 0.5269917

anova(model)

## Analysis of Variance Table
## 
## Response: perc_women_eng_region
##           Df Sum Sq Mean Sq F value    Pr(>F)    
## year_n     1 5.8240  5.8240  16.077 0.0005492 ***
## salary     1 4.9902  4.9902  13.776 0.0011481 ** 
## Residuals 23 8.3317  0.3622                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

postResample(pred = predict(model), obs = temp$perc_women_eng_region)

##       RMSE   Rsquared        MAE 
## 0.01261698 0.46523146 0.01003402