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In my last post, I examined the significance of the sector on the salary for different occupational groups using statistics from different regions. In previous posts I have shown a correlation between the salary and experience and also salary and education, In this post, I will examine the correlation between salary and sector using statistics for age.
The F-value from the Anova table is used as the single value to discriminate how much the region and salary correlates. For exploratory analysis, the Anova value seems good enough.
First, define libraries and functions.
library (tidyverse)## -- Attaching packages --------------------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.2.1 v purrr 0.3.3## v tibble 2.1.3 v dplyr 0.8.3## v tidyr 1.0.2 v stringr 1.4.0## v readr 1.3.1 v forcats 0.4.0## -- Conflicts ------------------------------------------------------ tidyverse_conflicts() --## x dplyr::filter() masks stats::filter()## x dplyr::lag() masks stats::lag()library (broom)library (car)## Loading required package: carData## ## Attaching package: 'car'## The following object is masked from 'package:dplyr':## ## recode## The following object is masked from 'package:purrr':## ## somelibrary (sjPlot)## Registered S3 methods overwritten by 'lme4':## method from## cooks.distance.influence.merMod car ## influence.merMod car ## dfbeta.influence.merMod car ## dfbetas.influence.merMod carreadfile <- 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/.
I have renamed the file to 000000D2_sector.csv because the filename 000000D2.csv was used in a previous post.
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 1-3 public sector 4-5 private sector
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_sector.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 = 0anova_table = 0for (i in unique(tb$`occuptional (SSYK 2012)`)){ temp <- filter(tb, `occuptional (SSYK 2012)` == i) if (dim(temp)[1] > 90){ model <- lm(log(salary) ~ poly(age_n, 3) + sex + year_n + sector, data = temp) summary_table <- rbind (summary_table, mutate (tidy (summary (model)), ssyk = i, interaction = "none")) anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i, interaction = "none")) model <- lm(log(salary) ~ poly(age_n, 3) * sector + sex + year_n, data = temp) summary_table <- rbind (summary_table, mutate (tidy (summary (model)), ssyk = i, interaction = "sector and age")) anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i, interaction = "sector and age")) model <- lm(log(salary) ~ poly(age_n, 3) + sector * sex + year_n, data = temp) summary_table <- rbind (summary_table, mutate (tidy (summary (model)), ssyk = i, interaction = "sector and sex")) anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i, interaction = "sector and sex")) model <- lm(log(salary) ~ poly(age_n, 3) + year_n * sector + sex, data = temp) summary_table <- rbind (summary_table, mutate (tidy (summary (model)), ssyk = i, interaction = "sector and year")) anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i, interaction = "sector and year")) model <- lm(log(salary) ~ poly(age_n, 3) * sector * sex * year_n, data = temp) summary_table <- rbind (summary_table, mutate (tidy (summary (model)), ssyk = i, interaction = "sector, year, age and sex")) anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i, interaction = "sector, year, age and sex")) }}## Note: model has aliased coefficients## sums of squares computed by model comparison## Note: model has aliased coefficients## sums of squares computed by model comparison## 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 (term.y == "sector") %>% filter (interaction == "none") %>% mutate (estimate = (exp(estimate) - 1) * 100) %>% ggplot () + geom_point (mapping = aes(x = estimate, y = statistic.y, colour = interaction)) + labs( x = "Increase in salaries (% / year)", y = "F-value for sector" ) 
Figure 1: The significance of the sector on the salary in Sweden, a comparison between different occupational groups, Year 2014 – 2018
merge(summary_table, anova_table, by = c("ssyk", "interaction"), all = TRUE) %>% filter (term.x == "year_n") %>% filter (contcol.y > 0) %>% # only look at the interactions between all four variables in the case with interaction sector, year, age and sex filter (!(contcol.y < 3 & interaction == "sector, year, age and sex")) %>% mutate (estimate = (exp(estimate) - 1) * 100) %>% ggplot () + geom_point (mapping = aes(x = estimate, y = statistic.y, colour = interaction)) + labs( x = "Increase in salaries (% / year)", y = "F-value for interaction" ) 
Figure 2: The significance of the interaction between sector, age, year and sex on the salary in Sweden, a comparison between different occupational groups, Year 2014 – 2018
The tables with all occupational groups sorted by F-value in descending order.
merge(summary_table, anova_table, c("ssyk", "interaction"), all = TRUE) %>% filter (term.x == "year_n") %>% filter (term.y == "sector") %>% filter (interaction == "none") %>% mutate (estimate = (exp(estimate) - 1) * 100) %>% select (ssyk, estimate, statistic.y, interaction) %>% rename (`F-value` = statistic.y) %>% rename (`Increase in salary` = estimate) %>% arrange (desc (`F-value`)) %>% knitr::kable( booktabs = TRUE, caption = 'Correlation for F-value (sector) and the yearly increase in salaries')| ssyk | Increase in salary | F-value | interaction |
|---|---|---|---|
| 218 Specialists within environmental and health protection | 2.3966875 | 456.4704739 | none |
| 261 Legal professionals | 2.6130221 | 304.1919940 | none |
| 411 Office assistants and other secretaries | 2.3419860 | 285.0349753 | none |
| 222 Nursing professionals | 4.6578230 | 280.4433905 | none |
| 515 Building caretakers and related workers | 2.2146338 | 270.0075297 | none |
| 819 Process control technicians | 2.5350438 | 268.3445857 | none |
| 242 Organisation analysts, policy administrators and human resource specialists | 2.0523696 | 259.5458010 | none |
| 251 ICT architects, systems analysts and test managers | 2.3512692 | 241.0997355 | none |
| 321 Medical and pharmaceutical technicians | 2.5801263 | 237.2341857 | none |
| 333 Business services agents | 2.3936321 | 218.8431989 | none |
| 334 Administrative and specialized secretaries | 2.0432078 | 197.0091850 | none |
| 243 Marketing and public relations professionals | 1.7547524 | 188.6715597 | none |
| 335 Tax and related government associate professionals | 2.4477931 | 179.6275210 | none |
| 342 Athletes, fitness instructors and recreational workers | 1.8086152 | 178.9426205 | none |
| 962 Newspaper distributors, janitors and other service workers | 1.9900356 | 178.5334093 | none |
| 213 Biologists, pharmacologists and specialists in agriculture and forestry | 1.8912397 | 161.3670379 | none |
| 265 Creative and performing artists | 2.0992262 | 156.9400225 | none |
| 241 Accountants, financial analysts and fund managers | 2.4619072 | 134.7081954 | none |
| 351 ICT operations and user support technicians | 2.7045039 | 117.0092304 | none |
| 331 Financial and accounting associate professionals | 2.1803329 | 111.0703889 | none |
| 264 Authors, journalists and linguists | 2.2865429 | 93.9690111 | none |
| 211 Physicists and chemists | 2.0010438 | 65.4196943 | none |
| 911 Cleaners and helpers | 1.9306757 | 64.9465316 | none |
| 231 University and higher education teachers | 2.3987358 | 58.9926983 | none |
| 541 Other surveillance and security workers | 2.2500326 | 54.4430468 | none |
| 815 Machine operators, textile, fur and leather products | 1.3517419 | 48.6411876 | none |
| 723 Machinery mechanics and fitters | 2.4251751 | 48.2416487 | none |
| 233 Secondary education teachers | 3.0390165 | 38.5627689 | none |
| 422 Client information clerks | 2.4686744 | 37.8718779 | none |
| 228 Specialists in health care not elsewhere classified | 2.1589844 | 36.5601628 | none |
| 941 Fast-food workers, food preparation assistants | 1.9960527 | 18.6514673 | none |
| 216 Architects and surveyors | 2.8323556 | 15.8227312 | none |
| 235 Teaching professionals not elsewhere classified | 1.6582824 | 14.3745577 | none |
| 532 Personal care workers in health services | 3.0076073 | 12.2147525 | none |
| 311 Physical and engineering science technicians | 2.7704145 | 12.1641422 | none |
| 234 Primary- and pre-school teachers | 3.4774983 | 7.3900661 | none |
| 511 Cabin crew, guides and related workers | -0.3443427 | 6.9533625 | none |
| 534 Attendants, personal assistants and related workers | 2.0233419 | 5.1208491 | none |
| 533 Health care assistants | 2.2271720 | 4.8058895 | none |
| 332 Insurance advisers, sales and purchasing agents | 2.2044676 | 4.2832833 | none |
| 531 Child care workers and teachers aides | 1.9105637 | 4.0479235 | none |
| 214 Engineering professionals | 2.7995742 | 2.1850611 | none |
| 341 Social work and religious associate professionals | 2.5651458 | 2.1164875 | none |
| 432 Stores and transport clerks | 1.9337354 | 0.7577500 | none |
| 611 Market gardeners and crop growers | 1.8041743 | 0.0643998 | none |
| 512 Cooks and cold-buffet managers | 2.7942944 | 0.0197082 | none |
merge(summary_table, anova_table, c("ssyk", "interaction"), all = TRUE) %>% filter (term.x == "year_n") %>% filter (contcol.y > 0) %>% filter (interaction == "sector and sex") %>% mutate (estimate = (exp(estimate) - 1) * 100) %>% select (ssyk, estimate, statistic.y, interaction) %>% rename (`F-value` = statistic.y) %>% rename (`Increase in salary` = estimate) %>% arrange (desc (`F-value`)) %>% knitr::kable( booktabs = TRUE, caption = 'Correlation for F-value (sector and sex) and the yearly increase in salaries')| ssyk | Increase in salary | F-value | interaction |
|---|---|---|---|
| 911 Cleaners and helpers | 1.9306757 | 109.1411280 | sector and sex |
| 335 Tax and related government associate professionals | 2.4477931 | 70.7824355 | sector and sex |
| 815 Machine operators, textile, fur and leather products | 1.3563289 | 31.8117555 | sector and sex |
| 333 Business services agents | 2.2899853 | 30.4752880 | sector and sex |
| 331 Financial and accounting associate professionals | 2.2023600 | 25.9296892 | sector and sex |
| 342 Athletes, fitness instructors and recreational workers | 1.8086152 | 25.0203935 | sector and sex |
| 241 Accountants, financial analysts and fund managers | 2.4786358 | 24.2528951 | sector and sex |
| 264 Authors, journalists and linguists | 2.2453619 | 21.8740576 | sector and sex |
| 533 Health care assistants | 2.2271720 | 21.5539071 | sector and sex |
| 243 Marketing and public relations professionals | 1.7263442 | 19.6730093 | sector and sex |
| 332 Insurance advisers, sales and purchasing agents | 2.2044676 | 19.3664844 | sector and sex |
| 611 Market gardeners and crop growers | 1.7542849 | 15.6290927 | sector and sex |
| 532 Personal care workers in health services | 3.0076073 | 12.5499477 | sector and sex |
| 213 Biologists, pharmacologists and specialists in agriculture and forestry | 1.8973847 | 11.5413223 | sector and sex |
| 321 Medical and pharmaceutical technicians | 2.5636636 | 9.5201918 | sector and sex |
| 242 Organisation analysts, policy administrators and human resource specialists | 2.0388525 | 7.8030347 | sector and sex |
| 351 ICT operations and user support technicians | 2.7045039 | 7.5568275 | sector and sex |
| 211 Physicists and chemists | 1.9926264 | 7.5217104 | sector and sex |
| 235 Teaching professionals not elsewhere classified | 1.6228416 | 5.7033533 | sector and sex |
| 512 Cooks and cold-buffet managers | 2.7942944 | 5.4926902 | sector and sex |
| 941 Fast-food workers, food preparation assistants | 1.9960527 | 3.8523689 | sector and sex |
| 432 Stores and transport clerks | 1.9247904 | 3.3361868 | sector and sex |
| 218 Specialists within environmental and health protection | 2.3902920 | 3.2726896 | sector and sex |
| 231 University and higher education teachers | 2.4049429 | 3.1968905 | sector and sex |
| 222 Nursing professionals | 4.6527974 | 2.8848508 | sector and sex |
| 265 Creative and performing artists | 2.0908231 | 2.4335097 | sector and sex |
| 962 Newspaper distributors, janitors and other service workers | 1.9900356 | 2.3091055 | sector and sex |
| 233 Secondary education teachers | 3.0478812 | 1.6616950 | sector and sex |
| 228 Specialists in health care not elsewhere classified | 2.2271410 | 1.4295653 | sector and sex |
| 819 Process control technicians | 2.5350438 | 1.1646979 | sector and sex |
| 541 Other surveillance and security workers | 2.2500326 | 1.1488532 | sector and sex |
| 534 Attendants, personal assistants and related workers | 2.0233419 | 1.1344410 | sector and sex |
| 261 Legal professionals | 2.6014900 | 1.1208624 | sector and sex |
| 531 Child care workers and teachers aides | 1.9034289 | 1.0441681 | sector and sex |
| 411 Office assistants and other secretaries | 2.3419860 | 0.8053533 | sector and sex |
| 214 Engineering professionals | 2.7938330 | 0.7610648 | sector and sex |
| 341 Social work and religious associate professionals | 2.5666541 | 0.6898733 | sector and sex |
| 422 Client information clerks | 2.4707255 | 0.4188795 | sector and sex |
| 251 ICT architects, systems analysts and test managers | 2.3512692 | 0.2019761 | sector and sex |
| 234 Primary- and pre-school teachers | 3.4774983 | 0.1910697 | sector and sex |
| 334 Administrative and specialized secretaries | 2.0473936 | 0.1673844 | sector and sex |
| 723 Machinery mechanics and fitters | 2.4270025 | 0.0946975 | sector and sex |
| 216 Architects and surveyors | 2.8323370 | 0.0480519 | sector and sex |
| 515 Building caretakers and related workers | 2.2148711 | 0.0466927 | sector and sex |
| 311 Physical and engineering science technicians | 2.7695781 | 0.0317138 | sector and sex |
| 511 Cabin crew, guides and related workers | -0.3452668 | 0.0263848 | sector and sex |
merge(summary_table, anova_table, c("ssyk", "interaction"), all = TRUE) %>% filter (term.x == "year_n") %>% filter (contcol.y > 0) %>% filter (interaction == "sector and age") %>% mutate (estimate = (exp(estimate) - 1) * 100) %>% select (ssyk, estimate, statistic.y, interaction) %>% rename (`F-value` = statistic.y) %>% rename (`Increase in salary` = estimate) %>% arrange (desc (`F-value`)) %>% knitr::kable( booktabs = TRUE, caption = 'Correlation for F-value (sector and age) and the yearly increase in salaries')| ssyk | Increase in salary | F-value | interaction |
|---|---|---|---|
| 251 ICT architects, systems analysts and test managers | 2.3512692 | 81.293931 | sector and age |
| 534 Attendants, personal assistants and related workers | 2.0233419 | 74.994233 | sector and age |
| 234 Primary- and pre-school teachers | 3.4774983 | 50.554556 | sector and age |
| 511 Cabin crew, guides and related workers | -0.3214703 | 49.726047 | sector and age |
| 331 Financial and accounting associate professionals | 2.1760115 | 43.716282 | sector and age |
| 432 Stores and transport clerks | 1.9372268 | 41.355837 | sector and age |
| 351 ICT operations and user support technicians | 2.7045039 | 37.992660 | sector and age |
| 241 Accountants, financial analysts and fund managers | 2.4925081 | 31.914364 | sector and age |
| 242 Organisation analysts, policy administrators and human resource specialists | 2.0984548 | 31.372391 | sector and age |
| 422 Client information clerks | 2.4763530 | 24.396462 | sector and age |
| 342 Athletes, fitness instructors and recreational workers | 1.8086152 | 21.878170 | sector and age |
| 332 Insurance advisers, sales and purchasing agents | 2.2044676 | 20.887588 | sector and age |
| 333 Business services agents | 2.5711782 | 20.728956 | sector and age |
| 261 Legal professionals | 2.6542145 | 19.114710 | sector and age |
| 233 Secondary education teachers | 3.1089611 | 18.460571 | sector and age |
| 541 Other surveillance and security workers | 2.2500326 | 18.166197 | sector and age |
| 243 Marketing and public relations professionals | 1.8298739 | 16.372110 | sector and age |
| 231 University and higher education teachers | 2.5000216 | 15.799814 | sector and age |
| 411 Office assistants and other secretaries | 2.3419860 | 15.436701 | sector and age |
| 235 Teaching professionals not elsewhere classified | 1.8467331 | 15.280487 | sector and age |
| 211 Physicists and chemists | 1.9949354 | 11.545117 | sector and age |
| 213 Biologists, pharmacologists and specialists in agriculture and forestry | 1.8835767 | 11.142152 | sector and age |
| 228 Specialists in health care not elsewhere classified | 2.0243679 | 10.920972 | sector and age |
| 335 Tax and related government associate professionals | 2.4477931 | 10.914843 | sector and age |
| 941 Fast-food workers, food preparation assistants | 1.9960527 | 10.718505 | sector and age |
| 341 Social work and religious associate professionals | 2.5816295 | 9.475763 | sector and age |
| 216 Architects and surveyors | 2.7872555 | 9.229637 | sector and age |
| 321 Medical and pharmaceutical technicians | 2.5872747 | 8.122166 | sector and age |
| 911 Cleaners and helpers | 1.9306757 | 8.084293 | sector and age |
| 815 Machine operators, textile, fur and leather products | 1.3751133 | 7.636766 | sector and age |
| 334 Administrative and specialized secretaries | 2.1355883 | 7.153296 | sector and age |
| 218 Specialists within environmental and health protection | 2.4620357 | 6.994422 | sector and age |
| 962 Newspaper distributors, janitors and other service workers | 1.9900356 | 6.838603 | sector and age |
| 531 Child care workers and teachers aides | 1.9226126 | 6.070516 | sector and age |
| 515 Building caretakers and related workers | 2.2192760 | 6.005165 | sector and age |
| 311 Physical and engineering science technicians | 2.7875316 | 5.421210 | sector and age |
| 723 Machinery mechanics and fitters | 2.4360682 | 5.406378 | sector and age |
| 819 Process control technicians | 2.5350438 | 5.043236 | sector and age |
| 264 Authors, journalists and linguists | 2.2247505 | 4.044872 | sector and age |
| 512 Cooks and cold-buffet managers | 2.7942944 | 3.928362 | sector and age |
| 533 Health care assistants | 2.2271720 | 3.673529 | sector and age |
| 265 Creative and performing artists | 2.0786906 | 2.538964 | sector and age |
| 214 Engineering professionals | 2.7863137 | 2.497814 | sector and age |
| 532 Personal care workers in health services | 3.0076073 | 2.339712 | sector and age |
| 222 Nursing professionals | 4.6262827 | 2.287950 | sector and age |
| 611 Market gardeners and crop growers | 1.8042107 | 1.716068 | sector and age |
merge(summary_table, anova_table, c("ssyk", "interaction"), all = TRUE) %>% filter (term.x == "year_n") %>% filter (contcol.y > 0) %>% filter (interaction == "sector and year") %>% mutate (estimate = (exp(estimate) - 1) * 100) %>% select (ssyk, estimate, statistic.y, interaction) %>% rename (`F-value` = statistic.y) %>% rename (`Increase in salary` = estimate) %>% arrange (desc (`F-value`)) %>% knitr::kable( booktabs = TRUE, caption = 'Correlation for F-value (sector and year) and the yearly increase in salaries')| ssyk | Increase in salary | F-value | interaction |
|---|---|---|---|
| 222 Nursing professionals | 3.2821623 | 52.9080079 | sector and year |
| 235 Teaching professionals not elsewhere classified | 2.7102815 | 24.3392922 | sector and year |
| 334 Administrative and specialized secretaries | 0.6824704 | 15.9484068 | sector and year |
| 531 Child care workers and teachers aides | 2.3704462 | 13.8844930 | sector and year |
| 422 Client information clerks | 3.2226939 | 12.4412439 | sector and year |
| 962 Newspaper distributors, janitors and other service workers | 2.5465829 | 10.9472608 | sector and year |
| 534 Attendants, personal assistants and related workers | 2.3719311 | 10.1007673 | sector and year |
| 532 Personal care workers in health services | 2.7590988 | 9.0616925 | sector and year |
| 611 Market gardeners and crop growers | 2.4285881 | 9.0355793 | sector and year |
| 233 Secondary education teachers | 3.4681159 | 8.2187247 | sector and year |
| 264 Authors, journalists and linguists | 3.2488108 | 6.2777560 | sector and year |
| 351 ICT operations and user support technicians | 3.4272576 | 4.8822064 | sector and year |
| 214 Engineering professionals | 3.4475215 | 4.3685520 | sector and year |
| 723 Machinery mechanics and fitters | 2.7726800 | 4.0300820 | sector and year |
| 511 Cabin crew, guides and related workers | -1.4636615 | 3.9854827 | sector and year |
| 243 Marketing and public relations professionals | 2.4174309 | 3.9092911 | sector and year |
| 311 Physical and engineering science technicians | 3.2975181 | 3.6534643 | sector and year |
| 533 Health care assistants | 2.0202998 | 3.4258034 | sector and year |
| 216 Architects and surveyors | 3.2766321 | 3.0286083 | sector and year |
| 512 Cooks and cold-buffet managers | 2.4761796 | 2.1189296 | sector and year |
| 231 University and higher education teachers | 2.7564149 | 1.9590690 | sector and year |
| 911 Cleaners and helpers | 2.1080113 | 1.3375758 | sector and year |
| 265 Creative and performing artists | 1.8252102 | 1.3073089 | sector and year |
| 341 Social work and religious associate professionals | 2.7179780 | 1.1525106 | sector and year |
| 332 Insurance advisers, sales and purchasing agents | 2.4698420 | 0.8846941 | sector and year |
| 515 Building caretakers and related workers | 2.0896486 | 0.8721029 | sector and year |
| 251 ICT architects, systems analysts and test managers | 2.1070969 | 0.8453000 | sector and year |
| 242 Organisation analysts, policy administrators and human resource specialists | 2.3074810 | 0.7627131 | sector and year |
| 213 Biologists, pharmacologists and specialists in agriculture and forestry | 2.1515840 | 0.7217108 | sector and year |
| 815 Machine operators, textile, fur and leather products | 1.5169216 | 0.7021796 | sector and year |
| 333 Business services agents | 2.6770984 | 0.6948266 | sector and year |
| 411 Office assistants and other secretaries | 2.4961111 | 0.6866667 | sector and year |
| 261 Legal professionals | 2.9024857 | 0.6474976 | sector and year |
| 342 Athletes, fitness instructors and recreational workers | 2.0356952 | 0.5210552 | sector and year |
| 541 Other surveillance and security workers | 2.1319348 | 0.4764820 | sector and year |
| 941 Fast-food workers, food preparation assistants | 2.1030085 | 0.4107102 | sector and year |
| 241 Accountants, financial analysts and fund managers | 2.1837720 | 0.3653365 | sector and year |
| 335 Tax and related government associate professionals | 2.2476351 | 0.3329319 | sector and year |
| 228 Specialists in health care not elsewhere classified | 2.0215985 | 0.1994037 | sector and year |
| 321 Medical and pharmaceutical technicians | 2.4986510 | 0.1694097 | sector and year |
| 234 Primary- and pre-school teachers | 3.5227087 | 0.0566620 | sector and year |
| 211 Physicists and chemists | 2.0509288 | 0.0256116 | sector and year |
| 819 Process control technicians | 2.5525388 | 0.0089722 | sector and year |
| 432 Stores and transport clerks | 1.9579284 | 0.0058151 | sector and year |
| 331 Financial and accounting associate professionals | 2.1471390 | 0.0023497 | sector and year |
| 218 Specialists within environmental and health protection | 2.3896946 | 0.0012880 | sector and year |
merge(summary_table, anova_table, c("ssyk", "interaction"), all = TRUE) %>% filter (term.x == "year_n") %>% filter (contcol.y > 1) %>% filter (interaction == "sector, year, age and sex") %>% filter (!(contcol.y < 3 & interaction == "sector, year, age and sex")) %>% mutate (estimate = (exp(estimate) - 1) * 100) %>% select (ssyk, estimate, statistic.y, interaction) %>% rename (`F-value` = statistic.y) %>% rename (`Increase in salary` = estimate) %>% arrange (desc (`F-value`)) %>% knitr::kable( booktabs = TRUE, caption = 'Correlation for F-value (sector, year, age and sex) and the yearly increase in salaries')| ssyk | Increase in salary | F-value | interaction |
|---|---|---|---|
| 214 Engineering professionals | 2.4648855 | 11.6391393 | sector, year, age and sex |
| 342 Athletes, fitness instructors and recreational workers | 2.0991670 | 8.9098726 | sector, year, age and sex |
| 216 Architects and surveyors | 3.2301466 | 7.6322806 | sector, year, age and sex |
| 228 Specialists in health care not elsewhere classified | 1.5176747 | 4.1043437 | sector, year, age and sex |
| 321 Medical and pharmaceutical technicians | 2.2641978 | 3.9184148 | sector, year, age and sex |
| 218 Specialists within environmental and health protection | 2.4573789 | 3.6168903 | sector, year, age and sex |
| 261 Legal professionals | 2.7581752 | 3.2902539 | sector, year, age and sex |
| 235 Teaching professionals not elsewhere classified | 2.4011513 | 3.0277439 | sector, year, age and sex |
| 335 Tax and related government associate professionals | 2.0292874 | 2.6128635 | sector, year, age and sex |
| 341 Social work and religious associate professionals | 2.6319084 | 2.6004044 | sector, year, age and sex |
| 233 Secondary education teachers | 3.5273156 | 2.2615012 | sector, year, age and sex |
| 331 Financial and accounting associate professionals | 2.4662860 | 1.9867325 | sector, year, age and sex |
| 941 Fast-food workers, food preparation assistants | 1.9900178 | 1.9221016 | sector, year, age and sex |
| 512 Cooks and cold-buffet managers | 2.5222219 | 1.9206611 | sector, year, age and sex |
| 962 Newspaper distributors, janitors and other service workers | 2.6603985 | 1.8150814 | sector, year, age and sex |
| 213 Biologists, pharmacologists and specialists in agriculture and forestry | 2.3962841 | 1.7496638 | sector, year, age and sex |
| 911 Cleaners and helpers | 2.1197350 | 1.5815397 | sector, year, age and sex |
| 511 Cabin crew, guides and related workers | -0.6957497 | 1.5410098 | sector, year, age and sex |
| 211 Physicists and chemists | 2.3310509 | 1.5257771 | sector, year, age and sex |
| 333 Business services agents | 2.9306066 | 1.5169994 | sector, year, age and sex |
| 819 Process control technicians | 2.5394398 | 1.4148181 | sector, year, age and sex |
| 723 Machinery mechanics and fitters | 2.9145883 | 1.2913243 | sector, year, age and sex |
| 234 Primary- and pre-school teachers | 3.5373214 | 1.2588294 | sector, year, age and sex |
| 533 Health care assistants | 2.0561764 | 1.1641262 | sector, year, age and sex |
| 222 Nursing professionals | 3.3141175 | 1.1593790 | sector, year, age and sex |
| 515 Building caretakers and related workers | 2.3554368 | 1.0916712 | sector, year, age and sex |
| 334 Administrative and specialized secretaries | -0.4841162 | 0.8627072 | sector, year, age and sex |
| 264 Authors, journalists and linguists | 3.8766371 | 0.8178463 | sector, year, age and sex |
| 541 Other surveillance and security workers | 2.1959143 | 0.8087407 | sector, year, age and sex |
| 531 Child care workers and teachers aides | 2.4624435 | 0.6787061 | sector, year, age and sex |
| 815 Machine operators, textile, fur and leather products | 1.2558500 | 0.6507638 | sector, year, age and sex |
| 242 Organisation analysts, policy administrators and human resource specialists | 2.1412580 | 0.6425880 | sector, year, age and sex |
| 411 Office assistants and other secretaries | 2.4063713 | 0.5077368 | sector, year, age and sex |
| 432 Stores and transport clerks | 2.1618484 | 0.5005162 | sector, year, age and sex |
| 422 Client information clerks | 3.3819183 | 0.4079113 | sector, year, age and sex |
| 265 Creative and performing artists | 1.7171084 | 0.4046591 | sector, year, age and sex |
| 241 Accountants, financial analysts and fund managers | 1.9650332 | 0.3867492 | sector, year, age and sex |
| 311 Physical and engineering science technicians | 3.0661705 | 0.3303477 | sector, year, age and sex |
| 611 Market gardeners and crop growers | 2.1758845 | 0.1794678 | sector, year, age and sex |
| 351 ICT operations and user support technicians | 3.3472478 | 0.1238188 | sector, year, age and sex |
| 332 Insurance advisers, sales and purchasing agents | 2.2161856 | 0.1214610 | sector, year, age and sex |
| 251 ICT architects, systems analysts and test managers | 2.1371036 | 0.1080593 | sector, year, age and sex |
| 534 Attendants, personal assistants and related workers | 2.3576411 | 0.0748416 | sector, year, age and sex |
| 532 Personal care workers in health services | 2.6448524 | 0.0675532 | sector, year, age and sex |
| 231 University and higher education teachers | 2.5212252 | 0.0231719 | sector, year, age and sex |
| 243 Marketing and public relations professionals | 2.1626640 | 0.0083390 | sector, year, age and sex |
Let’s check what we have found.
temp <- tb %>% filter(`occuptional (SSYK 2012)` == "218 Specialists within environmental and health protection") model <- lm (log(salary) ~ year_n + poly(age_n, 3) + sector + sex, data = temp) plot_model(model, type = "pred", terms = c("age_n", "sector"))## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.
Figure 3: Highest F-value sector, Specialists within environmental and health protection
temp <- tb %>% filter(`occuptional (SSYK 2012)` == "512 Cooks and cold-buffet managers") model <-lm (log(salary) ~ year_n + poly(age_n, 3) + sector + sex, data = temp) plot_model(model, type = "pred", terms = c("age_n", "sector"))## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.
Figure 4: Lowest F-value sector, Cooks and cold-buffet managers
temp <- tb %>% filter(`occuptional (SSYK 2012)` == "911 Cleaners and helpers") model <- lm (log(salary) ~ year_n + poly(age_n, 3) + sector * sex, data = temp) plot_model(model, type = "pred", terms = c("age_n", "sex", "sector"))## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.
Figure 5: Highest F-value interaction sector and gender, Cleaners and helpers
temp <- tb %>% filter(`occuptional (SSYK 2012)` == "511 Cabin crew, guides and related workers") model <- lm (log(salary) ~ year_n + poly(age_n, 3) + sector * sex, data = temp) plot_model(model, type = "pred", terms = c("age_n", "sex", "sector"))## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.
Figure 6: Lowest F-value interaction sector and gender, Cabin crew, guides and related workers
temp <- tb %>% filter(`occuptional (SSYK 2012)` == "251 ICT architects, systems analysts and test managers") model <- lm (log(salary) ~ year_n + poly(age_n, 3) * sector + sex, data = temp) plot_model(model, type = "pred", terms = c("age_n", "sector"))## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.
Figure 7: Highest F-value interaction sector and age, ICT architects, systems analysts and test managers
temp <- tb %>% filter(`occuptional (SSYK 2012)` == "611 Market gardeners and crop growers") model <- lm (log(salary) ~ year_n + poly(age_n, 3) * sector + sex, data = temp) plot_model(model, type = "pred", terms = c("age_n", "sector"))## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.
Figure 8: Lowest F-value interaction sector and age, Market gardeners and crop growers
temp <- tb %>% filter(`occuptional (SSYK 2012)` == "222 Nursing professionals") model <- lm (log(salary) ~ year_n * sector + poly(age_n, 3) + sex, data = temp) plot_model(model, type = "pred", terms = c("age_n", "year_n", "sector"))## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.
Figure 9: Highest F-value interaction sector and year, Nursing professionals
temp <- tb %>% filter(`occuptional (SSYK 2012)` == "218 Specialists within environmental and health protection") model <- lm (log(salary) ~ year_n * sector + poly(age_n, 3) + sex, data = temp) plot_model(model, type = "pred", terms = c("age_n", "year_n", "sector"))## Model has log-transformed response. Back-transforming predictions to original response scale. Standard errors are still on the log-scale.
Figure 10: Lowest F-value interaction sector and year, Specialists within environmental and health protection
temp <- tb %>% filter(`occuptional (SSYK 2012)` == "214 Engineering professionals") model <- lm (log(salary) ~ year_n * poly(age_n, 3) * sector * sex, data = temp) plot_model(model, type = "pred", terms = c("age_n", "year_n", "sex", "sector"))## 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 sector, age, year and gender, Engineering professionals
## TableGrob (2 x 1) "arrange": 2 grobs## z cells name grob## 1 1 (1-1,1-1) arrange gtable[layout]## 2 2 (2-2,1-1) arrange gtable[layout]temp <- tb %>% filter(`occuptional (SSYK 2012)` == "243 Marketing and public relations professionals") model <- lm (log(salary) ~ year_n * poly(age_n, 3) * sector * sex, data = temp) plot_model(model, type = "pred", terms = c("age_n", "year_n", "sex", "sector"))## 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 sector, age, year and gender, Marketing and public relations professionals
## TableGrob (2 x 1) "arrange": 2 grobs## z cells name grob## 1 1 (1-1,1-1) arrange gtable[layout]## 2 2 (2-2,1-1) arrange gtable[layout]To leave a comment for the author, please follow the link and comment on their blog: R Analystatistics Sweden .
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