Chapter 9 SSYK 214

9.1 Average monthly salary, SEK by region

Genomsnittlig grund- och månadslön samt kvinnors lön i procent av mäns lön efter region sektor, yrkesgrupp (SSYK 2012) och kön. År 2014 - 2018 Månadslön Samtliga sektorer 214 Cilvilingenjörsyrken Kön totalt 2017

readfile ("000000CG_1.csv") %>%
  left_join(nuts, by = c("region" = "NUTS2")) %>%
  right_join(map_ln_n, by = c("Länskod" = "lnkod_n")) %>%
  ggplot() +
    geom_polygon(mapping = aes(x = ggplot_long, y = ggplot_lat, group = lnkod, fill = relsalary)) +
    coord_equal() 

Figure 9.1: Engineers salaries in the different countys

salary_2017 <- readfile ("000000CG_1.csv") %>%
  left_join(nuts, by = c("region" = "NUTS2"))

readfile ("000000NL_3.csv") %>%   
  group_by (`region`, year) %>%   
  summarise (perc_women = perc_women (salary)) %>%
  mutate(perc_women_n = as.numeric(sub("%", "", perc_women))) %>%
  mutate(lnkod_n = as.numeric(substr(region, 1,2)))%>%
  right_join(salary_2017, by = c("lnkod_n" = "Länskod")) %>%
  ggscatter(x = "relsalary", y = "perc_women_n", 
    add = "reg.line", conf.int = TRUE, 
    cor.coef = TRUE, cor.method = "pearson")    

Figure 9.2: The correlation between the proportion of engineers who are women and the salaries of engineers in the region.

9.2 Number of men and women in SSYK 214 and the percentage of women in SSYK 214 Year 2014 - 2018

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 Number of employees All sectors 214, Architects, engineers and related professionals

readfile("000000CV_3.csv") %>%   
  group_by (year) %>%   
  mutate (perc_women = as.numeric (sub ("%", "", perc_women (salary)))) %>%
  ggplot(aes(x = year2)) +
  geom_line(mapping = aes(y = salary, colour = sex)) +
  geom_line(mapping = aes(y = perc_women * 2000)) +
  scale_y_continuous(sec.axis = sec_axis(~ . * 0.0005, name = "Percent women [%]"))

Figure 9.3: Number of men and women in SSYK 214 and the percentage of women in SSYK 214 Year 2014 - 2018

9.3 Number of men and women in SSYK 214 and the percentage of women in SSYK 214 Year 2003 - 2013

Average basic salary, monthly salary and women´s salary as a percentage of men´s salary by sector, occupational group (SSYK), sex and educational level (SUN). Year 2003 - 2013 Number of employees All sectors 214, Architects, engineers and related professionals

readfile("AM0110B4.csv") %>%   
  group_by (year) %>%   
  mutate (perc_women = as.numeric (sub ("%", "", perc_women (salary)))) %>%
  ggplot(aes(x = year2)) +
  geom_line(mapping = aes(y = salary, colour = sex)) +
  geom_line(mapping = aes(y = perc_women * 2000)) +
  scale_y_continuous(sec.axis = sec_axis(~ . * 0.0005, name = "Percent women [%]"))

Figure 9.4: Number of men and women in SSYK 214 and the percentage of women in SSYK 214 Year 2003 - 2013

9.4 Age distribution in the different countys

Employees 16-64 years by region of work, occupation (3-digit SSYK 2012), age and sex. Year 2014 - 2017 occupation=214, Architects, engineers and related professionals

readfile("000000NK.csv") %>% filter(year2 == 2017) %>%
  rowwise() %>% mutate(age2 = unlist(lapply(strsplit(substr(age, 1, 5), "-"), strtoi))[1]) %>% 
  rowwise() %>% mutate(age3 = unlist(lapply(strsplit(substr(age, 1, 5), "-"), strtoi))[2]) %>%
  ggplot() +
    geom_line(aes(x = (age2 + age3) / 2, y = salary, color = region)) +
    theme(legend.position="bottom") +
    facet_grid(. ~ sex)

Figure 9.5: The age distribution in the different countys for engineers, year 2017

9.5 Sector distribution in the different countys

Employees 16-64 years by region of work, occupation (3-digit SSYK 2012), sector and sex. Year 2014 - 2017 occupation=214, Architects, engineers and related professionals

readfile("000000RM_1.csv") %>% 
  filter(year2 == 2017) %>%
  mutate(lnkod_n = substr(region, 1,2))%>%
  ggplot(aes(x = lnkod_n, y = salary, fill = sector)) +
    geom_col(position = "fill") +
    theme(legend.position="bottom") +
    facet_grid(. ~ sex)

Figure 9.6: The sector distribution in the different countys for engineers, year 2017

9.6 Education distribution for engineers

Number of employees by sector, occuptional (SSYK 2012), sex, level of education and year 214 Engineering professionals All sectors Year 2017

edu <- readfile("000000CV_1.csv")
edu$`level of education` <- as.factor(edu$`level of education`)
edu$`level of education` <- factor(edu$`level of education`, levels(edu$`level of education`)[c(2, 3, 4, 6, 7, 5, 1)])
edu %>%
  filter(year2 == 2017) %>%  
  ggplot(aes(x = `occuptional  (SSYK 2012)`, y = salary, fill = `level of education`)) +
    geom_col(position = "fill") +
    theme(legend.position="bottom") +
    facet_grid(. ~ sex) 

Figure 9.7: The education distribution for engineers, year 2017

9.7 Average monthly pay increase by occuption year 2000 - 2013

Average monthly pay (total pay), non-manual workers private sector (SLP), SEK by occuptional (SSYK 2012), age, sex and year, Year 2000 - 2013
age=total
sex=total

Average monthly pay (total pay), non-manual workers private sector (SLP), SEK by occuptional (SSYK 2012), age, sex and year, Year 2014 - 2018
age=total
sex=total

readfile ("AM0103E6.csv") %>%   
  group_by (`occupational group (SSYK)`) %>%   
  summarise (tot = tot_dev (salary)) %>%  
  arrange (desc (tot)) %>%
  knitr::kable( 
  booktabs = TRUE,
  caption = 'Average monthly pay increase by occuption (SSYK 2012), Year 2000 - 2013')  

Table 9.1: Average monthly pay increase by occuption (SSYK 2012), Year 2000 - 2013

occupational group (SSYK)	tot
249 Psychologists, social work and related professionals	71.1%
723 Machinery mechanics and fitters	70.4%
822 Chemical-products machine operators	66.8%
829 Other machine operators and assemblers	66.7%
222 Health professionals (except nursing)	66.6%
0002 occupations unidentifiable	63.1%
722 Blacksmiths, tool-makers and related trades workers	62.0%
121 Directors and chief executives	61.5%
419 Other office clerks	61.4%
828 Assemblers	61.1%
731 Precision workers in metal and related materials	60.8%
815 Chemical-processing-plant operators	60.7%
522 Shop and stall salespersons and demonstrators	60.5%
131 Managers of small enterprises	59.7%
321 Agronomy and forestry technicians	58.0%
212 Mathematicians and statisticians	57.6%
221 Life science professionals	57.3%
919 Other sales and services elementary occupations	56.9%
324 Life science technicians	56.7%
413 Stores and transport clerks	56.4%
827 Food and related products machine operators	55.7%
331 Pre-primary education teaching associate professionals	55.4%
323 Nursing associate professionals	54.9%
246 Religious professionals	54.4%
341 Finance and sales associate professionals	54.3%
311 Physical and engineering science technicians	54.2%
512 Housekeeping and restaurant services workers	54.2%
347 Artistic, entertainment and sports associate professionals	54.1%
122 Production and operations managers	52.9%
348 Religious associate professionals	52.4%
713 Building finishers and related trades workers	51.3%
0000 All	51.2%
123 Other specialist managers	51.2%
914 Doorkeepers, newspaper and package deliverers and related workers	50.9%
315 Safety and quality inspectors	50.8%
412 Numerical clerks	50.8%
724 Electrical and electronic equipment mechanics and fitters	50.7%
346 Social work associate professionals	50.3%
342 Business services agents and trade brokers	50.2%
343 Administrative associate professionals	50.2%
825 Printing-, binding- and paper-products machine operators	50.0%
312 Computer associate professionals	48.5%
243 Archivists, librarians and related information professionals	48.2%
912 Helpers and cleaners	47.7%
422 Client information clerks	47.6%
513 Personal care and related workers	47.3%
913 Helpers in restaurants	47.0%
831 Locomotive-engine drivers and related worker	46.9%
712 Building frame and related trades workers	46.8%
816 Power-production and related plant operators	45.2%
411 Office secretaries and data entry operators	44.4%
112 Senior officials of special-interest organisations	43.9%
241 Business professionals	43.0%
514 Other personal services workers	42.7%
344 Customs, tax and related government associate professionals	42.0%
231 College, university and higher education teaching professionals	41.8%
211 Physicists, chemists and related professionals	41.6%
245 Writers and creative or performing artists	40.7%
832 Motor-vehicle drivers	40.6%
214 Architects, engineers and related professionals	40.3%
721 Metal moulders, welders, sheet-metal workers, structural-metal preparers and related trades workers	39.9%
322 Health associate professionals (except nursing)	39.3%
223 Nursing and midwifery professionals	39.2%
313 Optical and electronic equipment operators	38.9%
515 Protective services workers	38.7%
821 Metal- and mineral-products machine operators	38.6%
332 Other teaching associate professionals	37.1%
233 Primary education teaching professionals	36.2%
743 Garment and related trades workers	35.8%
242 Legal professionals	35.7%
421 Cashiers, tellers and related clerks	35.7%
833 Agricultural and other mobile-plant operators	35.5%
213 Computing professionals	35.4%
234 Special education teaching professionals	34.6%
235 Other teaching professionals	34.5%
511 Travel attendants and related workers	34.3%
933 Transport labourers and freight handlers	34.0%
248 Administrative professionals of special-interest organisations	33.7%
244 Social science and linguistics professionals (except social work professionals)	33.0%
232 Secondary education teaching professionals	32.1%
414 Library and filing clerks	31.2%
314 Ship and aircraft controllers and technicians	29.5%
915 Garbage collectors and related labourers	27.9%
611 Market gardeners and crop growers	27.1%
814 Wood-processing- and papermaking-plant operators	26.6%
932 Manufacturing labourers	25.4%
824 Wood-products machine operators	23.9%
714 Painters, building structure cleaners and related trades workers	22.7%
734 Craft printing and related trades workers	22.5%
612 Animal producers and related workers	21.1%
823 Rubber- and plastic-products machine operators	15.7%
247 Public service administrative professionals	15.1%
415 Mail carriers and sorting clerks	13.6%
124 Foreman	13.1%
111 Legislators and senior government officials	0%
834 Ships´ deck crews and related workers	0%
931 Mining and construction labourers	0%
812 Metal-processing-plant operators	-6.58%

readfile ("00000031.csv") %>%   
  group_by (`occuptional  (SSYK 2012)`) %>%   
  summarise (tot = parse_number (tot_dev (salary))) %>%  
  arrange (desc (tot)) %>%
  knitr::kable( 
  booktabs = TRUE,
  caption = 'Average monthly pay increase by occuption (SSYK 2012), Year 2014 - 2018')  

Table 9.1: Average monthly pay increase by occuption (SSYK 2012), Year 2014 - 2018

occuptional (SSYK 2012)	tot
222 Nursing professionals	27.600
149 Education managers not elsewhere classified	26.400
343 Photographers, interior decorators and entertainers	24.800
443 Elected representatives	19.500
334 Administrative and specialized secretaries	19.200
266 Social work and counselling professionals	18.400
171 Hotel and conference managers	18.300
161 Financial and insurance managers	18.100
173 Retail and wholesale trade managers	17.800
121 Finance managers	17.700
223 Nursing professionals (cont.)	17.700
122 Human resource managers	17.600
138 Forestry and agricultural production managers	17.300
132 Supply, logistics and transport managers	17.200
141 Primary and secondary schools and adult education managers	16.700
142 Preschool managers	16.000
152 Managers in social and curative care	15.600
234 Primary- and pre-school teachers	15.200
136 Production managers in construction and mining	15.000
344 Driving instructors and other instructors	14.800
123 Administration and planning managers	14.400
153 Elderly care managers	14.100
174 Sports, leisure and wellness managers	14.100
267 Religious professionals and deacons	14.100
265 Creative and performing artists	13.600
179 Other services managers not elsewhere classified	13.400
228 Specialists in health care not elsewhere classified	13.400
312 Construction and manufacturing supervisors	13.400
131 Information and communications technology service managers	13.200
321 Medical and pharmaceutical technicians	12.300
335 Tax and related government associate professionals	12.300
134 Architectural and engineering managers	12.200
217 Designers	12.200
151 Health care managers	11.800
311 Physical and engineering science technicians	11.700
224 Psychologists and psychotherapists	10.900
262 Museum curators and librarians and related professionals	10.600
125 Sales and marketing managers	10.400
233 Secondary education teachers	10.300
159 Other social services managers	10.200
332 Insurance advisers, sales and purchasing agents	9.870
218 Specialists within environmental and health protection	9.800
212 Mathematicians, actuaries and statisticians	9.740
324 Veterinary assistants	9.490
0000 All	9.440
524 Event seller and telemarketers	9.440
241 Accountants, financial analysts and fund managers	9.430
411 Office assistants and other secretaries	9.190
341 Social work and religious associate professionals	9.030
226 Dentists	8.980
231 University and higher education teachers	8.920
345 Culinary associate professionals	8.700
331 Financial and accounting associate professionals	8.610
251 ICT architects, systems analysts and test managers	8.310
124 Information, communication and public relations managers	8.280
261 Legal professionals	8.190
325 Dental hygienists	8.160
213 Biologists, pharmacologists and specialists in agriculture and forestry	8.140
232 Vocational education teachers	7.740
242 Organisation analysts, policy administrators and human resource specialists	7.590
137 Production managers in manufacturing	7.540
154 Managers and leaders within religious bodies	7.390
214 Engineering professionals	7.240
211 Physicists and chemists	6.820
422 Client information clerks	6.430
421 Croupiers, debt collectors and related workers	6.010
112 Managing directors and chief executives	5.840
133 Research and development managers	5.320
135 Real estate and head of administration manager	5.170
333 Business services agents	5.100
352 Broadcasting and audio-visual technicians	4.790
129 Administration and service managers not elsewhere classified	4.100
243 Marketing and public relations professionals	4.070
351 ICT operations and user support technicians	3.630
227 Naprapaths, physiotherapists, occupational therapists	3.230
315 Ship and aircraft controllers and technicians	3.190
264 Authors, journalists and linguists	2.250
342 Athletes, fitness instructors and recreational workers	2.110
216 Architects and surveyors	2.080
172 Restaurant managers	0.917
441 Library and filing clerks	-0.402
225 Veterinarians	-0.467
235 Teaching professionals not elsewhere classified	-2.720
0002 occupations unidentifiable	-4.510
111 Legislators and senior officials	-7.040
511 Cabin crew, guides and related workers	-7.610
221 Medical doctors	-8.990

9.8 SSYK 214, Architects, engineers and related professionals, Year 2000 - 2013

Average monthly pay, non-manual workers private sector (SLP) by occupational group (SSYK) age and sex. Year 2000 - 2013 Average monthly pay (total pay), non-manual workers private sector (SLP), SEK by occupational group (SSYK), age, sex and year

tb <- readfile("AM0103A9_1.csv") %>% 
  rowwise() %>% 
  mutate(age2 = unlist(lapply(strsplit(substr(age, 1, 5), "-"), strtoi))[1]) %>%  
  rowwise() %>% 
  mutate(age3 = unlist(lapply(strsplit(substr(age, 1, 5), "-"), strtoi))[2]) %>% 
  mutate(age4 = (age3 + age2) / 2) %>% 
  group_by (`occupational group (SSYK)`, age, sex) %>%   
  mutate (grouprelsal = relative_dev (salary))  

## Warning: Grouping rowwise data frame strips rowwise nature

tb %>%
  ggplot () +  
    geom_point (mapping = aes(x = year2,y = log(salary), colour = age, shape=sex))  

Figure 9.8: SSYK 214, Architects, engineers and related professionals, Year 2000 - 2013

model <- lm (log(salary) ~ year2 + sex + poly(age4, 3), data = tb)

tb <- bind_cols(tb, as_tibble(exp(predict(model, tb, interval = "confidence"))))

tb %>%
  ggplot () +  
    geom_point (mapping = aes(x = year2,y = log(fit), colour = age, shape=sex))

(#fig:ssyk00-13_2)SSYK, Year 2000 - 2013

summary(model) %>%  
  tidy() %>%
  knitr::kable( 
  booktabs = TRUE,
  caption = 'Summary from linear model fit')

(#tab:ssyk00-13_2)Summary from linear model fit

term	estimate	std.error	statistic	p.value
(Intercept)	-39.3602913	1.4040096	-28.034204	0.0000000
year2	0.0248351	0.0006997	35.492228	0.0000000
sexwomen	-0.0745348	0.0056586	-13.171933	0.0000000
poly(age4, 3)1	2.1477927	0.0437199	49.126166	0.0000000
poly(age4, 3)2	-1.3140489	0.0439104	-29.925667	0.0000000
poly(age4, 3)3	0.1728549	0.0437183	3.953832	0.0001018

Anova(model, type=2) %>% 
  tidy() %>% 
  knitr::kable( 
  booktabs = TRUE,
  caption = 'Anova report from linear model fit')  

(#tab:ssyk00-13_2)Anova report from linear model fit

term	sumsq	df	statistic	p.value
year2	2.4060060	1	1259.6982	0
sex	0.3313822	1	173.4998	0
poly(age4, 3)	6.3536207	3	1108.8424	0
Residuals	0.4488467	235	NA	NA

tb <- readfile("AM0103A9_2.csv") %>% 
  rowwise() %>% 
  mutate(age2 = unlist(lapply(strsplit(substr(age, 1, 5), "-"), strtoi))[1]) %>%  
  rowwise() %>% 
  mutate(age3 = unlist(lapply(strsplit(substr(age, 1, 5), "-"), strtoi))[2]) %>% 
  mutate(age4 = (age3 + age2) / 2) %>% 
  group_by (`occupational group (SSYK)`, age, sex) %>%   
  mutate (grouprelsal = relative_dev (salary))  

## Warning: Grouping rowwise data frame strips rowwise nature

model <- lm (log(salary) ~ `occupational group (SSYK)` + year2 + sex + poly(age4, 3), data = tb)
    
summary(model) %>%  
  tidy() %>%
  knitr::kable( 
  booktabs = TRUE,
  caption = 'Summary from linear model fit')

(#tab:ssyk00-13_2)Summary from linear model fit

term	estimate	std.error	statistic	p.value
(Intercept)	-48.9181797	0.4450215	-109.9231939	0.0000000
occupational group (SSYK)121 Directors and chief executives	0.6843097	0.0097694	70.0459713	0.0000000
occupational group (SSYK)122 Production and operations managers	0.1497668	0.0077836	19.2414263	0.0000000
occupational group (SSYK)123 Other specialist managers	0.3561121	0.0078044	45.6299353	0.0000000
occupational group (SSYK)124 Foreman	-0.0781592	0.0118826	-6.5776448	0.0000000
occupational group (SSYK)131 Managers of small enterprises	-0.0251403	0.0078604	-3.1983603	0.0013867
occupational group (SSYK)211 Physicists, chemists and related professionals	0.2084204	0.0081189	25.6711740	0.0000000
occupational group (SSYK)212 Mathematicians and statisticians	0.2004032	0.0348837	5.7448944	0.0000000
occupational group (SSYK)213 Computing professionals	0.1512162	0.0076259	19.8291953	0.0000000
occupational group (SSYK)214 Architects, engineers and related professionals	0.1216738	0.0076417	15.9223550	0.0000000
occupational group (SSYK)221 Life science professionals	0.1249274	0.0129063	9.6795668	0.0000000
occupational group (SSYK)222 Health professionals (except nursing)	0.3540252	0.0081056	43.6768676	0.0000000
occupational group (SSYK)223 Nursing and midwifery professionals	-0.0537161	0.0096257	-5.5804691	0.0000000
occupational group (SSYK)231 College, university and higher education teaching professionals	0.0520675	0.0103711	5.0204408	0.0000005
occupational group (SSYK)232 Secondary education teaching professionals	-0.2092601	0.0081292	-25.7418084	0.0000000
occupational group (SSYK)233 Primary education teaching professionals	-0.2351593	0.0091908	-25.5864570	0.0000000
occupational group (SSYK)235 Other teaching professionals	-0.1512582	0.0088117	-17.1655476	0.0000000
occupational group (SSYK)241 Business professionals	0.1536239	0.0075793	20.2689603	0.0000000
occupational group (SSYK)242 Legal professionals	0.3756703	0.0087341	43.0119789	0.0000000
occupational group (SSYK)244 Social science and linguistics professionals (except social work professionals)	-0.0037496	0.0845070	-0.0443704	0.9646101
occupational group (SSYK)245 Writers and creative or performing artists	-0.0242627	0.0075641	-3.2076131	0.0013429
occupational group (SSYK)246 Religious professionals	-0.0330001	0.0081970	-4.0258803	0.0000572
occupational group (SSYK)247 Public service administrative professionals	-0.0042949	0.0490043	-0.0876426	0.9301626
occupational group (SSYK)248 Administrative professionals of special-interest organisations	-0.0107107	0.0080066	-1.3377348	0.1810151
occupational group (SSYK)249 Psychologists, social work and related professionals	-0.1350674	0.0102828	-13.1353089	0.0000000
occupational group (SSYK)311 Physical and engineering science technicians	-0.0704933	0.0075566	-9.3286738	0.0000000
occupational group (SSYK)312 Computer associate professionals	-0.0709126	0.0076020	-9.3281629	0.0000000
occupational group (SSYK)313 Optical and electronic equipment operators	-0.2000192	0.0095361	-20.9750036	0.0000000
occupational group (SSYK)314 Ship and aircraft controllers and technicians	0.3906773	0.0102647	38.0601753	0.0000000
occupational group (SSYK)315 Safety and quality inspectors	-0.1200257	0.0091762	-13.0800884	0.0000000
occupational group (SSYK)321 Agronomy and forestry technicians	-0.1451979	0.0136048	-10.6725322	0.0000000
occupational group (SSYK)322 Health associate professionals (except nursing)	-0.1388401	0.0088585	-15.6730682	0.0000000
occupational group (SSYK)323 Nursing associate professionals	-0.0797149	0.0090905	-8.7690673	0.0000000
occupational group (SSYK)324 Life science technicians	-0.2069808	0.0110118	-18.7962311	0.0000000
occupational group (SSYK)331 Pre-primary education teaching associate professionals	-0.3033610	0.0099170	-30.5901460	0.0000000
occupational group (SSYK)332 Other teaching associate professionals	-0.2774992	0.0128234	-21.6400468	0.0000000
occupational group (SSYK)341 Finance and sales associate professionals	-0.0239146	0.0075566	-3.1647205	0.0015572
occupational group (SSYK)342 Business services agents and trade brokers	-0.1491105	0.0078277	-19.0490038	0.0000000
occupational group (SSYK)343 Administrative associate professionals	-0.1074541	0.0075641	-14.2058125	0.0000000
occupational group (SSYK)346 Social work associate professionals	-0.3102289	0.0080925	-38.3352361	0.0000000
occupational group (SSYK)347 Artistic, entertainment and sports associate professionals	-0.1286704	0.0083758	-15.3621676	0.0000000
occupational group (SSYK)411 Office secretaries and data entry operators	-0.2446566	0.0085685	-28.5531838	0.0000000
occupational group (SSYK)412 Numerical clerks	-0.2419657	0.0075791	-31.9252462	0.0000000
occupational group (SSYK)413 Stores and transport clerks	-0.2638604	0.0075790	-34.8145586	0.0000000
occupational group (SSYK)415 Mail carriers and sorting clerks	-0.4045545	0.0097712	-41.4026300	0.0000000
occupational group (SSYK)419 Other office clerks	-0.2812810	0.0075640	-37.1865647	0.0000000
occupational group (SSYK)421 Cashiers, tellers and related clerks	-0.2911851	0.0085149	-34.1973054	0.0000000
occupational group (SSYK)422 Client information clerks	-0.3466551	0.0076021	-45.6000045	0.0000000
occupational group (SSYK)511 Travel attendants and related workers	-0.1698378	0.0080115	-21.1992587	0.0000000
occupational group (SSYK)512 Housekeeping and restaurant services workers	-0.3675951	0.0112797	-32.5890874	0.0000000
occupational group (SSYK)513 Personal care and related workers	-0.3716465	0.0075867	-48.9868159	0.0000000
occupational group (SSYK)514 Other personal services workers	-0.3457294	0.0156694	-22.0640010	0.0000000
occupational group (SSYK)515 Protective services workers	-0.2028056	0.0120050	-16.8934447	0.0000000
occupational group (SSYK)522 Shop and stall salespersons and demonstrators	-0.2671818	0.0075790	-35.2527597	0.0000000
occupational group (SSYK)712 Building frame and related trades workers	-0.2663715	0.0166078	-16.0389248	0.0000000
occupational group (SSYK)713 Building finishers and related trades workers	-0.2953711	0.0112955	-26.1493579	0.0000000
occupational group (SSYK)723 Machinery mechanics and fitters	-0.2832357	0.0105255	-26.9095171	0.0000000
occupational group (SSYK)724 Electrical and electronic equipment mechanics and fitters	-0.2433953	0.0093842	-25.9367630	0.0000000
occupational group (SSYK)734 Craft printing and related trades workers	-0.2137465	0.0180896	-11.8159893	0.0000000
occupational group (SSYK)815 Chemical-processing-plant operators	-0.1040442	0.0425224	-2.4468097	0.0144308
occupational group (SSYK)816 Power-production and related plant operators	-0.2126282	0.0113508	-18.7323998	0.0000000
occupational group (SSYK)825 Printing-, binding- and paper-products machine operators	-0.1779737	0.0240425	-7.4024477	0.0000000
occupational group (SSYK)828 Assemblers	-0.2745193	0.0211832	-12.9592743	0.0000000
occupational group (SSYK)829 Other machine operators and assemblers	-0.1861654	0.0224749	-8.2832519	0.0000000
occupational group (SSYK)831 Locomotive-engine drivers and related worker	-0.1837192	0.0102368	-17.9469107	0.0000000
occupational group (SSYK)832 Motor-vehicle drivers	-0.4036730	0.0092552	-43.6156159	0.0000000
occupational group (SSYK)912 Helpers and cleaners	-0.4112420	0.0095967	-42.8526533	0.0000000
occupational group (SSYK)913 Helpers in restaurants	-0.4004462	0.0272415	-14.6998478	0.0000000
occupational group (SSYK)914 Doorkeepers, newspaper and package deliverers and related workers	-0.4233767	0.0112253	-37.7163717	0.0000000
occupational group (SSYK)919 Other sales and services elementary occupations	-0.4459274	0.0147337	-30.2659045	0.0000000
occupational group (SSYK)933 Transport labourers and freight handlers	-0.2855748	0.0845382	-3.3780563	0.0007330
year2	0.0295469	0.0002218	133.2286965	0.0000000
sexwomen	-0.0940155	0.0019313	-48.6800444	0.0000000
poly(age4, 3)1	8.0085927	0.0860976	93.0176060	0.0000000
poly(age4, 3)2	-5.4012785	0.0860881	-62.7413239	0.0000000
poly(age4, 3)3	0.6344314	0.0849507	7.4682340	0.0000000

Anova(model, type=2) %>% 
  tidy() %>% 
  knitr::kable( 
  booktabs = TRUE,
  caption = 'Anova report from linear model fit')

(#tab:ssyk00-13_2)Anova report from linear model fit

term	sumsq	df	statistic	p.value
occupational group (SSYK)	453.90944	70	912.2726	0
year2	126.16594	1	17749.8856	0
sex	16.84413	1	2369.7467	0
poly(age4, 3)	88.63996	3	4156.8249	0
Residuals	67.14914	9447	NA	NA

9.9 SSYK 214, Architects, engineers and related professionals, Year 2014 - 2018

Average monthly pay, non-manual workers private sector (SLP) by occupational group (SSYK 2012) age and sex. Year 2014 - 2018 Average monthly pay (total pay), non-manual workers private sector (SLP), SEK by occupational group (SSYK), age, sex and year

tb <- readfile("00000031_1.csv") %>% 
  rowwise() %>% 
  mutate(age2 = unlist(lapply(strsplit(substr(age, 1, 5), "-"), strtoi))[1]) %>%  
  rowwise() %>% 
  mutate(age3 = unlist(lapply(strsplit(substr(age, 1, 5), "-"), strtoi))[2]) %>% 
  mutate(age4 = (age3 + age2) / 2) %>% 
  group_by (`occuptional  (SSYK 2012)`, age, sex) %>%   
  mutate (grouprelsal = relative_dev (salary))  

## Warning: Grouping rowwise data frame strips rowwise nature

tb %>%
  ggplot () +  
    geom_point (mapping = aes(x = year2,y = log(salary), colour = age, shape=sex))  

Figure 9.9: SSYK 214, Architects, engineers and related professionals, Year 2014 - 2018

model <- lm (log(salary) ~ year2 + sex + poly(age4, 3), data = tb)

tb <- bind_cols(tb, as_tibble(exp(predict(model, tb, interval = "confidence"))))

tb %>%
  ggplot () +  
    geom_point (mapping = aes(x = year2,y = log(fit), colour = age, shape=sex))

(#fig:ssyk14-18_2)Model fit, SSYK 214, Architects, engineers and related professionals, Year 2014 - 2018

summary(model) %>%  
  tidy() %>%
  knitr::kable( 
  booktabs = TRUE,
  caption = 'Summary from linear model fit')

(#tab:ssyk14-18_2)Summary from linear model fit

term	estimate	std.error	statistic	p.value
(Intercept)	-27.9987545	4.1947424	-6.674726	0.0000000
year2	0.0191776	0.0020807	9.216871	0.0000000
sexwomen	-0.0353468	0.0059660	-5.924684	0.0000001
poly(age4, 3)1	1.5018782	0.0286694	52.386134	0.0000000
poly(age4, 3)2	-0.7321485	0.0287026	-25.508120	0.0000000
poly(age4, 3)3	0.0082994	0.0287225	0.288952	0.7732977

Anova(model, type=2) %>% 
  tidy() %>% 
  knitr::kable( 
  booktabs = TRUE,
  caption = 'Anova report from linear model fit')

(#tab:ssyk14-18_2)Anova report from linear model fit

term	sumsq	df	statistic	p.value
year2	0.0694613	1	84.95072	0e+00
sex	0.0287016	1	35.10188	1e-07
poly(age4, 3)	2.7897056	3	1137.26462	0e+00
Residuals	0.0719545	88	NA	NA

tb <- readfile("00000031_3.csv") %>% 
  rowwise() %>% 
  mutate(age2 = unlist(lapply(strsplit(substr(age, 1, 5), "-"), strtoi))[1]) %>%  
  rowwise() %>% 
  mutate(age3 = unlist(lapply(strsplit(substr(age, 1, 5), "-"), strtoi))[2]) %>% 
  mutate(age4 = (age3 + age2) / 2) %>% 
  group_by (`occuptional  (SSYK 2012)`, age, sex) %>%   
  mutate (grouprelsal = relative_dev (salary))

## Warning: Grouping rowwise data frame strips rowwise nature

model <- lm (log(salary) ~ `occuptional  (SSYK 2012)` + year2 + sex + poly(age4, 3), data = tb)
    
summary(model) %>%  
  tidy() %>%
  knitr::kable( 
  booktabs = TRUE,
  caption = 'Summary from linear model fit')

(#tab:ssyk14-18_2)Summary from linear model fit

term	estimate	std.error	statistic	p.value
(Intercept)	-37.5468927	2.0470713	-18.3417615	0.0000000
occuptional (SSYK 2012)112 Managing directors and chief executives	0.6170543	0.0192423	32.0676708	0.0000000
occuptional (SSYK 2012)121 Finance managers	0.4769831	0.0156019	30.5721499	0.0000000
occuptional (SSYK 2012)122 Human resource managers	0.4542501	0.0164617	27.5943225	0.0000000
occuptional (SSYK 2012)123 Administration and planning managers	0.4956227	0.0167515	29.5867070	0.0000000
occuptional (SSYK 2012)124 Information, communication and public relations managers	0.5278895	0.0246679	21.3998755	0.0000000
occuptional (SSYK 2012)125 Sales and marketing managers	0.3861924	0.0148564	25.9950674	0.0000000
occuptional (SSYK 2012)129 Administration and service managers not elsewhere classified	0.2823372	0.0148218	19.0488006	0.0000000
occuptional (SSYK 2012)131 Information and communications technology service managers	0.4022867	0.0158027	25.4568486	0.0000000
occuptional (SSYK 2012)132 Supply, logistics and transport managers	0.2973659	0.0166430	17.8673640	0.0000000
occuptional (SSYK 2012)133 Research and development managers	0.5276822	0.0161981	32.5768948	0.0000000
occuptional (SSYK 2012)134 Architectural and engineering managers	0.3935430	0.0161977	24.2961723	0.0000000
occuptional (SSYK 2012)135 Real estate and head of administration manager	0.2313332	0.0277684	8.3308092	0.0000000
occuptional (SSYK 2012)136 Production managers in construction and mining	0.2887767	0.0161857	17.8415086	0.0000000
occuptional (SSYK 2012)137 Production managers in manufacturing	0.1975406	0.0161121	12.2604253	0.0000000
occuptional (SSYK 2012)141 Primary and secondary schools and adult education managers	0.1834401	0.0890480	2.0600138	0.0394652
occuptional (SSYK 2012)151 Health care managers	0.1681348	0.0188836	8.9037297	0.0000000
occuptional (SSYK 2012)154 Managers and leaders within religious bodies	0.2966825	0.0456125	6.5044143	0.0000000
occuptional (SSYK 2012)159 Other social services managers	0.2081647	0.0175764	11.8434133	0.0000000
occuptional (SSYK 2012)161 Financial and insurance managers	0.7252319	0.0165534	43.8115622	0.0000000
occuptional (SSYK 2012)172 Restaurant managers	-0.0518488	0.0230186	-2.2524718	0.0243494
occuptional (SSYK 2012)173 Retail and wholesale trade managers	0.1104125	0.0175625	6.2868269	0.0000000
occuptional (SSYK 2012)179 Other services managers not elsewhere classified	0.0896892	0.0151243	5.9301557	0.0000000
occuptional (SSYK 2012)211 Physicists and chemists	0.1497958	0.0157317	9.5218952	0.0000000
occuptional (SSYK 2012)212 Mathematicians, actuaries and statisticians	0.1949668	0.0890529	2.1893368	0.0286330
occuptional (SSYK 2012)213 Biologists, pharmacologists and specialists in agriculture and forestry	0.0421748	0.0159456	2.6449250	0.0082046
occuptional (SSYK 2012)214 Engineering professionals	0.1572491	0.0141277	11.1305556	0.0000000
occuptional (SSYK 2012)216 Architects and surveyors	0.0515622	0.0173139	2.9780763	0.0029190
occuptional (SSYK 2012)217 Designers	-0.0081672	0.0154654	-0.5280988	0.5974617
occuptional (SSYK 2012)218 Specialists within environmental and health protection	0.0740458	0.0173017	4.2796822	0.0000192
occuptional (SSYK 2012)221 Medical doctors	0.5403667	0.0166303	32.4928738	0.0000000
occuptional (SSYK 2012)222 Nursing professionals	0.0609359	0.0146890	4.1484062	0.0000342
occuptional (SSYK 2012)223 Nursing professionals (cont.)	-0.0375099	0.0185202	-2.0253518	0.0429011
occuptional (SSYK 2012)224 Psychologists and psychotherapists	0.1310728	0.0634630	2.0653411	0.0389585
occuptional (SSYK 2012)225 Veterinarians	0.1451057	0.0246877	5.8776629	0.0000000
occuptional (SSYK 2012)226 Dentists	0.2061785	0.0217913	9.4614837	0.0000000
occuptional (SSYK 2012)227 Naprapaths, physiotherapists, occupational therapists	-0.1370381	0.0177376	-7.7258431	0.0000000
occuptional (SSYK 2012)228 Specialists in health care not elsewhere classified	0.0071140	0.0168624	0.4218871	0.6731313
occuptional (SSYK 2012)231 University and higher education teachers	0.1367054	0.0160981	8.4920376	0.0000000
occuptional (SSYK 2012)232 Vocational education teachers	-0.2934218	0.0634613	-4.6236362	0.0000039
occuptional (SSYK 2012)233 Secondary education teachers	-0.1041122	0.0351897	-2.9586030	0.0031095
occuptional (SSYK 2012)234 Primary- and pre-school teachers	-0.1872326	0.0143234	-13.0717619	0.0000000
occuptional (SSYK 2012)235 Teaching professionals not elsewhere classified	-0.1425854	0.0168496	-8.4622613	0.0000000
occuptional (SSYK 2012)241 Accountants, financial analysts and fund managers	0.2278752	0.0144328	15.7887542	0.0000000
occuptional (SSYK 2012)242 Organisation analysts, policy administrators and human resource specialists	0.1696041	0.0142889	11.8696265	0.0000000
occuptional (SSYK 2012)243 Marketing and public relations professionals	0.1407585	0.0144341	9.7518221	0.0000000
occuptional (SSYK 2012)251 ICT architects, systems analysts and test managers	0.1672958	0.0140985	11.8662120	0.0000000
occuptional (SSYK 2012)261 Legal professionals	0.3910677	0.0155923	25.0808377	0.0000000
occuptional (SSYK 2012)264 Authors, journalists and linguists	-0.0018197	0.0142888	-0.1273494	0.8986706
occuptional (SSYK 2012)265 Creative and performing artists	-0.0880117	0.0149099	-5.9029016	0.0000000
occuptional (SSYK 2012)266 Social work and counselling professionals	-0.0295346	0.0205064	-1.4402594	0.1498765
occuptional (SSYK 2012)267 Religious professionals and deacons	-0.0405199	0.0173192	-2.3395977	0.0193561
occuptional (SSYK 2012)311 Physical and engineering science technicians	0.0168089	0.0141598	1.1870890	0.2352668
occuptional (SSYK 2012)312 Construction and manufacturing supervisors	-0.0106277	0.0149022	-0.7131646	0.4757877
occuptional (SSYK 2012)315 Ship and aircraft controllers and technicians	0.2331542	0.0192622	12.1042123	0.0000000
occuptional (SSYK 2012)321 Medical and pharmaceutical technicians	-0.0730238	0.0143600	-5.0852059	0.0000004
occuptional (SSYK 2012)324 Veterinary assistants	-0.1810371	0.0205006	-8.8308107	0.0000000
occuptional (SSYK 2012)325 Dental hygienists	-0.1573366	0.0177376	-8.8702166	0.0000000
occuptional (SSYK 2012)331 Financial and accounting associate professionals	0.0449942	0.0142214	3.1638300	0.0015694
occuptional (SSYK 2012)332 Insurance advisers, sales and purchasing agents	0.0509263	0.0141278	3.6046962	0.0003165
occuptional (SSYK 2012)333 Business services agents	-0.0320470	0.0145514	-2.2023342	0.0277016
occuptional (SSYK 2012)334 Administrative and specialized secretaries	-0.0007863	0.0147644	-0.0532548	0.9575317
occuptional (SSYK 2012)335 Tax and related government associate professionals	-0.0126953	0.0145522	-0.8723958	0.3830477
occuptional (SSYK 2012)341 Social work and religious associate professionals	-0.2183480	0.0166441	-13.1186498	0.0000000
occuptional (SSYK 2012)342 Athletes, fitness instructors and recreational workers	-0.1492056	0.0147206	-10.1358383	0.0000000
occuptional (SSYK 2012)343 Photographers, interior decorators and entertainers	-0.1203248	0.0162674	-7.3966895	0.0000000
occuptional (SSYK 2012)344 Driving instructors and other instructors	-0.2170414	0.0277391	-7.8243728	0.0000000
occuptional (SSYK 2012)345 Culinary associate professionals	-0.3831146	0.0278107	-13.7757851	0.0000000
occuptional (SSYK 2012)351 ICT operations and user support technicians	-0.0335916	0.0142884	-2.3509619	0.0187758
occuptional (SSYK 2012)352 Broadcasting and audio-visual technicians	-0.2179292	0.0190592	-11.4343596	0.0000000
occuptional (SSYK 2012)411 Office assistants and other secretaries	-0.1911902	0.0141285	-13.5322664	0.0000000
occuptional (SSYK 2012)422 Client information clerks	-0.2841125	0.0141584	-20.0666661	0.0000000
occuptional (SSYK 2012)511 Cabin crew, guides and related workers	-0.1823307	0.0183293	-9.9474824	0.0000000
occuptional (SSYK 2012)524 Event seller and telemarketers	-0.2089114	0.0195200	-10.7024445	0.0000000
year2	0.0238570	0.0010154	23.4955442	0.0000000
sexwomen	-0.0767252	0.0030556	-25.1096548	0.0000000
poly(age4, 3)1	6.6940905	0.0918162	72.9074788	0.0000000
poly(age4, 3)2	-4.1563876	0.0920040	-45.1761784	0.0000000
poly(age4, 3)3	0.2201252	0.0896214	2.4561686	0.0140872

Anova(model, type=2) %>% 
  tidy() %>% 
  knitr::kable( 
  booktabs = TRUE,
  caption = 'Anova report from linear model fit')  

(#tab:ssyk14-18_2)Anova report from linear model fit

term	sumsq	df	statistic	p.value
occuptional (SSYK 2012)	180.651642	73	317.1308	0
year2	4.307761	1	552.0406	0
sex	4.919966	1	630.4948	0
poly(age4, 3)	56.139659	3	2398.1033	0
Residuals	29.629289	3797	NA	NA

9.10 SSYK 214, Architects, engineers and related professionals by region, Year 2000 - 2018

Average monthly pay, non-manual workers private sector (SLP) by region, occupational group (SSYK) and sex. Year 2000 - 2013 Average monthly pay (total pay), non-manual workers private sector (SLP) 214 Engineering professionals

tb <- readfile("AM0103H2_4.csv") %>%
    filter(year2 > 1994) %>%
    group_by (`occupational group (SSYK)`, region, sex) %>%   
    mutate (grouprelsal = relative_dev (salary))

model <- lm (log(salary) ~ year2 + region + sex, data = tb)
    
summary(model) %>%  
  tidy() %>%
  knitr::kable( 
  booktabs = TRUE,
  caption = 'Summary from linear model fit')

(#tab:ssyk00-13_3)Summary from linear model fit

term	estimate	std.error	statistic	p.value
(Intercept)	-46.0930565	1.0429843	-44.193433	0.0000000
year2	0.0282072	0.0005198	54.261845	0.0000000
regionSE11 Stockholm	0.0703349	0.0086749	8.107902	0.0000000
regionSE12 East-Central Sweden	-0.0157809	0.0086749	-1.819154	0.0701715
regionSE21 Småland and islands	-0.1108373	0.0087553	-12.659461	0.0000000
regionSE22 South Sweden	-0.0267388	0.0086749	-3.082338	0.0023012
regionSE23 West Sweden	-0.0314878	0.0086749	-3.629783	0.0003487
regionSE31 North-Central Sweden	-0.0709119	0.0086749	-8.174426	0.0000000
regionSE32 Central Norrland	-0.0526836	0.0090429	-5.825939	0.0000000
regionSE33 Upper Norrland	-0.0949739	0.0089418	-10.621353	0.0000000
sexwomen	-0.0997849	0.0041684	-23.938280	0.0000000

Anova(model, type=2) %>% 
  tidy() %>% 
  knitr::kable( 
  booktabs = TRUE,
  caption = 'Anova report from linear model fit') 

(#tab:ssyk00-13_3)Anova report from linear model fit

term	sumsq	df	statistic	p.value
year2	3.1019977	1	2944.34786	0
region	0.6452772	8	76.56036	0
sex	0.6037237	1	573.04126	0
Residuals	0.2454756	233	NA	NA

Average monthly pay, non-manual workers private sector (SLP) by region, occupational group (SSYK 2012) and sex. Year 2014 - 2018 Average monthly pay (total pay), non-manual workers private sector (SLP) 214 Engineering professionals

tb <- readfile("0000002T_1.csv") %>%
    filter(year2 > 1994) %>%
    group_by (`occuptional  (SSYK 2012)`, region, sex) %>%   
    mutate (grouprelsal = relative_dev (salary))

model <- lm (log(salary) ~ year2 + region + sex, data = tb)
    
summary(model) %>%  
  tidy() %>%
  knitr::kable( 
  booktabs = TRUE,
  caption = 'Summary from linear model fit')

(#tab:ssyk14-18_3)Summary from linear model fit

term	estimate	std.error	statistic	p.value
(Intercept)	-28.1357687	3.8206133	-7.364202	0.00e+00
year2	0.0192890	0.0018951	10.178127	0.00e+00
regionSE12 East-Central Sweden	-0.0564776	0.0107205	-5.268172	1.40e-06
regionSE21 Småland and islands	-0.1296477	0.0107205	-12.093398	0.00e+00
regionSE22 South Sweden	-0.0453027	0.0107205	-4.225790	7.06e-05
regionSE23 West Sweden	-0.0511164	0.0107205	-4.768079	9.80e-06
regionSE31 North-Central Sweden	-0.0979477	0.0107205	-9.136453	0.00e+00
regionSE32 Central Norrland	-0.1136594	0.0107205	-10.602028	0.00e+00
regionSE33 Upper Norrland	-0.1411647	0.0107205	-13.167691	0.00e+00
sexwomen	-0.0590904	0.0053603	-11.023773	0.00e+00

Anova(model, type=2) %>% 
  tidy() %>% 
  knitr::kable( 
  booktabs = TRUE,
  caption = 'Anova report from linear model fit') 

(#tab:ssyk14-18_3)Anova report from linear model fit

term	sumsq	df	statistic	p.value
year2	0.0595304	1	103.59427	0
region	0.1664982	7	41.39124	0
sex	0.0698335	1	121.52358	0
Residuals	0.0402255	70	NA	NA

9.11 Level of education , SSYK 214, Architects, engineers and related professionals

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 5 non-manual workers private sector 214 Engineering professionals

tb <- readfile("000000CY.csv") %>%
    group_by (`level of education`, sex) %>%   
    mutate (grouprelsal = relative_dev (salary))

model <- lm (log(salary) ~ `level of education` + sex + year2, data = tb)
    
summary(model) %>%  
  tidy() %>%
  knitr::kable( 
  booktabs = TRUE,
  caption = 'Level of education for SSYK 214')

(#tab:ssyk14-18_4)Level of education for SSYK 214

term	estimate	std.error	statistic	p.value
(Intercept)	-23.8760678	7.0878306	-3.3686002	0.0013476
level of educationpost-graduate education (ISCED97 6)	0.1123593	0.0179837	6.2478322	0.0000001
level of educationpost-secondary education 3 years or more (ISCED97 5A)	-0.0049891	0.0179837	-0.2774206	0.7824436
level of educationpost-secondary education, less than 3 years (ISCED97 4+5B)	-0.0408825	0.0179837	-2.2733079	0.0267275
level of educationprimary and secondary education 9-10 years (ISCED97 2)	-0.1207083	0.0199380	-6.0541888	0.0000001
level of educationupper secondary education 3 years (ISCED97 3A)	-0.0850009	0.0179837	-4.7265479	0.0000150
level of educationupper secondary education, 2 years or less (ISCED97 3C)	-0.1108685	0.0179837	-6.1649398	0.0000001
sexwomen	-0.0854343	0.0099224	-8.6102526	0.0000000
year2	0.0171685	0.0035157	4.8833120	0.0000086

Anova(model, type=2) %>% 
  tidy() %>% 
  knitr::kable( 
  booktabs = TRUE,
  caption = 'Anova report from linear model fit') 

(#tab:ssyk14-18_4)Anova report from linear model fit

term	sumsq	df	statistic	p.value
level of education	0.3709710	6	38.23488	0.0e+00
sex	0.1198839	1	74.13645	0.0e+00
year2	0.0385619	1	23.84674	8.6e-06
Residuals	0.0937901	58	NA	NA

9.12 The correlation between the proportion of engineers who are women and the salaries of engineers in the region. Year 2014 - 2018

Average basic salary, monthly salary and women´s salary as a percentage of men´s salary by region, sector, occupational group (SSYK 2012) and sex . Year 2014 - 2018 Monthly salaty All sectors 214 Engineering professionals

Average basic salary, monthly salary and women´s salary as a percentage of men´s salary by region, sector, occupational group (SSYK 2012) and sex . Year 2014 - 2018 Number of employees All sectors 214 Engineering professionals

tb <- readfile("000000CG_10.csv")
tb <- readfile("000000CD_10.csv") %>% 
  left_join(tb, by = c("region", "year", "sex")) %>%
  group_by (`region`, year) %>%   
  mutate (perc_women = as.numeric (sub ("%", "", perc_women (salary.x))))
  
model <- lm (log(salary.y) ~ year2.x + perc_women, data = tb)
    
summary(model) %>%  
  tidy() %>%
  knitr::kable( 
  booktabs = TRUE,
  caption = 'The correlation between the proportion of engineers who are women and the salaries of engineers in the region.')

(#tab:ssyk14-18_5)The correlation between the proportion of engineers who are women and the salaries of engineers in the region.

term	estimate	std.error	statistic	p.value
(Intercept)	-16.8472433	7.9797518	-2.111249	0.0381752
year2.x	0.0135156	0.0039623	3.411042	0.0010580
perc_women	0.0120174	0.0018213	6.598323	0.0000000

Anova(model, type=2) %>% 
  tidy() %>% 
  knitr::kable( 
  booktabs = TRUE,
  caption = 'Anova report from linear model fit') 

(#tab:ssyk14-18_5)Anova report from linear model fit

term	sumsq	df	statistic	p.value
year2.x	0.0249689	1	11.63521	0.001058
perc_women	0.0934314	1	43.53787	0.000000
Residuals	0.1566566	73	NA	NA

9.13 The correlation between the proportion of engineers who are women and the salaries of engineers in the region. Year 2003 - 2013

Average basic salary, monthly salary and women´s salary as a percentage of men´s salary by region, sector, occupational group (SSYK 2012) and sex . Year 2003 - 2013 Number of employees All sectors 214 Engineering professionals

tb <- readfile("AM0110A2.csv")
tb <- readfile("AM0110A4.csv") %>% 
  left_join(tb, by = c("region", "year", "sex")) %>%
  group_by (`region`, year) %>%   
  mutate (perc_women = as.numeric (sub ("%", "", perc_women (salary.x))))
  
model <- lm (log(salary.y) ~ year2.x + perc_women, data = tb)
    
summary(model) %>%  
  tidy() %>%
  knitr::kable( 
  booktabs = TRUE,
  caption = 'The correlation between the proportion of engineers who are women and the salaries of engineers in the region.')

(#tab:ssyk14-18_6)The correlation between the proportion of engineers who are women and the salaries of engineers in the region.

term	estimate	std.error	statistic	p.value
(Intercept)	-30.8708500	4.2567427	-7.252224	0
year2.x	0.0204578	0.0021284	9.611658	0
perc_women	0.0120317	0.0018553	6.485125	0

Anova(model, type=2) %>% 
  tidy() %>% 
  knitr::kable( 
  booktabs = TRUE,
  caption = 'Anova report from linear model fit') 

(#tab:ssyk14-18_6)Anova report from linear model fit

term	sumsq	df	statistic	p.value
year2.x	0.4587383	1	92.38397	0
perc_women	0.2088358	1	42.05684	0
Residuals	0.7299375	147	NA	NA

9.14 F-value for age compared to yearly increase in salary cleaned for the effect of age, Year 2004 - 2013

Average basic salary, monthly salary and women´s salary as a percentage of men´s salary by sector, occupational group (SSYK) , sex and age. Year 2004 - 2013 Monthly salary All sectors

tb <- readfile("AM0110E2.csv") %>%
  rowwise() %>% 
  mutate(age2 = unlist(lapply(strsplit(substr(age, 1, 5), "-"), strtoi))[1]) %>%  
  rowwise() %>% 
  mutate(age3 = unlist(lapply(strsplit(substr(age, 1, 5), "-"), strtoi))[2]) %>% 
  mutate(age4 = (age3 + age2) / 2)
  
summary_table = 0
anova_table = 0
for (i in unique(tb$`occupational group (SSYK)`)){
  temp <- filter(tb, `occupational group (SSYK)` == i)
  if (dim(temp)[1] > 50){
    model <- lm (log(salary) ~ year2 + sex + poly(age4, 3), data = temp)
    summary_table <- rbind (summary_table, mutate (mutate (tidy (summary (model)), ssyk = i), spannvidd = max (filter (temp, year2 == 2013)$salary) / min (filter (temp, year2 == 2013)$salary)))
    anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i))
  }
}

merge(summary_table, anova_table, by = "ssyk", all = TRUE) %>% 
  filter (term.y == "poly(age4, 3)") %>% 
  filter (term.x == "year2") %>%
  ggplot () + 
    geom_point (mapping = aes(x = estimate, y = statistic.y))

(#fig:ssyk14-18_7)F-value for age compared to yearly increase in salary cleaned for the effect of age

merge(summary_table, anova_table, by = "ssyk", all = TRUE) %>% 
  filter (term.y == "poly(age4, 3)") %>% 
  filter (term.x == "year2") %>%
  select (ssyk, estimate, statistic.y) %>%
  arrange (desc (statistic.y)) %>%
  knitr::kable( 
  booktabs = TRUE,
  caption = 'Correlation for F-value (age) and yearly increase in salaries cleaned for the effect of age') 

(#tab:ssyk14-18_7)Correlation for F-value (age) and yearly increase in salaries cleaned for the effect of age

ssyk	estimate	statistic.y
233 Primary education teaching professionals	0.0222522	1951.69188
222 Health professionals (except nursing)	0.0287220	1457.04286
231 College, university and higher education teaching professionals	0.0323762	1427.63618
122 Production and operations managers	0.0271575	1416.37235
232 Secondary education teaching professionals	0.0232175	1315.72047
312 Computer associate professionals	0.0261719	1293.59017
323 Nursing associate professionals	0.0275965	1253.47223
249 Psychologists, social work and related professionals	0.0284160	1224.76753
214 Architects, engineers and related professionals	0.0248582	1142.58491
331 Pre-primary education teaching associate professionals	0.0268390	999.04369
341 Finance and sales associate professionals	0.0280533	871.24552
011 Armed forces	0.0148934	842.31457
213 Computing professionals	0.0266410	684.64507
247 Public service administrative professionals	0.0337322	668.36080
415 Mail carriers and sorting clerks	0.0247880	649.49005
246 Religious professionals	0.0300239	649.17379
345 Police officers and detectives	0.0263753	625.49516
346 Social work associate professionals	0.0281937	620.00016
245 Writers and creative or performing artists	0.0205700	609.58554
211 Physicists, chemists and related professionals	0.0206138	597.42679
241 Business professionals	0.0258883	585.28238
322 Health associate professionals (except nursing)	0.0246437	488.16340
235 Other teaching professionals	0.0144847	450.42722
234 Special education teaching professionals	0.0243006	396.46505
343 Administrative associate professionals	0.0285844	395.88113
513 Personal care and related workers	0.0289085	390.16452
815 Chemical-processing-plant operators	0.0289092	379.39618
123 Other specialist managers	0.0308923	374.21391
311 Physical and engineering science technicians	0.0329471	365.90476
223 Nursing and midwifery professionals	0.0264022	338.30668
515 Protective services workers	0.0300250	330.90303
422 Client information clerks	0.0300153	322.90797
243 Archivists, librarians and related information professionals	0.0263611	312.02627
822 Chemical-products machine operators	0.0294656	305.02965
419 Other office clerks	0.0298857	300.21773
221 Life science professionals	0.0306660	286.98782
324 Life science technicians	0.0279558	261.87836
413 Stores and transport clerks	0.0317471	256.92774
314 Ship and aircraft controllers and technicians	0.0218090	237.48869
244 Social science and linguistics professionals (except social work professionals)	0.0229451	210.55221
342 Business services agents and trade brokers	0.0306404	207.12314
412 Numerical clerks	0.0300400	196.86360
411 Office secretaries and data entry operators	0.0269750	193.76835
829 Other machine operators and assemblers	0.0295291	190.23514
721 Metal moulders, welders, sheet-metal workers, structural-metal preparers and related trades workers	0.0295673	175.49681
823 Rubber- and plastic-products machine operators	0.0287661	167.38463
313 Optical and electronic equipment operators	0.0282529	162.50630
512 Housekeeping and restaurant services workers	0.0307579	153.68491
722 Blacksmiths, tool-makers and related trades workers	0.0295025	151.47248
315 Safety and quality inspectors	0.0327656	145.56840
511 Travel attendants and related workers	0.0248600	140.94823
344 Customs, tax and related government associate professionals	0.0320078	140.50202
347 Artistic, entertainment and sports associate professionals	0.0340164	126.91226
814 Wood-processing- and papermaking-plant operators	0.0310770	122.20339
932 Manufacturing labourers	0.0302280	113.71684
825 Printing-, binding- and paper-products machine operators	0.0289276	113.27582
131 Managers of small enterprises	0.0366800	104.47801
242 Legal professionals	0.0325003	103.25042
248 Administrative professionals of special-interest organisations	0.0217864	102.09489
723 Machinery mechanics and fitters	0.0309173	97.76839
714 Painters, building structure cleaners and related trades workers	0.0230825	89.43137
713 Building finishers and related trades workers	0.0288156	88.76996
212 Mathematicians and statisticians	0.0360177	87.18638
912 Helpers and cleaners	0.0295741	84.83107
611 Market gardeners and crop growers	0.0267796	82.14254
821 Metal- and mineral-products machine operators	0.0247308	79.50551
812 Metal-processing-plant operators	0.0280601	77.88761
828 Assemblers	0.0287109	77.38124
831 Locomotive-engine drivers and related worker	0.0315792	68.37296
734 Craft printing and related trades workers	0.0235737	66.46350
724 Electrical and electronic equipment mechanics and fitters	0.0365407	64.27696
913 Helpers in restaurants	0.0301832	60.46244
914 Doorkeepers, newspaper and package deliverers and related workers	0.0277470	58.60471
121 Directors and chief executives	0.0293868	55.09215
514 Other personal services workers	0.0274374	52.59568
833 Agricultural and other mobile-plant operators	0.0317821	51.72220
933 Transport labourers and freight handlers	0.0292293	46.02971
826 Textile-, fur- and leather-products machine operators	0.0268432	43.06312
824 Wood-products machine operators	0.0284347	42.56020
332 Other teaching associate professionals	0.0188570	42.34720
612 Animal producers and related workers	0.0247740	39.10006
522 Shop and stall salespersons and demonstrators	0.0326857	38.81291
321 Agronomy and forestry technicians	0.0382170	37.04074
915 Garbage collectors and related labourers	0.0228931	29.19059
816 Power-production and related plant operators	0.0267858	27.65173
712 Building frame and related trades workers	0.0288961	24.53035
414 Library and filing clerks	0.0284717	20.67900
832 Motor-vehicle drivers	0.0298189	20.31541
827 Food and related products machine operators	0.0289659	19.18984
421 Cashiers, tellers and related clerks	0.0241952	15.24343
919 Other sales and services elementary occupations	0.0257387	10.27841

9.15 F-value for age compared to yearly increase in salary cleaned for the effect of age, Year 2014 - 2018

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

tb <- readfile("000000D2_1.csv") %>%
  rowwise() %>% 
  mutate(age2 = unlist(lapply(strsplit(substr(age, 1, 5), "-"), strtoi))[1]) %>%  
  rowwise() %>% 
  mutate(age3 = unlist(lapply(strsplit(substr(age, 1, 5), "-"), strtoi))[2]) %>% 
  mutate(age4 = (age3 + age2) / 2)
  
summary_table = 0
anova_table = 0
for (i in unique(tb$`occuptional  (SSYK 2012)`)){
  temp <- filter(tb, `occuptional  (SSYK 2012)` == i)
  if (dim(temp)[1] > 50){
    model <- lm (log(salary) ~ year2 + sex + poly(age4, 3), data = temp)
    summary_table <- rbind (summary_table, mutate (mutate (tidy (summary (model)), ssyk = i), spannvidd = max (filter (temp, year2 == 2018)$salary) / min (filter (temp, year2 == 2018)$salary)))
    anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i))
  }
}

merge(summary_table, anova_table, by = "ssyk", all = TRUE) %>% 
  filter (term.y == "poly(age4, 3)") %>% 
  filter (term.x == "year2") %>%
  ggplot () + 
    geom_point (mapping = aes(x = estimate, y = statistic.y))

(#fig:ssyk14-18_8)F-value for age compared to yearly increase in salary cleaned for the effect of age

merge(summary_table, anova_table, by = "ssyk", all = TRUE) %>% 
  filter (term.y == "poly(age4, 3)") %>% 
  filter (term.x == "year2") %>%
  select (ssyk, estimate, statistic.y) %>%
  arrange (desc (statistic.y)) %>%
  knitr::kable( 
  booktabs = TRUE,
  caption = 'Correlation for F-value (age) and yearly increase in salaries cleaned for the effect of age') 

(#tab:ssyk14-18_8)Correlation for F-value (age) and yearly increase in salaries cleaned for the effect of age

ssyk	estimate	statistic.y
234 Primary- and pre-school teachers	0.0345563	1496.606988
231 University and higher education teachers	0.0254827	1127.841359
532 Personal care workers in health services	0.0285338	975.453302
233 Secondary education teachers	0.0294574	849.896621
235 Teaching professionals not elsewhere classified	0.0245885	812.791078
214 Engineering professionals	0.0192045	801.889286
242 Organisation analysts, policy administrators and human resource specialists	0.0223881	701.614565
332 Insurance advisers, sales and purchasing agents	0.0176134	664.442254
531 Child care workers and teachers aides	0.0216205	622.684596
222 Nursing professionals	0.0416796	611.433214
351 ICT operations and user support technicians	0.0211211	596.249682
266 Social work and counselling professionals	0.0317545	587.044065
251 ICT architects, systems analysts and test managers	0.0249600	555.431565
534 Attendants, personal assistants and related workers	0.0191811	428.571695
241 Accountants, financial analysts and fund managers	0.0270620	391.870288
533 Health care assistants	0.0205813	352.960310
422 Client information clerks	0.0175877	245.488178
321 Medical and pharmaceutical technicians	0.0285567	236.191372
335 Tax and related government associate professionals	0.0227003	198.582732
411 Office assistants and other secretaries	0.0250406	166.766194
331 Financial and accounting associate professionals	0.0217619	158.923954
311 Physical and engineering science technicians	0.0209631	154.667997
723 Machinery mechanics and fitters	0.0204855	142.874499
341 Social work and religious associate professionals	0.0255936	134.606247
515 Building caretakers and related workers	0.0191368	111.384579
541 Other surveillance and security workers	0.0239438	99.793943
333 Business services agents	0.0246292	80.618256
227 Naprapaths, physiotherapists, occupational therapists	0.0213986	80.052441
819 Process control technicians	0.0232825	65.100656
911 Cleaners and helpers	0.0169943	58.927493
941 Fast-food workers, food preparation assistants	0.0199578	40.150153
962 Newspaper distributors, janitors and other service workers	0.0141958	39.069854
611 Market gardeners and crop growers	0.0082242	37.208581
432 Stores and transport clerks	0.0217702	33.903777
834 Mobile plant operators	0.0251657	29.791104
512 Cooks and cold-buffet managers	0.0278549	22.750271
722 Blacksmiths, toolmakers and related trades workers	0.0192278	14.874887
522 Shop staff	0.0255930	14.735285
833 Heavy truck and bus drivers	0.0188392	4.531345
821 Assemblers	0.0283101	2.226122

9.16 Women’s urbanization, comparison between the SSYK, population dependence, Year 2003 - 2013

Average basic salary, monthly salary and women´s salary as a percentage of men´s salary by region, sector, occupational group (SSYK) and sex. Year 2003 - 2013 Number of employees All sectors

tb <- readfile("AM0110A4_2.csv")
  
summary_table = 0
anova_table = 0
tb <- tb %>%
  group_by (`region`, year, `occupational group (SSYK)`) %>%
  mutate (perc_women = as.numeric (sub ("%", "", perc_women (salary)))) %>%
  group_by(region, year) %>%
  mutate(sum_pop = sum(salary))
for (i in unique(tb$`occupational group (SSYK)`)){
  temp <- filter(tb, `occupational group (SSYK)` == i)
  if (dim(temp)[1] > 50){
    model <- lm(perc_women ~ sum_pop + year2, data = temp)
    summary_table <- rbind (summary_table, mutate (mutate (tidy (summary (model)), ssyk = i), spannvidd = max (filter (temp, year2 == 2013)$salary) / min (filter (temp, year2 == 2013)$salary)))
    anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i))
  }
}  

## Note: model has aliased coefficients
##       sums of squares computed by model comparison

merge(summary_table, anova_table, by = "ssyk", all = TRUE) %>% 
  filter (term.y == "sum_pop") %>% 
  filter (term.x == "sum_pop") %>%
  ggplot () + 
    geom_point (mapping = aes(x = estimate, y = statistic.y)) +
    labs(
      x = "estimate",
      y = "F value for SSYK"
    )   

(#fig:ssyk14-18_9)Womens urbanization, comparison between the SSYK, Year 2014 - 2018, positive x = tendency for women within SSYK to migrate to larger regions, negative x = tendency for women within SSYK to migrate to smaller regions

merge(summary_table, anova_table, by = "ssyk", all = TRUE) %>% 
  filter (term.y == "sum_pop") %>% 
  filter (term.x == "sum_pop") %>%
  select (ssyk, estimate, statistic.y) %>%
  arrange (desc (statistic.y)) %>%
  knitr::kable( 
  booktabs = TRUE,
  caption = 'Womens urbanization, comparison between the SSYK, Year 2003 - 2013')    

(#tab:ssyk14-18_9)Womens urbanization, comparison between the SSYK, Year 2003 - 2013

ssyk	estimate	statistic.y
321 Agronomy and forestry technicians	6.89e-05	1063.8292979
814 Wood-processing- and papermaking-plant operators	1.85e-05	564.8688266
221 Life science professionals	3.68e-05	252.4643108
826 Textile-, fur- and leather-products machine operators	-6.81e-05	250.8778596
311 Physical and engineering science technicians	6.70e-06	243.9579072
822 Chemical-products machine operators	4.16e-05	186.4551637
512 Housekeeping and restaurant services workers	-3.55e-05	165.2195745
222 Health professionals (except nursing)	8.80e-06	159.1321126
912 Helpers and cleaners	-3.35e-05	152.0551830
345 Police officers and detectives	6.50e-06	145.1628182
913 Helpers in restaurants	-3.05e-05	139.9663729
123 Other specialist managers	1.02e-05	133.9974558
214 Architects, engineers and related professionals	7.90e-06	90.7204155
232 Secondary education teaching professionals	4.90e-06	79.7756403
323 Nursing associate professionals	1.90e-06	77.2529656
828 Assemblers	-1.69e-05	63.4276805
515 Protective services workers	6.90e-06	58.8211210
131 Managers of small enterprises	9.90e-06	58.0455582
249 Psychologists, social work and related professionals	-3.70e-06	56.0577280
414 Library and filing clerks	-1.43e-05	51.0486557
412 Numerical clerks	-5.90e-06	40.4612509
816 Power-production and related plant operators	-5.50e-06	36.4018548
815 Chemical-processing-plant operators	2.77e-05	34.8057925
346 Social work associate professionals	-5.50e-06	33.6775476
413 Stores and transport clerks	6.50e-06	33.1596176
825 Printing-, binding- and paper-products machine operators	-1.31e-05	30.0905987
241 Business professionals	-6.00e-06	29.1297561
932 Manufacturing labourers	1.09e-05	28.6839067
331 Pre-primary education teaching associate professionals	1.50e-06	27.0837611
832 Motor-vehicle drivers	4.90e-06	26.1357552
341 Finance and sales associate professionals	5.50e-06	25.7516389
411 Office secretaries and data entry operators	-1.25e-05	25.4240116
247 Public service administrative professionals	3.70e-06	24.3480698
231 College, university and higher education teaching professionals	-3.20e-06	22.0484247
111 Legislators and senior government officials	6.70e-06	15.8424018
419 Other office clerks	-3.30e-06	15.5967644
933 Transport labourers and freight handlers	-9.30e-06	14.6766564
242 Legal professionals	-5.00e-06	13.4616919
514 Other personal services workers	-1.30e-05	12.7524231
513 Personal care and related workers	-2.60e-06	11.4225115
511 Travel attendants and related workers	1.77e-05	11.2070758
415 Mail carriers and sorting clerks	-6.80e-06	10.9993756
211 Physicists, chemists and related professionals	1.42e-05	10.7242720
315 Safety and quality inspectors	7.80e-06	9.4952835
223 Nursing and midwifery professionals	2.10e-06	8.7877674
313 Optical and electronic equipment operators	-1.70e-05	8.2244191
421 Cashiers, tellers and related clerks	-9.60e-06	6.6756545
344 Customs, tax and related government associate professionals	3.10e-06	6.6499868
246 Religious professionals	1.00e-05	6.6417584
833 Agricultural and other mobile-plant operators	-7.20e-06	6.3150838
122 Production and operations managers	-3.00e-06	6.1124364
821 Metal- and mineral-products machine operators	-2.90e-06	5.5545700
829 Other machine operators and assemblers	-3.90e-06	5.3658038
235 Other teaching professionals	-3.10e-06	4.8483487
322 Health associate professionals (except nursing)	1.40e-06	4.2865473
121 Directors and chief executives	5.70e-06	4.2290452
812 Metal-processing-plant operators	-5.30e-06	3.3375084
611 Market gardeners and crop growers	-6.80e-06	3.1968190
827 Food and related products machine operators	-6.20e-06	2.8037469
831 Locomotive-engine drivers and related worker	1.94e-05	2.2979810
919 Other sales and services elementary occupations	-2.30e-06	1.9045323
914 Doorkeepers, newspaper and package deliverers and related workers	2.00e-06	1.7352638
724 Electrical and electronic equipment mechanics and fitters	-2.20e-06	1.3265619
324 Life science technicians	-1.40e-06	1.2988170
422 Client information clerks	1.80e-06	1.1037460
245 Writers and creative or performing artists	1.30e-06	0.9519936
243 Archivists, librarians and related information professionals	-1.20e-06	0.8667095
343 Administrative associate professionals	-9.00e-07	0.8099945
244 Social science and linguistics professionals (except social work professionals)	-3.60e-06	0.7938810
011 Armed forces	-9.00e-07	0.7409832
712 Building frame and related trades workers	9.00e-07	0.6206295
342 Business services agents and trade brokers	-8.00e-07	0.4080769
823 Rubber- and plastic-products machine operators	-2.70e-06	0.3376290
713 Building finishers and related trades workers	3.00e-07	0.3320734
824 Wood-products machine operators	2.00e-06	0.2840136
522 Shop and stall salespersons and demonstrators	-8.00e-07	0.2706724
233 Primary education teaching professionals	2.00e-07	0.2217668
723 Machinery mechanics and fitters	-2.00e-07	0.1640692
312 Computer associate professionals	-3.00e-07	0.0551337
213 Computing professionals	-2.00e-07	0.0357450
234 Special education teaching professionals	-1.00e-07	0.0073434

9.17 Women’s urbanization, comparison between the SSYK, population dependence, Year 2014 - 2018

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

tb <- readfile("000000CD_13.csv")
  
summary_table = 0
anova_table = 0
tb <- tb %>%
  group_by (`region`, year, `occuptional  (SSYK 2012)`) %>%
  mutate (perc_women = as.numeric (sub ("%", "", perc_women (salary)))) %>%
  group_by(region, year) %>%
  mutate(sum_pop = sum(salary))
for (i in unique(tb$`occuptional  (SSYK 2012)`)){
  temp <- filter(tb, `occuptional  (SSYK 2012)` == i)
  if (dim(temp)[1] > 50){
    model <- lm(perc_women ~ sum_pop + year2, data = temp)
    summary_table <- rbind (summary_table, mutate (mutate (tidy (summary (model)), ssyk = i), spannvidd = max (filter (temp, year2 == 2018)$salary) / min (filter (temp, year2 == 2018)$salary)))
    anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i))
  }
}  

merge(summary_table, anova_table, by = "ssyk", all = TRUE) %>% 
  filter (term.y == "sum_pop") %>% 
  filter (term.x == "sum_pop") %>%
  ggplot () + 
    geom_point (mapping = aes(x = estimate, y = statistic.y)) +
    labs(
      x = "estimate",
      y = "F value for SSYK"
    )   

(#fig:ssyk14-18_10)Womens urbanization, comparison between the SSYK, Year 2014 - 2018, positive x = tendency for women within SSYK to migrate to larger regions, negative x = tendency for women within SSYK to migrate to smaller regions

merge(summary_table, anova_table, by = "ssyk", all = TRUE) %>% 
  filter (term.y == "sum_pop") %>% 
  filter (term.x == "sum_pop") %>%
  select (ssyk, estimate, statistic.y) %>%
  arrange (desc (statistic.y)) %>%
  knitr::kable( 
  booktabs = TRUE,
  caption = 'Womens urbanization, comparison between the SSYK, Year 2014 - 2018')    

(#tab:ssyk14-18_10)Womens urbanization, comparison between the SSYK, Year 2014 - 2018

ssyk	estimate	statistic.y
214 Engineering professionals	1.01e-05	295.7120388
441 Library and filing clerks	-1.93e-05	131.3075554
213 Biologists, pharmacologists and specialists in agriculture and forestry	3.75e-05	106.8084503
125 Sales and marketing managers	2.01e-05	97.2212141
232 Vocational education teachers	1.74e-05	77.1493980
134 Architectural and engineering managers	1.21e-05	76.7866348
311 Physical and engineering science technicians	7.70e-06	74.8367365
911 Cleaners and helpers	-2.84e-05	66.1971134
221 Medical doctors	8.10e-06	59.6028879
262 Museum curators and librarians and related professionals	-8.20e-06	53.7628051
241 Accountants, financial analysts and fund managers	-1.35e-05	53.5010341
141 Primary and secondary schools and adult education managers	8.30e-06	52.5065690
242 Organisation analysts, policy administrators and human resource specialists	-8.40e-06	49.1200001
129 Administration and service managers not elsewhere classified	1.53e-05	43.1139309
261 Legal professionals	-1.12e-05	42.8894144
224 Psychologists and psychotherapists	7.20e-06	33.7388945
234 Primary- and pre-school teachers	-2.60e-06	31.4538561
411 Office assistants and other secretaries	-5.10e-06	31.3747529
222 Nursing professionals	2.80e-06	29.9015182
962 Newspaper distributors, janitors and other service workers	-8.20e-06	27.1998547
332 Insurance advisers, sales and purchasing agents	7.80e-06	20.7025003
532 Personal care workers in health services	-3.50e-06	20.1222435
243 Marketing and public relations professionals	-1.12e-05	19.5869423
159 Other social services managers	5.30e-06	19.0970453
223 Nursing professionals (cont.)	2.60e-06	18.7455886
331 Financial and accounting associate professionals	-6.30e-06	18.4866717
335 Tax and related government associate professionals	-4.60e-06	17.7634619
515 Building caretakers and related workers	9.70e-06	15.2319657
227 Naprapaths, physiotherapists, occupational therapists	3.00e-06	14.7936021
266 Social work and counselling professionals	-1.50e-06	13.7487061
336 Police officers	1.80e-06	10.3872611
333 Business services agents	-6.60e-06	10.1427348
819 Process control technicians	2.50e-06	9.2296330
123 Administration and planning managers	7.00e-06	7.8311381
341 Social work and religious associate professionals	5.90e-06	6.9756456
422 Client information clerks	6.30e-06	5.8149395
151 Health care managers	-2.60e-06	5.5228329
251 ICT architects, systems analysts and test managers	-3.00e-06	4.6258473
541 Other surveillance and security workers	-2.40e-06	4.1592861
321 Medical and pharmaceutical technicians	3.10e-06	4.1444866
218 Specialists within environmental and health protection	3.00e-06	3.6257049
231 University and higher education teachers	-2.40e-06	3.3708760
233 Secondary education teachers	-1.30e-06	3.1488440
432 Stores and transport clerks	2.40e-06	2.3799201
351 ICT operations and user support technicians	2.20e-06	1.9880575
531 Child care workers and teachers aides	-1.00e-06	1.8373728
131 Information and communications technology service managers	-1.70e-06	0.5228535
153 Elderly care managers	5.00e-07	0.3368178
534 Attendants, personal assistants and related workers	-4.00e-07	0.1490330
235 Teaching professionals not elsewhere classified	5.00e-07	0.1244418
533 Health care assistants	2.00e-07	0.0104101

9.18 Women’s urbanization, comparison between the SSYK, salary dependence, Year 2003 - 2013

Average basic salary, monthly salary and women´s salary as a percentage of men´s salary by region, sector, occupational group (SSYK) and sex. Year 2003 - 2013 Number of employees All sectors

Monthly salary All sectors

tb <- readfile("AM0110A4_2.csv")

summary_table = 0
anova_table = 0
tb <- tb %>%
  group_by (`region`, year, `occupational group (SSYK)`) %>%
  mutate (perc_women = as.numeric (sub ("%", "", perc_women (salary)))) %>%
  group_by(region, year) %>%
  mutate(sum_pop = sum(salary))
tb <- readfile("AM0110A2_2.csv") %>% 
  inner_join(tb, by = c("region", "year", "sex", "occupational group (SSYK)"))  
for (i in unique(tb$`occupational group (SSYK)`)){
  temp <- filter(tb, `occupational group (SSYK)` == i)
  if (dim(temp)[1] > 50){
    model <- lm(perc_women ~ log(salary.x) + year2.x + sex, data = temp)
    summary_table <- rbind (summary_table, mutate (mutate (tidy (summary (model)), ssyk = i)))
    anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i))
  }
}  

## Note: model has aliased coefficients
##       sums of squares computed by model comparison

merge(summary_table, anova_table, by = "ssyk", all = TRUE) %>% 
  filter (term.y == "log(salary.x)") %>% 
  filter (term.x == "log(salary.x)") %>%
  ggplot () + 
    geom_point (mapping = aes(x = estimate, y = statistic.y)) +
    labs(
      x = "estimate",
      y = "F value for SSYK"
    )   

(#fig:ssyk14-18_11)Womens urbanization, comparison between the SSYK, Year 2003 - 2013, positive x = tendency for women within SSYK to migrate to regions with higher salaries, negative x = tendency for women within SSYK to migrate to regions with lower salaries

merge(summary_table, anova_table, by = "ssyk", all = TRUE) %>% 
  filter (term.y == "log(salary.x)") %>% 
  filter (term.x == "log(salary.x)") %>%
  select (ssyk, estimate, statistic.y) %>%
  arrange (desc (statistic.y)) %>%
  knitr::kable( 
  booktabs = TRUE,
  caption = 'Womens urbanization, comparison between the SSYK, Year 2003 - 2013')   

(#tab:ssyk14-18_11)Womens urbanization, comparison between the SSYK, Year 2003 - 2013

ssyk	estimate	statistic.y
123 Other specialist managers	25.8787109	170.3374227
311 Physical and engineering science technicians	26.1163477	159.9096161
211 Physicists, chemists and related professionals	65.0289762	106.5661612
214 Architects, engineers and related professionals	34.7999695	105.1626033
345 Police officers and detectives	23.3684266	84.0420219
131 Managers of small enterprises	29.3507967	82.6987698
221 Life science professionals	49.6106340	76.5555516
411 Office secretaries and data entry operators	-47.6314753	59.3312856
414 Library and filing clerks	-58.5878570	57.9436255
919 Other sales and services elementary occupations	-63.8548847	46.0637678
341 Finance and sales associate professionals	19.5694184	42.8489739
232 Secondary education teaching professionals	49.5262136	40.5058269
412 Numerical clerks	-28.7129046	35.0830272
222 Health professionals (except nursing)	-34.0820318	33.6768753
828 Assemblers	-73.8980268	30.3714326
822 Chemical-products machine operators	73.3334971	25.3239902
323 Nursing associate professionals	9.4754081	25.0575064
419 Other office clerks	-18.8841567	20.2438674
833 Agricultural and other mobile-plant operators	20.6964331	20.0214137
933 Transport labourers and freight handlers	-27.9227454	18.8553169
244 Social science and linguistics professionals (except social work professionals)	-24.2564582	17.4058195
315 Safety and quality inspectors	28.7227212	16.8591720
815 Chemical-processing-plant operators	-49.6794191	16.1639578
241 Business professionals	-11.1459268	16.0893376
821 Metal- and mineral-products machine operators	-16.2693685	14.9696946
511 Travel attendants and related workers	43.3101952	12.7213259
413 Stores and transport clerks	-33.1937595	12.5420915
247 Public service administrative professionals	11.0598948	12.1609171
249 Psychologists, social work and related professionals	-19.0157284	12.1357673
121 Directors and chief executives	9.6653091	11.8925557
344 Customs, tax and related government associate professionals	-23.7826041	11.5428566
111 Legislators and senior government officials	9.7549772	10.0145115
235 Other teaching professionals	-17.7058552	8.5403192
713 Building finishers and related trades workers	-8.5084387	8.3259510
213 Computing professionals	8.1182792	8.0631052
242 Legal professionals	-9.8978296	6.8935447
824 Wood-products machine operators	46.4966519	6.2881429
823 Rubber- and plastic-products machine operators	-63.1633132	5.9217639
816 Power-production and related plant operators	16.5252880	5.7949114
223 Nursing and midwifery professionals	13.8408709	5.7365939
723 Machinery mechanics and fitters	3.7086439	5.5167985
321 Agronomy and forestry technicians	54.1442164	5.1600245
832 Motor-vehicle drivers	16.4949754	4.9859931
512 Housekeeping and restaurant services workers	-117.4415992	4.8097428
346 Social work associate professionals	-23.0070114	4.6554733
522 Shop and stall salespersons and demonstrators	-19.5808129	4.4896508
342 Business services agents and trade brokers	-13.0699044	4.3229861
515 Protective services workers	-22.1098204	4.2145376
322 Health associate professionals (except nursing)	10.7280378	4.0198024
233 Primary education teaching professionals	5.9368753	3.1106372
829 Other machine operators and assemblers	-17.5601082	3.0869220
825 Printing-, binding- and paper-products machine operators	-29.3208137	2.9696055
245 Writers and creative or performing artists	6.1792176	2.8515571
913 Helpers in restaurants	69.8479894	2.3015343
932 Manufacturing labourers	22.8000144	1.9976237
826 Textile-, fur- and leather-products machine operators	84.8122054	1.9315719
812 Metal-processing-plant operators	-33.6103106	1.6710200
234 Special education teaching professionals	12.5779182	1.2721725
243 Archivists, librarians and related information professionals	-7.0838299	1.1275366
914 Doorkeepers, newspaper and package deliverers and related workers	7.7021356	1.0366771
122 Production and operations managers	-3.6643377	0.6933617
011 Armed forces	1.6956196	0.5823651
513 Personal care and related workers	-7.6874977	0.5218200
422 Client information clerks	-7.3272824	0.4888683
313 Optical and electronic equipment operators	-17.2262344	0.4686892
324 Life science technicians	-5.2305361	0.3998384
724 Electrical and electronic equipment mechanics and fitters	-3.9523747	0.3407023
421 Cashiers, tellers and related clerks	11.0290213	0.3239142
246 Religious professionals	17.1532898	0.3207119
415 Mail carriers and sorting clerks	-17.8325528	0.2506752
343 Administrative associate professionals	-1.3593611	0.1554629
331 Pre-primary education teaching associate professionals	-0.8601808	0.1418893
712 Building frame and related trades workers	1.9935962	0.1375431
514 Other personal services workers	-5.5426646	0.0700792
312 Computer associate professionals	-1.0839786	0.0581261
912 Helpers and cleaners	-13.8224170	0.0558828
831 Locomotive-engine drivers and related worker	-3.7374339	0.0256549
827 Food and related products machine operators	2.6007849	0.0116346
814 Wood-processing- and papermaking-plant operators	-0.8430777	0.0105995
231 College, university and higher education teaching professionals	-0.4741251	0.0039935
611 Market gardeners and crop growers	-1.0208046	0.0028677

9.19 Women’s urbanization, comparison between the SSYK, salary dependence, Year 2014 - 2018

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

Monthly salary All sectors

tb <- readfile("000000CD_13.csv")

summary_table = 0
anova_table = 0
tb <- tb %>%
  group_by (`region`, year, `occuptional  (SSYK 2012)`) %>%
  mutate (perc_women = as.numeric (sub ("%", "", perc_women (salary)))) %>%
  group_by(region, year) %>%
  mutate(sum_pop = sum(salary))
tb <- readfile("000000CG_3.csv") %>% 
  inner_join(tb, by = c("region", "year", "sex", "occuptional  (SSYK 2012)"))  
for (i in unique(tb$`occuptional  (SSYK 2012)`)){
  temp <- filter(tb, `occuptional  (SSYK 2012)` == i)
  if (dim(temp)[1] > 50){
    model <- lm(perc_women ~ log(salary.x) + year2.x + sex, data = temp)
    summary_table <- rbind (summary_table, mutate (mutate (tidy (summary (model)), ssyk = i)))
    anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i))
  }
}  
 
merge(summary_table, anova_table, by = "ssyk", all = TRUE) %>% 
  filter (term.y == "log(salary.x)") %>% 
  filter (term.x == "log(salary.x)") %>%
  ggplot () + 
    geom_point (mapping = aes(x = estimate, y = statistic.y)) +
    labs(
      x = "estimate",
      y = "F value for SSYK"
    )   

(#fig:ssyk14-18_12)Womens urbanization, comparison between the SSYK, Year 2014 - 2018, positive x = tendency for women within SSYK to migrate to regions with higher salaries, negative x = tendency for women within SSYK to migrate to regions with lower salaries

merge(summary_table, anova_table, by = "ssyk", all = TRUE) %>% 
  filter (term.y == "log(salary.x)") %>% 
  filter (term.x == "log(salary.x)") %>%
  select (ssyk, estimate, statistic.y) %>%
  arrange (desc (statistic.y)) %>%
  knitr::kable( 
  booktabs = TRUE,
  caption = 'Womens urbanization, comparison between the SSYK, Year 2014 - 2018')   

(#tab:ssyk14-18_12)Womens urbanization, comparison between the SSYK, Year 2014 - 2018

ssyk	estimate	statistic.y
241 Accountants, financial analysts and fund managers	-42.8934750	86.0593947
134 Architectural and engineering managers	60.2961665	79.1276065
214 Engineering professionals	41.4391605	71.4235616
141 Primary and secondary schools and adult education managers	46.8489999	48.7867193
962 Newspaper distributors, janitors and other service workers	-81.1783974	48.0232518
234 Primary- and pre-school teachers	-17.8295506	47.3934455
242 Organisation analysts, policy administrators and human resource specialists	-26.1623709	45.3860211
441 Library and filing clerks	-91.1524715	38.4639599
411 Office assistants and other secretaries	-35.0529614	36.9454704
129 Administration and service managers not elsewhere classified	37.6129265	34.3804780
232 Vocational education teachers	141.7560302	29.5383924
332 Insurance advisers, sales and purchasing agents	29.9895850	28.7795335
311 Physical and engineering science technicians	30.9551484	28.0931343
213 Biologists, pharmacologists and specialists in agriculture and forestry	97.5363529	26.9528526
331 Financial and accounting associate professionals	-15.9838334	26.9272578
541 Other surveillance and security workers	-79.1985728	24.0445092
262 Museum curators and librarians and related professionals	-28.8667633	20.0756006
261 Legal professionals	-25.9007200	18.2836308
532 Personal care workers in health services	-72.8339075	16.1279870
335 Tax and related government associate professionals	-19.8254572	14.8885361
534 Attendants, personal assistants and related workers	68.1094133	11.9819561
333 Business services agents	-20.6701954	10.8426996
125 Sales and marketing managers	27.4388500	9.6220304
221 Medical doctors	-27.0028420	9.4449119
222 Nursing professionals	9.7234828	9.3956916
266 Social work and counselling professionals	-9.9189934	8.9062253
227 Naprapaths, physiotherapists, occupational therapists	31.0932028	8.7061168
243 Marketing and public relations professionals	-28.1013433	8.3839999
341 Social work and religious associate professionals	64.4583221	5.9648445
159 Other social services managers	12.3012224	4.9830838
432 Stores and transport clerks	-36.5866571	4.8695284
224 Psychologists and psychotherapists	-35.4662944	4.7998447
231 University and higher education teachers	-29.2246040	4.2460382
336 Police officers	7.5340282	3.9921676
151 Health care managers	-8.8085169	3.9532513
422 Client information clerks	36.8198644	3.7283559
531 Child care workers and teachers aides	21.2685621	3.5426735
218 Specialists within environmental and health protection	10.7839688	2.9419753
153 Elderly care managers	5.7154589	1.4929492
123 Administration and planning managers	8.5155955	1.4830889
819 Process control technicians	5.4915573	1.3159714
515 Building caretakers and related workers	26.6623075	1.3077954
321 Medical and pharmaceutical technicians	-10.2383770	1.1683240
233 Secondary education teachers	-8.2149697	1.0646254
351 ICT operations and user support technicians	5.6346559	0.7442968
251 ICT architects, systems analysts and test managers	2.9326171	0.1852533
533 Health care assistants	-5.3262052	0.0939506
131 Information and communications technology service managers	1.3365896	0.0579040
235 Teaching professionals not elsewhere classified	1.5774773	0.0463449
223 Nursing professionals (cont.)	0.7538617	0.0203265
911 Cleaners and helpers	-2.4446248	0.0009310

9.20 Salary correlation with population in region, comparison between the SSYK, Year 2003 - 2013

Average basic salary, monthly salary and women´s salary as a percentage of men´s salary by region, sector, occupational group (SSYK) and sex. Year 2003 - 2013 Number of employees All sectors

Monthly salary All sectors

tb <- readfile("AM0110A4_2.csv")

summary_table = 0
anova_table = 0
tb <- tb %>%
  group_by (`region`, year, `occupational group (SSYK)`) %>%
  mutate (perc_women = as.numeric (sub ("%", "", perc_women (salary)))) %>%
  group_by(region, year) %>%
  mutate(sum_pop = sum(salary))
tb <- readfile("AM0110A2_2.csv") %>% 
  inner_join(tb, by = c("region", "year", "sex", "occupational group (SSYK)"))  
for (i in unique(tb$`occupational group (SSYK)`)){
  temp <- filter(tb, `occupational group (SSYK)` == i)
  if (dim(temp)[1] > 25){
    model <- lm(log(salary.x) ~ sum_pop + year2.x + sex, data = temp)
    summary_table <- rbind (summary_table, mutate (mutate (tidy (summary (model)), ssyk = i)))
    anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i))
  }
}  
 
merge(summary_table, anova_table, by = "ssyk", all = TRUE) %>% 
  filter (term.y == "sum_pop") %>% 
  filter (term.x == "sum_pop") %>%
  ggplot () + 
    geom_point (mapping = aes(x = estimate, y = statistic.y)) +
    labs(
      x = "estimate",
      y = "F value for SSYK"
    )   

(#fig:ssyk14-18_13)Salary correlation with population in region, comparison between the SSYK, Year 2003 - 2013, positive x = regions with higher population have higher salaries, negative x = regions with higher population have lower salaries

merge(summary_table, anova_table, by = "ssyk", all = TRUE) %>% 
  filter (term.y == "sum_pop") %>% 
  filter (term.x == "sum_pop") %>%
  select (ssyk, estimate, statistic.y) %>%
  arrange (desc (statistic.y)) %>%
  knitr::kable( 
  booktabs = TRUE,
  caption = 'Salary correlation with population in region, comparison between the SSYK, Year 2003 - 2013')  

(#tab:ssyk14-18_13)Salary correlation with population in region, comparison between the SSYK, Year 2003 - 2013

ssyk	estimate	statistic.y
311 Physical and engineering science technicians	2e-07	500.1280127
341 Finance and sales associate professionals	3e-07	460.0418364
123 Other specialist managers	4e-07	414.5703277
122 Production and operations managers	2e-07	389.7675125
213 Computing professionals	3e-07	334.3595272
212 Mathematicians and statisticians	8e-07	331.9425368
241 Business professionals	3e-07	329.9047688
312 Computer associate professionals	3e-07	322.6172830
247 Public service administrative professionals	2e-07	303.0453641
345 Police officers and detectives	2e-07	301.6616259
245 Writers and creative or performing artists	3e-07	293.8813897
343 Administrative associate professionals	2e-07	244.5345603
214 Architects, engineers and related professionals	2e-07	235.5867367
411 Office secretaries and data entry operators	2e-07	211.9374775
233 Primary education teaching professionals	1e-07	205.1829239
242 Legal professionals	3e-07	190.0943208
331 Pre-primary education teaching associate professionals	1e-07	154.3615009
315 Safety and quality inspectors	2e-07	149.7749605
131 Managers of small enterprises	3e-07	143.4464401
111 Legislators and senior government officials	5e-07	125.2831266
342 Business services agents and trade brokers	1e-07	120.2728134
232 Secondary education teaching professionals	0e+00	119.3366031
121 Directors and chief executives	6e-07	100.7071794
713 Building finishers and related trades workers	1e-07	96.3828334
419 Other office clerks	1e-07	94.2589519
221 Life science professionals	4e-07	93.4799243
323 Nursing associate professionals	1e-07	91.3032534
322 Health associate professionals (except nursing)	1e-07	83.0522375
235 Other teaching professionals	1e-07	78.4718137
522 Shop and stall salespersons and demonstrators	1e-07	67.0062003
243 Archivists, librarians and related information professionals	1e-07	65.8178073
412 Numerical clerks	1e-07	57.8108436
011 Armed forces	2e-07	52.3164331
422 Client information clerks	1e-07	50.7354313
248 Administrative professionals of special-interest organisations	3e-07	47.6430798
414 Library and filing clerks	1e-07	46.4721138
249 Psychologists, social work and related professionals	0e+00	36.2728079
723 Machinery mechanics and fitters	1e-07	34.9451359
919 Other sales and services elementary occupations	1e-07	33.0869788
313 Optical and electronic equipment operators	1e-07	31.7552441
246 Religious professionals	-1e-07	30.4865017
211 Physicists, chemists and related professionals	2e-07	25.9210523
244 Social science and linguistics professionals (except social work professionals)	3e-07	24.7634667
734 Craft printing and related trades workers	3e-07	22.2407552
223 Nursing and midwifery professionals	0e+00	21.2824918
821 Metal- and mineral-products machine operators	1e-07	20.7296809
344 Customs, tax and related government associate professionals	1e-07	20.5345482
347 Artistic, entertainment and sports associate professionals	2e-07	20.3832956
712 Building frame and related trades workers	1e-07	18.9127609
828 Assemblers	1e-07	18.5999873
611 Market gardeners and crop growers	1e-07	18.0628465
413 Stores and transport clerks	0e+00	15.9149525
346 Social work associate professionals	0e+00	14.8305597
827 Food and related products machine operators	1e-07	14.0340683
314 Ship and aircraft controllers and technicians	4e-07	13.9069035
824 Wood-products machine operators	1e-07	13.6820134
511 Travel attendants and related workers	2e-07	13.1825549
722 Blacksmiths, tool-makers and related trades workers	1e-07	13.1654083
825 Printing-, binding- and paper-products machine operators	1e-07	13.0839046
222 Health professionals (except nursing)	0e+00	13.0042385
724 Electrical and electronic equipment mechanics and fitters	1e-07	12.5063433
234 Special education teaching professionals	0e+00	12.4239877
915 Garbage collectors and related labourers	1e-07	12.2904717
913 Helpers in restaurants	0e+00	11.9951185
833 Agricultural and other mobile-plant operators	-1e-07	11.6923549
816 Power-production and related plant operators	1e-07	11.1704916
321 Agronomy and forestry technicians	1e-07	10.0898363
815 Chemical-processing-plant operators	1e-07	9.7664841
832 Motor-vehicle drivers	0e+00	9.1253799
822 Chemical-products machine operators	1e-07	9.0610781
512 Housekeeping and restaurant services workers	0e+00	7.9019662
914 Doorkeepers, newspaper and package deliverers and related workers	0e+00	6.8362319
711 Miners, shot firers, stonecutters and carvers	2e-07	6.7158723
826 Textile-, fur- and leather-products machine operators	0e+00	6.0247561
231 College, university and higher education teaching professionals	0e+00	5.1251920
823 Rubber- and plastic-products machine operators	0e+00	5.0401442
515 Protective services workers	0e+00	3.8642344
324 Life science technicians	0e+00	3.5543298
933 Transport labourers and freight handlers	0e+00	1.1327310
421 Cashiers, tellers and related clerks	0e+00	0.7926133
829 Other machine operators and assemblers	0e+00	0.7630612
721 Metal moulders, welders, sheet-metal workers, structural-metal preparers and related trades workers	0e+00	0.6870392
814 Wood-processing- and papermaking-plant operators	0e+00	0.2763579
415 Mail carriers and sorting clerks	0e+00	0.2720320
513 Personal care and related workers	0e+00	0.2681019
831 Locomotive-engine drivers and related worker	0e+00	0.1660888
912 Helpers and cleaners	0e+00	0.1424455
514 Other personal services workers	0e+00	0.0567542
812 Metal-processing-plant operators	0e+00	0.0337384
932 Manufacturing labourers	0e+00	0.0054044
731 Precision workers in metal and related materials	0e+00	0.0009893

9.21 Salary correlation with population in region, comparison between the SSYK, Year 2014 - 2018

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

Monthly salary All sectors

tb <- readfile("000000CD_13.csv")

summary_table = 0
anova_table = 0
tb <- tb %>%
  group_by (`region`, year, `occuptional  (SSYK 2012)`) %>%
  mutate (perc_women = as.numeric (sub ("%", "", perc_women (salary)))) %>%
  group_by(region, year) %>%
  mutate(sum_pop = sum(salary))
tb <- readfile("000000CG_3.csv") %>% 
  inner_join(tb, by = c("region", "year", "sex", "occuptional  (SSYK 2012)"))  
for (i in unique(tb$`occuptional  (SSYK 2012)`)){
  temp <- filter(tb, `occuptional  (SSYK 2012)` == i)
  if (dim(temp)[1] > 50){
    model <- lm(log(salary.x) ~ sum_pop + year2.x + sex, data = temp)
    summary_table <- rbind (summary_table, mutate (mutate (tidy (summary (model)), ssyk = i)))
    anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i))
  }
}  
 
merge(summary_table, anova_table, by = "ssyk", all = TRUE) %>% 
  filter (term.y == "sum_pop") %>% 
  filter (term.x == "sum_pop") %>%
  ggplot () + 
    geom_point (mapping = aes(x = estimate, y = statistic.y)) +
    labs(
      x = "estimate",
      y = "F value for SSYK"
    )   

(#fig:ssyk14-18_14)Salary correlation with population in region, comparison between the SSYK, Year 2014 - 2018, positive x = regions with higher population have higher salaries, negative x = regions with higher population have lower salaries

merge(summary_table, anova_table, by = "ssyk", all = TRUE) %>% 
  filter (term.y == "sum_pop") %>% 
  filter (term.x == "sum_pop") %>%
  select (ssyk, estimate, statistic.y) %>%
  arrange (desc (statistic.y)) %>%
  knitr::kable( 
  booktabs = TRUE,
  caption = 'Salary correlation with population in region, comparison between the SSYK, Year 2014 - 2018')  

(#tab:ssyk14-18_14)Salary correlation with population in region, comparison between the SSYK, Year 2014 - 2018

ssyk	estimate	statistic.y
335 Tax and related government associate professionals	2e-07	618.9172956
153 Elderly care managers	2e-07	415.5287723
242 Organisation analysts, policy administrators and human resource specialists	3e-07	282.5442245
336 Police officers	1e-07	216.4526824
159 Other social services managers	2e-07	203.0228798
351 ICT operations and user support technicians	2e-07	162.5763027
218 Specialists within environmental and health protection	2e-07	154.8681586
234 Primary- and pre-school teachers	1e-07	152.2797828
332 Insurance advisers, sales and purchasing agents	2e-07	151.3033690
214 Engineering professionals	2e-07	146.8224978
266 Social work and counselling professionals	1e-07	136.5776762
251 ICT architects, systems analysts and test managers	2e-07	128.5839528
241 Accountants, financial analysts and fund managers	3e-07	126.0001648
141 Primary and secondary schools and adult education managers	1e-07	122.8447285
411 Office assistants and other secretaries	1e-07	120.7887691
333 Business services agents	2e-07	115.2954418
331 Financial and accounting associate professionals	3e-07	112.3529484
243 Marketing and public relations professionals	2e-07	108.8218540
233 Secondary education teachers	1e-07	104.2794021
123 Administration and planning managers	3e-07	97.4300376
213 Biologists, pharmacologists and specialists in agriculture and forestry	3e-07	91.1555373
134 Architectural and engineering managers	2e-07	90.3221240
235 Teaching professionals not elsewhere classified	1e-07	88.1257247
261 Legal professionals	3e-07	86.7879979
533 Health care assistants	1e-07	82.7513076
311 Physical and engineering science technicians	1e-07	82.1150702
232 Vocational education teachers	1e-07	71.5588956
129 Administration and service managers not elsewhere classified	3e-07	71.4401227
131 Information and communications technology service managers	3e-07	63.2871266
515 Building caretakers and related workers	1e-07	60.2468588
223 Nursing professionals (cont.)	1e-07	58.4436025
222 Nursing professionals	1e-07	52.5801737
151 Health care managers	2e-07	50.2968913
125 Sales and marketing managers	3e-07	44.8688696
341 Social work and religious associate professionals	1e-07	42.3565421
321 Medical and pharmaceutical technicians	1e-07	40.9849447
231 University and higher education teachers	-1e-07	39.9354277
962 Newspaper distributors, janitors and other service workers	1e-07	28.3745772
441 Library and filing clerks	1e-07	22.9780940
262 Museum curators and librarians and related professionals	1e-07	18.4452802
227 Naprapaths, physiotherapists, occupational therapists	0e+00	15.8961515
422 Client information clerks	1e-07	13.7996026
531 Child care workers and teachers aides	0e+00	13.3373980
532 Personal care workers in health services	0e+00	10.2753463
224 Psychologists and psychotherapists	0e+00	7.2490748
432 Stores and transport clerks	0e+00	4.1238055
541 Other surveillance and security workers	0e+00	3.4769606
221 Medical doctors	0e+00	1.8500601
911 Cleaners and helpers	0e+00	0.8050857
819 Process control technicians	0e+00	0.1724911
534 Attendants, personal assistants and related workers	0e+00	0.0147750

9.22 Women with higher paid jobs migrate to regions with higher salaries, women with lower paid jobs migrate to regions with lower salaries, Year 2004 - 2013

Average basic salary, monthly salary and women´s salary as a percentage of men´s salary by sector, occupational group (SSYK) , sex and age. Year 2004 - 2013 Monthly salary All sectors

Average basic salary, monthly salary and women´s salary as a percentage of men´s salary by region, sector, occupational group (SSYK) and sex. Year 2003 - 2013 Number of employees All sectors

tb <- readfile("AM0110E2.csv") %>%
    rowwise() %>%
    mutate(age2 = unlist(lapply(strsplit(substr(age, 1, 5), "-"), strtoi))[1]) %>% 
    rowwise() %>%
    mutate(age3 = unlist(lapply(strsplit(substr(age, 1, 5), "-"), strtoi))[2]) %>%
    mutate(age4 = (age3 + age2) / 2)

model <- lm (log(salary) ~ `occupational group (SSYK)` + year2 + sex + poly(age4, 3), data = tb)

salary_table <- tidy (summary (model)) %>%
  mutate (ssyk = gsub("`occupational group \\(SSYK\\)`", "", term)) 
 
tb <- readfile("AM0110A4_2.csv")

summary_table = 0
anova_table = 0
tb <- tb %>%
    group_by (`region`, year, `occupational group (SSYK)`) %>%
    mutate (perc_women = as.numeric (sub ("%", "", perc_women (salary)))) %>%
    group_by(region, year) %>%
    mutate(sum_pop = sum(salary))
tb <- readfile("AM0110A2_2.csv") %>%
    inner_join(tb, by = c("region", "year", "sex", "occupational group (SSYK)")) 
for (i in unique(tb$`occupational group (SSYK)`)){
    temp <- filter(tb, `occupational group (SSYK)` == i)
    if (dim(temp)[1] > 50){
        model <- lm(perc_women ~ log(salary.x) + year2.x + sex, data = temp)
        summary_table <- rbind (summary_table, mutate (mutate (tidy (summary (model)), ssyk = i)))
        anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i))
    }
}  

## Note: model has aliased coefficients
##       sums of squares computed by model comparison

sumtab <- merge(salary_table, anova_table, by = "ssyk", all = TRUE)
merge(summary_table, sumtab, by = "ssyk", all = TRUE) %>%
  filter (term.y == "log(salary.x)") %>%
  filter (term == "log(salary.x)") %>%
  filter (statistic.y > 35)  %>%  # Filter noice with low relevance
  ggscatter(x = "estimate.x", y = "estimate.y",
  add = "reg.line", conf.int = TRUE,
  cor.coef = TRUE, cor.method = "pearson") +
  labs(
    x = "estimate for women urbanization",
    y = "estimate for SSYK salaries"
  ) 

(#fig:ssyk14-18_15)Women with higher paid jobs migrate to regions with higher salaries, women with lower paid jobs migrate to regions with lower salaries, positive x = tendency for women within SSYK to migrate to regions with higher salaries, positive y = SSYK with higher salaries than average for all SSYK

merge(summary_table, sumtab, by = "ssyk", all = TRUE) %>% 
  filter (term.y == "log(salary.x)") %>% 
  filter (term == "log(salary.x)") %>%
  filter (statistic.y > 35)  %>%  # Filter noice with low relevance
  select (ssyk, estimate.x, estimate.y) %>%
  arrange (desc (estimate.x)) %>%
  knitr::kable( 
  booktabs = TRUE,
  caption = 'Women with higher paid jobs migrate to regions with higher salaries, women with lower paid jobs migrate to regions with lower salaries')   

(#tab:ssyk14-18_15)Women with higher paid jobs migrate to regions with higher salaries, women with lower paid jobs migrate to regions with lower salaries

ssyk	estimate.x	estimate.y
211 Physicists, chemists and related professionals	65.02898	0.2541572
221 Life science professionals	49.61063	0.1475151
232 Secondary education teaching professionals	49.52621	-0.1317355
214 Architects, engineers and related professionals	34.79997	0.1784295
131 Managers of small enterprises	29.35080	0.0906872
311 Physical and engineering science technicians	26.11635	-0.0053539
123 Other specialist managers	25.87871	0.4774895
345 Police officers and detectives	23.36843	-0.0668591
341 Finance and sales associate professionals	19.56942	0.0591795
412 Numerical clerks	-28.71290	-0.1771011
411 Office secretaries and data entry operators	-47.63148	-0.2233204
414 Library and filing clerks	-58.58786	-0.3598463
919 Other sales and services elementary occupations	-63.85488	-0.3792034

9.23 Women with higher paid jobs migrate to regions with higher salaries, women with lower paid jobs migrate to regions with lower salaries, Year 2014 - 2018

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

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

tb <- readfile("000000D2_1.csv") %>%
    rowwise() %>%
    mutate(age2 = unlist(lapply(strsplit(substr(age, 1, 5), "-"), strtoi))[1]) %>%  
    rowwise() %>%
    mutate(age3 = unlist(lapply(strsplit(substr(age, 1, 5), "-"), strtoi))[2]) %>%
    mutate(age4 = (age3 + age2) / 2)

model <- lm (log(salary) ~ `occuptional  (SSYK 2012)` + year2 + sex + poly(age4, 3), data = tb)

salary_table <- tidy (summary (model)) %>% 
  mutate (ssyk = gsub("`occuptional  \\(SSYK 2012\\)`", "", term))

tb <- readfile("000000CD_13.csv")

summary_table = 0
anova_table = 0
tb <- tb %>%
    group_by (`region`, year, `occuptional  (SSYK 2012)`) %>%
    mutate (perc_women = as.numeric (sub ("%", "", perc_women (salary)))) %>%
    group_by(region, year) %>%
    mutate(sum_pop = sum(salary))
tb <- readfile("000000CG_3.csv") %>%
    inner_join(tb, by = c("region", "year", "sex", "occuptional  (SSYK 2012)"))  
for (i in unique(tb$`occuptional  (SSYK 2012)`)){
    temp <- filter(tb, `occuptional  (SSYK 2012)` == i)
    if (dim(temp)[1] > 50){
        model <- lm(perc_women ~ log(salary.x) + year2.x + sex, data = temp)
        summary_table <- rbind (summary_table, mutate (mutate (tidy (summary (model)), ssyk = i)))
        anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i))
    }
}   

sumtab <- merge(salary_table, anova_table, by = "ssyk", all = TRUE)
merge(summary_table, sumtab, by = "ssyk", all = TRUE) %>%
  filter (term.y == "log(salary.x)") %>%
  filter (term == "log(salary.x)") %>%
  filter (statistic.y > 15)  %>%  # Filter noice with low relevance
  ggscatter(x = "estimate.x", y = "estimate.y",
  add = "reg.line", conf.int = TRUE,
  cor.coef = TRUE, cor.method = "pearson") +
  labs(
    x = "estimate for women urbanization",
    y = "estimate for SSYK salaries"
  )  

(#fig:ssyk14-18_16)Women with higher paid jobs migrate to regions with higher salaries, women with lower paid jobs migrate to regions with lower salaries, positive x = tendency for women within SSYK to migrate to regions with higher salaries, positive y = SSYK with higher salaries than average for all SSYK

merge(summary_table, sumtab, by = "ssyk", all = TRUE) %>% 
  filter (term.y == "log(salary.x)") %>% 
  filter (term == "log(salary.x)") %>%
  filter (statistic.y > 15)  %>%  # Filter noice with low relevance
  select (ssyk, estimate.x, estimate.y) %>%
  arrange (desc (estimate.x)) %>%
  knitr::kable( 
  booktabs = TRUE,
  caption = 'Women with higher paid jobs migrate to regions with higher salaries, women with lower paid jobs migrate to regions with lower salaries')   

(#tab:ssyk14-18_16)Women with higher paid jobs migrate to regions with higher salaries, women with lower paid jobs migrate to regions with lower salaries

ssyk	estimate.x	estimate.y
232 Vocational education teachers	141.75603	-0.1505542
213 Biologists, pharmacologists and specialists in agriculture and forestry	97.53635	-0.0126828
134 Architectural and engineering managers	60.29617	0.3047370
141 Primary and secondary schools and adult education managers	46.84900	0.1827212
214 Engineering professionals	41.43916	0.0932335
129 Administration and service managers not elsewhere classified	37.61293	0.2519280
311 Physical and engineering science technicians	30.95515	-0.0457919
332 Insurance advisers, sales and purchasing agents	29.98959	-0.0151998
331 Financial and accounting associate professionals	-15.98383	-0.0353587
234 Primary- and pre-school teachers	-17.82955	-0.2498722
261 Legal professionals	-25.90072	0.2555572
242 Organisation analysts, policy administrators and human resource specialists	-26.16237	0.0290522
262 Museum curators and librarians and related professionals	-28.86676	-0.1797104
411 Office assistants and other secretaries	-35.05296	-0.2861178
241 Accountants, financial analysts and fund managers	-42.89348	0.1243897
532 Personal care workers in health services	-72.83391	-0.3338730
541 Other surveillance and security workers	-79.19857	-0.3033981
962 Newspaper distributors, janitors and other service workers	-81.17840	-0.4804970
441 Library and filing clerks	-91.15247	-0.4352854

9.24 The correlation between salary level and the F-value for age, Year 2004 - 2013

Average basic salary, monthly salary and women´s salary as a percentage of men´s salary by sector, occupational group (SSYK) , sex and age. Year 2004 - 2013 Monthly salary All sectors

tb <- readfile("AM0110E2.csv") %>%
    rowwise() %>%
    mutate(age2 = unlist(lapply(strsplit(substr(age, 1, 5), "-"), strtoi))[1]) %>% 
    rowwise() %>%
    mutate(age3 = unlist(lapply(strsplit(substr(age, 1, 5), "-"), strtoi))[2]) %>%
    mutate(age4 = (age3 + age2) / 2)
model <- lm (log(salary) ~ `occupational group (SSYK)` + year2 + sex + poly(age4, 3), data = tb)
salary_table <- tidy (summary (model)) %>%
  mutate (ssyk = gsub("`occupational group \\(SSYK\\)`", "", term))
  
summary_table = 0
anova_table = 0
for (i in unique(tb$`occupational group (SSYK)`)){
  temp <- filter(tb, `occupational group (SSYK)` == i)
  if (dim(temp)[1] > 50){
    model <- lm (log(salary) ~ year2 + sex + poly(age4, 3), data = temp)
    summary_table <- rbind (summary_table, mutate (mutate (tidy (summary (model)), ssyk = i), spannvidd = max (filter (temp, year2 == 2013)$salary) / min (filter (temp, year2 == 2013)$salary)))
    anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i))
  }
}

sumtab <- merge(salary_table, anova_table, by = "ssyk", all = TRUE)
sumtab <- merge(summary_table, sumtab, by = "ssyk", all = TRUE)
sumtab %>%
  filter (term.y == "poly(age4, 3)") %>%
  filter (term == "year2") %>% 
  ggscatter(x = "estimate.y", y = "statistic.y",
  add = "reg.line", conf.int = TRUE,
  cor.coef = TRUE, cor.method = "pearson") +
  labs(
    x = "Salary level",
    y = "F-value for age"
  ) 

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing non-finite values (stat_cor).

## Warning: Removed 1 rows containing missing values (geom_point).

(#fig:ssyk14-18_17)The correlation between salary level and the F-value for age

sumtab %>%
  filter (term.y == "poly(age4, 3)") %>%
  filter (term == "year2") %>% 
  select (ssyk, estimate.y, statistic.y) %>%
  arrange (desc (statistic.y)) %>%
  knitr::kable( 
  booktabs = TRUE,
  caption = 'The correlation between salary level and the F-value for age')    

(#tab:ssyk14-18_17)The correlation between salary level and the F-value for age

ssyk	estimate.y	statistic.y
233 Primary education teaching professionals	-0.1878374	1951.69188
222 Health professionals (except nursing)	0.4890067	1457.04286
231 College, university and higher education teaching professionals	0.0496420	1427.63618
122 Production and operations managers	0.2414125	1416.37235
232 Secondary education teaching professionals	-0.1317355	1315.72047
312 Computer associate professionals	-0.0305085	1293.59017
323 Nursing associate professionals	-0.0803320	1253.47223
249 Psychologists, social work and related professionals	-0.0794669	1224.76753
214 Architects, engineers and related professionals	0.1784295	1142.58491
331 Pre-primary education teaching associate professionals	-0.2677866	999.04369
341 Finance and sales associate professionals	0.0591795	871.24552
011 Armed forces	NA	842.31457
213 Computing professionals	0.1869678	684.64507
247 Public service administrative professionals	0.0143360	668.36080
415 Mail carriers and sorting clerks	-0.3291254	649.49005
246 Religious professionals	0.0763499	649.17379
345 Police officers and detectives	-0.0668591	625.49516
346 Social work associate professionals	-0.2451915	620.00016
245 Writers and creative or performing artists	0.0455956	609.58554
211 Physicists, chemists and related professionals	0.2541572	597.42679
241 Business professionals	0.1864641	585.28238
322 Health associate professionals (except nursing)	-0.1112299	488.16340
235 Other teaching professionals	-0.1224089	450.42722
234 Special education teaching professionals	-0.1168014	396.46505
343 Administrative associate professionals	-0.0679140	395.88113
513 Personal care and related workers	-0.3008597	390.16452
815 Chemical-processing-plant operators	-0.0707002	379.39618
123 Other specialist managers	0.4774895	374.21391
311 Physical and engineering science technicians	-0.0053539	365.90476
223 Nursing and midwifery professionals	-0.0277232	338.30668
515 Protective services workers	-0.2196469	330.90303
422 Client information clerks	-0.2710249	322.90797
243 Archivists, librarians and related information professionals	-0.1430274	312.02627
822 Chemical-products machine operators	-0.2021040	305.02965
419 Other office clerks	-0.2169749	300.21773
221 Life science professionals	0.1475151	286.98782
324 Life science technicians	-0.1718150	261.87836
413 Stores and transport clerks	-0.2212557	256.92774
314 Ship and aircraft controllers and technicians	0.4473610	237.48869
244 Social science and linguistics professionals (except social work professionals)	0.0949958	210.55221
342 Business services agents and trade brokers	-0.1074352	207.12314
412 Numerical clerks	-0.1771011	196.86360
411 Office secretaries and data entry operators	-0.2233204	193.76835
829 Other machine operators and assemblers	-0.2175313	190.23514
721 Metal moulders, welders, sheet-metal workers, structural-metal preparers and related trades workers	-0.2170469	175.49681
823 Rubber- and plastic-products machine operators	-0.2435825	167.38463
313 Optical and electronic equipment operators	-0.1316074	162.50630
512 Housekeeping and restaurant services workers	-0.3106191	153.68491
722 Blacksmiths, tool-makers and related trades workers	-0.2051246	151.47248
315 Safety and quality inspectors	0.0141641	145.56840
511 Travel attendants and related workers	-0.1208602	140.94823
344 Customs, tax and related government associate professionals	-0.1791629	140.50202
347 Artistic, entertainment and sports associate professionals	-0.0976138	126.91226
814 Wood-processing- and papermaking-plant operators	-0.0965608	122.20339
932 Manufacturing labourers	-0.2869549	113.71684
825 Printing-, binding- and paper-products machine operators	-0.1993668	113.27582
131 Managers of small enterprises	0.0906872	104.47801
242 Legal professionals	0.3163374	103.25042
248 Administrative professionals of special-interest organisations	0.0806722	102.09489
723 Machinery mechanics and fitters	-0.1994452	97.76839
714 Painters, building structure cleaners and related trades workers	-0.2225634	89.43137
713 Building finishers and related trades workers	-0.2487094	88.76996
212 Mathematicians and statisticians	0.2495026	87.18638
912 Helpers and cleaners	-0.4049861	84.83107
611 Market gardeners and crop growers	-0.3419262	82.14254
821 Metal- and mineral-products machine operators	-0.2103411	79.50551
812 Metal-processing-plant operators	-0.1144630	77.88761
828 Assemblers	-0.2463128	77.38124
831 Locomotive-engine drivers and related worker	-0.0533469	68.37296
734 Craft printing and related trades workers	-0.2145231	66.46350
724 Electrical and electronic equipment mechanics and fitters	-0.1837737	64.27696
913 Helpers in restaurants	-0.4174033	60.46244
914 Doorkeepers, newspaper and package deliverers and related workers	-0.3245340	58.60471
121 Directors and chief executives	0.7881691	55.09215
514 Other personal services workers	-0.2621824	52.59568
833 Agricultural and other mobile-plant operators	-0.2109833	51.72220
933 Transport labourers and freight handlers	-0.1857803	46.02971
826 Textile-, fur- and leather-products machine operators	-0.3318789	43.06312
824 Wood-products machine operators	-0.2506177	42.56020
332 Other teaching associate professionals	-0.1867732	42.34720
612 Animal producers and related workers	-0.3383252	39.10006
522 Shop and stall salespersons and demonstrators	-0.2085389	38.81291
321 Agronomy and forestry technicians	-0.0555436	37.04074
915 Garbage collectors and related labourers	-0.2630712	29.19059
816 Power-production and related plant operators	-0.1368992	27.65173
712 Building frame and related trades workers	-0.1834122	24.53035
414 Library and filing clerks	-0.3598463	20.67900
832 Motor-vehicle drivers	-0.2490619	20.31541
827 Food and related products machine operators	-0.2285315	19.18984
421 Cashiers, tellers and related clerks	-0.2324445	15.24343
919 Other sales and services elementary occupations	-0.3792034	10.27841

9.25 The correlation between salary level and the F-value for age, Year 2014 - 2018

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

tb <- readfile("000000D2_1.csv") %>%
    rowwise() %>%
    mutate(age2 = unlist(lapply(strsplit(substr(age, 1, 5), "-"), strtoi))[1]) %>% 
    rowwise() %>%
    mutate(age3 = unlist(lapply(strsplit(substr(age, 1, 5), "-"), strtoi))[2]) %>%
    mutate(age4 = (age3 + age2) / 2)
model <- lm (log(salary) ~ `occuptional  (SSYK 2012)` + year2 + sex + poly(age4, 3), data = tb)
salary_table <- tidy (summary (model)) %>%
  mutate (ssyk = gsub("`occuptional  \\(SSYK 2012\\)`", "", term))
  
tb <- readfile("000000D2_1.csv") %>%
  rowwise() %>%
  mutate(age2 = unlist(lapply(strsplit(substr(age, 1, 5), "-"), strtoi))[1]) %>% 
  rowwise() %>%
  mutate(age3 = unlist(lapply(strsplit(substr(age, 1, 5), "-"), strtoi))[2]) %>%
  mutate(age4 = (age3 + age2) / 2)
 
summary_table = 0
anova_table = 0
for (i in unique(tb$`occuptional  (SSYK 2012)`)){
  temp <- filter(tb, `occuptional  (SSYK 2012)` == i)
  if (dim(temp)[1] > 50){
    model <- lm (log(salary) ~ year2 + sex + poly(age4, 3), data = temp)
    summary_table <- rbind (summary_table, mutate (mutate (tidy (summary (model)), ssyk = i), spannvidd = max (filter (temp, year2 == 2018)$salary) / min (filter (temp, year2 == 2018)$salary)))
    anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i))
  }
}

sumtab <- merge(salary_table, anova_table, by = "ssyk", all = TRUE)
sumtab <- merge(summary_table, sumtab, by = "ssyk", all = TRUE)
sumtab %>%
  filter (term.y == "poly(age4, 3)") %>%
  filter (term == "year2") %>% 
  ggscatter(x = "estimate.y", y = "statistic.y",
  add = "reg.line", conf.int = TRUE,
  cor.coef = TRUE, cor.method = "pearson") +
  labs(
    x = "Salary level",
    y = "F-value for age"
  )   

(#fig:ssyk14-18_18)The correlation between salary level and the F-value for age

sumtab %>%
  filter (term.y == "poly(age4, 3)") %>%
  filter (term == "year2") %>% 
  select (ssyk, estimate.y, statistic.y) %>%
  arrange (desc (statistic.y)) %>%
  knitr::kable( 
  booktabs = TRUE,
  caption = 'The correlation between salary level and the F-value for age')   

(#tab:ssyk14-18_18)The correlation between salary level and the F-value for age

ssyk	estimate.y	statistic.y
234 Primary- and pre-school teachers	-0.2498722	1496.606988
231 University and higher education teachers	0.0465586	1127.841359
532 Personal care workers in health services	-0.3338730	975.453302
233 Secondary education teachers	-0.1547118	849.896621
235 Teaching professionals not elsewhere classified	-0.2224782	812.791078
214 Engineering professionals	0.0932335	801.889286
242 Organisation analysts, policy administrators and human resource specialists	0.0290522	701.614565
332 Insurance advisers, sales and purchasing agents	-0.0151998	664.442254
531 Child care workers and teachers aides	-0.4701506	622.684596
222 Nursing professionals	-0.0764471	611.433214
351 ICT operations and user support technicians	-0.1173323	596.249682
266 Social work and counselling professionals	-0.1714779	587.044065
251 ICT architects, systems analysts and test managers	0.0754768	555.431565
534 Attendants, personal assistants and related workers	-0.3479774	428.571695
241 Accountants, financial analysts and fund managers	0.1243897	391.870288
533 Health care assistants	-0.4125681	352.960310
422 Client information clerks	-0.3486152	245.488178
321 Medical and pharmaceutical technicians	-0.1748708	236.191372
335 Tax and related government associate professionals	-0.1926555	198.582732
411 Office assistants and other secretaries	-0.2861178	166.766194
331 Financial and accounting associate professionals	-0.0353587	158.923954
311 Physical and engineering science technicians	-0.0457919	154.667997
723 Machinery mechanics and fitters	-0.2614374	142.874499
341 Social work and religious associate professionals	-0.2778739	134.606247
515 Building caretakers and related workers	-0.3779723	111.384579
541 Other surveillance and security workers	-0.3033981	99.793943
333 Business services agents	-0.1463022	80.618256
227 Naprapaths, physiotherapists, occupational therapists	-0.2289576	80.052441
819 Process control technicians	-0.1789066	65.100656
911 Cleaners and helpers	-0.4893229	58.927493
941 Fast-food workers, food preparation assistants	-0.4850005	40.150153
962 Newspaper distributors, janitors and other service workers	-0.4804970	39.069854
611 Market gardeners and crop growers	-0.4232930	37.208581
432 Stores and transport clerks	-0.2908432	33.903777
834 Mobile plant operators	-0.2298379	29.791104
512 Cooks and cold-buffet managers	-0.3980305	22.750271
722 Blacksmiths, toolmakers and related trades workers	-0.2900944	14.874887
522 Shop staff	-0.2750987	14.735285
833 Heavy truck and bus drivers	-0.3147488	4.531345
821 Assemblers	-0.3142495	2.226122

9.26 Animations , SSYK 214, Architects, engineers and related professionals

Approximaton with B-spline
Average monthly pay (total pay), non-manual workers private sector (SLP),
SEK by occuptional group (SSYK), age, sex and year 2000-2013
214 Engineering professionals
sex=total

readfile("AM0103A9.csv") %>% rowwise() %>% mutate(age2 = unlist(lapply(strsplit(substr(age, 1, 5), "-"), strtoi))[1]) %>%  
rowwise() %>% mutate(age3 = unlist(lapply(strsplit(substr(age, 1, 5), "-"), strtoi))[2]) %>%   
 ggplot(mapping = aes(x = year2 - (age2 + age3) / 2, y = salary)) +  
   geom_point() +   
   geom_smooth(method = lm, formula = y ~ splines::bs(x, 8), se = FALSE) +  
   transition_time(year2) +  
   labs(title = "Year: {frame_time}") +  
    scale_x_continuous(name = "Year of birth") +  
   scale_y_continuous(name = "Salary")  
anim_save("2000-2013.gif", width = 1000, height = 1000)   

Average monthly pay (total pay), non-manual workers private sector (SLP),
SEK by occuptional (SSYK 2012), age, sex and year 2014-2018
214 Engineering professionals
sex=total

readfile("00000031_2.csv") %>% rowwise() %>% mutate(age2 = unlist(lapply(strsplit(substr(age, 1, 5), "-"), strtoi))[1]) %>%  
rowwise() %>% mutate(age3 = unlist(lapply(strsplit(substr(age, 1, 5), "-"), strtoi))[2]) %>%   
 ggplot(mapping = aes(x = year2 - (age2 + age3) / 2, y = salary)) +  
   geom_point() +   
   geom_smooth(method = lm, formula = y ~ splines::bs(x, 8), se = FALSE) +  
   transition_time(year2) +  
   labs(title = "Year: {frame_time}") +  
    scale_x_continuous(name = "Year of birth") +  
   scale_y_continuous(name = "Salary")    
anim_save("2014-2018.gif", width = 1000, height = 1000)    

Average monthly pay (total pay), non-manual workers private sector (SLP),
SEK by occuptional (SSYK 2012), age, sex and year 2000-2013
214 Engineering professionals
sex=total
Growth of salaries by age group

csvfile <- readfile("AM0103A9.csv") %>%   
  rowwise() %>%   
  mutate(age2 = unlist(lapply(strsplit(substr(age, 1, 5), "-"), strtoi))[1]) %>%  
  rowwise() %>% mutate(age3 = unlist(lapply(strsplit(substr(age, 1, 5), "-"), strtoi))[2])    
yearwise <- group_split(csvfile %>% group_by(year2))      
ageGroupGrowth = data.frame()     
for (i in 1:13){  
  temp <- cbind(year = 1999 + i, age = (yearwise[[i]]$age2 + yearwise[[i]]$age3) / 2, growth = yearwise[[i+1]]$relsalary / yearwise[[i]]$relsalary) - 1  
  ageGroupGrowth <- rbind(ageGroupGrowth, temp)  
}     
ageGroupGrowth[, 'age'] <- factor(ageGroupGrowth[, 'age'])        
ageGroupGrowth %>%    
  ggplot(mapping = aes(x = age, y = growth)) +  
  geom_bar(stat = "identity") +  
  transition_time(as.numeric(year)) +  
  labs(title = "Year: {frame_time}") +  
  scale_x_discrete(name = "Age group") +  
  scale_y_continuous(name = "Salary increase (%)")    
anim_save("AgeGroupGrowth2000-2013.gif", width = 1000, height = 1000)    

Average monthly pay (total pay), non-manual workers private sector (SLP),
SEK by occuptional (SSYK 2012), age, sex and year 2000-2013
214 Engineering professionals
sex=total
Individual salary increase by birthyear, estimated from B-spline approximation

csvfile <- readfile("AM0103A9.csv") %>%   
  rowwise() %>%   
  mutate(age2 = unlist(lapply(strsplit(substr(age, 1, 5), "-"), strtoi))[1]) %>%  
  rowwise() %>% mutate(age3 = unlist(lapply(strsplit(substr(age, 1, 5), "-"), strtoi))[2])  
indGrowth = data.frame()  
for (i in 2000:2012){  
  yearfile <- filter(csvfile, year2 == i)  
  x = yearfile$year2 - (yearfile$age2 + yearfile$age3) / 2  
  model = lm(yearfile$salary ~ splines::bs(x, 8))  
  X1 = data.frame(x = unlist(map2( i - 62, i - 22, seq)))  
  Y1 = predict(model, X1)   
  yearfile <- filter(csvfile, year2 == i + 1)  
  x = yearfile$year2 - (yearfile$age2 + yearfile$age3) / 2  
  model = lm(yearfile$salary ~ splines::bs(x, 8))  
  X2 = data.frame(x = unlist(map2( i + 1 - 62, i + 1 - 22, seq)))      
  Y2 = predict(model, X2)  
  growth = Y2[1:40] / Y1[2:41]    
  temp <- as_tibble(cbind(year = i+1, by=X2[1:40,1], growth=growth))  
  indGrowth <- rbind(indGrowth, temp)  
}   
indGrowth %>%     
  ggplot(mapping = aes(x = by, y = growth)) +  
  geom_line() +  
 transition_time(year) +  
  labs(title = "Year: {frame_time}") +  
  scale_x_continuous(name = "Year of birth") +  
  scale_y_continuous(name = "Salary increase (%)")   
anim_save("indGrowth2000-2013.gif", width = 1000, height = 1000)  

Average monthly pay (total pay), non-manual workers private sector (SLP),
SEK by occuptional (SSYK 2012), age, sex and year 2000-2013
214 Engineering professionals
sex=total
Changes in the salary structure part by birthyear, salary structure part is
defined as the derivative of the age / salary function, salary structure
part defines how much the salaries needs to increase each year so that the
structure remains unchanged.

csvfile <- readfile("AM0103A9.csv") %>%   
  rowwise() %>%   
  mutate(age2 = unlist(lapply(strsplit(substr(age, 1, 5), "-"), strtoi))[1]) %>%  
  rowwise() %>% mutate(age3 = unlist(lapply(strsplit(substr(age, 1, 5), "-"), strtoi))[2])    
salaryStructure = data.frame()  
for (i in min(csvfile$year2):max(csvfile$year2)){  
  yearfile <- filter(csvfile, year2 == i)  
  x = yearfile$year2 - (yearfile$age2 + yearfile$age3) / 2  
  model = lm(yearfile$salary ~ splines::bs(x, 8))  
  X = data.frame(x = unlist(map2( i - 62, i - 22, seq, length = 100)))  
  Y = predict(model, X)   
  dX = rowMeans(embed(X$x, 2))  
  dY = -diff(Y) / diff(X$x) / Y  
  temp <- as_tibble(cbind(year = i, dX = dX, dY = dY))  
  salaryStructure <- rbind(salaryStructure, temp)  
}   
salaryStructure %>%   
  ggplot(mapping = aes(x = dX, y = dY)) +  
  geom_line() +  
  transition_time(year) +  
  labs(title = "Year: {frame_time}") +  
  scale_x_continuous(name = "Year of birth") +  
  scale_y_continuous(name = "Structural increase (%)")   
anim_save("salaryStructure2000-2013.gif", width = 1000, height = 1000)