Chapter 14 R&D expenditures

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

Population 16-74 years of age by region, highest level of education, age and sex. Year 1985 - 2018 total, 16-74 years

Regional distribution of enterprises R&D. Every other year 2007 - 2017 Intramural R&D, SEK million

Total R&D person years performed by employees of a higher education institution by region, sex and field of science. Every second year 2013 - 2017 Total R&D person years County men, women All fields of science and technology

tb <- readfile("000000NL_1.csv") %>%
  group_by (`region`, year) %>%  
  mutate (perc_women = as.numeric (sub ("%", "", perc_women (salary)))) %>%
  mutate (sum_ing = sum(salary))  
tb <- readfile("UF0506A1_2.csv") %>%
  group_by(`level of education`, region, year, sex) %>%
  mutate(utbregno = sum(salary)) %>%
  group_by(region, year, sex) %>% mutate(perc_edu = utbregno / sum(utbregno)) %>%
  group_by(`level of education`, region, year) %>%
  mutate (sum_edu = sum(utbregno)) %>%
  filter (`level of education` == "post-secondary education 3 years or more (ISCED97 5A)") %>%
  right_join(tb, by = c("region", "year", "sex")) %>%
  mutate (perc_eng = sum_ing / sum_edu)
tb <- readfile("000002O5.csv") %>%
  right_join(tb, by = c("region", "year"))
tb <- readfile("000002LR.csv") %>%
  right_join(tb, by = c("region", "year"))
  
tb1 <- tb %>% select(
  salary.x.x, 
  salary.y, 
  salary.x, 
  year2.x, 
  utbregno, 
  perc_edu, 
  sum_edu, 
  salary.y.y, 
  perc_women, 
  sum_ing, 
  perc_eng)

chart.Correlation(tb1, histogram=TRUE, pch=19)

Figure 14.1: Correlation matrix between R&D expenditures, number of engineers and level of education

tb <- readfile("000000NL_1.csv") %>%
  group_by (`region`, year) %>%  
  mutate (perc_women = as.numeric (sub ("%", "", perc_women (salary)))) %>%
  mutate (sum_ing = sum(salary))  
tb <- readfile("UF0506A1_2.csv") %>%
  group_by(`level of education`, region, year, sex) %>%
  mutate(utbregno = sum(salary)) %>%
  group_by(region, year, sex) %>% mutate(perc_edu = utbregno / sum(utbregno)) %>%
  group_by(`level of education`, region, year) %>%
  mutate (sum_edu = sum(utbregno)) %>%
  filter (`level of education` == "post-secondary education 3 years or more (ISCED97 5A)") %>%
  right_join(tb, by = c("region", "year", "sex")) %>%
  mutate (perc_eng = sum_ing / sum_edu)
tb <- readfile("000002O5.csv") %>%
  right_join(tb, by = c("region", "year"))
tb <- readfile("000002LR.csv") %>%
  right_join(tb, by = c("region", "year"))
  
tb1 <- tb %>% select(
  salary.x.x, 
  salary.y, 
  salary.x, 
  year2.x, 
  utbregno, 
  perc_edu, 
  sum_edu, 
  salary.y.y, 
  perc_women, 
  sum_ing, 
  perc_eng)
  
get_clust_tendency(
  tb1, 
  n = 50,
  gradient = list(low = "steelblue",  high = "white"))  

## $hopkins_stat
## [1] 0.04789467
## 
## $plot

Figure 14.2: Clustering tendancy for data

tb <- readfile("000000NL_1.csv") %>%
  group_by (`region`, year) %>%  
  mutate (perc_women = as.numeric (sub ("%", "", perc_women (salary)))) %>%
  mutate (sum_ing = sum(salary))  
tb <- readfile("UF0506A1_2.csv") %>%
  group_by(`level of education`, region, year, sex) %>%
  mutate(utbregno = sum(salary)) %>%
  group_by(region, year, sex) %>% mutate(perc_edu = utbregno / sum(utbregno)) %>%
  group_by(`level of education`, region, year) %>%
  mutate (sum_edu = sum(utbregno)) %>%
  filter (`level of education` == "post-secondary education 3 years or more (ISCED97 5A)") %>%
  right_join(tb, by = c("region", "year", "sex")) %>%
  mutate (perc_eng = sum_ing / sum_edu)
tb <- readfile("000002O5.csv") %>%
  right_join(tb, by = c("region", "year"))
tb <- readfile("000002LR.csv") %>%
  right_join(tb, by = c("region", "year"))
  
tb1 <- tb %>% select(
  salary.x.x, 
  salary.y, 
  salary.x, 
  year2.x, 
  utbregno, 
  perc_edu, 
  sum_edu, 
  salary.y.y, 
  perc_women, 
  sum_ing, 
  perc_eng)
  
tb2 <- tb1 %>% na.omit() %>% scale()  
  
intern <- clValid(tb2, nClust = 2:6, 
              clMethods = c("hierarchical","kmeans","pam"),
              validation = "internal")

## Warning in clValid(tb2, nClust = 2:6, clMethods = c("hierarchical",
## "kmeans", : rownames for data not specified, using 1:nrow(data)

summary(intern) %>% 
  tidy() %>% 
  knitr::kable( 
    booktabs = TRUE,
    caption = 'Clustering validation measures')           

## 
## Clustering Methods:
##  hierarchical kmeans pam 
## 
## Cluster sizes:
##  2 3 4 5 6 
## 
## Validation Measures:
##                                  2       3       4       5       6
##                                                                   
## hierarchical Connectivity   5.6464 10.0151 13.9048 20.1786 22.8548
##              Dunn           0.4585  0.5304  0.5388  0.4915  0.4915
##              Silhouette     0.6346  0.5555  0.3919  0.4039  0.4050
## kmeans       Connectivity   5.6464 10.1651 18.7151 20.1786 22.8548
##              Dunn           0.4585  0.5385  0.4235  0.4915  0.4915
##              Silhouette     0.6346  0.4999  0.4037  0.4039  0.4050
## pam          Connectivity   5.6464 16.8294 23.2329 33.0845 36.8401
##              Dunn           0.4585  0.2863  0.1590  0.1590  0.1579
##              Silhouette     0.6346  0.2690  0.2254  0.2326  0.2533
## 
## Optimal Scores:
## 
##              Score  Method       Clusters
## Connectivity 5.6464 hierarchical 2       
## Dunn         0.5388 hierarchical 4       
## Silhouette   0.6346 hierarchical 2

## Warning in kable_markdown(x, padding = padding, ...): The table should have
## a header (column names)

Table: (#tab:rndexpenditures3)Clustering validation measures

|| || || ||

tb <- readfile("000000NL_1.csv") %>%
  group_by (`region`, year) %>%  
  mutate (perc_women = as.numeric (sub ("%", "", perc_women (salary)))) %>%
  mutate (sum_ing = sum(salary))  
tb <- readfile("UF0506A1_2.csv") %>%
  group_by(`level of education`, region, year, sex) %>%
  mutate(utbregno = sum(salary)) %>%
  group_by(region, year, sex) %>% mutate(perc_edu = utbregno / sum(utbregno)) %>%
  group_by(`level of education`, region, year) %>%
  mutate (sum_edu = sum(utbregno)) %>%
  filter (`level of education` == "post-secondary education 3 years or more (ISCED97 5A)") %>%
  right_join(tb, by = c("region", "year", "sex")) %>%
  mutate (perc_eng = sum_ing / sum_edu)
tb <- readfile("000002O5.csv") %>%
  right_join(tb, by = c("region", "year"))
tb <- readfile("000002LR.csv") %>%
  right_join(tb, by = c("region", "year"))
  
tb1 <- tb %>% select(
  salary.x.x, 
  salary.y, 
  salary.x, 
  year2.x, 
  utbregno, 
  perc_edu, 
  sum_edu, 
  salary.y.y, 
  perc_women, 
  sum_ing, 
  perc_eng)
  
tb2 <- tb1 %>% na.omit() %>% scale()  
  
d <- dist(tb2, method = "euclidean")

res.hc <- hclust(d, method = "ward.D2" )

grp <- cutree(res.hc, k = 2)

plot(res.hc, cex = 0.6) # plot tree
rect.hclust(res.hc, k = 2, border = 2:5)

Figure 14.3: Hierarchical clustering using Ward’s method

14.1 Correlation between R&D person years performed by employees of a higher education institution by region and per cent of engineers who are women

tb <- readfile("000000NL_1.csv") %>%
  group_by (`region`, year) %>%  
  mutate (perc_women = as.numeric (sub ("%", "", perc_women (salary)))) %>%
  mutate (sum_ing = sum(salary))  
tb <- readfile("UF0506A1_2.csv") %>%
  group_by(`level of education`, region, year, sex) %>%
  mutate(utbregno = sum(salary)) %>%
  group_by(region, year, sex) %>% mutate(perc_edu = utbregno / sum(utbregno)) %>%
  group_by(`level of education`, region, year) %>%
  mutate (sum_edu = sum(utbregno)) %>%
  filter (`level of education` == "post-secondary education 3 years or more (ISCED97 5A)") %>%
  right_join(tb, by = c("region", "year", "sex")) %>%
  mutate (perc_eng = sum_ing / sum_edu)
tb <- readfile("000002O5.csv") %>%
  right_join(tb, by = c("region", "year"))
tb <- readfile("000002LR.csv") %>%
  right_join(tb, by = c("region", "year"))
  
tb1 <- tb %>% select(
  salary.x.x, 
  salary.y, 
  salary.x, 
  year2.x, 
  utbregno, 
  perc_edu, 
  sum_edu, 
  salary.y.y, 
  perc_women, 
  sum_ing, 
  perc_eng)
  
tb2 <- tb1

tb3 <- tb2 %>% scale()  
  
d <- dist(tb3, method = "euclidean")

res.hc <- hclust(d, method = "ward.D2" )

grp <- cutree(res.hc, k = 2)

tb4 <- tb2 %>% mutate(grp)

model1 <- lm(perc_women ~ ., data = filter(tb4, grp == 1))

summary(model1) %>% 
  tidy() %>%
  knitr::kable( 
  booktabs = TRUE,
  caption = 'Summary of model data for group 1, number of population with 3 years or more post-secondary education > 193000')

Table 14.2: Summary of model data for group 1, number of population with 3 years or more post-secondary education > 193000

term	estimate	std.error	statistic	p.value
(Intercept)	-3232.4031087	0	-3.956486e+12	0.0000000
salary.x.x	0.0058631	0	2.877303e+12	0.0000000
salary.y	-0.0001395	0	-2.654362e+12	0.0000000
salary.x	0.0000000	0	8.429982e-01	0.4611338
year2.x	1.6116179	0	3.981184e+12	0.0000000
perc_edu	0.0000000	0	-1.035823e+00	0.3764532
sum_edu	-0.0000488	0	-1.887907e+12	0.0000000
salary.y.y	0.0000000	0	1.588736e-01	0.8838614
sum_ing	0.0001593	0	1.705022e+12	0.0000000

model2 <- lm(perc_women ~ ., data = filter(tb4, grp == 2))

summary(model2) %>% 
  tidy() %>%
  knitr::kable( 
  booktabs = TRUE,
  caption = 'Women engineers and population with 3 years or more post-secondary education')

Table 14.2: Women engineers and population with 3 years or more post-secondary education

term	estimate	std.error	statistic	p.value
(Intercept)	-405.3202427	834.1392847	-0.4859143	0.6288587
salary.x.x	0.0033701	0.0011007	3.0617573	0.0033322
salary.y	-0.0002970	0.0007141	-0.4159109	0.6790101
salary.x	-0.0002938	0.0003383	-0.8686163	0.3886372
year2.x	0.2109100	0.4144858	0.5088473	0.6127896
perc_edu	25.8430631	25.0172384	1.0330102	0.3058874
sum_edu	0.0000128	0.0001935	0.0661902	0.9474541
salary.y.y	-0.0001661	0.0011022	-0.1506891	0.8807438
sum_ing	0.0021199	0.0017251	1.2288449	0.2240926
perc_eng	-93.1069852	49.9236663	-1.8649869	0.0672449

Anova(model2, type=2) %>% 
  tidy() %>% 
  knitr::kable( 
  booktabs = TRUE,
  caption = 'Anova report from linear model fit') 

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

Table 14.2: Anova report from linear model fit

term	sumsq	df	statistic	p.value
salary.x.x	95.2758135	1	9.3743575	0.0033322
salary.y	1.7580924	1	0.1729819	0.6790101
salary.x	NA	0	NA	NA
year2.x	2.6315775	1	0.2589256	0.6127896
utbregno	NA	0	NA	NA
perc_edu	10.8455204	1	1.0671101	0.3058874
sum_edu	0.0445275	1	0.0043811	0.9474541
salary.y.y	0.2307836	1	0.0227072	0.8807438
sum_ing	15.3474169	1	1.5100598	0.2240926
perc_eng	35.3502704	1	3.4781762	0.0672449
grp	NA	0	NA	NA
Residuals	589.4801006	58	NA	NA

tb %>% mutate(grp) %>%
  ggplot(aes(x = salary.x.x, y = perc_women, colour = region, size = year2.x)) +
  geom_point() +
  theme(legend.position="bottom") +
  facet_grid(. ~ grp) +
  labs(
    x = "Total R&D person years performed by employees of a higher education institution by region",
    y = "Per cent of engineers who are women"
  )

## Warning: Removed 92 rows containing missing values (geom_point).

Figure 14.4: Correlation between R&D person years performed by employees of a higher education institution by region and per cent of engineers who are women

14.2 Correlation between total R&D person years performed by employees of a higher education institution by region and number of the population who are engineers

tb <- readfile("000000NL_1.csv") %>%
  group_by (`region`, year) %>%  
  mutate (perc_women = as.numeric (sub ("%", "", perc_women (salary)))) %>%
  mutate (sum_ing = sum(salary))  
tb <- readfile("UF0506A1_2.csv") %>%
  group_by(`level of education`, region, year, sex) %>%
  mutate(utbregno = sum(salary)) %>%
  group_by(region, year, sex) %>% mutate(perc_edu = utbregno / sum(utbregno)) %>%
  group_by(`level of education`, region, year) %>%
  mutate (sum_edu = sum(utbregno)) %>%
  filter (`level of education` == "post-secondary education 3 years or more (ISCED97 5A)") %>%
  right_join(tb, by = c("region", "year", "sex")) %>%
  mutate (perc_eng = sum_ing / sum_edu)
tb <- readfile("000002O5.csv") %>%
  right_join(tb, by = c("region", "year"))
tb <- readfile("000002LR.csv") %>%
  right_join(tb, by = c("region", "year"))
  
tb1 <- tb %>% select(
  salary.x.x, 
  salary.y, 
  salary.x, 
  year2.x, 
  utbregno, 
  perc_edu, 
  sum_edu, 
  salary.y.y, 
  perc_women, 
  sum_ing, 
  perc_eng)
  
tb2 <- tb1

tb3 <- tb2 %>% scale()  
  
d <- dist(tb3, method = "euclidean")

res.hc <- hclust(d, method = "ward.D2" )

grp <- cutree(res.hc, k = 2)

tb4 <- tb2 %>% mutate(grp)

model1 <- lm(sum_edu ~ ., data = filter(tb4, grp == 1))

summary(model1) %>% 
  tidy() %>%
  knitr::kable( 
  booktabs = TRUE,
  caption = 'Summary of model data for group 1, number of population with 3 years or more post-secondary education > 193000')

Table 14.3: Summary of model data for group 1, number of population with 3 years or more post-secondary education > 193000

term	estimate	std.error	statistic	p.value
(Intercept)	-6.620101e+07	2.06e-05	-3.207153e+12	0.0000000
salary.x.x	1.200785e+02	0.00e+00	4.127007e+12	0.0000000
salary.y	-2.857963e+00	0.00e+00	-2.686799e+12	0.0000000
salary.x	0.000000e+00	0.00e+00	8.426335e-01	0.4613091
year2.x	3.300663e+04	0.00e+00	3.196360e+12	0.0000000
perc_edu	-2.000000e-07	2.00e-07	-1.035078e+00	0.3767507
salary.y.y	0.000000e+00	0.00e+00	1.588732e-01	0.8838617
perc_women	-2.048043e+04	0.00e+00	-1.887715e+12	0.0000000
sum_ing	3.263440e+00	0.00e+00	1.834379e+12	0.0000000

model2 <- lm(sum_edu ~ ., data = filter(tb4, grp == 2))

summary(model2) %>% 
  tidy() %>%
  knitr::kable( 
  booktabs = TRUE,
  caption = 'Summary of model data for group 2, number of population with 3 years or more post-secondary education < 193000')

Table 14.3: Summary of model data for group 2, number of population with 3 years or more post-secondary education < 193000

term	estimate	std.error	statistic	p.value
(Intercept)	-1.039565e+06	5.503698e+05	-1.8888490	0.0639155
salary.x.x	3.840863e+00	6.272259e-01	6.1235728	0.0000001
salary.y	-5.649046e-01	4.794746e-01	-1.1781742	0.2435386
salary.x	1.570842e+00	1.039662e-01	15.1091537	0.0000000
year2.x	5.265551e+02	2.732082e+02	1.9273035	0.0588444
perc_edu	-9.880334e+04	1.118214e+04	-8.8358203	0.0000000
salary.y.y	2.899929e+00	6.437333e-01	4.5048608	0.0000327
perc_women	5.896028e+00	8.907710e+01	0.0661902	0.9474541
sum_ing	1.640474e+00	1.165766e+00	1.4072073	0.1647024
perc_eng	-8.237740e+04	3.314969e+04	-2.4850124	0.0158610

Anova(model2, type=2) %>% 
  tidy() %>% 
  knitr::kable( 
  booktabs = TRUE,
  caption = 'Anova report from linear model fit') 

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

Table 14.3: Anova report from linear model fit

term	sumsq	df	statistic	p.value
salary.x.x	175405754.82	1	37.4981435	0.0000001
salary.y	6493114.71	1	1.3880944	0.2435386
salary.x	NA	0	NA	NA
year2.x	17375378.31	1	3.7144986	0.0588444
utbregno	NA	0	NA	NA
perc_edu	365197516.66	1	78.0717195	0.0000000
salary.y.y	94928546.25	1	20.2937712	0.0000327
perc_women	20493.73	1	0.0043811	0.9474541
sum_ing	9262969.01	1	1.9802323	0.1647024
perc_eng	28886250.67	1	6.1752864	0.0158610
grp	NA	0	NA	NA
Residuals	271307665.61	58	NA	NA

tb %>% mutate(grp) %>%
  ggplot(aes(x = salary.x.x, y = sum_edu, colour = region, size = year2.x)) +
  geom_point() +
  theme(legend.position="bottom") +
  facet_grid(. ~ grp) +
  labs(
    x = "Total R&D person years performed by employees of a higher education institution by region",
    y = "Number of the population with 3 years or more post-secondary education"
  )

## Warning: Removed 92 rows containing missing values (geom_point).

Figure 14.5: Correlation between total R&D person years performed by employees of a higher education institution by region and number of the population who are engineers

14.3 Correlation between regional distribution of enterprises R&D and number of the population who are engineers

tb <- readfile("000000NL_1.csv") %>%
  group_by (`region`, year) %>%  
  mutate (perc_women = as.numeric (sub ("%", "", perc_women (salary)))) %>%
  mutate (sum_ing = sum(salary))  
tb <- readfile("UF0506A1_2.csv") %>%
  group_by(`level of education`, region, year, sex) %>%
  mutate(utbregno = sum(salary)) %>%
  group_by(region, year, sex) %>% mutate(perc_edu = utbregno / sum(utbregno)) %>%
  group_by(`level of education`, region, year) %>%
  mutate (sum_edu = sum(utbregno)) %>%
  filter (`level of education` == "post-secondary education 3 years or more (ISCED97 5A)") %>%
  right_join(tb, by = c("region", "year", "sex")) %>%
  mutate (perc_eng = sum_ing / sum_edu)
tb <- readfile("000002O5.csv") %>%
  right_join(tb, by = c("region", "year"))
tb <- readfile("000002LR.csv") %>%
  right_join(tb, by = c("region", "year"))
  
tb1 <- tb %>% select(
  salary.x.x, 
  salary.y, 
  salary.x, 
  year2.x, 
  utbregno, 
  perc_edu, 
  sum_edu, 
  salary.y.y, 
  perc_women, 
  sum_ing, 
  perc_eng)
  
tb2 <- tb1

tb3 <- tb2 %>% scale()  
  
d <- dist(tb3, method = "euclidean")

res.hc <- hclust(d, method = "ward.D2" )

grp <- cutree(res.hc, k = 2)

tb4 <- tb2 %>% mutate(grp)

model1 <- lm(sum_ing ~ ., data = filter(tb4, grp == 1))

summary(model1) %>% 
  tidy() %>%
  knitr::kable( 
  booktabs = TRUE,
  caption = 'Summary of model data for group 1, number of population with 3 years or more post-secondary education > 193000')

Table 14.4: Summary of model data for group 1, number of population with 3 years or more post-secondary education > 193000

term	estimate	std.error	statistic	p.value
(Intercept)	2.028566e+07	8.7e-06	2.328515e+12	0.0000000
salary.x.x	-3.679508e+01	0.0e+00	-2.518763e+12	0.0000000
salary.y	8.757519e-01	0.0e+00	3.309505e+12	0.0000000
salary.x	0.000000e+00	0.0e+00	-8.432274e-01	0.4610237
year2.x	-1.011406e+04	0.0e+00	-2.323401e+12	0.0000000
perc_edu	1.000000e-07	1.0e-07	1.035901e+00	0.3764222
sum_edu	3.064252e-01	0.0e+00	1.834501e+12	0.0000000
salary.y.y	0.000000e+00	0.0e+00	-1.591774e-01	0.8836417
perc_women	6.275719e+03	0.0e+00	1.704962e+12	0.0000000

model2 <- lm(sum_ing ~ ., data = filter(tb4, grp == 2))

summary(model2) %>% 
  tidy() %>%
  knitr::kable( 
  booktabs = TRUE,
  caption = 'Summary of model data for group 2, number of population with 3 years or more post-secondary education < 193000')

Table 14.4: Summary of model data for group 2, number of population with 3 years or more post-secondary education < 193000

term	estimate	std.error	statistic	p.value
(Intercept)	21196.0979826	6.274442e+04	0.3378164	0.7367212
salary.x.x	-0.1087238	8.799350e-02	-1.2355896	0.2215925
salary.y	0.3015347	3.633450e-02	8.2988643	0.0000000
salary.x	0.0278309	2.532140e-02	1.0991060	0.2762633
year2.x	-11.1555186	3.118023e+01	-0.3577753	0.7218102
perc_edu	-3.9728911	1.897053e+03	-0.0020942	0.9983362
sum_edu	0.0201251	1.430150e-02	1.4072073	0.1647024
salary.y.y	0.1947262	7.879540e-02	2.4712903	0.0164209
perc_women	11.9696948	9.740607e+00	1.2288449	0.2240926
perc_eng	23016.3036434	2.404771e+03	9.5711002	0.0000000

Anova(model2, type=2) %>% 
  tidy() %>% 
  knitr::kable( 
  booktabs = TRUE,
  caption = 'Anova report from linear model fit') 

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##       sums of squares computed by model comparison

Table 14.4: Anova report from linear model fit

term	sumsq	df	statistic	p.value
salary.x.x	8.760967e+04	1	1.5266817	0.2215925
salary.y	3.952218e+06	1	68.8711485	0.0000000
salary.x	NA	0	NA	NA
year2.x	7.345550e+03	1	0.1280032	0.7218102
utbregno	NA	0	NA	NA
perc_edu	2.516853e-01	1	0.0000044	0.9983362
sum_edu	1.136370e+05	1	1.9802323	0.1647024
salary.y.y	3.504702e+05	1	6.1072758	0.0164209
perc_women	8.665581e+04	1	1.5100598	0.2240926
perc_eng	5.256871e+06	1	91.6059592	0.0000000
grp	NA	0	NA	NA
Residuals	3.328370e+06	58	NA	NA

tb %>% mutate(grp) %>%
  ggplot(aes(x = salary.y, y = sum_ing, colour = region, size = year2.x)) +
  geom_point() +
  theme(legend.position="bottom") +
  facet_grid(. ~ grp) +
  labs(
    x = "Regional distribution of enterprises R&D",
    y = "Number of the population who are engineers"
  )

## Warning: Removed 96 rows containing missing values (geom_point).

Figure 14.6: Correlation between regional distribution of enterprises R&D and number of the population who are engineers

14.4 F-value for total R&D person years performed by employees of a higher education institution by region compared to the per cent in the ssyk who are women

tb <- readfile("000000NL_10.csv") %>%
  filter(salary > 0) %>%
  group_by (`region`, year, `occupation (SSYK 2012)`) %>%  
  mutate (perc_women = as.numeric (sub ("%", "", perc_women (salary)))) %>%
  mutate (sum_pop = sum(salary))
tb <- readfile("UF0506A1_2.csv") %>%
  group_by(`level of education`, region, year, sex) %>%
  mutate(utbregno = sum(salary)) %>%
  group_by(region, year, sex) %>% mutate(perc_edu = utbregno / sum(utbregno)) %>%
  group_by(`level of education`, region, year) %>%
  mutate (sum_edu = sum(utbregno)) %>%
  filter (`level of education` == "post-secondary education 3 years or more (ISCED97 5A)") %>%
  right_join(tb, by = c("region", "year", "sex"))
tb <- readfile("000002O5.csv") %>%
  right_join(tb, by = c("region", "year"))
tb <- readfile("000002LR.csv") %>%
  right_join(tb, by = c("region", "year"))
 
summary_table = 0
anova_table = 0
 
for (i in unique(tb$`occupation (SSYK 2012)`)){
  temp <- filter(tb, `occupation (SSYK 2012)` == i)
  if (dim(temp)[1] > 50){
    temp1 <- temp %>% select(
      salary.x.x, 
      salary.y, 
      salary.x, 
      year2.x, 
      utbregno, 
      perc_edu, 
      sum_edu, 
      salary.y.y, 
      perc_women, 
      sum_pop) 
    model <- lm(perc_women ~ ., data = temp1)
    summary_table <- rbind (summary_table, mutate (tidy (summary (model)), ssyk = i))
    anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i))
  }
}  

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

Figure 14.7: F-value for total R&D person years performed by employees of a higher education institution by region compared to the per cent in the ssyk who are women

merge(summary_table, anova_table, by = "ssyk", all = TRUE) %>% 
  filter (term.y == "salary.x.x") %>%
  filter (term.x == "salary.x.x") %>%
  select (ssyk, estimate, statistic.y, p.value.x) %>%
  arrange (desc (statistic.y)) %>%
  knitr::kable( 
  booktabs = TRUE,
  caption = 'Correlation F-value for total R&D person years performed by employees of a higher education institution by region compared to the per cent in the ssyk who are women') 

Table 14.5: Correlation F-value for total R&D person years performed by employees of a higher education institution by region compared to the per cent in the ssyk who are women

ssyk	estimate	statistic.y	p.value.x
223 Nursing professionals (cont.)	-0.0021847	42.9874337	0.0000000
133 Research and development managers	0.0083902	42.1282180	0.0000000
911 Cleaners and helpers	-0.0031481	30.4949545	0.0000005
214 Engineering professionals	0.0037136	29.4918323	0.0000007
532 Personal care workers in health services	-0.0015852	29.3433060	0.0000008
819 Process control technicians	0.0038908	28.2396997	0.0000012
816 Machine operators, food and related products	-0.0065585	25.5003602	0.0000033
831 Train operators and related workers	0.0051004	22.8343195	0.0000096
441 Library and filing clerks	-0.0053517	18.2666386	0.0000587
533 Health care assistants	-0.0025602	17.4669370	0.0000821
742 Electronics and telecommunications installers and repairers	0.0014082	16.3741388	0.0001307
341 Social work and religious associate professionals	-0.0035096	14.9875992	0.0002380
331 Financial and accounting associate professionals	-0.0021209	14.9509488	0.0002419
217 Designers	-0.0056023	14.7517188	0.0002639
612 Animal breeders and keepers	0.0066739	13.9349503	0.0003785
741 Electrical equipment installers and repairers	0.0005040	13.2134939	0.0005224
266 Social work and counselling professionals	-0.0016673	12.4220161	0.0007471
335 Tax and related government associate professionals	0.0020394	12.3131587	0.0007851
129 Administration and service managers not elsewhere classified	0.0025508	11.4164758	0.0011853
136 Production managers in construction and mining	0.0012363	11.2564630	0.0012766
262 Museum curators and librarians and related professionals	-0.0035065	11.2463194	0.0012826
534 Attendants, personal assistants and related workers	-0.0019943	10.3956401	0.0019098
315 Ship and aircraft controllers and technicians	-0.0045320	10.1634542	0.0021313
342 Athletes, fitness instructors and recreational workers	0.0029032	9.9933679	0.0023105
137 Production managers in manufacturing	0.0016759	9.9039930	0.0024108
442 Postmen and postal facility workers	-0.0030138	9.6760633	0.0026878
222 Nursing professionals	-0.0008717	9.6301397	0.0027475
961 Recycling collectors	0.0020107	8.7335737	0.0042366
141 Primary and secondary schools and adult education managers	0.0025721	8.6905331	0.0043264
173 Retail and wholesale trade managers	0.0025886	8.0036341	0.0060645
226 Dentists	0.0023061	7.5457303	0.0076178
522 Shop staff	-0.0009155	7.2177187	0.0089835
324 Veterinary assistants	-0.0027451	6.9426985	0.0103838
941 Fast-food workers, food preparation assistants	-0.0020659	6.7648528	0.0113053
179 Other services managers not elsewhere classified	0.0024200	6.1110480	0.0158325
962 Newspaper distributors, janitors and other service workers	-0.0014595	5.7758223	0.0188628
011 Commissioned armed forces officers	0.0018557	5.7606943	0.0225819
151 Health care managers	-0.0014428	5.6771270	0.0198676
761 Butchers, bakers and food processors	-0.0048643	5.5296151	0.0214763
933 Dockers and ground personnel	-0.0027279	5.5139056	0.0217382
216 Architects and surveyors	0.0026707	5.3769744	0.0232872
524 Event seller and telemarketers	-0.0041018	5.2568266	0.0248265
171 Hotel and conference managers	0.0039124	5.0297831	0.0280383
516 Other service related workers	0.0034150	4.6830041	0.0338279
227 Naprapaths, physiotherapists, occupational therapists	-0.0009828	4.6796386	0.0338900
422 Client information clerks	-0.0030092	4.4994022	0.0373994
512 Cooks and cold-buffet managers	-0.0019104	4.3182771	0.0413216
233 Secondary education teachers	0.0010123	4.0168391	0.0488636
622 Aquaculture and fishery workers	-0.0097019	3.9164394	0.0536916
336 Police officers	0.0010844	3.8947723	0.0523293
267 Religious professionals and deacons	0.0023399	3.7759601	0.0559592
513 Waiters and bartenders	-0.0014844	3.6328609	0.0606978
134 Architectural and engineering managers	0.0016000	3.5744014	0.0627576
241 Accountants, financial analysts and fund managers	0.0015864	3.4010994	0.0693251
814 Machine operators, rubber, plastic and paper products	-0.0030154	3.3694448	0.0706039
243 Marketing and public relations professionals	0.0016505	3.3132277	0.0729391
125 Sales and marketing managers	0.0017105	3.3018629	0.0734214
242 Organisation analysts, policy administrators and human resource specialists	0.0008063	3.2978973	0.0735905
511 Cabin crew, guides and related workers	-0.0031202	3.2821751	0.0742651
154 Managers and leaders within religious bodies	0.0050036	3.2727535	0.0746727
261 Legal professionals	-0.0017166	3.2698937	0.0747969
833 Heavy truck and bus drivers	0.0005226	3.1911286	0.0783082
251 ICT architects, systems analysts and test managers	-0.0007897	3.1387507	0.0807433
323 Complementary medicine therapists and associate professionals	0.0046128	3.0515323	0.0929376
621 Forestry and related workers	-0.0024541	2.8220385	0.0973736
311 Physical and engineering science technicians	0.0005470	2.7194903	0.1035477
235 Teaching professionals not elsewhere classified	0.0011338	2.5639599	0.1137648
234 Primary- and pre-school teachers	-0.0004935	2.4977831	0.1184517
111 Legislators and senior officials	0.0020444	2.4860681	0.1193039
135 Real estate and head of administration manager	0.0019923	2.4650672	0.1208489
312 Construction and manufacturing supervisors	-0.0006152	2.4463760	0.1222431
711 Carpenters, bricklayers and construction workers	-0.0002231	2.4155741	0.1245804
344 Driving instructors and other instructors	0.0017355	2.2698551	0.1363488
142 Preschool managers	0.0008262	2.2660249	0.1366747
535 Dental nurses	0.0002232	2.2049343	0.1442464
211 Physicists and chemists	0.0069629	2.1522227	0.1467794
221 Medical doctors	0.0005451	2.0392607	0.1576682
731 Precision-instrument makers and handicraft workers	-0.0027707	1.9570165	0.1661861
265 Creative and performing artists	-0.0020493	1.9103658	0.1712556
523 Cashiers and related clerks	0.0013473	1.8953772	0.1729226
732 Printing trades workers	-0.0016976	1.8435992	0.1788294
832 Car, van and motorcycle drivers	-0.0006866	1.7109754	0.1950781
912 Washers, window cleaners and other cleaning workers	-0.0021987	1.6002302	0.2100068
218 Specialists within environmental and health protection	0.0011416	1.5283769	0.2204305
721 Sheet and structural metal workers, moulders and welders, and related workers	-0.0003508	1.5270235	0.2206327
174 Sports, leisure and wellness managers	0.0027791	1.4404413	0.2340573
352 Broadcasting and audio-visual technicians	0.0024915	1.4295443	0.2358169
332 Insurance advisers, sales and purchasing agents	-0.0008288	1.3393333	0.2510302
931 Construction labourers	0.0006596	1.2716008	0.2633726
161 Financial and insurance managers	-0.0012599	1.1884196	0.2793346
159 Other social services managers	0.0010474	1.1752419	0.2819944
264 Authors, journalists and linguists	0.0010905	1.1700774	0.2830457
122 Human resource managers	0.0011227	1.1166814	0.2942165
213 Biologists, pharmacologists and specialists in agriculture and forestry	0.0037044	1.0984348	0.2981646
812 Metal processing and finishing plant operators	0.0007560	0.9926111	0.3226897
817 Wood processing and papermaking plant operators	-0.0006418	0.9574332	0.3312527
815 Machine operators, textile, fur and leather products	-0.0021553	0.9166440	0.3416065
421 Croupiers, debt collectors and related workers	-0.0026206	0.8565411	0.3579341
112 Managing directors and chief executives	0.0008931	0.8513428	0.3592977
921 Berry pickers and planters	-0.0017895	0.8406439	0.3623166
834 Mobile plant operators	-0.0003834	0.8402559	0.3624267
432 Stores and transport clerks	0.0005341	0.8314583	0.3649369
952 Street and market vendors	0.0085325	0.7679452	0.3865076
411 Office assistants and other secretaries	0.0002730	0.7184723	0.3994939
333 Business services agents	-0.0005225	0.7134311	0.4011443
515 Building caretakers and related workers	-0.0006190	0.6727421	0.4148424
443 Elected representatives	0.0024228	0.6691477	0.4161649
138 Forestry and agricultural production managers	-0.0014463	0.6431126	0.4256006
224 Psychologists and psychotherapists	0.0005386	0.6199679	0.4336799
0002 occupations unidentifiable	-0.0002714	0.5472013	0.4619024
228 Specialists in health care not elsewhere classified	-0.0004884	0.5202476	0.4731046
152 Managers in social and curative care	-0.0009334	0.4856045	0.4881700
031 Armed forces occupations, other ranks	-0.0005560	0.4666810	0.4971920
752 Wood treaters, cabinet-makers and related trades workers	-0.0004295	0.4578213	0.5008424
811 Mining and mineral processing plant operators	0.0003087	0.4435441	0.5077736
149 Education managers not elsewhere classified	-0.0016034	0.3970769	0.5306841
613 Mixed crop and animal breeders	-0.0006563	0.3717801	0.5439821
124 Information, communication and public relations managers	0.0009398	0.3445555	0.5590734
225 Veterinarians	-0.0007481	0.3217750	0.5723321
132 Supply, logistics and transport managers	0.0004625	0.3005788	0.5852395
835 Ships deck crews and related workers	0.0012289	0.2918848	0.5916792
723 Machinery mechanics and fitters	-0.0002402	0.2378631	0.6272564
541 Other surveillance and security workers	0.0004745	0.2360714	0.6285542
818 Other stationary plant and machine operators	-0.0007873	0.2130011	0.6458361
712 Roofers, floor layers, plumbers and pipefitters	0.0000576	0.1841870	0.6690989
722 Blacksmiths, toolmakers and related trades workers	0.0002775	0.1718315	0.6797390
345 Culinary associate professionals	-0.0006867	0.1482087	0.7014040
153 Elderly care managers	0.0002252	0.1473160	0.7022606
351 ICT operations and user support technicians	0.0002231	0.1352132	0.7141808
932 Manufacturing labourers	-0.0004441	0.1278619	0.7217200
531 Child care workers and teachers aides	-0.0001199	0.0905656	0.7643389
232 Vocational education teachers	0.0002095	0.0651001	0.7993470
821 Assemblers	0.0002757	0.0642040	0.8007029
813 Machine operators, chemical and pharmaceutical products	-0.0007226	0.0623108	0.8036439
131 Information and communications technology service managers	-0.0002405	0.0616734	0.8045872
753 Tailors, upholsterers and leather craftsmen	0.0006227	0.0535214	0.8177107
121 Finance managers	-0.0001982	0.0414551	0.8392444
514 Hairdressers, beauty and body therapists	0.0001204	0.0384115	0.8451791
713 Painters, Lacquerers, Chimney-sweepers and related trades workers	-0.0001074	0.0307946	0.8611989
325 Dental hygienists	-0.0000642	0.0264478	0.8713159
611 Market gardeners and crop growers	0.0001257	0.0248903	0.8750886
212 Mathematicians, actuaries and statisticians	0.0005591	0.0246993	0.8756058
172 Restaurant managers	-0.0001507	0.0180974	0.8933667
343 Photographers, interior decorators and entertainers	-0.0001898	0.0137057	0.9071341
321 Medical and pharmaceutical technicians	0.0001454	0.0095114	0.9225832
123 Administration and planning managers	0.0001391	0.0062321	0.9372996
021 Non-commissioned armed forces officers	-0.0001006	0.0029503	0.9569599
334 Administrative and specialized secretaries	0.0000210	0.0002274	0.9880116
231 University and higher education teachers	0.0000435	0.0001790	0.9893644

14.5 F-value for regional distribution of enterprises R&D compared to the per cent in the ssyk who are women

tb <- readfile("000000NL_10.csv") %>%
  filter(salary > 0) %>%
  group_by (`region`, year, `occupation (SSYK 2012)`) %>%  
  mutate (perc_women = as.numeric (sub ("%", "", perc_women (salary)))) %>%
  mutate (sum_pop = sum(salary))
tb <- readfile("UF0506A1_2.csv") %>%
  group_by(`level of education`, region, year, sex) %>%
  mutate(utbregno = sum(salary)) %>%
  group_by(region, year, sex) %>% mutate(perc_edu = utbregno / sum(utbregno)) %>%
  group_by(`level of education`, region, year) %>%
  mutate (sum_edu = sum(utbregno)) %>%
  filter (`level of education` == "post-secondary education 3 years or more (ISCED97 5A)") %>%
  right_join(tb, by = c("region", "year", "sex"))
tb <- readfile("000002O5.csv") %>%
  right_join(tb, by = c("region", "year"))
tb <- readfile("000002LR.csv") %>%
  right_join(tb, by = c("region", "year"))
  
summary_table = 0
anova_table = 0
 
for (i in unique(tb$`occupation (SSYK 2012)`)){
  temp <- filter(tb, `occupation (SSYK 2012)` == i)
  if (dim(temp)[1] > 50){
    temp1 <- temp %>% select(
      salary.x.x, 
      salary.y, 
      salary.x, 
      year2.x, 
      utbregno, 
      perc_edu, 
      sum_edu, 
      salary.y.y, 
      perc_women, 
      sum_pop) 
    model <- lm(perc_women ~ ., data = temp1)
    summary_table <- rbind (summary_table, mutate (tidy (summary (model)), ssyk = i))
    anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i))
  }
}  

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

Figure 14.8: F-value for regional distribution of enterprises R&D compared to the per cent in the ssyk who are women

merge(summary_table, anova_table, by = "ssyk", all = TRUE) %>% 
  filter (term.y == "salary.y") %>%
  filter (term.x == "salary.y") %>%
  select (ssyk, estimate, statistic.y, p.value.x) %>%
  arrange (desc (statistic.y)) %>%
  knitr::kable( 
  booktabs = TRUE,
  caption = 'F-value for regional distribution of enterprises R&D compared to the per cent in the ssyk who are women') 

Table 14.6: F-value for regional distribution of enterprises R&D compared to the per cent in the ssyk who are women

ssyk	estimate	statistic.y	p.value.x
932 Manufacturing labourers	-0.0006457	7.8674667	0.0064883
621 Forestry and related workers	-0.0005947	6.8164730	0.0110114
0002 occupations unidentifiable	-0.0001579	6.2446815	0.0147719
731 Precision-instrument makers and handicraft workers	0.0013486	6.1740680	0.0153227
138 Forestry and agricultural production managers	-0.0006651	5.5593064	0.0215007
962 Newspaper distributors, janitors and other service workers	0.0002656	5.4134525	0.0228402
812 Metal processing and finishing plant operators	-0.0002946	5.2690891	0.0248445
611 Market gardeners and crop growers	-0.0003365	5.1078794	0.0268863
161 Financial and insurance managers	0.0004603	4.7476685	0.0326584
124 Information, communication and public relations managers	0.0006090	4.5956230	0.0354799
243 Marketing and public relations professionals	-0.0003370	4.4046033	0.0393999
811 Mining and mineral processing plant operators	-0.0001685	4.2831471	0.0424713
225 Veterinarians	-0.0003855	4.1685923	0.0448967
622 Aquaculture and fishery workers	0.0014431	4.1407094	0.0475260
136 Production managers in construction and mining	0.0001504	3.7806116	0.0558121
834 Mobile plant operators	0.0001575	3.6064765	0.0616182
315 Ship and aircraft controllers and technicians	-0.0004471	3.3671367	0.0706981
335 Tax and related government associate professionals	-0.0002298	3.3401053	0.0718122
351 ICT operations and user support technicians	0.0001968	3.3268733	0.0723646
821 Assemblers	-0.0004344	3.3020157	0.0734149
132 Supply, logistics and transport managers	0.0003189	3.2846847	0.0741570
173 Retail and wholesale trade managers	-0.0002465	2.9120508	0.0922907
323 Complementary medicine therapists and associate professionals	0.0006325	2.8737549	0.1024563
723 Machinery mechanics and fitters	0.0001751	2.8455679	0.0960156
333 Business services agents	-0.0001954	2.4998182	0.1183044
512 Cooks and cold-buffet managers	-0.0003148	2.4604203	0.1211939
262 Museum curators and librarians and related professionals	0.0002545	2.4324609	0.1232928
921 Berry pickers and planters	0.0006166	2.3615517	0.1288032
815 Machine operators, textile, fur and leather products	0.0006943	2.3454131	0.1300960
421 Croupiers, debt collectors and related workers	-0.0010400	2.3131308	0.1328551
931 Construction labourers	-0.0001629	2.2504645	0.1381364
218 Specialists within environmental and health protection	0.0002906	2.1906384	0.1432756
111 Legislators and senior officials	0.0003404	2.1245424	0.1493651
722 Blacksmiths, toolmakers and related trades workers	-0.0002308	1.9985660	0.1618178
129 Administration and service managers not elsewhere classified	0.0001815	1.9790145	0.1638565
819 Process control technicians	-0.0001879	1.9684353	0.1649721
217 Designers	0.0003557	1.9121452	0.1710589
336 Police officers	0.0001246	1.8613448	0.1767787
233 Secondary education teachers	0.0001264	1.8172563	0.1819255
134 Architectural and engineering managers	0.0002561	1.8170276	0.1819527
713 Painters, Lacquerers, Chimney-sweepers and related trades workers	-0.0001725	1.8033889	0.1835807
933 Dockers and ground personnel	0.0002727	1.7849490	0.1859325
123 Administration and planning managers	-0.0004179	1.6888772	0.1979529
121 Finance managers	0.0002150	1.6793019	0.1992142
912 Washers, window cleaners and other cleaning workers	0.0004237	1.6503922	0.2030812
174 Sports, leisure and wellness managers	0.0005197	1.6177940	0.2075500
833 Heavy truck and bus drivers	0.0000728	1.5477745	0.2175559
151 Health care managers	0.0001466	1.5471463	0.2176483
816 Machine operators, food and related products	0.0002940	1.4607410	0.2308221
242 Organisation analysts, policy administrators and human resource specialists	0.0000952	1.4329401	0.2352668
753 Tailors, upholsterers and leather craftsmen	0.0006041	1.3448186	0.2500710
432 Stores and transport clerks	0.0001284	1.2955273	0.2588581
531 Child care workers and teachers aides	0.0000775	1.2605842	0.2653229
171 Hotel and conference managers	0.0003330	1.1982262	0.2773761
533 Health care assistants	0.0001185	1.1454670	0.2881250
524 Event seller and telemarketers	-0.0003307	1.1302900	0.2913162
343 Photographers, interior decorators and entertainers	-0.0002936	1.1215825	0.2931677
952 Street and market vendors	0.0006296	1.1028561	0.3004513
761 Butchers, bakers and food processors	0.0003954	1.0896234	0.3000958
341 Social work and religious associate professionals	0.0001781	1.0386322	0.3116000
226 Dentists	0.0001967	1.0232630	0.3151812
441 Library and filing clerks	0.0002325	0.9321916	0.3375713
011 Commissioned armed forces officers	-0.0001262	0.9208536	0.3446761
442 Postmen and postal facility workers	0.0001607	0.8966173	0.3469020
211 Physicists and chemists	0.0004656	0.8888187	0.3489946
135 Real estate and head of administration manager	-0.0002082	0.8887805	0.3490049
231 University and higher education teachers	-0.0002251	0.8545843	0.3583900
264 Authors, journalists and linguists	-0.0001682	0.8186704	0.3686293
541 Other surveillance and security workers	-0.0001442	0.7956533	0.3754093
752 Wood treaters, cabinet-makers and related trades workers	-0.0001048	0.7726790	0.3823549
742 Electronics and telecommunications installers and repairers	-0.0000558	0.7406660	0.3923446
721 Sheet and structural metal workers, moulders and welders, and related workers	-0.0000462	0.7370372	0.3935007
817 Wood processing and papermaking plant operators	0.0000886	0.6793195	0.4126612
612 Animal breeders and keepers	-0.0002631	0.6712576	0.4153552
732 Printing trades workers	-0.0001864	0.6707332	0.4155366
137 Production managers in manufacturing	0.0000850	0.6411173	0.4259778
266 Social work and counselling professionals	-0.0000727	0.6200694	0.4336424
814 Machine operators, rubber, plastic and paper products	-0.0002487	0.6167562	0.4348681
228 Specialists in health care not elsewhere classified	0.0000981	0.5908623	0.4446353
711 Carpenters, bricklayers and construction workers	0.0000215	0.5306474	0.4687307
234 Primary- and pre-school teachers	0.0000435	0.5079513	0.4783629
523 Cashiers and related clerks	-0.0001238	0.4893701	0.4864940
232 Vocational education teachers	-0.0001063	0.4848386	0.4885121
324 Veterinary assistants	-0.0001322	0.4820243	0.4898379
813 Machine operators, chemical and pharmaceutical products	0.0004432	0.4403635	0.5092241
422 Client information clerks	0.0001506	0.4120946	0.5229756
214 Engineering professionals	0.0001653	0.4107554	0.5236497
818 Other stationary plant and machine operators	-0.0002147	0.3804577	0.5393313
515 Building caretakers and related workers	0.0000906	0.3579344	0.5515595
832 Car, van and motorcycle drivers	0.0000565	0.3571153	0.5520140
443 Elected representatives	0.0003277	0.3507870	0.5556039
532 Personal care workers in health services	-0.0000325	0.3324210	0.5660604
179 Other services managers not elsewhere classified	0.0001052	0.3204798	0.5731045
021 Non-commissioned armed forces officers	0.0001772	0.2991253	0.5875473
216 Architects and surveyors	0.0001041	0.2893135	0.5923429
031 Armed forces occupations, other ranks	-0.0000769	0.2884937	0.5932074
514 Hairdressers, beauty and body therapists	0.0000578	0.2729764	0.6029686
511 Cabin crew, guides and related workers	0.0001527	0.2698108	0.6050743
154 Managers and leaders within religious bodies	-0.0002664	0.2653019	0.6081008
332 Insurance advisers, sales and purchasing agents	0.0000535	0.2549145	0.6151992
222 Nursing professionals	-0.0000275	0.2511228	0.6178357
224 Psychologists and psychotherapists	-0.0000643	0.2379413	0.6271999
153 Elderly care managers	0.0000544	0.2311075	0.6321820
261 Legal professionals	-0.0000782	0.2174508	0.6424167
312 Construction and manufacturing supervisors	-0.0000359	0.1906820	0.6636748
149 Education managers not elsewhere classified	-0.0001966	0.1829755	0.6701606
223 Nursing professionals (cont.)	0.0000270	0.1810968	0.6717195
221 Medical doctors	-0.0000259	0.1710916	0.6803902
241 Accountants, financial analysts and fund managers	0.0000619	0.1637486	0.6869450
334 Administrative and specialized secretaries	-0.0000959	0.1603409	0.6900452
122 Human resource managers	-0.0000645	0.1282636	0.7213019
516 Other service related workers	0.0001031	0.1217120	0.7282175
961 Recycling collectors	0.0000425	0.1182514	0.7319551
342 Athletes, fitness instructors and recreational workers	0.0000608	0.1147586	0.7357900
911 Cleaners and helpers	-0.0000359	0.1122787	0.7385526
352 Broadcasting and audio-visual technicians	-0.0001250	0.1113099	0.7396411
345 Culinary associate professionals	0.0000869	0.1054971	0.7462851
831 Train operators and related workers	0.0000639	0.1041019	0.7479373
522 Shop staff	-0.0000201	0.1010053	0.7515594
213 Biologists, pharmacologists and specialists in agriculture and forestry	0.0001097	0.0993153	0.7535772
235 Teaching professionals not elsewhere classified	0.0000380	0.0855566	0.7707570
159 Other social services managers	-0.0000491	0.0847614	0.7717945
941 Fast-food workers, food preparation assistants	0.0000427	0.0799479	0.7781907
411 Office assistants and other secretaries	-0.0000162	0.0794263	0.7788962
133 Research and development managers	0.0001401	0.0680307	0.7950260
835 Ships deck crews and related workers	-0.0001794	0.0601924	0.8073076
712 Roofers, floor layers, plumbers and pipefitters	-0.0000061	0.0566234	0.8126000
325 Dental hygienists	-0.0000176	0.0544556	0.8162173
141 Primary and secondary schools and adult education managers	0.0000366	0.0532732	0.8181264
311 Physical and engineering science technicians	0.0000198	0.0510731	0.8218558
613 Mixed crop and animal breeders	-0.0000450	0.0504070	0.8230014
112 Managing directors and chief executives	-0.0000344	0.0490575	0.8253474
131 Information and communications technology service managers	0.0000323	0.0373463	0.8473135
534 Attendants, personal assistants and related workers	-0.0000222	0.0358542	0.8503572
172 Restaurant managers	-0.0000327	0.0311408	0.8604290
251 ICT architects, systems analysts and test managers	0.0000131	0.0293397	0.8644842
142 Preschool managers	0.0000158	0.0233120	0.8790816
321 Medical and pharmaceutical technicians	-0.0000328	0.0220096	0.8824820
152 Managers in social and curative care	-0.0000345	0.0208776	0.8855222
513 Waiters and bartenders	-0.0000189	0.0206471	0.8861515
227 Naprapaths, physiotherapists, occupational therapists	-0.0000124	0.0199742	0.8880095
535 Dental nurses	-0.0000036	0.0168930	0.8971423
125 Sales and marketing managers	0.0000147	0.0090545	0.9244596
331 Financial and accounting associate professionals	0.0000072	0.0059991	0.9384805
267 Religious professionals and deacons	-0.0000119	0.0023085	0.9618142
212 Mathematicians, actuaries and statisticians	-0.0000249	0.0016135	0.9680824
344 Driving instructors and other instructors	0.0000078	0.0009629	0.9753328
265 Creative and performing artists	0.0000052	0.0004044	0.9840117
741 Electrical equipment installers and repairers	0.0000003	0.0001314	0.9908852

14.6 F-value for total R&D person years performed by employees of a higher education institution by region and number of the population in the ssyk

tb <- readfile("000000NL_10.csv") %>%
  filter(salary > 0) %>%
  group_by (`region`, year, `occupation (SSYK 2012)`) %>%  
  mutate (perc_women = as.numeric (sub ("%", "", perc_women (salary)))) %>%
  mutate (sum_pop = sum(salary)) 
tb <- readfile("UF0506A1_2.csv") %>%
  group_by(`level of education`, region, year, sex) %>%
  mutate(utbregno = sum(salary)) %>%
  group_by(region, year, sex) %>% mutate(perc_edu = utbregno / sum(utbregno)) %>%
  group_by(`level of education`, region, year) %>%
  mutate (sum_edu = sum(utbregno)) %>%
  filter (`level of education` == "post-secondary education 3 years or more (ISCED97 5A)") %>%
  right_join(tb, by = c("region", "year", "sex"))
tb <- readfile("000002O5.csv") %>%
  right_join(tb, by = c("region", "year"))
tb <- readfile("000002LR.csv") %>%
  right_join(tb, by = c("region", "year"))
  
summary_table = 0
anova_table = 0
 
for (i in unique(tb$`occupation (SSYK 2012)`)){
  temp <- filter(tb, `occupation (SSYK 2012)` == i)
  if (dim(temp)[1] > 50){
    temp1 <- temp %>% select(
      salary.x.x, 
      salary.y, 
      salary.x, 
      year2.x, 
      utbregno, 
      perc_edu, 
      sum_edu, 
      salary.y.y, 
      perc_women, 
      sum_pop) 
    model <- lm(sum_pop ~ ., data = temp1)
    summary_table <- rbind (summary_table, mutate (tidy (summary (model)), ssyk = i))
    anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i))
  }
}  

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

Figure 14.9: F-value for total R&D person years performed by employees of a higher education institution by region and number of the population in the ssyk

merge(summary_table, anova_table, by = "ssyk", all = TRUE) %>% 
  filter (term.y == "salary.x.x") %>%
  filter (term.x == "salary.x.x") %>%
  select (ssyk, estimate, statistic.y, p.value.x) %>%
  arrange (desc (statistic.y)) %>%
  knitr::kable( 
  booktabs = TRUE,
  caption = 'F-value for total R&D person years performed by employees of a higher education institution by region and number of the population in the ssyk') 

Table 14.7: F-value for total R&D person years performed by employees of a higher education institution by region and number of the population in the ssyk

ssyk	estimate	statistic.y	p.value.x
211 Physicists and chemists	0.1463558	58.0362031	0.0000000
411 Office assistants and other secretaries	-0.7418784	33.9587364	0.0000002
422 Client information clerks	-0.4022829	22.3770565	0.0000111
345 Culinary associate professionals	-0.0198665	19.1732781	0.0000403
213 Biologists, pharmacologists and specialists in agriculture and forestry	0.0740779	15.6583774	0.0001778
122 Human resource managers	-0.0806716	14.3185096	0.0003193
511 Cabin crew, guides and related workers	-0.0275086	14.1021568	0.0003514
621 Forestry and related workers	-0.0516183	13.4380725	0.0004723
812 Metal processing and finishing plant operators	-0.2192918	13.3306195	0.0005128
231 University and higher education teachers	0.2117384	12.8144288	0.0006253
225 Veterinarians	0.0202851	12.7088438	0.0006559
334 Administrative and specialized secretaries	-0.1507574	12.4085159	0.0007517
133 Research and development managers	0.0465105	12.3546361	0.0007938
331 Financial and accounting associate professionals	-0.2324933	9.8898466	0.0024271
216 Architects and surveyors	-0.0297791	9.6219620	0.0027583
261 Legal professionals	-0.0976863	9.1598261	0.0034448
344 Driving instructors and other instructors	-0.0146249	9.0040863	0.0037145
343 Photographers, interior decorators and entertainers	-0.0176099	8.8024704	0.0040967
531 Child care workers and teachers aides	-0.2996087	8.2569096	0.0053513
232 Vocational education teachers	-0.0436771	7.7179267	0.0069898
817 Wood processing and papermaking plant operators	-0.1994336	7.4406100	0.0080805
262 Museum curators and librarians and related professionals	0.0264295	6.8038803	0.0110823
742 Electronics and telecommunications installers and repairers	-0.0450196	6.2047048	0.0150811
534 Attendants, personal assistants and related workers	0.3433275	6.1292707	0.0156833
124 Information, communication and public relations managers	-0.0186241	5.9783154	0.0169658
831 Train operators and related workers	-0.0402474	5.7522148	0.0191763
243 Marketing and public relations professionals	-0.2371783	5.7483964	0.0191365
141 Primary and secondary schools and adult education managers	-0.0240571	5.6421717	0.0202369
171 Hotel and conference managers	-0.0060783	5.6243691	0.0204278
031 Armed forces occupations, other ranks	0.0834141	5.6177319	0.0210675
532 Personal care workers in health services	1.2202192	5.2938515	0.0243410
514 Hairdressers, beauty and body therapists	-0.0337248	4.5475585	0.0364252
941 Fast-food workers, food preparation assistants	-0.1765814	4.4958363	0.0374727
335 Tax and related government associate professionals	-0.0977686	4.4595177	0.0382275
523 Cashiers and related clerks	-0.0353361	4.0807251	0.0471494
264 Authors, journalists and linguists	-0.0511786	4.0400609	0.0482329
342 Athletes, fitness instructors and recreational workers	-0.0392351	3.9455483	0.0508564
179 Other services managers not elsewhere classified	-0.0099504	3.9147611	0.0517440
153 Elderly care managers	0.0357532	3.8588155	0.0534001
214 Engineering professionals	0.3662656	3.5939076	0.0620620
212 Mathematicians, actuaries and statisticians	0.0114794	3.4928962	0.0661394
111 Legislators and senior officials	-0.0088274	3.4241806	0.0684086
266 Social work and counselling professionals	-0.0616859	3.3121156	0.0729861
336 Police officers	-0.0226565	3.2996371	0.0735162
721 Sheet and structural metal workers, moulders and welders, and related workers	-0.1536410	3.2888635	0.0739773
722 Blacksmiths, toolmakers and related trades workers	-0.3582090	3.0124220	0.0869671
441 Library and filing clerks	0.0085926	2.9690978	0.0892221
221 Medical doctors	-0.0437246	2.8247567	0.0972157
834 Mobile plant operators	-0.1731126	2.7713912	0.1003701
132 Supply, logistics and transport managers	0.0221364	2.7713222	0.1003742
932 Manufacturing labourers	0.0178137	2.6899696	0.1054052
541 Other surveillance and security workers	-0.0927008	2.6671869	0.1068642
242 Organisation analysts, policy administrators and human resource specialists	-0.1727573	2.6239929	0.1096929
421 Croupiers, debt collectors and related workers	-0.0056013	2.5698599	0.1134856
112 Managing directors and chief executives	-0.0715940	2.5351362	0.1157800
159 Other social services managers	-0.0163546	2.4373636	0.1229218
911 Cleaners and helpers	0.1361728	2.3144913	0.1326146
753 Tailors, upholsterers and leather craftsmen	-0.0093093	2.2781682	0.1356443
952 Street and market vendors	0.0029578	2.2756346	0.1399138
152 Managers in social and curative care	-0.0100460	2.1356902	0.1483175
516 Other service related workers	-0.0087183	2.1148072	0.1502870
174 Sports, leisure and wellness managers	-0.0030682	2.0916549	0.1525058
813 Machine operators, chemical and pharmaceutical products	0.0198509	2.0334205	0.1585168
142 Preschool managers	-0.0073834	2.0108663	0.1605504
233 Secondary education teachers	0.0148162	1.9776662	0.1639982
315 Ship and aircraft controllers and technicians	-0.0320213	1.9299456	0.1691062
622 Aquaculture and fishery workers	0.0171978	1.6964738	0.1990962
613 Mixed crop and animal breeders	-0.0175992	1.6872578	0.1981655
224 Psychologists and psychotherapists	0.0115318	1.4934392	0.2257253
533 Health care assistants	0.1191213	1.4877238	0.2266062
962 Newspaper distributors, janitors and other service workers	0.0529368	1.4755997	0.2284888
138 Forestry and agricultural production managers	-0.0026924	1.2888059	0.2605688
333 Business services agents	-0.0176886	1.2808529	0.2615485
811 Mining and mineral processing plant operators	-0.0408799	1.2334579	0.2708272
921 Berry pickers and planters	-0.0047961	1.1956664	0.2778857
723 Machinery mechanics and fitters	-0.1785306	1.1354512	0.2902259
241 Accountants, financial analysts and fund managers	0.0611027	1.0926428	0.2994322
149 Education managers not elsewhere classified	-0.0015607	1.0682708	0.3049454
833 Heavy truck and bus drivers	-0.0980038	0.9266111	0.3390121
325 Dental hygienists	0.0072419	0.9042218	0.3451747
443 Elected representatives	-0.0013574	0.9023337	0.3454721
515 Building caretakers and related workers	-0.0476197	0.7949414	0.3756218
265 Creative and performing artists	0.0099944	0.7761485	0.3812943
136 Production managers in construction and mining	0.0171352	0.7716266	0.3826774
267 Religious professionals and deacons	0.0033847	0.7441644	0.3912346
819 Process control technicians	-0.0757045	0.7158311	0.4003573
172 Restaurant managers	-0.0078559	0.6287919	0.4304407
226 Dentists	-0.0050437	0.6141053	0.4358527
522 Shop staff	-0.0683203	0.5594339	0.4569587
933 Dockers and ground personnel	-0.0137935	0.5177664	0.4742262
234 Primary- and pre-school teachers	0.1638680	0.4596416	0.4999951
251 ICT architects, systems analysts and test managers	0.3146785	0.4507707	0.5041478
323 Complementary medicine therapists and associate professionals	0.0005862	0.4300329	0.5179629
161 Financial and insurance managers	0.0157011	0.4288542	0.5146678
121 Finance managers	-0.0074248	0.4219395	0.5180669
442 Postmen and postal facility workers	-0.0192643	0.4193823	0.5193340
752 Wood treaters, cabinet-makers and related trades workers	-0.0307698	0.4072236	0.5254350
612 Animal breeders and keepers	-0.0113570	0.3997059	0.5292719
218 Specialists within environmental and health protection	-0.0038586	0.3909445	0.5338079
931 Construction labourers	-0.0082315	0.3868301	0.5360201
228 Specialists in health care not elsewhere classified	0.0075191	0.3624893	0.5490449
312 Construction and manufacturing supervisors	-0.0155602	0.3409234	0.5611472
351 ICT operations and user support technicians	-0.0739311	0.3295452	0.5677412
961 Recycling collectors	0.0073579	0.3224956	0.5719032
611 Market gardeners and crop growers	0.0170079	0.3213459	0.5725878
821 Assemblers	-0.1199391	0.2933950	0.5897489
912 Washers, window cleaners and other cleaning workers	0.0051673	0.2652329	0.6081474
235 Teaching professionals not elsewhere classified	-0.0167530	0.2514236	0.6176256
137 Production managers in manufacturing	-0.0228467	0.2495196	0.6189580
123 Administration and planning managers	-0.0074163	0.2407100	0.6252067
352 Broadcasting and audio-visual technicians	0.0101539	0.2390439	0.6264044
217 Designers	0.0078311	0.2239504	0.6374980
818 Other stationary plant and machine operators	-0.0153782	0.2237146	0.6376749
223 Nursing professionals (cont.)	0.0318760	0.2228286	0.6383407
816 Machine operators, food and related products	0.0166026	0.2136065	0.6453684
513 Waiters and bartenders	-0.0057509	0.1904920	0.6638318
832 Car, van and motorcycle drivers	-0.0195279	0.1858496	0.6676997
524 Event seller and telemarketers	-0.0059479	0.1687607	0.6824528
311 Physical and engineering science technicians	0.0530238	0.1575890	0.6925768
135 Real estate and head of administration manager	-0.0021021	0.1347974	0.7146009
021 Non-commissioned armed forces officers	0.0033013	0.1314770	0.7188633
0002 occupations unidentifiable	-0.0589769	0.1209906	0.7289917
432 Stores and transport clerks	0.0536965	0.1051802	0.7466531
125 Sales and marketing managers	0.0238198	0.1041027	0.7479089
134 Architectural and engineering managers	0.0045784	0.0964988	0.7569825
324 Veterinary assistants	-0.0018865	0.0886965	0.7667358
731 Precision-instrument makers and handicraft workers	0.0016801	0.0816069	0.7759633
713 Painters, Lacquerers, Chimney-sweepers and related trades workers	0.0082431	0.0726258	0.7883330
332 Insurance advisers, sales and purchasing agents	0.0389329	0.0717852	0.7895319
814 Machine operators, rubber, plastic and paper products	-0.0172823	0.0599319	0.8073098
173 Retail and wholesale trade managers	0.0017316	0.0485386	0.8262583
712 Roofers, floor layers, plumbers and pipefitters	0.0088647	0.0470323	0.8289322
741 Electrical equipment installers and repairers	-0.0123605	0.0460158	0.8307620
711 Carpenters, bricklayers and construction workers	-0.0227412	0.0450086	0.8325961
131 Information and communications technology service managers	0.0079229	0.0270484	0.8698332
011 Commissioned armed forces officers	-0.0024839	0.0238994	0.8781434
129 Administration and service managers not elsewhere classified	0.0021438	0.0228879	0.8801781
222 Nursing professionals	0.0105296	0.0194864	0.8893761
732 Printing trades workers	0.0021334	0.0178872	0.8939842
512 Cooks and cold-buffet managers	0.0057852	0.0163700	0.8985540
835 Ships deck crews and related workers	-0.0003782	0.0065494	0.9358580
154 Managers and leaders within religious bodies	0.0001459	0.0059884	0.9385351
815 Machine operators, textile, fur and leather products	0.0008588	0.0049907	0.9438789
535 Dental nurses	0.0013194	0.0043521	0.9476813
341 Social work and religious associate professionals	-0.0018668	0.0037401	0.9514066
321 Medical and pharmaceutical technicians	0.0021093	0.0036609	0.9519228
761 Butchers, bakers and food processors	0.0018300	0.0036546	0.9519644
151 Health care managers	-0.0006433	0.0026781	0.9588732
227 Naprapaths, physiotherapists, occupational therapists	0.0001902	0.0000911	0.9924113

14.7 F-value for regional distribution of enterprises R&D compared to the number of the population in the ssyk

tb <- readfile("000000NL_10.csv") %>%
  filter(salary > 0) %>%
  group_by (`region`, year, `occupation (SSYK 2012)`) %>%  
  mutate (perc_women = as.numeric (sub ("%", "", perc_women (salary)))) %>%
  mutate (sum_pop = sum(salary)) 
tb <- readfile("UF0506A1_2.csv") %>%
  group_by(`level of education`, region, year, sex) %>%
  mutate(utbregno = sum(salary)) %>%
  group_by(region, year, sex) %>% mutate(perc_edu = utbregno / sum(utbregno)) %>%
  group_by(`level of education`, region, year) %>%
  mutate (sum_edu = sum(utbregno)) %>%
  filter (`level of education` == "post-secondary education 3 years or more (ISCED97 5A)") %>%
  right_join(tb, by = c("region", "year", "sex"))
tb <- readfile("000002O5.csv") %>%
  right_join(tb, by = c("region", "year"))
tb <- readfile("000002LR.csv") %>%
  right_join(tb, by = c("region", "year"))
  
summary_table = 0
anova_table = 0
 
for (i in unique(tb$`occupation (SSYK 2012)`)){
  temp <- filter(tb, `occupation (SSYK 2012)` == i)
  if (dim(temp)[1] > 50){
    temp1 <- temp %>% select(
      salary.x.x, 
      salary.y, 
      salary.x, 
      year2.x, 
      utbregno, 
      perc_edu, 
      sum_edu, 
      salary.y.y, 
      perc_women, 
      sum_pop) 
    model <- lm(sum_pop ~ ., data = temp1)  
    summary_table <- rbind (summary_table, mutate (tidy (summary (model)), ssyk = i))
    anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i))
  }
}  

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

Figure 14.10: F-value for regional distribution of enterprises R&D compared to the number of the population in the ssyk

merge(summary_table, anova_table, by = "ssyk", all = TRUE) %>% 
  filter (term.y == "salary.y") %>%
  filter (term.x == "salary.y") %>%
  select (ssyk, estimate, statistic.y, p.value.x) %>%
  arrange (desc (statistic.y)) %>%
  knitr::kable( 
  booktabs = TRUE,
  caption = 'F-value for regional distribution of enterprises R&D compared to the number of the population in the ssyk') 

Table 14.8: F-value for regional distribution of enterprises R&D compared to the number of the population in the ssyk

ssyk	estimate	statistic.y	p.value.x
133 Research and development managers	0.0288360	86.5766200	0.0000000
731 Precision-instrument makers and handicraft workers	0.0093522	57.5823738	0.0000000
214 Engineering professionals	0.3468839	53.0273935	0.0000000
835 Ships deck crews and related workers	0.0071463	46.0241635	0.0000000
622 Aquaculture and fishery workers	0.0090426	39.7498695	0.0000001
512 Cooks and cold-buffet managers	0.0460682	32.5713459	0.0000002
224 Psychologists and psychotherapists	0.0074879	21.8187489	0.0000138
411 Office assistants and other secretaries	0.1121404	21.7044739	0.0000145
335 Tax and related government associate professionals	0.0365611	17.5140675	0.0000805
722 Blacksmiths, toolmakers and related trades workers	0.1946837	17.4296331	0.0000834
136 Production managers in construction and mining	0.0144288	16.7004996	0.0001137
211 Physicists and chemists	0.0095241	15.4773228	0.0001923
344 Driving instructors and other instructors	0.0038660	14.4772864	0.0002977
941 Fast-food workers, food preparation assistants	0.0540376	14.4405037	0.0003026
311 Physical and engineering science technicians	0.1136168	12.6588107	0.0006709
134 Architectural and engineering managers	0.0099713	10.6873560	0.0016649
813 Machine operators, chemical and pharmaceutical products	-0.0097243	10.1934751	0.0021475
228 Specialists in health care not elsewhere classified	0.0067824	9.3376485	0.0031617
342 Athletes, fitness instructors and recreational workers	0.0105651	9.1049243	0.0035375
421 Croupiers, debt collectors and related workers	0.0023281	8.0259161	0.0060407
831 Train operators and related workers	-0.0071873	7.2206450	0.0090238
821 Assemblers	0.1238140	6.7585852	0.0113415
713 Painters, Lacquerers, Chimney-sweepers and related trades workers	0.0159076	6.5312493	0.0127421
151 Health care managers	-0.0055369	6.0031620	0.0167473
226 Dentists	0.0033322	5.8488952	0.0181536
179 Other services managers not elsewhere classified	0.0021802	5.7851798	0.0187704
931 Construction labourers	0.0057264	5.7761637	0.0189371
742 Electronics and telecommunications installers and repairers	0.0072694	5.6302928	0.0203640
723 Machinery mechanics and fitters	0.0825639	5.5954821	0.0207416
721 Sheet and structural metal workers, moulders and welders, and related workers	0.0365659	5.3911909	0.0231119
441 Library and filing clerks	-0.0019345	5.2024844	0.0255579
142 Preschool managers	-0.0021390	5.1165637	0.0267614
137 Production managers in manufacturing	0.0185958	5.0117363	0.0283120
312 Construction and manufacturing supervisors	0.0117376	4.8582539	0.0307573
818 Other stationary plant and machine operators	-0.0140275	4.7414708	0.0327686
336 Police officers	-0.0044063	4.7323619	0.0329313
933 Dockers and ground personnel	0.0069166	4.7062109	0.0335014
741 Electrical equipment installers and repairers	0.0229156	4.6643992	0.0341726
811 Mining and mineral processing plant operators	0.0134146	4.2522831	0.0431994
218 Specialists within environmental and health protection	0.0025719	3.9990073	0.0493541
911 Cleaners and helpers	0.0266583	3.6556016	0.0599165
812 Metal processing and finishing plant operators	0.0205694	3.3799495	0.0704268
952 Street and market vendors	0.0002171	3.2864816	0.0779721
132 Supply, logistics and transport managers	0.0050782	3.2389740	0.0761542
541 Other surveillance and security workers	0.0160347	2.8988953	0.0930148
532 Personal care workers in health services	0.1439794	2.7076463	0.1042885
524 Event seller and telemarketers	-0.0038354	2.5348984	0.1157968
819 Process control technicians	0.0220138	2.5213688	0.1167568
712 Roofers, floor layers, plumbers and pipefitters	0.0119500	2.4248608	0.1238704
711 Carpenters, bricklayers and construction workers	0.0322593	2.2702921	0.1363116
516 Other service related workers	0.0016233	2.2289965	0.1398724
021 Non-commissioned armed forces officers	0.0022965	2.1718683	0.1485818
834 Mobile plant operators	0.0310669	2.1690198	0.1452354
535 Dental nurses	0.0051430	2.0914876	0.1547579
752 Wood treaters, cabinet-makers and related trades workers	0.0128855	2.0622667	0.1553769
153 Elderly care managers	0.0050779	2.0399807	0.1575959
832 Car, van and motorcycle drivers	-0.0113870	2.0368134	0.1579143
351 ICT operations and user support technicians	0.0327500	2.0031284	0.1613464
912 Washers, window cleaners and other cleaning workers	0.0026571	1.9933215	0.1623618
223 Nursing professionals (cont.)	0.0140486	1.9523609	0.1666841
325 Dental hygienists	0.0020148	1.9359896	0.1688486
232 Vocational education teachers	0.0042122	1.9140758	0.1708459
523 Cashiers and related clerks	0.0043954	1.9105586	0.1712342
514 Hairdressers, beauty and body therapists	0.0040006	1.8990590	0.1725113
833 Heavy truck and bus drivers	0.0273845	1.8757354	0.1751359
011 Commissioned armed forces officers	0.0033562	1.8373543	0.1850555
352 Broadcasting and audio-visual technicians	-0.0049245	1.8103728	0.1827449
345 Culinary associate professionals	-0.0010181	1.8087789	0.1829353
031 Armed forces occupations, other ranks	0.0083822	1.7282458	0.1937226
422 Client information clerks	0.0199932	1.6625489	0.2014442
213 Biologists, pharmacologists and specialists in agriculture and forestry	-0.0025708	1.6540879	0.2025819
231 University and higher education teachers	0.0061336	1.6401569	0.2044718
515 Building caretakers and related workers	0.0132252	1.5466976	0.2177143
534 Attendants, personal assistants and related workers	0.0310932	1.5047835	0.2239893
753 Tailors, upholsterers and leather craftsmen	0.0014844	1.5027277	0.2243027
814 Machine operators, rubber, plastic and paper products	0.0161551	1.4924592	0.2258761
613 Mixed crop and animal breeders	0.0030563	1.4729629	0.2289007
161 Financial and insurance managers	-0.0052858	1.4055083	0.2397562
267 Religious professionals and deacons	0.0009268	1.4000808	0.2406569
533 Health care assistants	0.0188154	1.3914583	0.2420965
222 Nursing professionals	0.0159570	1.3476573	0.2495764
135 Real estate and head of administration manager	0.0011096	1.2879930	0.2602351
315 Ship and aircraft controllers and technicians	-0.0041387	1.1852662	0.2799682
138 Forestry and agricultural production managers	-0.0004167	1.1690386	0.2837204
152 Managers in social and curative care	0.0013286	1.1645932	0.2841675
154 Managers and leaders within religious bodies	0.0003646	1.1325556	0.2908369
141 Primary and secondary schools and adult education managers	0.0019010	1.1246890	0.2925054
234 Primary- and pre-school teachers	0.0491506	1.1236326	0.2927304
815 Machine operators, textile, fur and leather products	0.0025770	1.1032847	0.2971085
221 Medical doctors	0.0044678	1.0952462	0.2988615
334 Administrative and specialized secretaries	-0.0081430	1.0555935	0.3077100
125 Sales and marketing managers	-0.0111966	0.9053781	0.3445717
333 Business services agents	0.0029231	0.8503344	0.3595807
266 Social work and counselling professionals	0.0057216	0.8423190	0.3618416
522 Shop staff	0.0145573	0.8147077	0.3697841
227 Naprapaths, physiotherapists, occupational therapists	0.0032989	0.7916877	0.3765952
817 Wood processing and papermaking plant operators	0.0101785	0.6597192	0.4194525
174 Sports, leisure and wellness managers	0.0003034	0.6424601	0.4254958
122 Human resource managers	-0.0031279	0.6384414	0.4269407
321 Medical and pharmaceutical technicians	0.0040967	0.6343873	0.4284058
432 Stores and transport clerks	0.0248619	0.6080341	0.4381206
443 Elected representatives	-0.0001984	0.5522305	0.4599305
921 Berry pickers and planters	-0.0006515	0.5063660	0.4790478
341 Social work and religious associate professionals	0.0037216	0.4804464	0.4904814
732 Printing trades workers	-0.0018385	0.4095441	0.5242608
131 Information and communications technology service managers	-0.0052744	0.4053263	0.5263986
173 Retail and wholesale trade managers	0.0006960	0.3376285	0.5630414
251 ICT architects, systems analysts and test managers	-0.0450284	0.3248615	0.5704998
611 Market gardeners and crop growers	-0.0032925	0.3217809	0.5723286
962 Newspaper distributors, janitors and other service workers	0.0046302	0.3158434	0.5758863
265 Creative and performing artists	-0.0010876	0.3123452	0.5780032
111 Legislators and senior officials	-0.0004090	0.2179765	0.6420156
129 Administration and service managers not elsewhere classified	-0.0010255	0.2029696	0.6537073
761 Butchers, bakers and food processors	0.0022963	0.1826299	0.6704161
264 Authors, journalists and linguists	-0.0020383	0.1796760	0.6729333
212 Mathematicians, actuaries and statisticians	-0.0004593	0.1759297	0.6762778
0002 occupations unidentifiable	-0.0126771	0.1747811	0.6771589
123 Administration and planning managers	-0.0011076	0.1572826	0.6928603
241 Accountants, financial analysts and fund managers	-0.0039146	0.1462714	0.7032668
159 Other social services managers	-0.0007028	0.1451539	0.7043478
261 Legal professionals	-0.0020287	0.1170421	0.7332756
124 Information, communication and public relations managers	-0.0004947	0.1168495	0.7334867
531 Child care workers and teachers aides	-0.0062484	0.1055742	0.7461957
332 Insurance advisers, sales and purchasing agents	0.0067977	0.1013902	0.7511024
323 Complementary medicine therapists and associate professionals	0.0000388	0.0935605	0.7622313
243 Marketing and public relations professionals	-0.0048685	0.0705013	0.7913777
233 Secondary education teachers	0.0004656	0.0568840	0.8121775
816 Machine operators, food and related products	0.0013640	0.0546090	0.8159014
216 Architects and surveyors	-0.0003675	0.0489906	0.8254644
932 Manufacturing labourers	0.0004533	0.0440839	0.8342990
331 Financial and accounting associate professionals	0.0024876	0.0416569	0.8388592
442 Postmen and postal facility workers	0.0009841	0.0398276	0.8423886
961 Recycling collectors	-0.0004322	0.0376276	0.8467468
343 Photographers, interior decorators and entertainers	0.0002024	0.0348484	0.8524461
171 Hotel and conference managers	-0.0000772	0.0291067	0.8650182
217 Designers	0.0003867	0.0205982	0.8862855
262 Museum curators and librarians and related professionals	0.0002080	0.0176921	0.8945604
235 Teaching professionals not elsewhere classified	0.0007020	0.0135568	0.9076375
513 Waiters and bartenders	-0.0002214	0.0103510	0.9192499
149 Education managers not elsewhere classified	-0.0000257	0.0087979	0.9255422
621 Forestry and related workers	-0.0002280	0.0086074	0.9263429
121 Finance managers	-0.0001575	0.0063544	0.9366886
612 Animal breeders and keepers	0.0001910	0.0041293	0.9489439
225 Veterinarians	0.0000556	0.0037471	0.9513610
242 Organisation analysts, policy administrators and human resource specialists	-0.0007742	0.0016248	0.9679605
511 Cabin crew, guides and related workers	0.0000334	0.0006216	0.9801799
324 Veterinary assistants	-0.0000116	0.0001087	0.9917130
112 Managing directors and chief executives	-0.0000401	0.0000302	0.9956320
172 Restaurant managers	-0.0000054	0.0000106	0.9974156