Unemployment & Employment Rates in Turkey (2014-2020)
Group Rhapsody
Dec 2, 2020
Importing Necessary Libraries
Reading the Datasets
First dataset we used is the Unemployed Job Searching People by the Channel.
job_search_overall <- read_xls("/Users/Emre - PC/Desktop/MEF Assignments/R Exercises/Group Project/search_channel.xls", range = "TÜRKİYE!A6:P91", col_names = c("year", "month", "total_unemployed", "to_employers", "to_relatives", "to_emp_office", "to_emp_agencies", "to_newspaper", "insert_ad_to_newspaper", "take_interview", "look_place_equip_to_est_bus", "look_credit_license_to_est_bus", "wait_call_from_emp_office", "wait_result_of_app", "wait_result_of_comp_for_public_sec", "others"))
job_search_male <- read_xls("/Users/Emre - PC/Desktop/MEF Assignments/R Exercises/Group Project/search_channel.xls", range = "TÜRKİYE!R6:AG91", col_names = c("year", "month", "total_unemployed", "to_employers", "to_relatives", "to_emp_office", "to_emp_agencies", "to_newspaper", "insert_ad_to_newspaper", "take_interview", "look_place_equip_to_est_bus", "look_credit_license_to_est_bus", "wait_call_from_emp_office", "wait_result_of_app", "wait_result_of_comp_for_public_sec", "others"))
job_search_female <- read_xls("/Users/Emre - PC/Desktop/MEF Assignments/R Exercises/Group Project/search_channel.xls", range = "TÜRKİYE!AI6:AX91", col_names = c("year", "month", "total_unemployed", "to_employers", "to_relatives", "to_emp_office", "to_emp_agencies", "to_newspaper", "insert_ad_to_newspaper", "take_interview", "look_place_equip_to_est_bus", "look_credit_license_to_est_bus", "wait_call_from_emp_office", "wait_result_of_app", "wait_result_of_comp_for_public_sec", "others"))Second dataset is Employed & Unemployed by Educational Level.
educational_level_overall <- read_xls("/Users/Emre - PC/Desktop/MEF Assignments/R Exercises/Group Project/educational_level.xls", range = "TÜRKİYE!A7:Q92", col_names = c("year", "month", "lf_illeterate", "lf_less_than_hs", "lf_highschool", "lf_voc_hs", "lf_higher_ed","emp_illeterate", "emp_less_than_hs", "emp_highschool", "emp_voc_hs", "emp_higher_ed","unemp_illeterate", "unemp_less_than_hs", "unemp_highschool", "unemp_voc_hs", "unemp_higher_ed" ))Third dataset is Unemployment by occupational group.
occ_group_overall <- read_xls("/Users/Emre - PC/Desktop/MEF Assignments/R Exercises/Group Project/occupational_group.xls", range = "TÜRKİYE!A7:L92", col_names = c("year", "month", "total_unemployed", "manager", "prof", "tech", "cleric", "service", "agricul", "trade", "operator", "elemantary")) %>%
mutate(month = gsub("^.*?- ", "", month))
occ_group_male <- read_xls("/Users/Emre - PC/Desktop/MEF Assignments/R Exercises/Group Project/occupational_group.xls", range = "TÜRKİYE!N7:Y92", col_names = c("year", "month", "total_unemployed", "manager", "prof", "tech", "cleric", "service", "agricul", "trade", "operator", "elemantary")) %>%
mutate(month = gsub("^.*?- ", "", month))
occ_group_female <- read_xls("/Users/Emre - PC/Desktop/MEF Assignments/R Exercises/Group Project/occupational_group.xls", range = "TÜRKİYE!AA6:AL91", col_names = c("year", "month", "total_unemployed", "manager", "prof", "tech", "cleric", "service", "agricul", "trade", "operator", "elemantary")) %>%
mutate(month = gsub("^.*?- ", "", month))Fourth dataset is Labour force status of higher education graduates by the last graduated field of education.
last_graduated_major <- read_xls("/Users/Emre - PC/Desktop/MEF Assignments/R Exercises/Group Project/major_field.xls", range = "TURKIYE!A6:Y41", col_names = c("year", "statistics", "higher_ed_grad", "education", "arts", "humanities", "languages", "social_sci", "journalism", "business", "law", "biology_env_related_sci", "physical_sci", "math_stat", "info_communication_tech", "engineering", "manufacturing_processing", "architecture_construction", "agriculture_forestry_fishery", "veterinary", "health", "welfare", "personal_services", "occupational_health_transport_services", "security_services"))Exploratory Data Analysis
Overview Regarding Unemployment in Turkey (2014-2020)
As observed, there is an upward trend in unemployment from 2014 to 2020 in Turkey. Unemployment was 4.3 million as of January 2020 while it was 2.8 million in Jan 2014. In 2018, unemployment has soared enormously , which might be related to the sanctions imposed by US.
Despite the upward trend in unemployment, there is a repeated fluctuation periods in unemployment history. To explore whether it is due to seasonality effect, we will be investigating the data in the part called “Seasonality Effect on Unemployment”.
occ_group_overall_m <- occ_group_overall %>%
filter(month != "Annual") %>%
mutate(year = factor(year),
month = factor(month, levels = c("January","February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December")))
occ_group_overall_m <- occ_group_overall_m %>%
mutate(date = factor(paste(year, month), levels = paste(year, month)))
ggplot(occ_group_overall_m, aes(date, total_unemployed)) +
geom_line(aes(group = 1)) +
labs(
x = "Date",
y = "Unemployment (in thousands)") +
theme(axis.ticks.x = element_blank(),
axis.text.x = element_text(angle = 90, size = 5, hjust = 1))
In the next analysis, we’ll examine whether the unemployment differentiate by gender.
Unemployment by Gender
Both genders have similar upward trend over time. However, males have higher unemployment than females. We’ll be covering this difference deeply in the part, Unemployment by Occupational Group and Gender.
occ_group_male_y <- occ_group_male %>%
filter(month == "Annual") %>%
mutate(year = factor(year))
year_male_x <- rep(unique(occ_group_male_y$year), times = (ncol(occ_group_male_y)-3))
group_male_y <- rep(colnames(occ_group_male_y[-c(1,2,3)]),
each = length(unique(year_male_x)))
values_male_z <- array(unlist(occ_group_male_y[1:6,4:12]))
gender_male_q <- rep("M", length(year_male_x))
og_male_df <- data.frame(year_male_x, group_male_y, values_male_z, gender_male_q)
occ_group_female_y <- occ_group_female %>%
filter(month == "Annual") %>%
mutate(year = factor(year))
year_female_x <- rep(unique(occ_group_female_y$year), times = (ncol(occ_group_female_y)-3))
group_female_y <- rep(colnames(occ_group_female_y[-c(1,2,3)]),
each = length(unique(year_female_x)))
values_female_z <- array(unlist(occ_group_female_y[1:6,4:12]))
gender_female_q <- rep("F", length(year_female_x))
og_female_df <- data.frame(year_female_x, group_female_y, values_female_z, gender_female_q)
colnames(og_male_df) <- c("year", "group", "values", "gender")
colnames(og_female_df) <- c("year", "group", "values", "gender")
total_og_df <- rbind(og_male_df, og_female_df)
total_og_df2 <- total_og_df %>%
group_by(gender, year) %>%
summarize(total = sum(values))
total_og_df2$gender <- as.factor(total_og_df2$gender)
levels(total_og_df2$gender) <- c("Female", "Male")
ggplot(total_og_df2, aes(x = year ,y = total, group = gender, color = gender)) +
geom_line() +
labs(x = "Year",
y = "Unemployment (in thousands)",
title = "Unemployment by Gender",
color = "Gender") +
theme(plot.title = element_text(hjust = 0.5))
job_search_female$month <- str_split_fixed(job_search_female$month, " - ", 2)[,2]
job_search_male$month <- str_split_fixed(job_search_male$month, " - ", 2)[,2]
monthly_jobsearch_male <- filter(job_search_male, !grepl("Annual", month))
monthly_jobsearch_female <- filter(job_search_female, !grepl("Annual", month))
unemp_gr_male<-monthly_jobsearch_male%>%
transmute(month_year = paste(year, month, sep = " "), total_unemployed)
unemp_gr_female<-monthly_jobsearch_female%>%
transmute(month_year = paste(year, month, sep = " "), total_unemployed)
unemp_merged <-merge(unemp_gr_female,unemp_gr_male, by="month_year", all=TRUE, sort = FALSE)
unemp_merged <- unemp_merged %>%
rename(female=total_unemployed.x, male=total_unemployed.y)
unemp_merged$month_year <- as.character(unemp_merged$month_year)
unemp_merged$month_year <- factor(unemp_merged$month_year, level=unemp_merged$month_year)
ggplot(unemp_merged, aes(x=factor(month_year))) +
geom_line(aes(y=female, color="Female", group=1))+
geom_line(aes(y=male, color="Male", group=1))+
theme(axis.text.x = element_text(angle=90, size=5, hjust = 1),
plot.title = element_text(hjust = 0.5))+
labs(title = "Unemployment by Gender Over the Years",
x="Year",
y="Unemployment (in thousands)",
color="Gender")
Seasonality Effect on Unemployment
There are two peaks in a year on average: Winter and Summer. Besides the seasonal jobs, graduation might also be the contributor to these peaks.
ggplot(occ_group_overall_m, aes(factor(month), total_unemployed, group=year, color=year)) +
geom_line() +
labs(
x = "Month",
y = "Unemployment (in thousands)",
title = "Unemployment by Seasonality",
color = "Year") +
theme(axis.text.x = element_text(angle = 90, hjust = 1),
plot.title = element_text(hjust = 0.5))
Unemployment by Occupational Group
From 2014 to 2019, total unemployment has increased by 57%. Occupational groups with the highest growth in unemployment are professionals (108%), elementary occupations (64%) and technicians and associate professionals (55%) ; whereas skilled agricultural, forestry and fishery workers (0%), managers (20%) and clerical support workers (26%) are the ones with the lowest growth. The graph below displays the distribution of unemployment by occupational group from 2014 to 2019. The highest proportion of unemployment in 2019 belongs to service and sales workers (24% of all unemployment), followed by elementary occupations (19%) and craft and related trades workers (14%).
occ_group_overall_y <- occ_group_overall %>%
filter(month == "Annual") %>%
mutate(year = factor(year))
year_x <- rep(unique(occ_group_overall_y$year), times = (ncol(occ_group_overall_y)-3))
group_y <- rep(colnames(occ_group_overall_y[-c(1,2,3)]),
each = length(unique(year_x)))
values_z <- array(unlist(occ_group_overall_y[1:6,4:12]))
og_df <- data.frame(year_x, group_y, values_z)
og_df_rate <- og_df %>%
group_by(year_x) %>%
mutate(Percentage = round(values_z/sum(values_z) * 100, 1))
og_df_rate$group_y <- as.factor(og_df_rate$group_y)
levels(og_df_rate$group_y) <- c("Skilled agricultural, forestry and fishery workers", "Clerical support workers", "Elemantary occupations", "Managers", "Plant and machine operators and assemblers", "Professionals", "Service and sales workers", "Technicians and associate professionals", "Craft and related trades workers")
ggplot(og_df_rate, aes(x = year_x, y = Percentage)) +
geom_bar(stat = "identity", aes(fill = as.factor(group_y))) +
geom_text(aes(label = paste0(Percentage, "%"), vjust = -1)) +
facet_wrap(~group_y) +
ggtitle("Distribution of Unemployment by Occupational Group") +
theme(legend.position = "none", plot.title = element_text(hjust = 0.5)) +
xlab(label = "Year") +
scale_y_continuous(limits = c(0,30))
Unemployment by Occupational Group and Gender
In the following graph, the average unemployment between 2014 and 2019 are taken into account rather than displaying bars for each year. According to graph, occupational groups with the highest male percentage are plant and machine operators and assemblers (92%), skilled agricultural, forestry and fishery workers (91%) and craft and related trades workers (91%). Occupational groups with the lowest percentage of male participation are clerical support workers (34%), professionals (41%) and technicians and associate professionals (52%).
An interesting point is that even there are some occupational groups requiring almost no physical power, but there are still huge differences between male and female participation. For instance, 74% of unemployed managers are males where as 60% of all unemployed are males. The difference between these two percentage might have several reasons. One reason might be linked to the educational level of unemployed. Another reason might be linked to the willingness to work after education since willingness to work is a criteria to decide whether a person is unemployed or not. Last but not least, pregnancy and raising child might be another reason that keeps the female participation lower.
total_og_df4 <- total_og_df %>%
group_by(group, gender) %>%
summarize(average_p_year = mean(values))
total_og_df4$gender <- as.factor(total_og_df4$gender)
levels(total_og_df4$gender) <- c("Female", "Male")
total_og_df4$group <- as.factor(total_og_df4$group)
levels(total_og_df4$group) <- c("Agricul. & Forestry & Fishery", "Clerical Support", "Elemantary Occu.", "Managers", "Plant & Machine Oper./Assmb.", "Professionals", "Service & Sales", "Technicians & Assoc. Prof.", "Craft & Related Trades")
ggplot(total_og_df4, aes(group, average_p_year, fill = gender)) +
geom_col(position = "fill") +
labs(x = "",
y = "Percentage",
title = "Unemployment by Occupational Group and Gender",
fill = "Gender") +
theme(axis.text.x = element_text(angle = 90, size = 10, hjust = 1),
plot.title = element_text(hjust = 0.5))
Labour Force Status of Higher Education Graduates by Their Major
In the following table, unemployment rate for the graduates of the corresponding majors are provided. The last column in the table called ‘trend’ represents the percentage of change in unemployment rate from 2014 to 2019.
Top 5 majors with the highest trend in unemployment rate are represented in the graph. Graduates of health have the highest increase in unemployment rate proportionally even though it has been one of the majors with the lowest unemployment rate. Unemployment rate for the graduates of health are 3.14 and 11.4 for 2014 and 2020, respectively. Therefore, the unemployment rate is raised by 252% in comparison with 2014. Despite this, health still remained its position as one of the lowest unemployment rate among the other major.
5 majors with the lowest trend in unemployment rate are represented in the second graph. Graduates of journalism and information have the lowest increase, even negative increase, in unemployment rate proportionally. However, after considering that it has been the major with the highest unemployment rate in 2014, it can be assumed that the number of people interested in journalism and information might started to take alternative majors into account.
After all analysis, it should be kept in mind that these statistics regarding employment & unemployment do not represent whether people work on their own field of major or not. Therefore, when considering people working on the field that do not related to their major, employment rate on the field of major could be even lower.
deneme_major <- last_graduated_major %>%
gather(key = "major", value = "value", -year, -statistics)
deneme_major_issizoran <- filter(deneme_major, grepl("İşsizlik oranı", statistics))
major_issizoran_wide <- spread(deneme_major_issizoran, year, value) %>%
subset(select = -c(statistics))
major_issizoran_wide$trend <- round(((major_issizoran_wide$`2019`/major_issizoran_wide$`2014`)-1)*100,1)
major_issizoran_wide$major <- as.factor(major_issizoran_wide$major)
levels(major_issizoran_wide$major) <- c("Agriculture, forestry and fishery","Architecture and construction", "Arts", "Biology & Env. Sci.", "Business & Admin.", "Education", "Engineering and engineering trades", "Health", "Higher education graduate", "Humanities", "Info. & Comm. Tech.", "Journalism & Info.", "Languages", "Law", "Manufacturing and processing", "Mathematics and statistics", "Occupational health and transport services", "Personal Services", "Physical science", "Security Services", "Social and behavioural sciences", "Veterinary", "Welfare (Social services)")
major_top5 <- major_issizoran_wide %>%
top_n(5, trend)
major_last5 <- major_issizoran_wide %>%
arrange(trend) %>%
head(5)
kable(major_issizoran_wide)| major | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | trend |
|---|---|---|---|---|---|---|---|
| Agriculture, forestry and fishery | 11.627907 | 15.277778 | 12.676056 | 12.4 | 12.0 | 14.6 | 25.6 |
| Architecture and construction | 10.869565 | 10.212766 | 12.643678 | 13.5 | 13.7 | 18.0 | 65.6 |
| Arts | 16.312057 | 16.184971 | 21.182266 | 20.4 | 17.8 | 19.7 | 20.8 |
| Biology & Env. Sci. | 14.666667 | 20.238095 | 14.117647 | 16.0 | 16.9 | 14.5 | -1.1 |
| Business & Admin. | 13.639302 | 13.261851 | 13.777326 | 13.2 | 13.3 | 14.3 | 4.8 |
| Education | 7.379135 | 7.840909 | 8.643216 | 9.7 | 9.8 | 10.2 | 38.2 |
| Engineering and engineering trades | 8.761329 | 8.757062 | 9.419355 | 9.0 | 10.3 | 12.1 | 38.1 |
| Health | 3.142857 | 5.013193 | 6.338028 | 9.6 | 9.6 | 11.4 | 262.7 |
| Higher education graduate | 10.648392 | 11.010342 | 12.013929 | 12.7 | 12.4 | 13.7 | 28.7 |
| Humanities | 6.707317 | 9.042553 | 10.526316 | 13.2 | 13.2 | 15.8 | 135.6 |
| Info. & Comm. Tech. | 16.806723 | 17.142857 | 16.964286 | 19.9 | 14.0 | 16.7 | -0.6 |
| Journalism & Info. | 29.166667 | 17.391304 | 19.230769 | 19.1 | 23.8 | 21.8 | -25.3 |
| Languages | 8.108108 | 10.743802 | 13.768116 | 16.4 | 14.9 | 14.7 | 81.3 |
| Law | 7.339449 | 6.802721 | 5.988024 | 11.2 | 8.3 | 8.4 | 14.5 |
| Manufacturing and processing | 13.178295 | 12.328767 | 18.367347 | 14.2 | 11.9 | 17.1 | 29.8 |
| Mathematics and statistics | 9.302326 | 9.677419 | 14.130435 | 14.0 | 9.9 | 12.6 | 35.4 |
| Occupational health and transport services | 12.500000 | 18.750000 | 23.529412 | 18.0 | 17.0 | 18.6 | 48.8 |
| Personal Services | 12.949640 | 15.384615 | 16.666667 | 14.3 | 12.2 | 11.0 | -15.1 |
| Physical science | 14.285714 | 12.738853 | 12.048193 | 15.7 | 16.9 | 16.5 | 15.5 |
| Security Services | 2.521008 | 3.278688 | 4.651163 | 5.0 | 6.3 | 5.8 | 130.1 |
| Social and behavioural sciences | 11.195446 | 11.627907 | 13.499112 | 15.2 | 15.3 | 16.1 | 43.8 |
| Veterinary | 6.666667 | 8.163265 | 7.272727 | 10.7 | 8.9 | 11.2 | 68.0 |
| Welfare (Social services) | 19.230769 | 23.809524 | 24.000000 | 26.4 | 24.3 | 24.0 | 24.8 |
ggplot(major_top5, aes(factor(major, levels = major), trend, fill = major)) +
geom_col(position = "dodge") +
theme(legend.position = "none",
plot.title = element_text(hjust = 0.5)) +
guides(fill=guide_legend(title="Major")) +
labs(x = "",
y = "Percentage of Change from 2014 to 2019",
title = "Top 5 Major by Unemployment Rate Trend")
ggplot(major_last5, aes(factor(major, levels = major), trend, fill = major)) +
geom_col(position = "dodge") +
theme(legend.position = "none",
plot.title = element_text(hjust = 0.5)) +
guides(fill=guide_legend(title="Major")) +
labs(x = "",
y = "Percentage of Change from 2014 to 2019",
title = "Lowest 5 Major by Unemployment Rate Trend")
Unemployed Job Searching People by the Channel
Establish Own Business: Represents people who are applying for loans, doing research or looking for a place to start a business. Turkish Employment Office: Represents people who have contacted or waited a call from Turkish Employment Office Newspaper: Represents people who are looking for a job on newspaper or put an advertisement on the newspaper to find a job Friends and Relatives: Asking friends and relatives for a job Employment Agencies: Represents people who have contacted with private employment agencies Direct Application to Employers: People who applied for jobs directly to companies Other: Job searching methods that cannot be categorized
In these six charts, different job searching methods were analyzed between the years 2014 and 2019. The percentage change in direct job applications, asking for a job to relatives and applications by newspapers were decreasing last six years. The decline in direct application may be because of fall in new job oppurtunities in companies in these period. Moreover, It is observed that the rate of job search with newspapers has decreased due to the shift to online channels. On the other hand, It is clearly seen that jop applications by Employment Agencies and Turkish Employment Office has increased last six years. The main explanation for this increase among methods can be summarized by those who cannot find a job by their own effort, apply for a job through ISKUR or other private employment agencies. Among these six job searching methods, the biggest percentage change can be seen in application through Turkish Employment Office (ISKUR) where percentage was 14.2% in 2014 and rose 24.3% in 2019.
job_search_overall_c <- job_search_overall %>%
mutate(month = gsub("^.*?- ", "", month)) %>%
filter(month == "Annual") %>%
mutate(year = factor(year),
establish_own_bus = look_place_equip_to_est_bus + look_credit_license_to_est_bus,
newspaper = to_newspaper + insert_ad_to_newspaper,
iskur = to_emp_office + wait_call_from_emp_office,
waiting_result = wait_result_of_app + wait_result_of_comp_for_public_sec) %>%
select(year, establish_own_bus, newspaper, iskur, to_emp_agencies, to_employers, to_relatives)
Year <- rep(unique(job_search_overall_c$year), times = (ncol(job_search_overall_c)-1))
Channel <- rep(colnames(job_search_overall_c[-c(1,1)]),
each = length(unique(Year)))
Count <- array(unlist(job_search_overall_c[1:6,2:7]))
js_df <- data.frame(Year, Channel, Count)
js_df_rate <- js_df %>%
group_by(Year) %>%
mutate(Percentage = round(Count/sum(Count) * 100, 1))
js_df_rate$Channel <- as.factor(js_df_rate$Channel)
levels(js_df_rate$Channel) <- c("Establish Own Business", "Turkish Employment Office", "Newspaper",
"Employment Agencies", "Direct Application to Employers", "Friends & Relatives")
ggplot(js_df_rate, aes(x = Year, y = Percentage)) +
geom_bar(stat = "identity", aes(fill = as.factor(Channel))) +
geom_text(aes(label = paste0(Percentage, "%"), vjust = -1)) +
facet_wrap(~Channel) +
ggtitle("Rates of Job Search Channel Among All Applications") +
theme(legend.position = "none", plot.title = element_text(hjust = 0.5)) +
scale_y_continuous(limits = c(0,40))
Number of Applications Made to Private and State Employment Agencies By Months
The chart below shows us the comparison of the application numbers that made to the Turkish employment office, called İşkur, and other private employment agencies from 2014 to 2020 by months. The difference of the approach of this chart from the previous graph is, since the awaiting number of applications that sent to the private agencies are unknown, they are not included. However, in the graph above, the number of applications and the number of waiting for the result are considered together.
Since the beginning of 2014, the number of applications are quite close each other until July of 2018. Then, the gap starts to gradually expand in 6 months from that date. Even the upward trend of private agencies continues, the Turkish employment office’s application numbers have surged and doubled itself at the beginning of 2019. After a slight fluctuation, the government’s office applications starts to decrease and it reaches to the closest point with the private agencies, at the around of 1.3 million application in August of 2020. The private agencies’ application number increases without a fall since April and one of the reason might be the restrictions due to Covid-19, hence the gap have became closer to 50 thousand applications.
annual_jobsearch <- filter(job_search_overall, grepl("Annual", month))
annual_jobsearch_melt <- annual_jobsearch %>%
select(year, month, total_unemployed, to_employers, to_relatives, to_emp_office, to_emp_agencies, to_newspaper, insert_ad_to_newspaper, take_interview, look_place_equip_to_est_bus, look_credit_license_to_est_bus, wait_call_from_emp_office, wait_result_of_app, wait_result_of_comp_for_public_sec, others)%>%
gather(key="type", value = "value", -month, -year)
job_search_overall$month <- str_split_fixed(job_search_overall$month, " - ", 2)[,2]
monthly_jobsearch <- filter(job_search_overall, !grepl("Annual", month))
unemp_gr<-monthly_jobsearch%>%
transmute(month_year = paste(year, month, sep = " "), total_unemployed)
applied_office_types<- monthly_jobsearch %>%
transmute(month_year = paste(year, month, sep = " "), to_emp_office, to_emp_agencies)
applied_office_types_melted <- applied_office_types %>%
melt(id.vars="month_year", variable.name= "Office_Type", value.name="Value")
applied_office_types$month_year <- as.character(applied_office_types$month_year)
applied_office_types$month_year <- factor(applied_office_types$month_year, level=applied_office_types$month_year)
ggplot(applied_office_types, aes(x=factor(month_year))) +
geom_line(aes(y=to_emp_office, color="Turkish Employee Office", group=1))+
geom_line(aes(y=to_emp_agencies, color="Employee Agency", group=1))+
theme(axis.text.x = element_text(angle =90, size=5, hjust=1),
plot.title = element_text(hjust = 0.5))+
labs(title="The Comparison of Employment Agencies Over The Months",
x="",
y="Employee Provider Type (in thousands)",
color="Provider")
The Trend of Establishing Own Business
In our dataset, establishing the own business splits into 2 parts. First, people who are in an action such as; looking for a place or looking for equipment etc. Second, people who having the idea and doing some research such as; looking for loan options, searching for license etc. In the following chart, the trend and roughly relation between them can be seen.
The people who are in action to open their own businesses and the ones who are considering owning a business have quite similar numbers from 2014 to 2020. However, from the beginning of 2020 they have both drastically risen. Especially when the most of the companies have started to work from home, around March and April, they have both spiked. Supposably, the Covid-19 effected a lot of companies and thousands of people had to leave their works without getting any payment, and this may have effected people’s decisions to establish their own businesses in a very short time.
own_bus_df <- monthly_jobsearch %>%
transmute(month_year = paste(year, month, sep = " "), look_place_equip_to_est_bus, look_credit_license_to_est_bus) %>%
rename(in_action=look_place_equip_to_est_bus, planning_for = look_credit_license_to_est_bus)
own_bus_df$month_year <- as.character(own_bus_df$month_year)
own_bus_df$month_year <- factor(own_bus_df$month_year, level=own_bus_df$month_year)
ggplot(own_bus_df, aes(x=factor(month_year))) +
geom_line(aes(y=in_action, color="In Action", group=1))+
geom_line(aes(y=planning_for, color="Planning", group=1))+
theme(axis.text.x = element_text(angle =90, size=5, hjust=1),
plot.title = element_text(hjust = 0.5))+
labs(title="The Trend of Establishing Own Business Idea Over The Years",
x="Year",
y="Number of People (in thousands)",
color="Current Status")
Employed & Unemployed by Educational Level
yearly_educational <- filter(educational_level_overall, grepl("Annual", month))
total_labour_yearly_educational <- yearly_educational%>%
transmute(year=as.character(year),illeterate=lf_illeterate, less_than_hs=lf_less_than_hs, highschool=lf_highschool, voc_hs=lf_voc_hs, higher_ed=lf_higher_ed)
total_labour_yearly_educational_melt <- total_labour_yearly_educational %>%
gather(key="type", value="value", -year) %>%
group_by(year) %>%
mutate(Percentage= round(value/sum(value) * 100, 1))
emp_yearly_educational <- yearly_educational %>%
transmute(year=as.character(year), illeterate=emp_illeterate, less_than_hs=emp_less_than_hs, highschool=emp_highschool, voc_hs=emp_voc_hs, higher_ed=emp_higher_ed)
emp_yearly_educational_melt<-emp_yearly_educational %>%
gather(key="Education_Level", value="Person", -year ) %>%
group_by(year) %>%
mutate(Percentage= round(Person/sum(Person) * 100, 1))
emp_yearly_educational_melt$Education_Level <- as.factor(emp_yearly_educational_melt$Education_Level)
levels(emp_yearly_educational_melt$Education_Level) <- c("Higher Education", "Highschool", "Illeterate", "Less Than Highschool", "Vocational Highschool")
unemp_yearly_educational <- yearly_educational %>%
transmute(year=as.character(year), illeterate=unemp_illeterate, less_than_hs=unemp_less_than_hs, highschool=unemp_highschool, voc_hs=unemp_voc_hs, higher_ed=unemp_higher_ed)
unemp_yearly_educational_melt<-unemp_yearly_educational %>%
gather(key="Education_Level", value="Person", -year ) %>%
group_by(year) %>%
mutate(Percentage= round(Person/sum(Person) * 100, 1))
unemp_yearly_educational_melt$Education_Level <- as.factor(unemp_yearly_educational_melt$Education_Level)
levels(unemp_yearly_educational_melt$Education_Level) <- c("Higher Education", "Highschool", "Illeterate", "Less Than Highschool", "Vocational Highschool")As we can depict from the table and graphs below, the employment and unemployment rates for the people, who have a higher education rise in all years, except 2019. While employment rate going up and reaches to the three fourth of the all employed people in 2019, the unemployment rate decreases for 2019. On the other hand, for the people who have less than a highschool education, the employment and unemployment rates decreasing steadily over the years. Furthermore, even though the rates of employment of vocational highschool graduates are stands still over the years, the unemployment rates have a slight upward trend. As long as downward trend of the group of less than highschool and upward trend of the group of vocational highschool keep gooing, it may be concluded as, most of the unemployed people would be from the vocational highschool graduated cluster, instead of less than highschool graduated cluster in a long run.
total_employed <- emp_yearly_educational %>%
group_by(year)%>%
transmute(total_employed = illeterate + less_than_hs+highschool+voc_hs+higher_ed)
total_unemployed <- unemp_yearly_educational %>%
group_by(year)%>%
transmute(total_unemployed = illeterate + less_than_hs+highschool+voc_hs+higher_ed)
total_emp_unemp <- merge(total_employed, total_unemployed)
kable(total_emp_unemp, col.names = c("Year", "Total Employed", "Total Unemployed"), format = "html", align = "c", format.args = list(big.mark = ",", scientific = FALSE), caption="Number of Employment and Unemployment in Thous.")| Year | Total Employed | Total Unemployed |
|---|---|---|
| 2014 | 25,933 | 2,853 |
| 2015 | 26,621 | 3,058 |
| 2016 | 27,205 | 3,330 |
| 2017 | 28,189 | 3,455 |
| 2018 | 28,738 | 3,537 |
| 2019 | 28,080 | 4,468 |
#Employment Graph
ggplot(emp_yearly_educational_melt, aes(x = year, y = Percentage)) +
geom_bar(stat = "identity", aes(fill = as.factor(Education_Level))) +
coord_cartesian(ylim=c(0,70))+
geom_text(aes(label = paste0(Percentage, "%"), vjust = -1), size = 3) +
facet_wrap(~ Education_Level)+
labs(title="Employment Rates by Education Level",
y="% of People")+
theme(legend.position = "none", plot.title = element_text(hjust = 0.5))
#Unemployment Graph
ggplot(unemp_yearly_educational_melt, aes(x = year, y = Percentage)) +
geom_bar(stat = "identity", aes(fill = as.factor(Education_Level))) +
coord_cartesian(ylim=c(0,70))+
geom_text(aes(label = paste0(Percentage, "%"), vjust = -1), size = 3) +
facet_wrap(~ Education_Level)+
labs(title="Unemployment Rates by Education Level",
y="% of People")+
theme(legend.position = "none", plot.title = element_text(hjust = 0.5))
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