The significance of the region on the salary in Sweden, a comparison between different occupational groups

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In my last post, I found that the region has a significant impact on the salary of engineers. Is the significance of the region unique to engineers or are there similar correlations in other occupational groups?

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

The F-value from the Anova table is used as the single value to discriminate how much the region and salary correlates. For exploratory analysis, the Anova value seems good enough.

First, define libraries and functions.

library (tidyverse) 
## -- Attaching packages -------------------------------------- tidyverse 1.3.0 --
## v ggplot2 3.2.1     v purrr   0.3.3
## v tibble  2.1.3     v dplyr   0.8.3
## v tidyr   1.0.2     v stringr 1.4.0
## v readr   1.3.1     v forcats 0.4.0
## -- Conflicts ----------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library (broom) 
library (car)
## Loading required package: carData
## 
## Attaching package: 'car'
## The following object is masked from 'package:dplyr':
## 
##     recode
## The following object is masked from 'package:purrr':
## 
##     some
library (swemaps) # devtools::install_github('reinholdsson/swemaps')
library(sjPlot)
## Registered S3 methods overwritten by 'lme4':
##   method                          from
##   cooks.distance.influence.merMod car 
##   influence.merMod                car 
##   dfbeta.influence.merMod         car 
##   dfbetas.influence.merMod        car
## Install package "strengejacke" from GitHub (`devtools::install_github("strengejacke/strengejacke")`) to load all sj-packages at once!
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 = salary) %>%  
    drop_na() %>%  
    mutate (year_n = parse_number (year))
}
nuts <- read.csv("nuts.csv") %>%
  mutate(NUTS2_sh = substr(NUTS2, 1, 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
map_ln_n <- map_ln %>%
  mutate(lnkod_n = as.numeric(lnkod)) 

The data table is downloaded from Statistics Sweden. It is saved as a comma-delimited file without heading, 000000CG.csv, http://www.statistikdatabasen.scb.se/pxweb/en/ssd/.

The table: 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

In the plot and tables, you can also find information on how the increase in salaries per year for each occupational group is affected when the interactions are taken into account.

tb <- readfile ("000000CG.csv") %>%
  left_join(nuts %>% distinct (NUTS2_en, NUTS2_sh), by = c("region" = "NUTS2_en")) 
## Warning: Column `region`/`NUTS2_en` joining character vector and factor,
## coercing into character vector
tb_map <- readfile ("000000CG.csv") %>%
  left_join(nuts, by = c("region" = "NUTS2_en")) %>%  
  right_join(map_ln_n, by = c("Länskod" = "lnkod_n"))
## Warning: Column `region`/`NUTS2_en` joining character vector and factor,
## coercing into character vector
summary_table = vector()
anova_table = vector()
for (i in unique(tb$`occuptional  (SSYK 2012)`)){
  temp <- filter(tb, `occuptional  (SSYK 2012)` == i)
  if (dim(temp)[1] > 75){
    model <- lm(log(salary) ~ region + sex + year_n, data = temp)
    summary_table <- rbind (summary_table, mutate (tidy (summary (model)), ssyk = i, interaction = "none"))
    anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i, interaction = "none"))  
  
    model <- lm(log(salary) ~ region * sex + year_n, data = temp)
    summary_table <- rbind (summary_table, mutate (tidy (summary (model)), ssyk = i, interaction = "region and sex"))
    anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i, interaction = "region and sex"))
    
    model <- lm(log(salary) ~ region * year_n + sex, data = temp)
    summary_table <- rbind (summary_table, mutate (tidy (summary (model)), ssyk = i, interaction = "region and year"))
    anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i, interaction = "region and year"))   
    
    model <- lm(log(salary) ~ region * year_n * sex, data = temp)
    summary_table <- rbind (summary_table, mutate (tidy (summary (model)), ssyk = i, interaction = "region, year and sex"))
    anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i, interaction = "region, year and sex"))     
  }
}
## Note: model has aliased coefficients
##       sums of squares computed by model comparison
## Note: model has aliased coefficients
##       sums of squares computed by model comparison
anova_table <- anova_table %>% rowwise() %>% mutate(contcol = str_count(term, ":")) 

summary_table <- summary_table %>% rowwise() %>% mutate(contcol = str_count(term, ":"))

merge(summary_table, anova_table, by = c("ssyk", "interaction"), all = TRUE) %>%
  filter (term.x == "year_n") %>%
  filter (term.y == "region") %>%    
  filter (interaction == "none") %>%
  
  mutate (estimate = (exp(estimate) - 1) * 100) %>%  
  ggplot () +
    geom_point (mapping = aes(x = estimate, y = statistic.y, colour = interaction)) +
    labs(
      x = "Increase in salaries (% / year)",
      y = "F-value for interaction"
    )   

The significance of the region on the salary in Sweden, a comparison between different occupational groups, Year 2014 - 2018

Figure 1: The significance of the region on the salary in Sweden, a comparison between different occupational groups, Year 2014 – 2018

merge(summary_table, anova_table, by = c("ssyk", "interaction"), all = TRUE) %>%
  filter (term.x == "year_n") %>%
  filter (contcol.y > 0) %>%    
  # only look at the interactions between all three variables in the case with interaction region, year and sex
  filter (!(contcol.y == 1 & interaction == "region, year and sex")) %>% 
  
  mutate (estimate = (exp(estimate) - 1) * 100) %>%  
  ggplot () +
    geom_point (mapping = aes(x = estimate, y = statistic.y, colour = interaction)) +
    labs(
      x = "Increase in salaries (% / year)",
      y = "F-value for interaction"
    )   

The significance of the interaction between region, year and sex on the salary in Sweden, a comparison between different occupational groups, Year 2014 - 2018

Figure 2: The significance of the interaction between region, year and sex on the salary in Sweden, a comparison between different occupational groups, Year 2014 – 2018

The tables with all occupational groups sorted by F-value in descending order.

merge(summary_table, anova_table, c("ssyk", "interaction"), all = TRUE) %>%
  filter (term.x == "year_n") %>%
  filter (term.y == "region") %>%   
  filter (interaction == "none") %>%
  mutate (estimate = (exp(estimate) - 1) * 100) %>%  
  select (ssyk, estimate, statistic.y, interaction) %>%
  rename (`F-value for age` = statistic.y) %>%
  rename (`Increase in salary` = estimate) %>%
  arrange (desc (`F-value for age`)) %>%
  knitr::kable(
    booktabs = TRUE,
    caption = 'Correlation for F-value (region) and the yearly increase in salaries')
Table 2: Correlation for F-value (region) and the yearly increase in salaries
ssyk Increase in salary F-value for age interaction
234 Primary- and pre-school teachers 3.505364 140.459561 none
242 Organisation analysts, policy administrators and human resource specialists 2.141124 137.227070 none
159 Other social services managers 2.412305 126.200697 none
141 Primary and secondary schools and adult education managers 3.543591 118.692580 none
335 Tax and related government associate professionals 2.133970 108.141526 none
251 ICT architects, systems analysts and test managers 2.196568 103.500079 none
332 Insurance advisers, sales and purchasing agents 1.910500 71.550568 none
336 Police officers 3.280076 70.269911 none
241 Accountants, financial analysts and fund managers 2.040018 64.180404 none
235 Teaching professionals not elsewhere classified 2.210947 58.449386 none
351 ICT operations and user support technicians 1.868739 49.435480 none
233 Secondary education teachers 3.377886 43.017847 none
331 Financial and accounting associate professionals 2.016965 42.321607 none
214 Engineering professionals 2.002819 40.294194 none
218 Specialists within environmental and health protection 2.831305 39.372062 none
533 Health care assistants 2.001888 36.961185 none
261 Legal professionals 2.304699 35.545371 none
231 University and higher education teachers 2.682606 30.352218 none
243 Marketing and public relations professionals 1.217703 29.319658 none
222 Nursing professionals 4.012209 27.254061 none
123 Administration and planning managers 4.455192 25.902695 none
264 Authors, journalists and linguists 1.218409 24.973290 none
531 Child care workers and teachers aides 2.276723 23.659505 none
333 Business services agents 2.601340 22.810204 none
311 Physical and engineering science technicians 2.532877 20.769863 none
266 Social work and counselling professionals 2.965491 19.825931 none
411 Office assistants and other secretaries 2.060828 19.127036 none
534 Attendants, personal assistants and related workers 1.956992 16.198710 none
341 Social work and religious associate professionals 2.309857 12.736283 none
321 Medical and pharmaceutical technicians 2.613786 12.424504 none
232 Vocational education teachers 2.990624 11.524474 none
221 Medical doctors 0.902755 9.634942 none
422 Client information clerks 1.843752 8.137514 none
512 Cooks and cold-buffet managers 2.756431 8.061511 none
962 Newspaper distributors, janitors and other service workers 1.740067 7.765145 none
522 Shop staff 2.903890 6.830648 none
532 Personal care workers in health services 2.705999 5.078780 none
432 Stores and transport clerks 1.705670 4.865026 none
541 Other surveillance and security workers 2.389682 4.371317 none
941 Fast-food workers, food preparation assistants 2.227375 3.219358 none
833 Heavy truck and bus drivers 1.912077 2.247326 none
911 Cleaners and helpers 2.016100 1.711840 none
merge(summary_table, anova_table, c("ssyk", "interaction"), all = TRUE) %>%
  filter (term.x == "year_n") %>%
  filter (contcol.y > 0) %>%   
  filter (interaction == "region and sex") %>%
  mutate (estimate = (exp(estimate) - 1) * 100) %>%  
  select (ssyk, estimate, statistic.y, interaction) %>%
  rename (`F-value for age` = statistic.y) %>%
  rename (`Increase in salary` = estimate) %>%
  arrange (desc (`F-value for age`)) %>%
  knitr::kable(
    booktabs = TRUE,
    caption = 'Correlation for F-value (region and sex) and the yearly increase in salaries')
Table 3: Correlation for F-value (region and sex) and the yearly increase in salaries
ssyk Increase in salary F-value for age interaction
331 Financial and accounting associate professionals 2.0169648 23.9056822 region and sex
911 Cleaners and helpers 2.0160997 9.0815382 region and sex
321 Medical and pharmaceutical technicians 2.6137864 7.7168336 region and sex
231 University and higher education teachers 2.6826060 5.6873851 region and sex
159 Other social services managers 2.4123053 5.3090755 region and sex
242 Organisation analysts, policy administrators and human resource specialists 2.1411239 5.0798948 region and sex
533 Health care assistants 2.0018877 4.4194926 region and sex
531 Child care workers and teachers aides 2.2767234 4.1363582 region and sex
261 Legal professionals 2.3101043 3.8795657 region and sex
335 Tax and related government associate professionals 2.1339700 3.8162101 region and sex
218 Specialists within environmental and health protection 2.7191768 3.4940939 region and sex
241 Accountants, financial analysts and fund managers 2.0400185 3.3402726 region and sex
541 Other surveillance and security workers 2.3896824 3.0479843 region and sex
351 ICT operations and user support technicians 1.8593806 3.0366516 region and sex
235 Teaching professionals not elsewhere classified 2.2109466 2.9986405 region and sex
512 Cooks and cold-buffet managers 2.7564313 2.9661198 region and sex
234 Primary- and pre-school teachers 3.5053638 2.7061253 region and sex
214 Engineering professionals 2.0028190 2.6793275 region and sex
232 Vocational education teachers 2.9906243 2.5473580 region and sex
532 Personal care workers in health services 2.7059985 2.3861956 region and sex
332 Insurance advisers, sales and purchasing agents 1.9105004 2.3851151 region and sex
243 Marketing and public relations professionals 1.2005440 2.3538771 region and sex
264 Authors, journalists and linguists 1.2184087 2.3283437 region and sex
251 ICT architects, systems analysts and test managers 2.1965679 2.3071242 region and sex
141 Primary and secondary schools and adult education managers 3.5435906 2.2283878 region and sex
534 Attendants, personal assistants and related workers 1.9569919 2.0176966 region and sex
941 Fast-food workers, food preparation assistants 2.2273749 1.9439585 region and sex
522 Shop staff 2.9038903 1.9435000 region and sex
411 Office assistants and other secretaries 2.0399065 1.8087907 region and sex
833 Heavy truck and bus drivers 1.9298918 1.7646741 region and sex
333 Business services agents 2.6013400 1.5532994 region and sex
123 Administration and planning managers 4.4564515 1.3795888 region and sex
962 Newspaper distributors, janitors and other service workers 1.7400674 1.3302289 region and sex
422 Client information clerks 1.8437523 1.3111491 region and sex
266 Social work and counselling professionals 2.9654913 1.1228380 region and sex
432 Stores and transport clerks 1.7056704 1.0829037 region and sex
311 Physical and engineering science technicians 2.5162972 0.8523619 region and sex
336 Police officers 3.2800755 0.8458087 region and sex
233 Secondary education teachers 3.3778859 0.7369872 region and sex
221 Medical doctors 0.8945233 0.5918577 region and sex
341 Social work and religious associate professionals 2.3098566 0.3654042 region and sex
222 Nursing professionals 4.0122087 0.1103390 region and sex
merge(summary_table, anova_table, c("ssyk", "interaction"), all = TRUE) %>%
  filter (term.x == "year_n") %>%
  filter (contcol.y > 0) %>%   
  filter (interaction == "region and year") %>%
  mutate (estimate = (exp(estimate) - 1) * 100) %>%  
  select (ssyk, estimate, statistic.y, interaction) %>%
  rename (`F-value for age` = statistic.y) %>%
  rename (`Increase in salary` = estimate) %>%
  arrange (desc (`F-value for age`)) %>%
  knitr::kable(
    booktabs = TRUE,
    caption = 'Correlation for F-value (region and year) and the yearly increase in salaries')
Table 4: Correlation for F-value (region and year) and the yearly increase in salaries
ssyk Increase in salary F-value for age interaction
222 Nursing professionals 4.594527 9.5756908 region and year
234 Primary- and pre-school teachers 4.741438 8.4929762 region and year
962 Newspaper distributors, janitors and other service workers 1.014436 5.5221538 region and year
218 Specialists within environmental and health protection 2.960551 3.7726084 region and year
341 Social work and religious associate professionals 1.179665 3.6377998 region and year
141 Primary and secondary schools and adult education managers 4.464391 3.4565788 region and year
231 University and higher education teachers 2.190339 3.1807419 region and year
531 Child care workers and teachers aides 1.946134 3.1384371 region and year
123 Administration and planning managers 3.202935 3.0929465 region and year
351 ICT operations and user support technicians 1.818022 2.8907900 region and year
335 Tax and related government associate professionals 2.794241 2.6351058 region and year
311 Physical and engineering science technicians 1.836449 2.5772805 region and year
336 Police officers 3.238027 2.2608889 region and year
221 Medical doctors 1.577680 2.1873354 region and year
266 Social work and counselling professionals 4.269070 2.0856790 region and year
534 Attendants, personal assistants and related workers 2.012733 2.0738388 region and year
333 Business services agents 1.929570 2.0713461 region and year
532 Personal care workers in health services 2.854891 2.0354065 region and year
264 Authors, journalists and linguists 1.020091 1.9403580 region and year
332 Insurance advisers, sales and purchasing agents 3.197316 1.9390313 region and year
232 Vocational education teachers 3.729880 1.7811629 region and year
512 Cooks and cold-buffet managers 2.937534 1.7459584 region and year
411 Office assistants and other secretaries 1.870052 1.6496037 region and year
235 Teaching professionals not elsewhere classified 2.263332 1.5866915 region and year
233 Secondary education teachers 3.800949 1.5667053 region and year
159 Other social services managers 2.939365 1.3862925 region and year
241 Accountants, financial analysts and fund managers 3.112492 1.3754541 region and year
251 ICT architects, systems analysts and test managers 2.189907 1.2977035 region and year
941 Fast-food workers, food preparation assistants 1.418897 1.2189448 region and year
261 Legal professionals 2.620417 1.1684118 region and year
242 Organisation analysts, policy administrators and human resource specialists 2.212719 1.1514498 region and year
833 Heavy truck and bus drivers 1.823095 1.0719499 region and year
911 Cleaners and helpers 1.438852 0.9563272 region and year
432 Stores and transport clerks 2.093243 0.8517395 region and year
331 Financial and accounting associate professionals 1.079578 0.7062153 region and year
522 Shop staff 2.500507 0.6743597 region and year
533 Health care assistants 2.008445 0.6349934 region and year
214 Engineering professionals 2.153132 0.5758246 region and year
243 Marketing and public relations professionals 1.702978 0.5743298 region and year
422 Client information clerks 1.363946 0.5084936 region and year
321 Medical and pharmaceutical technicians 2.820296 0.4615622 region and year
541 Other surveillance and security workers 2.488850 0.4542178 region and year
merge(summary_table, anova_table, c("ssyk", "interaction"), all = TRUE) %>%
  filter (term.x == "year_n") %>%
  filter (contcol.y > 1) %>%   
  filter (interaction == "region, year and sex") %>%
  filter (!(contcol.y == 1 & interaction == "region, year and sex")) %>%   
  mutate (estimate = (exp(estimate) - 1) * 100) %>%  
  select (ssyk, estimate, statistic.y, interaction) %>%
  rename (`F-value for age` = statistic.y) %>%
  rename (`Increase in salary` = estimate) %>%
  arrange (desc (`F-value for age`)) %>%
  knitr::kable(
    booktabs = TRUE,
    caption = 'Correlation for F-value (region, year and sex) and the yearly increase in salaries')
Table 5: Correlation for F-value (region, year and sex) and the yearly increase in salaries
ssyk Increase in salary F-value for age interaction
531 Child care workers and teachers aides 1.6212785 4.6701912 region, year and sex
541 Other surveillance and security workers 2.8239443 3.3698474 region, year and sex
218 Specialists within environmental and health protection 2.4738659 3.2049100 region, year and sex
331 Financial and accounting associate professionals 0.1908239 2.8847688 region, year and sex
351 ICT operations and user support technicians 0.3582487 2.5229634 region, year and sex
221 Medical doctors -0.2578495 2.3158388 region, year and sex
411 Office assistants and other secretaries 1.9518139 1.7466381 region, year and sex
141 Primary and secondary schools and adult education managers 4.5500329 1.7360681 region, year and sex
512 Cooks and cold-buffet managers 3.9252389 1.7021536 region, year and sex
251 ICT architects, systems analysts and test managers 2.0097672 1.5905499 region, year and sex
242 Organisation analysts, policy administrators and human resource specialists 2.0886458 1.5752902 region, year and sex
941 Fast-food workers, food preparation assistants 1.0629882 1.5615772 region, year and sex
232 Vocational education teachers 3.8326506 1.5493239 region, year and sex
235 Teaching professionals not elsewhere classified 1.2656111 1.2406770 region, year and sex
911 Cleaners and helpers 1.7251553 1.2122343 region, year and sex
332 Insurance advisers, sales and purchasing agents 3.6964107 0.9875563 region, year and sex
833 Heavy truck and bus drivers 1.8960959 0.9786211 region, year and sex
532 Personal care workers in health services 3.0120859 0.9603131 region, year and sex
335 Tax and related government associate professionals 2.8095715 0.9471462 region, year and sex
266 Social work and counselling professionals 4.1855410 0.9152029 region, year and sex
123 Administration and planning managers 3.9661945 0.8530921 region, year and sex
159 Other social services managers 2.6152072 0.8505913 region, year and sex
533 Health care assistants 1.8690208 0.7855258 region, year and sex
231 University and higher education teachers 1.6422359 0.7732961 region, year and sex
241 Accountants, financial analysts and fund managers 3.3332928 0.7520525 region, year and sex
333 Business services agents 0.9959201 0.7345545 region, year and sex
233 Secondary education teachers 3.7756980 0.6833044 region, year and sex
534 Attendants, personal assistants and related workers 1.8119543 0.6586806 region, year and sex
243 Marketing and public relations professionals 1.6359856 0.6487796 region, year and sex
214 Engineering professionals 2.2789556 0.6307550 region, year and sex
234 Primary- and pre-school teachers 4.7865061 0.6132542 region, year and sex
264 Authors, journalists and linguists 1.0415688 0.5809769 region, year and sex
222 Nursing professionals 5.2654653 0.5702894 region, year and sex
962 Newspaper distributors, janitors and other service workers 1.4942008 0.5531934 region, year and sex
522 Shop staff 2.9107242 0.5165112 region, year and sex
321 Medical and pharmaceutical technicians 3.4804846 0.4863120 region, year and sex
341 Social work and religious associate professionals 0.7370630 0.3372004 region, year and sex
311 Physical and engineering science technicians 2.1802298 0.3313909 region, year and sex
261 Legal professionals 2.2233855 0.2620376 region, year and sex
432 Stores and transport clerks 1.7998663 0.1870227 region, year and sex
422 Client information clerks 1.0842242 0.1733253 region, year and sex
336 Police officers 3.2164461 0.1482551 region, year and sex

Let’s check what we have found.

temp <- tb %>%
  filter(`occuptional  (SSYK 2012)` == "234 Primary- and pre-school teachers")
 
model <-lm (log(salary) ~ year_n + sex + NUTS2_sh, data = temp) 
 
plot_model(model, type = "pred", terms = c("NUTS2_sh"))
## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.

Highest F-value region, Primary- and pre-school teachers

Figure 3: Highest F-value region, Primary- and pre-school teachers

tb_map %>%
    filter(`occuptional  (SSYK 2012)` == "234 Primary- and pre-school teachers") %>%
  ggplot() +
    geom_polygon(mapping = aes(x = ggplot_long, y = ggplot_lat, group = lnkod, fill = salary)) +
      facet_grid(. ~ year) + 
    coord_equal() 

Highest F-value region, Primary- and pre-school teachers

Figure 4: Highest F-value region, Primary- and pre-school teachers

temp <- tb %>%
  filter(`occuptional  (SSYK 2012)` == "911 Cleaners and helpers")
 
model <-lm (log(salary) ~ year_n + sex + NUTS2_sh, data = temp) 
 
plot_model(model, type = "pred", terms = c("NUTS2_sh"))
## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.

Lowest F-value region, Cleaners and helpers

Figure 5: Lowest F-value region, Cleaners and helpers

temp <- tb %>%
  filter(`occuptional  (SSYK 2012)` == "331 Financial and accounting associate professionals")
 
model <-lm (log(salary) ~ year_n + sex * NUTS2_sh, data = temp) 
 
plot_model(model, type = "pred", terms = c("NUTS2_sh", "sex"))
## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.

Highest F-value interaction gender and region, Financial and accounting associate professionals

Figure 6: Highest F-value interaction gender and region, Financial and accounting associate professionals

tb_map %>%
    filter(`occuptional  (SSYK 2012)` == "331 Financial and accounting associate professionals") %>%
  filter (sex == "men") %>%
  ggplot() +
    geom_polygon(mapping = aes(x = ggplot_long, y = ggplot_lat, group = lnkod, fill = salary)) +
      facet_grid(. ~ year) + 
    coord_equal() 

Highest F-value interaction gender and region, Financial and accounting associate professionals

Figure 7: Highest F-value interaction gender and region, Financial and accounting associate professionals

tb_map %>%
    filter(`occuptional  (SSYK 2012)` == "331 Financial and accounting associate professionals") %>%
  filter (sex == "women") %>%  
  ggplot() +
    geom_polygon(mapping = aes(x = ggplot_long, y = ggplot_lat, group = lnkod, fill = salary)) +
      facet_grid(. ~ year) + 
    coord_equal() 

Highest F-value interaction gender and region, Financial and accounting associate professionals

Figure 8: Highest F-value interaction gender and region, Financial and accounting associate professionals

temp <- tb %>%
  filter(`occuptional  (SSYK 2012)` == "222 Nursing professionals")
 
model <-lm (log(salary) ~ year_n + sex * NUTS2_sh, data = temp) 
 
plot_model(model, type = "pred", terms = c("NUTS2_sh", "sex"))
## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.

Lowest F-value interaction gender and region, Nursing professionals

Figure 9: Lowest F-value interaction gender and region, Nursing professionals

temp <- tb %>%
  filter(`occuptional  (SSYK 2012)` == "222 Nursing professionals")
 
model <-lm (log(salary) ~ year_n * NUTS2_sh + sex , data = temp) 
 
plot_model(model, type = "pred", terms = c("NUTS2_sh", "year_n"))
## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.

Highest F-value interaction year and region, Nursing professionals

Figure 10: Highest F-value interaction year and region, Nursing professionals

tb_map %>%
  filter(`occuptional  (SSYK 2012)` == "222 Nursing professionals") %>%
  ggplot() +
    geom_polygon(mapping = aes(x = ggplot_long, y = ggplot_lat, group = lnkod, fill = salary)) +
      facet_grid(. ~ year) + 
    coord_equal() 

Highest F-value interaction year and region, Nursing professionals

Figure 11: Highest F-value interaction year and region, Nursing professionals

temp <- tb %>%
  filter(`occuptional  (SSYK 2012)` == "541 Other surveillance and security workers")
 
model <-lm (log(salary) ~ year_n * NUTS2_sh + sex , data = temp) 
 
plot_model(model, type = "pred", terms = c("NUTS2_sh", "year_n"))
## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.

Lowest F-value interaction year and region, Other surveillance and security workers

Figure 12: Lowest F-value interaction year and region, Other surveillance and security workers

temp <- tb %>%
  filter(`occuptional  (SSYK 2012)` == "531 Child care workers and teachers aides")
 
model <-lm (log(salary) ~ year_n * NUTS2_sh * sex , data = temp) 
 
plot_model(model, type = "pred", terms = c("NUTS2_sh", "year_n", "sex"))
## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.

Highest F-value interaction region, year and gender, Child care workers and teachers aides

Figure 13: Highest F-value interaction region, year and gender, Child care workers and teachers aides

tb_map %>%
  filter(`occuptional  (SSYK 2012)` == "531 Child care workers and teachers aides") %>%
  filter (sex == "men") %>%
  ggplot() +
    geom_polygon(mapping = aes(x = ggplot_long, y = ggplot_lat, group = lnkod, fill = salary)) +
      facet_grid(. ~ year) + 
    coord_equal() 

Highest F-value interaction region, year and gender, Child care workers and teachers aides

Figure 14: Highest F-value interaction region, year and gender, Child care workers and teachers aides

tb_map %>%
    filter(`occuptional  (SSYK 2012)` == "531 Child care workers and teachers aides") %>%
  filter (sex == "women") %>%  
  ggplot() +
    geom_polygon(mapping = aes(x = ggplot_long, y = ggplot_lat, group = lnkod, fill = salary)) +
      facet_grid(. ~ year) + 
    coord_equal() 

Highest F-value interaction region, year and gender, Child care workers and teachers aides

Figure 15: Highest F-value interaction region, year and gender, Child care workers and teachers aides

temp <- tb %>%
  filter(`occuptional  (SSYK 2012)` == "336 Police officers")
 
model <-lm (log(salary) ~ year_n * NUTS2_sh * sex , data = temp) 
 
plot_model(model, type = "pred", terms = c("NUTS2_sh", "year_n", "sex"))
## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.

Lowest F-value interaction region, year and gender, Police officers

Figure 16: Lowest F-value interaction region, year and gender, Police officers

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