In my last post, I found that the interaction between the different predictors has a significant impact on the salary of engineers. Is the significance of the interactions on wages unique to engineers or are there similar correlations in other occupational groups?
We start by examining the interaction between age, year, gender.
The F-value from the Anova table is used as the single value to discriminate how much education and salary correlates. For exploratory analysis, the Anova value seems good enough.
First, define libraries and functions.
library (tidyverse) ## -- Attaching packages --------------------------------------- tidyverse 1.2.1 --## v ggplot2 3.2.0 v purrr 0.3.2
## v tibble 2.1.3 v dplyr 0.8.3
## v tidyr 1.0.0 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':
##
## somelibrary(sjPlot)
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))
}The data table is downloaded from Statistics Sweden. It is saved as a comma-delimited file without heading, 000000D2.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 sector, occupational group (SSYK 2012), sex and age. Year 2014 - 2018 Monthly salary All sectors
I will use a continuous predictor, a polynomial of degree three, to fit the contribution of age to the salary.
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("000000D2.csv") %>%
rowwise() %>%
mutate(age_l = unlist(lapply(strsplit(substr(age, 1, 5), "-"), strtoi))[1]) %>%
rowwise() %>%
mutate(age_h = unlist(lapply(strsplit(substr(age, 1, 5), "-"), strtoi))[2]) %>%
mutate(age_n = (age_l + age_h) / 2)
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] > 30){
model <-lm (log(salary) ~ year_n + sex * poly(age_n, 3), data = temp)
summary_table <- bind_rows (summary_table, mutate (tidy (summary (model)), ssyk = i, interaction = "sex and age"))
anova_table <- bind_rows (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i, interaction = "sex and age"))
model <-lm (log(salary) ~ year_n * sex + poly(age_n, 3), data = temp)
summary_table <- bind_rows (summary_table, mutate (tidy (summary (model)), ssyk = i, interaction = "sex and year"))
anova_table <- bind_rows (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i, interaction = "sex and year"))
model <-lm (log(salary) ~ sex + year_n * poly(age_n, 3), data = temp)
summary_table <- bind_rows (summary_table, mutate (tidy (summary (model)), ssyk = i, interaction = "year and age"))
anova_table <- bind_rows (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i, interaction = "year and age"))
model <-lm (log(salary) ~ sex * year_n * poly(age_n, 3), data = temp)
summary_table <- bind_rows (summary_table, mutate (tidy (summary (model)), ssyk = i, interaction = "sex, year and age"))
anova_table <- bind_rows (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i, interaction = "sex, year and age"))
}
}## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparisonanova_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 (contcol.y > 0) %>%
# only look at the interactions between all three variables
filter (!(contcol.y == 1 & interaction == "sex, year and age")) %>%
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"
) 
Figure 1: The significance of the interaction between age, year, 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 (contcol.y > 0) %>%
filter (interaction == "sex and age") %>%
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 (sex and age) and the yearly increase in salaries')| ssyk | Increase in salary | F-value for age | interaction |
|---|---|---|---|
| 335 Tax and related government associate professionals | 2.2959882 | 159.4793522 | sex and age |
| 331 Financial and accounting associate professionals | 2.2766227 | 42.0381959 | sex and age |
| 241 Accountants, financial analysts and fund managers | 2.7431544 | 20.6684827 | sex and age |
| 531 Child care workers and teachers aides | 2.1421968 | 20.6043679 | sex and age |
| 242 Organisation analysts, policy administrators and human resource specialists | 2.2640553 | 20.5912541 | sex and age |
| 234 Primary- and pre-school teachers | 3.5160288 | 18.7575727 | sex and age |
| 713 Painters, Lacquerers, Chimney-sweepers and related trades workers | 3.1994881 | 17.5772847 | sex and age |
| 911 Cleaners and helpers | 1.7714885 | 16.3428281 | sex and age |
| 227 Naprapaths, physiotherapists, occupational therapists | 2.1685852 | 15.4642863 | sex and age |
| 151 Health care managers | 2.5609524 | 14.4293656 | sex and age |
| 231 University and higher education teachers | 2.5810115 | 13.7386699 | sex and age |
| 533 Health care assistants | 2.0794512 | 13.0025749 | sex and age |
| 541 Other surveillance and security workers | 2.4232740 | 12.9292988 | sex and age |
| 267 Religious professionals and deacons | 3.1863563 | 12.7247444 | sex and age |
| 321 Medical and pharmaceutical technicians | 2.9289943 | 12.1381897 | sex and age |
| 522 Shop staff | 2.7119318 | 12.0951539 | sex and age |
| 213 Biologists, pharmacologists and specialists in agriculture and forestry | 1.4590279 | 9.6061073 | sex and age |
| 821 Assemblers | 2.8365295 | 9.2734220 | sex and age |
| 422 Client information clerks | 1.7743297 | 8.7718831 | sex and age |
| 262 Museum curators and librarians and related professionals | 2.3203065 | 8.5847669 | sex and age |
| 159 Other social services managers | 2.5439991 | 7.8781716 | sex and age |
| 123 Administration and planning managers | 4.3275243 | 7.3452011 | sex and age |
| 161 Financial and insurance managers | 3.4953296 | 7.2879455 | sex and age |
| 818 Other stationary plant and machine operators | 2.4062997 | 7.1387013 | sex and age |
| 333 Business services agents | 2.7489045 | 6.9293991 | sex and age |
| 722 Blacksmiths, toolmakers and related trades workers | 1.9353634 | 6.8656109 | sex and age |
| 511 Cabin crew, guides and related workers | 0.6997964 | 6.7913009 | sex and age |
| 261 Legal professionals | 3.0821252 | 6.6602603 | sex and age |
| 311 Physical and engineering science technicians | 2.1777128 | 6.2915291 | sex and age |
| 816 Machine operators, food and related products | 2.0069383 | 5.6187226 | sex and age |
| 814 Machine operators, rubber, plastic and paper products | 2.4830804 | 5.3245175 | sex and age |
| 336 Police officers | 2.9217134 | 5.3169761 | sex and age |
| 812 Metal processing and finishing plant operators | 1.7680426 | 5.1597022 | sex and age |
| 351 ICT operations and user support technicians | 2.1345745 | 4.8748330 | sex and age |
| 411 Office assistants and other secretaries | 2.5356754 | 4.7575794 | sex and age |
| 741 Electrical equipment installers and repairers | 2.4746226 | 4.7218323 | sex and age |
| 342 Athletes, fitness instructors and recreational workers | 1.6415741 | 4.6480979 | sex and age |
| 819 Process control technicians | 2.3555614 | 4.6407029 | sex and age |
| 214 Engineering professionals | 1.9299724 | 4.5859413 | sex and age |
| 228 Specialists in health care not elsewhere classified | 2.7352142 | 4.5394356 | sex and age |
| 962 Newspaper distributors, janitors and other service workers | 1.4297059 | 4.5028895 | sex and age |
| 312 Construction and manufacturing supervisors | 3.5808422 | 4.3879763 | sex and age |
| 232 Vocational education teachers | 3.0245050 | 4.0584628 | sex and age |
| 212 Mathematicians, actuaries and statisticians | 2.4369530 | 3.9838403 | sex and age |
| 211 Physicists and chemists | 2.0943491 | 3.9727058 | sex and age |
| 334 Administrative and specialized secretaries | 2.9702746 | 3.6129743 | sex and age |
| 222 Nursing professionals | 4.2511717 | 3.5676086 | sex and age |
| 121 Finance managers | 3.2704547 | 3.3530172 | sex and age |
| 243 Marketing and public relations professionals | 1.4890095 | 3.2225018 | sex and age |
| 932 Manufacturing labourers | 2.6991481 | 3.1523625 | sex and age |
| 817 Wood processing and papermaking plant operators | 2.9341892 | 3.1259551 | sex and age |
| 532 Personal care workers in health services | 2.8944753 | 3.0544887 | sex and age |
| 217 Designers | 2.5526515 | 3.0235916 | sex and age |
| 136 Production managers in construction and mining | 2.7406649 | 2.5807238 | sex and age |
| 732 Printing trades workers | 1.9355361 | 2.3243296 | sex and age |
| 534 Attendants, personal assistants and related workers | 1.9366235 | 2.2843662 | sex and age |
| 441 Library and filing clerks | 2.1267903 | 2.2496759 | sex and age |
| 235 Teaching professionals not elsewhere classified | 2.4893302 | 2.1506250 | sex and age |
| 131 Information and communications technology service managers | 4.0848134 | 2.1380093 | sex and age |
| 834 Mobile plant operators | 2.5374407 | 2.0839971 | sex and age |
| 611 Market gardeners and crop growers | 0.7905144 | 2.0157799 | sex and age |
| 223 Nursing professionals (cont.) | 3.0862129 | 1.9488266 | sex and age |
| 512 Cooks and cold-buffet managers | 2.8246521 | 1.9384343 | sex and age |
| 352 Broadcasting and audio-visual technicians | 0.6430997 | 1.9263743 | sex and age |
| 264 Authors, journalists and linguists | 1.6003330 | 1.8718057 | sex and age |
| 221 Medical doctors | 1.4464671 | 1.8334236 | sex and age |
| 133 Research and development managers | 1.3868102 | 1.8214105 | sex and age |
| 432 Stores and transport clerks | 2.2008861 | 1.7861746 | sex and age |
| 523 Cashiers and related clerks | 0.4338337 | 1.7438312 | sex and age |
| 132 Supply, logistics and transport managers | 1.2583558 | 1.7194546 | sex and age |
| 216 Architects and surveyors | 2.4463103 | 1.6944331 | sex and age |
| 137 Production managers in manufacturing | 2.5139252 | 1.6853597 | sex and age |
| 813 Machine operators, chemical and pharmaceutical products | 2.5781771 | 1.6607729 | sex and age |
| 332 Insurance advisers, sales and purchasing agents | 1.7769452 | 1.6356527 | sex and age |
| 515 Building caretakers and related workers | 2.0061217 | 1.5892818 | sex and age |
| 266 Social work and counselling professionals | 3.2075276 | 1.5751088 | sex and age |
| 815 Machine operators, textile, fur and leather products | 1.2919985 | 1.5542036 | sex and age |
| 251 ICT architects, systems analysts and test managers | 2.5274101 | 1.4087670 | sex and age |
| 125 Sales and marketing managers | 1.9020892 | 1.3550073 | sex and age |
| 134 Architectural and engineering managers | 2.4540118 | 1.2227887 | sex and age |
| 961 Recycling collectors | 2.2632593 | 1.1906325 | sex and age |
| 723 Machinery mechanics and fitters | 2.0663308 | 1.1041179 | sex and age |
| 344 Driving instructors and other instructors | 2.8774550 | 1.0799384 | sex and age |
| 833 Heavy truck and bus drivers | 1.9017745 | 1.0503103 | sex and age |
| 122 Human resource managers | 3.6123261 | 0.9673152 | sex and age |
| 226 Dentists | 2.2610744 | 0.9612771 | sex and age |
| 224 Psychologists and psychotherapists | 2.7435122 | 0.9185294 | sex and age |
| 711 Carpenters, bricklayers and construction workers | 1.8248715 | 0.9020636 | sex and age |
| 761 Butchers, bakers and food processors | 1.6307538 | 0.8629405 | sex and age |
| 141 Primary and secondary schools and adult education managers | 3.5738599 | 0.8021870 | sex and age |
| 831 Train operators and related workers | 1.7928726 | 0.7956301 | sex and age |
| 179 Other services managers not elsewhere classified | 2.7619304 | 0.7557586 | sex and age |
| 218 Specialists within environmental and health protection | 2.6146940 | 0.7229000 | sex and age |
| 516 Other service related workers | 2.1156393 | 0.7101924 | sex and age |
| 524 Event seller and telemarketers | 2.0545466 | 0.5044158 | sex and age |
| 752 Wood treaters, cabinet-makers and related trades workers | 2.6808750 | 0.4777104 | sex and age |
| 265 Creative and performing artists | 2.5614482 | 0.4592989 | sex and age |
| 912 Washers, window cleaners and other cleaning workers | 2.5642100 | 0.3968101 | sex and age |
| 153 Elderly care managers | 3.3657167 | 0.3924072 | sex and age |
| 343 Photographers, interior decorators and entertainers | 3.4433785 | 0.3697008 | sex and age |
| 513 Waiters and bartenders | 2.1711800 | 0.2718709 | sex and age |
| 129 Administration and service managers not elsewhere classified | 2.2086175 | 0.2151892 | sex and age |
| 941 Fast-food workers, food preparation assistants | 2.0158249 | 0.2048116 | sex and age |
| 233 Secondary education teachers | 2.9895590 | 0.1966141 | sex and age |
| 152 Managers in social and curative care | 3.9369039 | 0.1858560 | sex and age |
| 341 Social work and religious associate professionals | 2.5917033 | 0.1642278 | sex and age |
merge(summary_table, anova_table, c("ssyk", "interaction"), all = TRUE) %>%
filter (term.x == "year_n") %>%
filter (contcol.y > 0) %>%
filter (interaction == "sex 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 (sex and year) and the yearly increase in salaries')| ssyk | Increase in salary | F-value for age | interaction |
|---|---|---|---|
| 131 Information and communications technology service managers | 2.3096913 | 18.9885627 | sex and year |
| 741 Electrical equipment installers and repairers | 2.8954090 | 15.1782057 | sex and year |
| 813 Machine operators, chemical and pharmaceutical products | 1.9077963 | 11.5897622 | sex and year |
| 151 Health care managers | 0.0045519 | 10.7526199 | sex and year |
| 211 Physicists and chemists | 1.3915123 | 7.4977359 | sex and year |
| 221 Medical doctors | 0.6538773 | 7.3600653 | sex and year |
| 816 Machine operators, food and related products | 1.5717231 | 4.9339943 | sex and year |
| 235 Teaching professionals not elsewhere classified | 2.0310346 | 4.5698800 | sex and year |
| 524 Event seller and telemarketers | 3.5081268 | 4.4316751 | sex and year |
| 711 Carpenters, bricklayers and construction workers | 2.2254597 | 4.4174399 | sex and year |
| 932 Manufacturing labourers | 2.1290954 | 4.1545847 | sex and year |
| 262 Museum curators and librarians and related professionals | 1.7899210 | 4.1395912 | sex and year |
| 227 Naprapaths, physiotherapists, occupational therapists | 1.2922853 | 3.7328350 | sex and year |
| 441 Library and filing clerks | 1.6621943 | 3.1588539 | sex and year |
| 732 Printing trades workers | 1.4914084 | 2.9105428 | sex and year |
| 611 Market gardeners and crop growers | 1.3821474 | 2.7246518 | sex and year |
| 266 Social work and counselling professionals | 2.9450799 | 2.6820347 | sex and year |
| 123 Administration and planning managers | 5.2325129 | 2.4711124 | sex and year |
| 422 Client information clerks | 1.3545144 | 2.2011024 | sex and year |
| 261 Legal professionals | 2.2865093 | 2.1206991 | sex and year |
| 214 Engineering professionals | 1.5742827 | 2.0990600 | sex and year |
| 815 Machine operators, textile, fur and leather products | 1.0046815 | 2.0981886 | sex and year |
| 267 Religious professionals and deacons | 4.2599077 | 1.9296670 | sex and year |
| 218 Specialists within environmental and health protection | 2.2923893 | 1.9224272 | sex and year |
| 122 Human resource managers | 4.8586001 | 1.7650927 | sex and year |
| 251 ICT architects, systems analysts and test managers | 2.1136658 | 1.7424405 | sex and year |
| 534 Attendants, personal assistants and related workers | 1.8244819 | 1.7041840 | sex and year |
| 232 Vocational education teachers | 2.7671447 | 1.4897367 | sex and year |
| 134 Architectural and engineering managers | 2.0704051 | 1.3888211 | sex and year |
| 333 Business services agents | 3.0955032 | 1.3284140 | sex and year |
| 834 Mobile plant operators | 2.8742266 | 1.2866972 | sex and year |
| 814 Machine operators, rubber, plastic and paper products | 2.2917499 | 1.2780326 | sex and year |
| 121 Finance managers | 2.5063899 | 1.2719390 | sex and year |
| 511 Cabin crew, guides and related workers | 1.2549099 | 1.2614665 | sex and year |
| 137 Production managers in manufacturing | 2.3152928 | 1.2488175 | sex and year |
| 819 Process control technicians | 2.5792156 | 1.2321895 | sex and year |
| 752 Wood treaters, cabinet-makers and related trades workers | 2.3616877 | 1.2158818 | sex and year |
| 311 Physical and engineering science technicians | 2.5830714 | 1.1623245 | sex and year |
| 264 Authors, journalists and linguists | 1.1529306 | 1.1155040 | sex and year |
| 343 Photographers, interior decorators and entertainers | 4.1985542 | 1.1106546 | sex and year |
| 243 Marketing and public relations professionals | 0.9815532 | 1.0732378 | sex and year |
| 541 Other surveillance and security workers | 2.5761973 | 1.0256130 | sex and year |
| 336 Police officers | 3.1412715 | 1.0138419 | sex and year |
| 216 Architects and surveyors | 2.9471943 | 0.9876359 | sex and year |
| 233 Secondary education teachers | 2.7734258 | 0.9441665 | sex and year |
| 351 ICT operations and user support technicians | 1.9038540 | 0.7819692 | sex and year |
| 132 Supply, logistics and transport managers | 1.9099255 | 0.7677975 | sex and year |
| 342 Athletes, fitness instructors and recreational workers | 1.9068283 | 0.7090386 | sex and year |
| 912 Washers, window cleaners and other cleaning workers | 2.8087342 | 0.7016091 | sex and year |
| 321 Medical and pharmaceutical technicians | 3.1597738 | 0.6977518 | sex and year |
| 962 Newspaper distributors, janitors and other service workers | 1.1887397 | 0.6955963 | sex and year |
| 818 Other stationary plant and machine operators | 2.6877777 | 0.6820875 | sex and year |
| 432 Stores and transport clerks | 2.3817426 | 0.6776753 | sex and year |
| 411 Office assistants and other secretaries | 2.2627780 | 0.6775943 | sex and year |
| 341 Social work and religious associate professionals | 2.7258687 | 0.6031036 | sex and year |
| 153 Elderly care managers | 3.1660345 | 0.5974951 | sex and year |
| 129 Administration and service managers not elsewhere classified | 2.5786963 | 0.4952633 | sex and year |
| 723 Machinery mechanics and fitters | 2.2281119 | 0.4619171 | sex and year |
| 265 Creative and performing artists | 2.8167822 | 0.4064823 | sex and year |
| 161 Financial and insurance managers | 4.5271109 | 0.3869820 | sex and year |
| 513 Waiters and bartenders | 2.4344083 | 0.3385613 | sex and year |
| 761 Butchers, bakers and food processors | 1.5483458 | 0.3298306 | sex and year |
| 179 Other services managers not elsewhere classified | 3.0293121 | 0.3105108 | sex and year |
| 831 Train operators and related workers | 1.6824270 | 0.3089261 | sex and year |
| 133 Research and development managers | 1.1872438 | 0.2947620 | sex and year |
| 961 Recycling collectors | 2.1940100 | 0.2898964 | sex and year |
| 213 Biologists, pharmacologists and specialists in agriculture and forestry | 1.6757443 | 0.2654079 | sex and year |
| 722 Blacksmiths, toolmakers and related trades workers | 1.7877696 | 0.2513383 | sex and year |
| 224 Psychologists and psychotherapists | 2.5048041 | 0.2303968 | sex and year |
| 231 University and higher education teachers | 2.4211367 | 0.2112476 | sex and year |
| 817 Wood processing and papermaking plant operators | 2.8257298 | 0.2058922 | sex and year |
| 212 Mathematicians, actuaries and statisticians | 2.1580785 | 0.1932679 | sex and year |
| 331 Financial and accounting associate professionals | 1.8922388 | 0.1893544 | sex and year |
| 335 Tax and related government associate professionals | 2.1448303 | 0.1877449 | sex and year |
| 136 Production managers in construction and mining | 2.9832656 | 0.1877118 | sex and year |
| 941 Fast-food workers, food preparation assistants | 2.1405063 | 0.1755668 | sex and year |
| 234 Primary- and pre-school teachers | 3.4559768 | 0.1540937 | sex and year |
| 222 Nursing professionals | 4.3218411 | 0.1292660 | sex and year |
| 241 Accountants, financial analysts and fund managers | 2.9051016 | 0.1277434 | sex and year |
| 533 Health care assistants | 2.0375910 | 0.1236315 | sex and year |
| 312 Construction and manufacturing supervisors | 3.4952751 | 0.1144032 | sex and year |
| 332 Insurance advisers, sales and purchasing agents | 1.6955017 | 0.0981232 | sex and year |
| 217 Designers | 2.3869024 | 0.0963789 | sex and year |
| 159 Other social services managers | 2.6064759 | 0.0820633 | sex and year |
| 352 Broadcasting and audio-visual technicians | 0.7640921 | 0.0744931 | sex and year |
| 532 Personal care workers in health services | 2.9147895 | 0.0673681 | sex and year |
| 334 Administrative and specialized secretaries | 2.8414994 | 0.0628304 | sex and year |
| 821 Assemblers | 2.7912579 | 0.0461133 | sex and year |
| 523 Cashiers and related clerks | 0.2414249 | 0.0391983 | sex and year |
| 516 Other service related workers | 2.1949695 | 0.0374522 | sex and year |
| 242 Organisation analysts, policy administrators and human resource specialists | 2.3180587 | 0.0370499 | sex and year |
| 515 Building caretakers and related workers | 1.8961579 | 0.0213149 | sex and year |
| 911 Cleaners and helpers | 1.6893962 | 0.0203530 | sex and year |
| 512 Cooks and cold-buffet managers | 2.7763370 | 0.0188044 | sex and year |
| 223 Nursing professionals (cont.) | 3.1057971 | 0.0161602 | sex and year |
| 226 Dentists | 2.2246798 | 0.0121797 | sex and year |
| 833 Heavy truck and bus drivers | 1.8878919 | 0.0062890 | sex and year |
| 141 Primary and secondary schools and adult education managers | 3.6011327 | 0.0059097 | sex and year |
| 812 Metal processing and finishing plant operators | 1.7636861 | 0.0046745 | sex and year |
| 228 Specialists in health care not elsewhere classified | 2.8440246 | 0.0042559 | sex and year |
| 344 Driving instructors and other instructors | 2.8525929 | 0.0037639 | sex and year |
| 152 Managers in social and curative care | 3.9152106 | 0.0030755 | sex and year |
| 522 Shop staff | 2.5756910 | 0.0016560 | sex and year |
| 531 Child care workers and teachers aides | 2.1809614 | 0.0010117 | sex and year |
| 713 Painters, Lacquerers, Chimney-sweepers and related trades workers | 2.8325569 | 0.0006941 | sex and year |
| 125 Sales and marketing managers | 1.9006017 | 0.0000073 | sex and year |
merge(summary_table, anova_table, c("ssyk", "interaction"), all = TRUE) %>%
filter (term.x == "year_n") %>%
filter (contcol.y > 0) %>%
filter (interaction == "year and age") %>%
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 (year and age) and the yearly increase in salaries')| ssyk | Increase in salary | F-value for age | interaction |
|---|---|---|---|
| 515 Building caretakers and related workers | 1.9559116 | 15.9023802 | year and age |
| 821 Assemblers | 2.8879833 | 9.2570096 | year and age |
| 223 Nursing professionals (cont.) | 3.0862129 | 7.5229917 | year and age |
| 812 Metal processing and finishing plant operators | 1.7681082 | 5.9064137 | year and age |
| 912 Washers, window cleaners and other cleaning workers | 2.5642100 | 5.6899347 | year and age |
| 516 Other service related workers | 2.1152246 | 5.2080039 | year and age |
| 264 Authors, journalists and linguists | 1.6003330 | 4.9115575 | year and age |
| 235 Teaching professionals not elsewhere classified | 2.4893302 | 4.7634036 | year and age |
| 251 ICT architects, systems analysts and test managers | 2.5274101 | 4.4833008 | year and age |
| 532 Personal care workers in health services | 2.8944753 | 4.3339760 | year and age |
| 441 Library and filing clerks | 2.1267903 | 4.2334206 | year and age |
| 333 Business services agents | 2.3588054 | 4.1952156 | year and age |
| 332 Insurance advisers, sales and purchasing agents | 1.7769452 | 4.1089082 | year and age |
| 132 Supply, logistics and transport managers | 1.6502566 | 3.9217350 | year and age |
| 833 Heavy truck and bus drivers | 1.9017745 | 3.6601456 | year and age |
| 932 Manufacturing labourers | 2.6991481 | 3.6464745 | year and age |
| 432 Stores and transport clerks | 2.2008861 | 3.5354072 | year and age |
| 222 Nursing professionals | 4.2308421 | 3.4741301 | year and age |
| 818 Other stationary plant and machine operators | 2.4062997 | 3.4540268 | year and age |
| 161 Financial and insurance managers | 4.0364493 | 3.3856627 | year and age |
| 233 Secondary education teachers | 2.9895590 | 3.1794015 | year and age |
| 234 Primary- and pre-school teachers | 3.5160288 | 3.1553505 | year and age |
| 228 Specialists in health care not elsewhere classified | 2.8875413 | 3.1501241 | year and age |
| 152 Managers in social and curative care | 3.9235201 | 2.8784925 | year and age |
| 137 Production managers in manufacturing | 2.6954172 | 2.6701651 | year and age |
| 352 Broadcasting and audio-visual technicians | 0.6216704 | 2.5914276 | year and age |
| 334 Administrative and specialized secretaries | 2.8445143 | 2.5139090 | year and age |
| 815 Machine operators, textile, fur and leather products | 1.2919985 | 2.4449179 | year and age |
| 941 Fast-food workers, food preparation assistants | 2.0158249 | 2.4202939 | year and age |
| 266 Social work and counselling professionals | 3.2170157 | 2.4011601 | year and age |
| 216 Architects and surveyors | 2.6170069 | 2.3614098 | year and age |
| 343 Photographers, interior decorators and entertainers | 3.4645037 | 2.2302218 | year and age |
| 533 Health care assistants | 2.0794512 | 2.1161718 | year and age |
| 961 Recycling collectors | 2.2500279 | 1.9762282 | year and age |
| 211 Physicists and chemists | 2.0943491 | 1.9599317 | year and age |
| 342 Athletes, fitness instructors and recreational workers | 1.6415741 | 1.7856668 | year and age |
| 761 Butchers, bakers and food processors | 1.6561457 | 1.6846191 | year and age |
| 214 Engineering professionals | 1.9496371 | 1.6729919 | year and age |
| 153 Elderly care managers | 3.3564767 | 1.6605420 | year and age |
| 411 Office assistants and other secretaries | 2.5356754 | 1.6094669 | year and age |
| 534 Attendants, personal assistants and related workers | 1.9366235 | 1.5775466 | year and age |
| 336 Police officers | 2.8967989 | 1.5454215 | year and age |
| 541 Other surveillance and security workers | 2.4232740 | 1.5351778 | year and age |
| 232 Vocational education teachers | 3.0621355 | 1.5199772 | year and age |
| 513 Waiters and bartenders | 2.1711800 | 1.3797110 | year and age |
| 212 Mathematicians, actuaries and statisticians | 2.3025215 | 1.3794017 | year and age |
| 531 Child care workers and teachers aides | 2.1977955 | 1.3438941 | year and age |
| 134 Architectural and engineering managers | 2.4421872 | 1.3309754 | year and age |
| 732 Printing trades workers | 1.9468028 | 1.2928325 | year and age |
| 344 Driving instructors and other instructors | 3.0880108 | 1.2771979 | year and age |
| 831 Train operators and related workers | 1.7390209 | 1.2652502 | year and age |
| 351 ICT operations and user support technicians | 2.1345745 | 1.2472259 | year and age |
| 713 Painters, Lacquerers, Chimney-sweepers and related trades workers | 2.7921442 | 1.2381889 | year and age |
| 261 Legal professionals | 3.0754624 | 1.2256187 | year and age |
| 159 Other social services managers | 2.5439991 | 1.2079146 | year and age |
| 136 Production managers in construction and mining | 2.6565246 | 1.1618915 | year and age |
| 611 Market gardeners and crop growers | 0.8091723 | 1.1274767 | year and age |
| 813 Machine operators, chemical and pharmaceutical products | 2.5686676 | 1.1016766 | year and age |
| 722 Blacksmiths, toolmakers and related trades workers | 1.9484997 | 1.0809628 | year and age |
| 241 Accountants, financial analysts and fund managers | 2.7431544 | 1.0545884 | year and age |
| 133 Research and development managers | 1.3880860 | 1.0327136 | year and age |
| 711 Carpenters, bricklayers and construction workers | 1.7284301 | 0.9998010 | year and age |
| 819 Process control technicians | 2.3555614 | 0.9854613 | year and age |
| 814 Machine operators, rubber, plastic and paper products | 2.4830804 | 0.9597551 | year and age |
| 179 Other services managers not elsewhere classified | 2.7619304 | 0.9365528 | year and age |
| 524 Event seller and telemarketers | 2.0545466 | 0.9061984 | year and age |
| 752 Wood treaters, cabinet-makers and related trades workers | 2.7414213 | 0.8802318 | year and age |
| 817 Wood processing and papermaking plant operators | 2.9341892 | 0.8608835 | year and age |
| 331 Financial and accounting associate professionals | 2.1684226 | 0.8564160 | year and age |
| 962 Newspaper distributors, janitors and other service workers | 1.4297059 | 0.8546703 | year and age |
| 341 Social work and religious associate professionals | 2.5900830 | 0.8191473 | year and age |
| 217 Designers | 2.5526515 | 0.7984661 | year and age |
| 741 Electrical equipment installers and repairers | 2.2566285 | 0.7858381 | year and age |
| 311 Physical and engineering science technicians | 2.1298064 | 0.7798156 | year and age |
| 511 Cabin crew, guides and related workers | 0.6997964 | 0.7635704 | year and age |
| 123 Administration and planning managers | 4.3275243 | 0.7609043 | year and age |
| 512 Cooks and cold-buffet managers | 2.8246521 | 0.6943099 | year and age |
| 265 Creative and performing artists | 2.5485214 | 0.6640336 | year and age |
| 312 Construction and manufacturing supervisors | 3.6001239 | 0.6252099 | year and age |
| 221 Medical doctors | 1.4542955 | 0.6061171 | year and age |
| 131 Information and communications technology service managers | 4.2967213 | 0.6059112 | year and age |
| 243 Marketing and public relations professionals | 1.4435592 | 0.5717083 | year and age |
| 122 Human resource managers | 3.8549956 | 0.5660338 | year and age |
| 321 Medical and pharmaceutical technicians | 2.8974259 | 0.5491370 | year and age |
| 151 Health care managers | 2.2391207 | 0.5385478 | year and age |
| 226 Dentists | 2.2660904 | 0.5172035 | year and age |
| 218 Specialists within environmental and health protection | 2.6146940 | 0.4971584 | year and age |
| 816 Machine operators, food and related products | 2.0069383 | 0.4950654 | year and age |
| 224 Psychologists and psychotherapists | 2.7435122 | 0.4914927 | year and age |
| 121 Finance managers | 3.2407711 | 0.4712382 | year and age |
| 227 Naprapaths, physiotherapists, occupational therapists | 2.1711768 | 0.4559658 | year and age |
| 523 Cashiers and related clerks | 0.4288843 | 0.4465303 | year and age |
| 422 Client information clerks | 1.7743297 | 0.4222086 | year and age |
| 911 Cleaners and helpers | 1.7149158 | 0.4213080 | year and age |
| 834 Mobile plant operators | 2.5488197 | 0.3653360 | year and age |
| 522 Shop staff | 2.5915625 | 0.3222133 | year and age |
| 125 Sales and marketing managers | 1.9745892 | 0.3192256 | year and age |
| 242 Organisation analysts, policy administrators and human resource specialists | 2.2640553 | 0.2791956 | year and age |
| 262 Museum curators and librarians and related professionals | 2.3543764 | 0.2615687 | year and age |
| 141 Primary and secondary schools and adult education managers | 3.6123368 | 0.2597121 | year and age |
| 231 University and higher education teachers | 2.5810115 | 0.2269531 | year and age |
| 723 Machinery mechanics and fitters | 2.0728261 | 0.2269456 | year and age |
| 335 Tax and related government associate professionals | 2.2959882 | 0.2050003 | year and age |
| 213 Biologists, pharmacologists and specialists in agriculture and forestry | 1.4590279 | 0.1806503 | year and age |
| 267 Religious professionals and deacons | 3.1655046 | 0.1747193 | year and age |
| 129 Administration and service managers not elsewhere classified | 2.1762142 | 0.0684575 | year and age |
merge(summary_table, anova_table, c("ssyk", "interaction"), all = TRUE) %>%
filter (term.x == "year_n") %>%
filter (contcol.y > 1) %>%
filter (interaction == "sex, year and age") %>%
filter (!(contcol.y == 1 & interaction == "sex, year and age")) %>%
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 (sex, year and age) and the yearly increase in salaries')| ssyk | Increase in salary | F-value for age | interaction |
|---|---|---|---|
| 161 Financial and insurance managers | 6.7014383 | 12.8607103 | sex, year and age |
| 212 Mathematicians, actuaries and statisticians | 1.9911366 | 10.4095062 | sex, year and age |
| 267 Religious professionals and deacons | 4.1658118 | 8.7837115 | sex, year and age |
| 227 Naprapaths, physiotherapists, occupational therapists | 2.4303726 | 8.1151159 | sex, year and age |
| 152 Managers in social and curative care | 2.5213877 | 6.6049915 | sex, year and age |
| 713 Painters, Lacquerers, Chimney-sweepers and related trades workers | 2.9730902 | 6.1709248 | sex, year and age |
| 343 Photographers, interior decorators and entertainers | 0.5060734 | 6.1295901 | sex, year and age |
| 151 Health care managers | -1.2649194 | 5.9426940 | sex, year and age |
| 261 Legal professionals | 2.3125463 | 5.5530411 | sex, year and age |
| 216 Architects and surveyors | 3.1604358 | 4.3669656 | sex, year and age |
| 341 Social work and religious associate professionals | 2.5727375 | 4.1594012 | sex, year and age |
| 441 Library and filing clerks | 1.3204015 | 3.5932816 | sex, year and age |
| 833 Heavy truck and bus drivers | 1.8961398 | 3.4082814 | sex, year and age |
| 432 Stores and transport clerks | 2.1649036 | 3.3329059 | sex, year and age |
| 344 Driving instructors and other instructors | 2.4848718 | 3.3062803 | sex, year and age |
| 342 Athletes, fitness instructors and recreational workers | 1.9068283 | 3.2624869 | sex, year and age |
| 741 Electrical equipment installers and repairers | 2.8664232 | 3.1590750 | sex, year and age |
| 235 Teaching professionals not elsewhere classified | 2.0310346 | 3.1408651 | sex, year and age |
| 228 Specialists in health care not elsewhere classified | 3.3736609 | 2.9090416 | sex, year and age |
| 711 Carpenters, bricklayers and construction workers | 2.1627237 | 2.7534146 | sex, year and age |
| 961 Recycling collectors | 2.1566912 | 2.7467393 | sex, year and age |
| 911 Cleaners and helpers | 1.7263476 | 2.4634242 | sex, year and age |
| 262 Museum curators and librarians and related professionals | 2.3015865 | 2.3970735 | sex, year and age |
| 222 Nursing professionals | 4.2407032 | 2.3527290 | sex, year and age |
| 213 Biologists, pharmacologists and specialists in agriculture and forestry | 1.6757443 | 2.3028311 | sex, year and age |
| 334 Administrative and specialized secretaries | 2.7956288 | 2.2055190 | sex, year and age |
| 513 Waiters and bartenders | 0.8749792 | 2.1159294 | sex, year and age |
| 523 Cashiers and related clerks | 2.8459448 | 2.0567141 | sex, year and age |
| 534 Attendants, personal assistants and related workers | 1.8244819 | 1.9933909 | sex, year and age |
| 816 Machine operators, food and related products | 1.5717231 | 1.9024147 | sex, year and age |
| 817 Wood processing and papermaking plant operators | 2.8257298 | 1.8237211 | sex, year and age |
| 242 Organisation analysts, policy administrators and human resource specialists | 2.3180587 | 1.7886047 | sex, year and age |
| 332 Insurance advisers, sales and purchasing agents | 1.7859408 | 1.7585242 | sex, year and age |
| 515 Building caretakers and related workers | 2.0556672 | 1.7184190 | sex, year and age |
| 132 Supply, logistics and transport managers | 2.2475088 | 1.6580301 | sex, year and age |
| 241 Accountants, financial analysts and fund managers | 2.9051016 | 1.3893091 | sex, year and age |
| 211 Physicists and chemists | 1.3915123 | 1.3625703 | sex, year and age |
| 218 Specialists within environmental and health protection | 2.2923893 | 1.3335963 | sex, year and age |
| 732 Printing trades workers | 1.4520363 | 1.3253827 | sex, year and age |
| 818 Other stationary plant and machine operators | 2.6877777 | 1.3237424 | sex, year and age |
| 232 Vocational education teachers | 2.7950272 | 1.1768965 | sex, year and age |
| 541 Other surveillance and security workers | 2.5928421 | 1.1184021 | sex, year and age |
| 834 Mobile plant operators | 2.8552448 | 1.1025428 | sex, year and age |
| 352 Broadcasting and audio-visual technicians | 0.8738449 | 1.0491745 | sex, year and age |
| 159 Other social services managers | 2.5733968 | 1.0457667 | sex, year and age |
| 131 Information and communications technology service managers | 2.3673892 | 1.0310987 | sex, year and age |
| 815 Machine operators, textile, fur and leather products | 1.0046815 | 0.9282509 | sex, year and age |
| 941 Fast-food workers, food preparation assistants | 2.4028452 | 0.8902366 | sex, year and age |
| 912 Washers, window cleaners and other cleaning workers | 2.8087342 | 0.8797368 | sex, year and age |
| 524 Event seller and telemarketers | 3.4988451 | 0.8544225 | sex, year and age |
| 121 Finance managers | 2.5670784 | 0.8466459 | sex, year and age |
| 512 Cooks and cold-buffet managers | 3.2163958 | 0.8442390 | sex, year and age |
| 136 Production managers in construction and mining | 2.8467904 | 0.7932921 | sex, year and age |
| 814 Machine operators, rubber, plastic and paper products | 2.2917499 | 0.7793006 | sex, year and age |
| 214 Engineering professionals | 1.6492316 | 0.7761337 | sex, year and age |
| 813 Machine operators, chemical and pharmaceutical products | 1.9063935 | 0.7563922 | sex, year and age |
| 531 Child care workers and teachers aides | 2.0950582 | 0.7028050 | sex, year and age |
| 265 Creative and performing artists | 2.9175166 | 0.6854106 | sex, year and age |
| 336 Police officers | 3.1628844 | 0.6829463 | sex, year and age |
| 123 Administration and planning managers | 5.2325129 | 0.6568238 | sex, year and age |
| 226 Dentists | 2.2002919 | 0.6424133 | sex, year and age |
| 821 Assemblers | 2.7298723 | 0.6272515 | sex, year and age |
| 266 Social work and counselling professionals | 2.9030963 | 0.6105511 | sex, year and age |
| 333 Business services agents | 3.4608843 | 0.6059347 | sex, year and age |
| 122 Human resource managers | 2.8367433 | 0.6024562 | sex, year and age |
| 264 Authors, journalists and linguists | 1.1529306 | 0.5903280 | sex, year and age |
| 134 Architectural and engineering managers | 2.1160537 | 0.5768053 | sex, year and age |
| 153 Elderly care managers | 3.0364263 | 0.5758293 | sex, year and age |
| 125 Sales and marketing managers | 1.6739767 | 0.5704022 | sex, year and age |
| 133 Research and development managers | 1.1597044 | 0.5683059 | sex, year and age |
| 932 Manufacturing labourers | 2.1290954 | 0.5548487 | sex, year and age |
| 312 Construction and manufacturing supervisors | 3.4591641 | 0.5377424 | sex, year and age |
| 335 Tax and related government associate professionals | 2.1448303 | 0.5243855 | sex, year and age |
| 533 Health care assistants | 2.0013978 | 0.4993126 | sex, year and age |
| 221 Medical doctors | 0.6685600 | 0.4775631 | sex, year and age |
| 819 Process control technicians | 2.6501438 | 0.4743982 | sex, year and age |
| 243 Marketing and public relations professionals | 1.0617941 | 0.4741832 | sex, year and age |
| 611 Market gardeners and crop growers | 1.4116246 | 0.4502343 | sex, year and age |
| 231 University and higher education teachers | 2.4211367 | 0.4173814 | sex, year and age |
| 233 Secondary education teachers | 2.7734258 | 0.3928290 | sex, year and age |
| 962 Newspaper distributors, janitors and other service workers | 1.1318853 | 0.3918519 | sex, year and age |
| 137 Production managers in manufacturing | 2.1660640 | 0.3369015 | sex, year and age |
| 141 Primary and secondary schools and adult education managers | 3.4089930 | 0.3318747 | sex, year and age |
| 422 Client information clerks | 1.3506000 | 0.3234139 | sex, year and age |
| 511 Cabin crew, guides and related workers | 1.2549099 | 0.3045993 | sex, year and age |
| 179 Other services managers not elsewhere classified | 3.0293121 | 0.3041590 | sex, year and age |
| 223 Nursing professionals (cont.) | 2.8788243 | 0.2989413 | sex, year and age |
| 234 Primary- and pre-school teachers | 3.4559768 | 0.2757739 | sex, year and age |
| 532 Personal care workers in health services | 2.9147895 | 0.2709784 | sex, year and age |
| 722 Blacksmiths, toolmakers and related trades workers | 1.8671965 | 0.2638641 | sex, year and age |
| 831 Train operators and related workers | 1.5460450 | 0.2570093 | sex, year and age |
| 331 Financial and accounting associate professionals | 1.8020273 | 0.2535867 | sex, year and age |
| 411 Office assistants and other secretaries | 2.2627780 | 0.1869850 | sex, year and age |
| 812 Metal processing and finishing plant operators | 1.7305642 | 0.1741774 | sex, year and age |
| 311 Physical and engineering science technicians | 2.6472370 | 0.1720296 | sex, year and age |
| 761 Butchers, bakers and food processors | 1.6172221 | 0.1659462 | sex, year and age |
| 224 Psychologists and psychotherapists | 2.4333553 | 0.1445774 | sex, year and age |
| 321 Medical and pharmaceutical technicians | 3.2440727 | 0.1215590 | sex, year and age |
| 217 Designers | 2.3869024 | 0.1138331 | sex, year and age |
| 351 ICT operations and user support technicians | 1.7567973 | 0.1112492 | sex, year and age |
| 516 Other service related workers | 2.8434718 | 0.0882348 | sex, year and age |
| 129 Administration and service managers not elsewhere classified | 2.6395673 | 0.0513667 | sex, year and age |
| 723 Machinery mechanics and fitters | 2.2720551 | 0.0252031 | sex, year and age |
| 752 Wood treaters, cabinet-makers and related trades workers | 2.2552676 | 0.0214662 | sex, year and age |
| 522 Shop staff | 2.8964681 | 0.0194987 | sex, year and age |
| 251 ICT architects, systems analysts and test managers | 2.2488355 | 0.0081645 | sex, year and age |
Let’s check what we have found.
temp <- tb %>%
filter(`occuptional (SSYK 2012)` == "335 Tax and related government associate professionals")
model <-lm (log(salary) ~ year_n + sex * poly(age_n, 3), data = temp)
plot_model(model, type = "pred", terms = c("age_n", "sex"))## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.
Figure 2: Highest F-value interaction sex and age, Tax and related government associate professionals
temp <- tb %>%
filter(`occuptional (SSYK 2012)` == "341 Social work and religious associate professionals")
model <-lm (log(salary) ~ year_n + sex * poly(age_n, 3), data = temp)
plot_model(model, type = "pred", terms = c("age_n", "sex"))## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.
Figure 3: Lowest F-value interaction sex and age, Social work and religious associate professionals
temp <- tb %>%
filter(`occuptional (SSYK 2012)` == "131 Information and communications technology service managers")
model <-lm (log(salary) ~ year_n * sex + poly(age_n, 3), data = temp)
plot_model(model, type = "pred", terms = c("year_n", "sex"))## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.
Figure 4: Highest F-value interaction sex and year, Information and communications technology service managers
temp <- tb %>%
filter(`occuptional (SSYK 2012)` == "125 Sales and marketing managers")
model <-lm (log(salary) ~ year_n * sex + poly(age_n, 3), data = temp)
plot_model(model, type = "pred", terms = c("year_n", "sex"))## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.
Figure 5: Lowest F-value interaction sex and year, Sales and marketing managers
temp <- tb %>%
filter(`occuptional (SSYK 2012)` == "515 Building caretakers and related workers")
model <-lm (log(salary) ~ sex + year_n * poly(age_n, 3), data = temp)
plot_model(model, type = "pred", terms = c("age_n", "year_n"))## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.
Figure 6: Highest F-value interaction year and age, Building caretakers and related workers
temp <- tb %>%
filter(`occuptional (SSYK 2012)` == "129 Administration and service managers not elsewhere classified")
model <-lm (log(salary) ~ sex + year_n * poly(age_n, 3), data = temp)
plot_model(model, type = "pred", terms = c("age_n", "year_n"))## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.
Figure 7: Lowest F-value interaction year and age, Administration and service managers not elsewhere classified
temp <- tb %>%
filter(`occuptional (SSYK 2012)` == "161 Financial and insurance managers")
model <-lm (log(salary) ~ sex * year_n * poly(age_n, 3), data = temp)
plot_model(model, type = "pred", terms = c("age_n", "year_n", "sex"))## Warning in predict.lm(model, newdata = fitfram, type = "response", se.fit =
## se, : prediction from a rank-deficient fit may be misleading## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.
Figure 8: Highest F-value interaction sex, year and age, Financial and insurance managers
temp <- tb %>%
filter(`occuptional (SSYK 2012)` == "251 ICT architects, systems analysts and test managers")
model <-lm (log(salary) ~ sex * year_n * poly(age_n, 3), data = temp)
plot_model(model, type = "pred", terms = c("age_n", "year_n", "sex"))## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.
Figure 9: Lowest F-value interaction sex, year and age, ICT architects, systems analysts and test managers
We proceed by examining the interaction between education, year, gender.
The data table is downloaded from Statistics Sweden. It is saved as a comma-delimited file without heading, 000000CY.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 sector, occupational group (SSYK 2012), sex and educational level (SUN). Year 2014 - 2018 Monthly salary All sectors
I will use a categorical predictor to fit the contribution of education to the salary.
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("000000CY.csv") %>%
mutate(edulevel = `level of education`)
numedulevel <- read.csv("edulevel.csv")
tbnum <- tb %>%
right_join(numedulevel, by = c("level of education" = "level.of.education")) %>%
filter(!is.na(eduyears)) %>%
mutate(eduyears = factor(eduyears))## Warning: Column `level of education`/`level.of.education` joining character
## vector and factor, coercing into character vectorsummary_table = vector()
anova_table = vector()
for (i in unique(tbnum$`occuptional (SSYK 2012)`)){
temp <- filter(tbnum, `occuptional (SSYK 2012)` == i)
if (dim(temp)[1] > 30){
model <-lm (log(salary) ~ year_n + sex * edulevel, data = temp)
summary_table <- rbind (summary_table, mutate (tidy (summary (model)), ssyk = i, interaction = "sex and edulevel"))
anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i, interaction = "sex and edulevel"))
model <-lm (log(salary) ~ year_n * sex + edulevel, data = temp)
summary_table <- rbind (summary_table, mutate (tidy (summary (model)), ssyk = i, interaction = "sex and year"))
anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i, interaction = "sex and year"))
model <-lm (log(salary) ~ sex + year_n * edulevel, data = temp)
summary_table <- rbind (summary_table, mutate (tidy (summary (model)), ssyk = i, interaction = "year and edulevel"))
anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i, interaction = "year and edulevel"))
model <-lm (log(salary) ~ year_n * sex * edulevel, data = temp)
summary_table <- rbind (summary_table, mutate (tidy (summary (model)), ssyk = i, interaction = "sex, year and edulevel"))
anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i, interaction = "sex, year and edulevel"))
}
} ## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
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## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
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## Note: model has aliased coefficients
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## Note: model has aliased coefficients
## sums of squares computed by model comparison
## Note: model has aliased coefficients
## sums of squares computed by model comparisonanova_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 (contcol.y > 0) %>%
filter (!(contcol.y == 1 & interaction == "sex, year and edulevel")) %>%
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 education"
) 
Figure 10: The significance of education, year, sex and the interaction between them on the salary in Sweden, a comparison between different occupational groups, Year 2014 - 2018
The table 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 (contcol.y > 0) %>%
filter (interaction == "sex and edulevel") %>%
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 (sex and edulevel) and the yearly increase in salaries')| ssyk | Increase in salary | F-value for age | interaction |
|---|---|---|---|
| 231 University and higher education teachers | 2.2958141 | 34.5015790 | sex and edulevel |
| 531 Child care workers and teachers aides | 2.0849482 | 22.9893835 | sex and edulevel |
| 534 Attendants, personal assistants and related workers | 1.9853599 | 17.8765982 | sex and edulevel |
| 159 Other social services managers | 2.5200022 | 17.2526856 | sex and edulevel |
| 331 Financial and accounting associate professionals | 1.9408245 | 15.3185884 | sex and edulevel |
| 232 Vocational education teachers | 2.8905545 | 13.2302391 | sex and edulevel |
| 235 Teaching professionals not elsewhere classified | 1.8910322 | 12.9225823 | sex and edulevel |
| 533 Health care assistants | 1.9894190 | 12.7840342 | sex and edulevel |
| 532 Personal care workers in health services | 2.8496725 | 9.1729109 | sex and edulevel |
| 335 Tax and related government associate professionals | 2.3353756 | 7.9120778 | sex and edulevel |
| 151 Health care managers | 2.3371897 | 7.8893324 | sex and edulevel |
| 312 Construction and manufacturing supervisors | 3.2514155 | 7.5021320 | sex and edulevel |
| 221 Medical doctors | 1.6287887 | 7.2076142 | sex and edulevel |
| 261 Legal professionals | 3.0480065 | 6.8746713 | sex and edulevel |
| 213 Biologists, pharmacologists and specialists in agriculture and forestry | 1.5450027 | 6.5884385 | sex and edulevel |
| 123 Administration and planning managers | 3.2940731 | 6.5099919 | sex and edulevel |
| 332 Insurance advisers, sales and purchasing agents | 2.2092499 | 6.4609540 | sex and edulevel |
| 218 Specialists within environmental and health protection | 2.7392976 | 6.4333433 | sex and edulevel |
| 815 Machine operators, textile, fur and leather products | 1.2987260 | 5.9964706 | sex and edulevel |
| 352 Broadcasting and audio-visual technicians | 1.1989274 | 5.9008142 | sex and edulevel |
| 234 Primary- and pre-school teachers | 2.9842522 | 5.5772411 | sex and edulevel |
| 179 Other services managers not elsewhere classified | 2.7707830 | 5.5622073 | sex and edulevel |
| 818 Other stationary plant and machine operators | 2.3348813 | 5.3288394 | sex and edulevel |
| 122 Human resource managers | 2.8136311 | 5.2385563 | sex and edulevel |
| 831 Train operators and related workers | 1.8323618 | 4.7619015 | sex and edulevel |
| 133 Research and development managers | 1.2477491 | 4.7380058 | sex and edulevel |
| 242 Organisation analysts, policy administrators and human resource specialists | 1.7745769 | 4.6468566 | sex and edulevel |
| 432 Stores and transport clerks | 1.8812238 | 4.5189085 | sex and edulevel |
| 819 Process control technicians | 2.2014594 | 4.3967289 | sex and edulevel |
| 441 Library and filing clerks | 1.8816062 | 4.2560137 | sex and edulevel |
| 311 Physical and engineering science technicians | 1.8230392 | 4.1225856 | sex and edulevel |
| 422 Client information clerks | 1.8028560 | 4.0863532 | sex and edulevel |
| 513 Waiters and bartenders | 2.8449379 | 3.9818863 | sex and edulevel |
| 217 Designers | 2.7808393 | 3.7385345 | sex and edulevel |
| 125 Sales and marketing managers | 2.3036799 | 3.4839265 | sex and edulevel |
| 214 Engineering professionals | 2.2866957 | 3.3293074 | sex and edulevel |
| 132 Supply, logistics and transport managers | 1.2954135 | 3.2963695 | sex and edulevel |
| 821 Assemblers | 2.8859212 | 3.2954380 | sex and edulevel |
| 341 Social work and religious associate professionals | 2.5669973 | 3.2470601 | sex and edulevel |
| 523 Cashiers and related clerks | 1.4161452 | 3.1231802 | sex and edulevel |
| 161 Financial and insurance managers | 2.8561469 | 3.0300851 | sex and edulevel |
| 411 Office assistants and other secretaries | 2.2467740 | 2.9941491 | sex and edulevel |
| 941 Fast-food workers, food preparation assistants | 1.8006179 | 2.9874932 | sex and edulevel |
| 342 Athletes, fitness instructors and recreational workers | 1.7794145 | 2.9204408 | sex and edulevel |
| 334 Administrative and specialized secretaries | 3.1996557 | 2.7701174 | sex and edulevel |
| 216 Architects and surveyors | 2.0531986 | 2.7656716 | sex and edulevel |
| 511 Cabin crew, guides and related workers | 0.5200769 | 2.6492869 | sex and edulevel |
| 812 Metal processing and finishing plant operators | 0.7894188 | 2.4826989 | sex and edulevel |
| 251 ICT architects, systems analysts and test managers | 2.5832621 | 2.4743375 | sex and edulevel |
| 817 Wood processing and papermaking plant operators | 2.8508683 | 2.3671829 | sex and edulevel |
| 524 Event seller and telemarketers | 1.7483645 | 2.3363353 | sex and edulevel |
| 121 Finance managers | 2.5622573 | 2.3103331 | sex and edulevel |
| 522 Shop staff | 2.1891308 | 2.1309636 | sex and edulevel |
| 723 Machinery mechanics and fitters | 1.9652100 | 2.1236645 | sex and edulevel |
| 321 Medical and pharmaceutical technicians | 2.7193546 | 2.0749292 | sex and edulevel |
| 911 Cleaners and helpers | 1.8900136 | 1.9883146 | sex and edulevel |
| 137 Production managers in manufacturing | 1.8204783 | 1.9834843 | sex and edulevel |
| 541 Other surveillance and security workers | 2.4854095 | 1.8987495 | sex and edulevel |
| 962 Newspaper distributors, janitors and other service workers | 1.5168559 | 1.7259970 | sex and edulevel |
| 834 Mobile plant operators | 2.3493104 | 1.6776513 | sex and edulevel |
| 131 Information and communications technology service managers | 4.5217451 | 1.5468919 | sex and edulevel |
| 264 Authors, journalists and linguists | 0.9398759 | 1.5389406 | sex and edulevel |
| 732 Printing trades workers | 1.3754920 | 1.4116364 | sex and edulevel |
| 961 Recycling collectors | 2.5026241 | 1.3670042 | sex and edulevel |
| 333 Business services agents | 2.6647133 | 1.2102215 | sex and edulevel |
| 932 Manufacturing labourers | 2.9938554 | 1.1866761 | sex and edulevel |
| 241 Accountants, financial analysts and fund managers | 3.0649247 | 1.0785958 | sex and edulevel |
| 343 Photographers, interior decorators and entertainers | 3.7751554 | 1.0765630 | sex and edulevel |
| 512 Cooks and cold-buffet managers | 2.3823637 | 1.0684103 | sex and edulevel |
| 833 Heavy truck and bus drivers | 1.6067155 | 1.0284369 | sex and edulevel |
| 129 Administration and service managers not elsewhere classified | 2.3080253 | 1.0114151 | sex and edulevel |
| 173 Retail and wholesale trade managers | 4.5823781 | 0.8462927 | sex and edulevel |
| 233 Secondary education teachers | 2.3727154 | 0.7689329 | sex and edulevel |
| 611 Market gardeners and crop growers | 1.2948139 | 0.7273208 | sex and edulevel |
| 243 Marketing and public relations professionals | 0.6033059 | 0.6504950 | sex and edulevel |
| 136 Production managers in construction and mining | 1.9290646 | 0.6163258 | sex and edulevel |
| 711 Carpenters, bricklayers and construction workers | 1.8229891 | 0.5364368 | sex and edulevel |
| 816 Machine operators, food and related products | 1.7935335 | 0.4643469 | sex and edulevel |
| 262 Museum curators and librarians and related professionals | 3.0179014 | 0.4478464 | sex and edulevel |
| 722 Blacksmiths, toolmakers and related trades workers | 1.9606924 | 0.4286145 | sex and edulevel |
| 351 ICT operations and user support technicians | 1.7201155 | 0.3979107 | sex and edulevel |
| 265 Creative and performing artists | 3.0842617 | 0.3975144 | sex and edulevel |
| 516 Other service related workers | 2.8114718 | 0.3611032 | sex and edulevel |
| 813 Machine operators, chemical and pharmaceutical products | 2.2201342 | 0.3378979 | sex and edulevel |
| 515 Building caretakers and related workers | 2.6332939 | 0.2296460 | sex and edulevel |
| 134 Architectural and engineering managers | 2.8463094 | 0.1876642 | sex and edulevel |
| 266 Social work and counselling professionals | 2.4259163 | 0.1355394 | sex and edulevel |
| 814 Machine operators, rubber, plastic and paper products | 2.4479515 | 0.0917245 | sex and edulevel |
merge(summary_table, anova_table, c("ssyk", "interaction"), all = TRUE) %>%
filter (term.x == "year_n") %>%
filter (contcol.y > 0) %>%
filter (interaction == "sex 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 (sex and year) and the yearly increase in salaries')| ssyk | Increase in salary | F-value for age | interaction |
|---|---|---|---|
| 262 Museum curators and librarians and related professionals | 1.3370390 | 12.5685564 | sex and year |
| 813 Machine operators, chemical and pharmaceutical products | 1.7231905 | 10.6533739 | sex and year |
| 131 Information and communications technology service managers | 2.7462737 | 10.0982495 | sex and year |
| 151 Health care managers | 1.3373535 | 9.1937238 | sex and year |
| 815 Machine operators, textile, fur and leather products | 0.7515225 | 8.5275034 | sex and year |
| 243 Marketing and public relations professionals | -0.5550314 | 7.7320191 | sex and year |
| 932 Manufacturing labourers | 2.4564479 | 5.1323005 | sex and year |
| 816 Machine operators, food and related products | 1.4102081 | 4.6641162 | sex and year |
| 311 Physical and engineering science technicians | 2.6295635 | 4.4514388 | sex and year |
| 819 Process control technicians | 2.5143689 | 3.3882796 | sex and year |
| 533 Health care assistants | 1.7959772 | 3.3597299 | sex and year |
| 351 ICT operations and user support technicians | 1.3030832 | 3.3339379 | sex and year |
| 221 Medical doctors | 1.0703216 | 3.2379210 | sex and year |
| 264 Authors, journalists and linguists | 0.2830418 | 3.2324009 | sex and year |
| 541 Other surveillance and security workers | 2.6683785 | 3.0177453 | sex and year |
| 511 Cabin crew, guides and related workers | 1.3314937 | 2.7734531 | sex and year |
| 834 Mobile plant operators | 2.6262371 | 2.7299923 | sex and year |
| 159 Other social services managers | 2.1787998 | 2.6997604 | sex and year |
| 173 Retail and wholesale trade managers | 6.2849030 | 2.6920564 | sex and year |
| 341 Social work and religious associate professionals | 2.7993782 | 2.4785285 | sex and year |
| 312 Construction and manufacturing supervisors | 3.5850660 | 2.1887133 | sex and year |
| 723 Machinery mechanics and fitters | 2.3375170 | 2.1272013 | sex and year |
| 235 Teaching professionals not elsewhere classified | 1.3782887 | 2.1168491 | sex and year |
| 821 Assemblers | 2.5894715 | 2.0183158 | sex and year |
| 122 Human resource managers | 1.6158738 | 1.8995024 | sex and year |
| 123 Administration and planning managers | 4.0238453 | 1.7601948 | sex and year |
| 334 Administrative and specialized secretaries | 3.7714265 | 1.6845647 | sex and year |
| 512 Cooks and cold-buffet managers | 2.7497179 | 1.4835714 | sex and year |
| 179 Other services managers not elsewhere classified | 3.2090223 | 1.4601245 | sex and year |
| 732 Printing trades workers | 1.1043814 | 1.3695629 | sex and year |
| 432 Stores and transport clerks | 1.6848001 | 1.3440502 | sex and year |
| 411 Office assistants and other secretaries | 1.9008129 | 1.1244333 | sex and year |
| 814 Machine operators, rubber, plastic and paper products | 2.2707050 | 1.0815608 | sex and year |
| 911 Cleaners and helpers | 2.0298327 | 1.0400501 | sex and year |
| 962 Newspaper distributors, janitors and other service workers | 1.2842640 | 1.0400405 | sex and year |
| 812 Metal processing and finishing plant operators | 0.0598298 | 0.9982723 | sex and year |
| 321 Medical and pharmaceutical technicians | 3.0095453 | 0.9569319 | sex and year |
| 352 Broadcasting and audio-visual technicians | 0.7887304 | 0.9352994 | sex and year |
| 441 Library and filing clerks | 1.6143560 | 0.9017484 | sex and year |
| 343 Photographers, interior decorators and entertainers | 4.3863478 | 0.8400912 | sex and year |
| 817 Wood processing and papermaking plant operators | 2.6936338 | 0.7010849 | sex and year |
| 422 Client information clerks | 1.6081275 | 0.6920726 | sex and year |
| 333 Business services agents | 3.0014789 | 0.6872118 | sex and year |
| 137 Production managers in manufacturing | 2.2137062 | 0.6712665 | sex and year |
| 331 Financial and accounting associate professionals | 1.6611645 | 0.6470988 | sex and year |
| 232 Vocational education teachers | 2.6579475 | 0.6330528 | sex and year |
| 532 Personal care workers in health services | 2.7713176 | 0.6314612 | sex and year |
| 121 Finance managers | 2.1097794 | 0.6254650 | sex and year |
| 833 Heavy truck and bus drivers | 1.7707251 | 0.5974334 | sex and year |
| 513 Waiters and bartenders | 2.3751929 | 0.4976799 | sex and year |
| 265 Creative and performing artists | 2.6869389 | 0.4912352 | sex and year |
| 161 Financial and insurance managers | 3.3678037 | 0.4848632 | sex and year |
| 534 Attendants, personal assistants and related workers | 1.7516818 | 0.4712625 | sex and year |
| 266 Social work and counselling professionals | 2.2254012 | 0.4681457 | sex and year |
| 332 Insurance advisers, sales and purchasing agents | 2.4028894 | 0.4468183 | sex and year |
| 524 Event seller and telemarketers | 2.0532844 | 0.4218714 | sex and year |
| 242 Organisation analysts, policy administrators and human resource specialists | 1.5992807 | 0.3854059 | sex and year |
| 711 Carpenters, bricklayers and construction workers | 1.9691994 | 0.3716610 | sex and year |
| 941 Fast-food workers, food preparation assistants | 1.6155630 | 0.3607242 | sex and year |
| 261 Legal professionals | 3.3823847 | 0.3303860 | sex and year |
| 516 Other service related workers | 2.5058837 | 0.3278309 | sex and year |
| 342 Athletes, fitness instructors and recreational workers | 1.9757715 | 0.2936025 | sex and year |
| 134 Architectural and engineering managers | 3.0210896 | 0.2305112 | sex and year |
| 132 Supply, logistics and transport managers | 1.2733534 | 0.2184407 | sex and year |
| 522 Shop staff | 2.0517358 | 0.2003355 | sex and year |
| 214 Engineering professionals | 2.2103539 | 0.1363967 | sex and year |
| 136 Production managers in construction and mining | 1.8048620 | 0.1361198 | sex and year |
| 129 Administration and service managers not elsewhere classified | 2.3857822 | 0.1359729 | sex and year |
| 722 Blacksmiths, toolmakers and related trades workers | 2.0295905 | 0.1151279 | sex and year |
| 213 Biologists, pharmacologists and specialists in agriculture and forestry | 1.6901080 | 0.1061134 | sex and year |
| 515 Building caretakers and related workers | 2.7062205 | 0.1013957 | sex and year |
| 611 Market gardeners and crop growers | 1.3388971 | 0.0784413 | sex and year |
| 241 Accountants, financial analysts and fund managers | 3.1531437 | 0.0419040 | sex and year |
| 523 Cashiers and related clerks | 1.2219886 | 0.0365257 | sex and year |
| 251 ICT architects, systems analysts and test managers | 2.6453968 | 0.0341629 | sex and year |
| 125 Sales and marketing managers | 2.2616762 | 0.0175115 | sex and year |
| 133 Research and development managers | 1.1102691 | 0.0171085 | sex and year |
| 217 Designers | 2.7155067 | 0.0160870 | sex and year |
| 218 Specialists within environmental and health protection | 2.5604843 | 0.0138650 | sex and year |
| 234 Primary- and pre-school teachers | 3.0003110 | 0.0070748 | sex and year |
| 233 Secondary education teachers | 2.3980983 | 0.0066879 | sex and year |
| 231 University and higher education teachers | 2.2687967 | 0.0051000 | sex and year |
| 831 Train operators and related workers | 1.8251453 | 0.0043574 | sex and year |
| 335 Tax and related government associate professionals | 2.3241995 | 0.0038471 | sex and year |
| 531 Child care workers and teachers aides | 2.0954991 | 0.0031907 | sex and year |
| 216 Architects and surveyors | 2.0729695 | 0.0031382 | sex and year |
| 818 Other stationary plant and machine operators | 2.3325473 | 0.0016111 | sex and year |
| 961 Recycling collectors | 2.4977953 | 0.0008583 | sex and year |
merge(summary_table, anova_table, c("ssyk", "interaction"), all = TRUE) %>%
filter (term.x == "year_n") %>%
filter (contcol.y > 0) %>%
filter (interaction == "year and edulevel") %>%
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 (year and edulevel) and the yearly increase in salaries')| ssyk | Increase in salary | F-value for age | interaction |
|---|---|---|---|
| 534 Attendants, personal assistants and related workers | -0.1402810 | 17.4936553 | year and edulevel |
| 812 Metal processing and finishing plant operators | -5.1520587 | 15.9949732 | year and edulevel |
| 262 Museum curators and librarians and related professionals | 1.9840758 | 8.0470057 | year and edulevel |
| 234 Primary- and pre-school teachers | 3.8838951 | 5.4208619 | year and edulevel |
| 522 Shop staff | 0.0698877 | 5.0158953 | year and edulevel |
| 129 Administration and service managers not elsewhere classified | 4.1673228 | 4.7271849 | year and edulevel |
| 161 Financial and insurance managers | 4.5135081 | 4.0324041 | year and edulevel |
| 516 Other service related workers | 7.2670210 | 3.1139823 | year and edulevel |
| 125 Sales and marketing managers | 2.3472066 | 3.0940253 | year and edulevel |
| 131 Information and communications technology service managers | 2.1201215 | 2.8775913 | year and edulevel |
| 243 Marketing and public relations professionals | 0.7324120 | 2.7082606 | year and edulevel |
| 335 Tax and related government associate professionals | 1.1765682 | 2.5942601 | year and edulevel |
| 242 Organisation analysts, policy administrators and human resource specialists | 1.3687826 | 2.5767431 | year and edulevel |
| 511 Cabin crew, guides and related workers | 1.6565700 | 2.4973120 | year and edulevel |
| 541 Other surveillance and security workers | 2.9299467 | 2.4531985 | year and edulevel |
| 233 Secondary education teachers | 3.2921464 | 2.3912796 | year and edulevel |
| 221 Medical doctors | 2.6099777 | 2.3282085 | year and edulevel |
| 343 Photographers, interior decorators and entertainers | 1.7484483 | 2.3120145 | year and edulevel |
| 352 Broadcasting and audio-visual technicians | 1.1530191 | 2.3008338 | year and edulevel |
| 821 Assemblers | 3.4717300 | 2.2599823 | year and edulevel |
| 241 Accountants, financial analysts and fund managers | 1.6197117 | 2.2273108 | year and edulevel |
| 515 Building caretakers and related workers | 3.4401456 | 2.1128708 | year and edulevel |
| 121 Finance managers | 2.3907091 | 1.8807514 | year and edulevel |
| 232 Vocational education teachers | 3.0442130 | 1.7782730 | year and edulevel |
| 218 Specialists within environmental and health protection | 3.3885291 | 1.7324927 | year and edulevel |
| 173 Retail and wholesale trade managers | 7.4640395 | 1.6906345 | year and edulevel |
| 266 Social work and counselling professionals | 3.4582880 | 1.6867797 | year and edulevel |
| 524 Event seller and telemarketers | 0.5903028 | 1.6179929 | year and edulevel |
| 251 ICT architects, systems analysts and test managers | 3.4450065 | 1.5962935 | year and edulevel |
| 732 Printing trades workers | 1.4035774 | 1.4933929 | year and edulevel |
| 151 Health care managers | 1.9531112 | 1.4056639 | year and edulevel |
| 261 Legal professionals | 2.4524403 | 1.3545649 | year and edulevel |
| 818 Other stationary plant and machine operators | 1.1073347 | 1.3304879 | year and edulevel |
| 123 Administration and planning managers | 1.8272030 | 1.3139512 | year and edulevel |
| 932 Manufacturing labourers | 2.0273905 | 1.2593432 | year and edulevel |
| 311 Physical and engineering science technicians | 1.2484897 | 1.2546202 | year and edulevel |
| 723 Machinery mechanics and fitters | 3.1964544 | 1.2328322 | year and edulevel |
| 265 Creative and performing artists | 1.7733798 | 1.2051886 | year and edulevel |
| 531 Child care workers and teachers aides | 1.6218079 | 1.0967008 | year and edulevel |
| 411 Office assistants and other secretaries | 4.8834688 | 1.0316452 | year and edulevel |
| 235 Teaching professionals not elsewhere classified | 2.8482974 | 1.0232382 | year and edulevel |
| 214 Engineering professionals | 2.0110404 | 1.0077700 | year and edulevel |
| 333 Business services agents | 1.7631050 | 0.9966769 | year and edulevel |
| 264 Authors, journalists and linguists | 1.4793444 | 0.9754283 | year and edulevel |
| 512 Cooks and cold-buffet managers | 2.1856120 | 0.9514593 | year and edulevel |
| 342 Athletes, fitness instructors and recreational workers | 2.1163882 | 0.9498070 | year and edulevel |
| 133 Research and development managers | 0.5839858 | 0.9449465 | year and edulevel |
| 217 Designers | 3.2594826 | 0.9229512 | year and edulevel |
| 134 Architectural and engineering managers | 1.2211597 | 0.9193392 | year and edulevel |
| 334 Administrative and specialized secretaries | 1.9614167 | 0.9145418 | year and edulevel |
| 961 Recycling collectors | 0.5439750 | 0.8972133 | year and edulevel |
| 332 Insurance advisers, sales and purchasing agents | 2.2720763 | 0.8912591 | year and edulevel |
| 137 Production managers in manufacturing | 1.8909772 | 0.8796878 | year and edulevel |
| 816 Machine operators, food and related products | 1.5011716 | 0.8683619 | year and edulevel |
| 611 Market gardeners and crop growers | 0.9702078 | 0.7969390 | year and edulevel |
| 122 Human resource managers | 1.9316303 | 0.7634885 | year and edulevel |
| 833 Heavy truck and bus drivers | 1.2362398 | 0.6907621 | year and edulevel |
| 136 Production managers in construction and mining | 3.2044445 | 0.6886988 | year and edulevel |
| 813 Machine operators, chemical and pharmaceutical products | 1.6181230 | 0.6737919 | year and edulevel |
| 513 Waiters and bartenders | 1.8661549 | 0.6546343 | year and edulevel |
| 711 Carpenters, bricklayers and construction workers | 1.1969785 | 0.6416119 | year and edulevel |
| 532 Personal care workers in health services | 2.8020249 | 0.6328864 | year and edulevel |
| 834 Mobile plant operators | 2.4022634 | 0.6302037 | year and edulevel |
| 819 Process control technicians | 1.7061994 | 0.6114937 | year and edulevel |
| 159 Other social services managers | 3.2304608 | 0.5909851 | year and edulevel |
| 432 Stores and transport clerks | 1.8449906 | 0.5743293 | year and edulevel |
| 533 Health care assistants | 2.1616545 | 0.5736881 | year and edulevel |
| 321 Medical and pharmaceutical technicians | 2.5297791 | 0.5488535 | year and edulevel |
| 911 Cleaners and helpers | 2.2245045 | 0.5336872 | year and edulevel |
| 331 Financial and accounting associate professionals | 1.2100081 | 0.5288551 | year and edulevel |
| 817 Wood processing and papermaking plant operators | 3.5093276 | 0.5181038 | year and edulevel |
| 216 Architects and surveyors | 1.9919142 | 0.4832745 | year and edulevel |
| 722 Blacksmiths, toolmakers and related trades workers | 1.7270402 | 0.4520330 | year and edulevel |
| 351 ICT operations and user support technicians | 2.1384843 | 0.4518060 | year and edulevel |
| 523 Cashiers and related clerks | 0.8593031 | 0.4308086 | year and edulevel |
| 831 Train operators and related workers | 1.9370280 | 0.4298998 | year and edulevel |
| 312 Construction and manufacturing supervisors | 2.7306910 | 0.4009186 | year and edulevel |
| 941 Fast-food workers, food preparation assistants | 1.6227045 | 0.3683117 | year and edulevel |
| 441 Library and filing clerks | 1.5960181 | 0.3611492 | year and edulevel |
| 213 Biologists, pharmacologists and specialists in agriculture and forestry | 0.7902735 | 0.3333037 | year and edulevel |
| 179 Other services managers not elsewhere classified | 2.4531669 | 0.2396802 | year and edulevel |
| 422 Client information clerks | 2.0649425 | 0.1625848 | year and edulevel |
| 132 Supply, logistics and transport managers | 1.1893479 | 0.1619384 | year and edulevel |
| 341 Social work and religious associate professionals | 2.4925171 | 0.1313061 | year and edulevel |
| 962 Newspaper distributors, janitors and other service workers | 1.3746098 | 0.0752161 | year and edulevel |
| 231 University and higher education teachers | 2.1855704 | 0.0653639 | year and edulevel |
| 815 Machine operators, textile, fur and leather products | 1.3138483 | 0.0462972 | year and edulevel |
| 814 Machine operators, rubber, plastic and paper products | 2.5812272 | 0.0446220 | year and edulevel |
merge(summary_table, anova_table, c("ssyk", "interaction"), all = TRUE) %>%
filter (term.x == "year_n") %>%
filter (contcol.y > 1) %>%
filter (interaction == "sex, year and edulevel") %>%
filter (!(contcol.y == 1 & interaction == "sex, year and edulevel")) %>%
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 (sex, year and edulevel) and the yearly increase in salaries')| ssyk | Increase in salary | F-value for age | interaction |
|---|---|---|---|
| 311 Physical and engineering science technicians | 0.3007069 | 7.6368150 | sex, year and edulevel |
| 261 Legal professionals | 1.9749741 | 7.2332096 | sex, year and edulevel |
| 334 Administrative and specialized secretaries | 1.6645488 | 4.9962163 | sex, year and edulevel |
| 151 Health care managers | 2.0072983 | 4.6943513 | sex, year and edulevel |
| 232 Vocational education teachers | 2.7387801 | 4.1907918 | sex, year and edulevel |
| 264 Authors, journalists and linguists | 2.0863140 | 4.0644218 | sex, year and edulevel |
| 711 Carpenters, bricklayers and construction workers | 1.1969785 | 4.0519757 | sex, year and edulevel |
| 121 Finance managers | 2.0641822 | 3.6513567 | sex, year and edulevel |
| 122 Human resource managers | 1.0779576 | 3.5481108 | sex, year and edulevel |
| 137 Production managers in manufacturing | -0.0152177 | 3.3778729 | sex, year and edulevel |
| 523 Cashiers and related clerks | 1.0766683 | 3.1321131 | sex, year and edulevel |
| 159 Other social services managers | 2.0475832 | 2.6301550 | sex, year and edulevel |
| 234 Primary- and pre-school teachers | 3.4992304 | 2.5722281 | sex, year and edulevel |
| 818 Other stationary plant and machine operators | 1.1073347 | 2.5439043 | sex, year and edulevel |
| 342 Athletes, fitness instructors and recreational workers | 2.7582176 | 2.4744045 | sex, year and edulevel |
| 531 Child care workers and teachers aides | 1.4341298 | 2.4314811 | sex, year and edulevel |
| 522 Shop staff | -0.5891626 | 1.9849918 | sex, year and edulevel |
| 331 Financial and accounting associate professionals | 0.4425476 | 1.9602026 | sex, year and edulevel |
| 332 Insurance advisers, sales and purchasing agents | 2.2947388 | 1.9422377 | sex, year and edulevel |
| 533 Health care assistants | 1.6253210 | 1.9216616 | sex, year and edulevel |
| 343 Photographers, interior decorators and entertainers | 1.0634866 | 1.7202512 | sex, year and edulevel |
| 235 Teaching professionals not elsewhere classified | 3.0233562 | 1.4927454 | sex, year and edulevel |
| 233 Secondary education teachers | 3.0407628 | 1.4358037 | sex, year and edulevel |
| 813 Machine operators, chemical and pharmaceutical products | 1.6181230 | 1.3871806 | sex, year and edulevel |
| 217 Designers | 3.6821293 | 1.2946162 | sex, year and edulevel |
| 534 Attendants, personal assistants and related workers | 0.3597122 | 1.2943980 | sex, year and edulevel |
| 541 Other surveillance and security workers | 3.1102372 | 1.2828090 | sex, year and edulevel |
| 125 Sales and marketing managers | 0.7835022 | 1.2779771 | sex, year and edulevel |
| 129 Administration and service managers not elsewhere classified | 4.9504748 | 1.2735961 | sex, year and edulevel |
| 819 Process control technicians | 2.4986543 | 1.2299209 | sex, year and edulevel |
| 911 Cleaners and helpers | 2.8014931 | 1.2021688 | sex, year and edulevel |
| 341 Social work and religious associate professionals | 2.5010265 | 1.1808832 | sex, year and edulevel |
| 723 Machinery mechanics and fitters | 3.1964544 | 1.1493456 | sex, year and edulevel |
| 611 Market gardeners and crop growers | 0.1434970 | 1.1324088 | sex, year and edulevel |
| 243 Marketing and public relations professionals | -4.1275797 | 1.1078823 | sex, year and edulevel |
| 351 ICT operations and user support technicians | 2.6270604 | 1.0821259 | sex, year and edulevel |
| 821 Assemblers | 3.5727179 | 0.9895740 | sex, year and edulevel |
| 524 Event seller and telemarketers | 0.1957456 | 0.9768868 | sex, year and edulevel |
| 335 Tax and related government associate professionals | 1.6651038 | 0.9445517 | sex, year and edulevel |
| 242 Organisation analysts, policy administrators and human resource specialists | 1.7377916 | 0.9296940 | sex, year and edulevel |
| 831 Train operators and related workers | 1.9370280 | 0.9170162 | sex, year and edulevel |
| 515 Building caretakers and related workers | 4.2685600 | 0.9019820 | sex, year and edulevel |
| 422 Client information clerks | 2.6186600 | 0.8683975 | sex, year and edulevel |
| 512 Cooks and cold-buffet managers | 1.9342162 | 0.8474145 | sex, year and edulevel |
| 123 Administration and planning managers | 1.6423465 | 0.8377722 | sex, year and edulevel |
| 312 Construction and manufacturing supervisors | 3.6769309 | 0.7940555 | sex, year and edulevel |
| 241 Accountants, financial analysts and fund managers | 1.4634289 | 0.7813243 | sex, year and edulevel |
| 812 Metal processing and finishing plant operators | -4.9761347 | 0.7523221 | sex, year and edulevel |
| 516 Other service related workers | 7.0743258 | 0.7425455 | sex, year and edulevel |
| 432 Stores and transport clerks | 1.6337081 | 0.7279130 | sex, year and edulevel |
| 221 Medical doctors | 2.5016353 | 0.6902677 | sex, year and edulevel |
| 134 Architectural and engineering managers | 1.2211597 | 0.6693583 | sex, year and edulevel |
| 333 Business services agents | 1.0012648 | 0.6482391 | sex, year and edulevel |
| 441 Library and filing clerks | 0.8265796 | 0.6267038 | sex, year and edulevel |
| 962 Newspaper distributors, janitors and other service workers | 1.6061203 | 0.5933810 | sex, year and edulevel |
| 941 Fast-food workers, food preparation assistants | 0.6914853 | 0.5829709 | sex, year and edulevel |
| 321 Medical and pharmaceutical technicians | 2.4540505 | 0.5434559 | sex, year and edulevel |
| 352 Broadcasting and audio-visual technicians | 1.9435613 | 0.5379284 | sex, year and edulevel |
| 161 Financial and insurance managers | 4.9347200 | 0.5294277 | sex, year and edulevel |
| 511 Cabin crew, guides and related workers | 3.0644405 | 0.5132864 | sex, year and edulevel |
| 932 Manufacturing labourers | 2.0273905 | 0.5026755 | sex, year and edulevel |
| 179 Other services managers not elsewhere classified | 2.2046970 | 0.4872775 | sex, year and edulevel |
| 532 Personal care workers in health services | 2.6931703 | 0.4842910 | sex, year and edulevel |
| 173 Retail and wholesale trade managers | 10.5993797 | 0.4694386 | sex, year and edulevel |
| 133 Research and development managers | 1.3071833 | 0.4418744 | sex, year and edulevel |
| 231 University and higher education teachers | 2.0503501 | 0.3707303 | sex, year and edulevel |
| 834 Mobile plant operators | 2.4022634 | 0.3545803 | sex, year and edulevel |
| 265 Creative and performing artists | 1.1809261 | 0.3382118 | sex, year and edulevel |
| 266 Social work and counselling professionals | 3.1792487 | 0.3328237 | sex, year and edulevel |
| 251 ICT architects, systems analysts and test managers | 3.9660691 | 0.3290868 | sex, year and edulevel |
| 815 Machine operators, textile, fur and leather products | 0.5896274 | 0.3224206 | sex, year and edulevel |
| 131 Information and communications technology service managers | -0.0347601 | 0.3179017 | sex, year and edulevel |
| 732 Printing trades workers | 1.4035774 | 0.2933972 | sex, year and edulevel |
| 132 Supply, logistics and transport managers | 1.9589258 | 0.2574186 | sex, year and edulevel |
| 411 Office assistants and other secretaries | 4.8892850 | 0.2517369 | sex, year and edulevel |
| 833 Heavy truck and bus drivers | 1.6848227 | 0.2443507 | sex, year and edulevel |
| 722 Blacksmiths, toolmakers and related trades workers | 2.1732826 | 0.2354179 | sex, year and edulevel |
| 816 Machine operators, food and related products | 0.8583028 | 0.1593265 | sex, year and edulevel |
| 214 Engineering professionals | 1.9785162 | 0.1445402 | sex, year and edulevel |
| 262 Museum curators and librarians and related professionals | 1.4727922 | 0.1389862 | sex, year and edulevel |
| 136 Production managers in construction and mining | 3.9874104 | 0.1239311 | sex, year and edulevel |
| 817 Wood processing and papermaking plant operators | 3.5093276 | 0.0936398 | sex, year and edulevel |
| 513 Waiters and bartenders | 2.5267044 | 0.0838668 | sex, year and edulevel |
| 814 Machine operators, rubber, plastic and paper products | 2.5812272 | 0.0552916 | sex, year and edulevel |
| 218 Specialists within environmental and health protection | 2.7640467 | 0.0345293 | sex, year and edulevel |
| 213 Biologists, pharmacologists and specialists in agriculture and forestry | 0.7145156 | 0.0285926 | sex, year and edulevel |
| 961 Recycling collectors | 0.5439750 | 0.0142411 | sex, year and edulevel |
| 216 Architects and surveyors | 1.9398000 | 0.0071724 | sex, year and edulevel |
temp <- tbnum %>%
filter(`occuptional (SSYK 2012)` == "231 University and higher education teachers")
model <-lm (log(salary) ~ year_n + sex * eduyears, data = temp)
plot_model(model, type = "pred", terms = c("eduyears", "sex"))## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.
Figure 11: Highest F-value interaction sex and education level, University and higher education teachers
temp <- tbnum %>%
filter(`occuptional (SSYK 2012)` == "814 Machine operators, rubber, plastic and paper products")
model <-lm (log(salary) ~ year_n + sex * eduyears, data = temp)
plot_model(model, type = "pred", terms = c("eduyears", "sex"))## Warning in predict.lm(model, newdata = fitfram, type = "response", se.fit =
## se, : prediction from a rank-deficient fit may be misleading## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.
Figure 12: Lowest F-value interaction sex and education level, Machine operators, rubber, plastic and paper products
temp <- tbnum %>%
filter(`occuptional (SSYK 2012)` == "534 Attendants, personal assistants and related workers")
model <-lm (log(salary) ~ sex + year_n * eduyears, data = temp)
plot_model(model, type = "pred", terms = c("year_n", "eduyears"))## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.
Figure 13: Highest F-value interaction year and education level, Attendants, personal assistants and related workers
temp <- tbnum %>%
filter(`occuptional (SSYK 2012)` == "814 Machine operators, rubber, plastic and paper products")
model <-lm (log(salary) ~ sex + year_n * eduyears, data = temp)
plot_model(model, type = "pred", terms = c("year_n", "eduyears"))## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.
Figure 14: Lowest F-value interaction year and education level, Machine operators, rubber, plastic and paper products
temp <- tbnum %>%
filter(`occuptional (SSYK 2012)` == "262 Museum curators and librarians and related professionals")
model <-lm (log(salary) ~ sex * year_n + edulevel, data = temp)
plot_model(model, type = "pred", terms = c("year_n", "sex"))## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.
Figure 15: Highest F-value interaction sex and year, Museum curators and librarians and related professionals
temp <- tbnum %>%
filter(`occuptional (SSYK 2012)` == "961 Recycling collectors")
model <-lm (log(salary) ~ sex * year_n + edulevel, data = temp)
plot_model(model, type = "pred", terms = c("year_n", "sex"))## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.
Figure 16: Lowest F-value interaction sex and year, Recycling collectors
temp <- tbnum %>%
filter(`occuptional (SSYK 2012)` == "311 Physical and engineering science technicians")
model <-lm (log(salary) ~ sex * year_n * eduyears, data = temp)
plot_model(model, type = "pred", terms = c("year_n", "eduyears", "sex")) ## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.
Figure 17: Highest F-value interaction sex, year and education level, Physical and engineering science technicians
temp <- tbnum %>%
filter(`occuptional (SSYK 2012)` == "216 Architects and surveyors")
model <-lm (log(salary) ~ sex * year_n * eduyears, data = temp)
plot_model(model, type = "pred", terms = c("year_n", "eduyears", "sex")) ## Warning in predict.lm(model, newdata = fitfram, type = "response", se.fit =
## se, : prediction from a rank-deficient fit may be misleading## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.
Figure 18: Lowest F-value interaction sex, year and education level, Architects and surveyors