Has Covid-19 pandemic affected the unemployment rates?

Introduction

While many countries have gone into lockdown to combat the spread of the virus, unemployment numbers have been rising sharply. How high could the unemployment rate go is my question and I will try to figure that out using data.

In this project, I am trying to figure out of this crisis has disproportionately affected the youth in the job market and I will specifically concentrate on the a few of the European countries. In this project, I have used unemployment data by sex and age for the European Union (obtained from the EU Open Data Portal) - Linked here!.

Analysis and Visualization

In the analysis, I have cleaned and transformed the data, and then plotted the graph which compares how the total unemployment and youth unemployment rate varies across specific countries in Europe.

suppressMessages(library(tidyverse))
suppressMessages(library(ggplot2))


unemployment_data <- read_csv("C:/Users/rohit/OneDrive/Desktop/unemployment-data.csv")


df_unemp_data <- separate(unemployment_data, "s_adj,age,unit,sex,geo\\time", c("seasonal_adjustment", "age_group", "data_unit", "sex","country"),sep = ",", remove = TRUE)

df_unemp_data <- mutate_at(df_unemp_data, vars(age_group, data_unit, sex, country), as.factor)

df_unemp_filtered <- df_unemp_data %>% filter(data_unit == "PC_ACT", is.na(country) == FALSE, seasonal_adjustment == "SA") %>% select("age_group":"2019-01","country")

unemp_pivot <- df_unemp_filtered %>% gather(key = 'month', value = 'unemployment_rate', c(-age_group, -data_unit, -sex, -country))

unemp_pivot$data_unit <- NULL


unemp_pivot$unemployment_rate <- unemp_pivot$unemployment_rate %>% trimws(which = c("right")) %>% as.numeric

country_list <- c("IT","NL","FR","PT","ES","SE")
df_plot_unemployment <- unemp_pivot %>% filter(sex == 'T', country %in% country_list, !age_group == "Y25-74", !month %in% c("2019/01","2019/02","2019/03","2019/04"))

ggplot(df_plot_unemployment, aes(x = month, y = unemployment_rate, color = country)) + geom_line(aes(group = country)) + xlab("Year and Month") +
  ylab("Unemployemnt Rate") + theme(axis.text.x = element_text(angle = 90), panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
    panel.background = element_blank(),axis.line = element_line(colour = "black")) + coord_fixed(0.5) +
    facet_wrap(~age_group, labeller = labeller(age_group = c("TOTAL" = "Total Unemployment Rate", "Y_LT25" = "Unemployment Rate for Youth (Less than 25)")))

Conclusion

As seen in the graph above for most countries, there is a sharp rise in unemployment which starts in early 2020, which is exactly when the pandemic hit Europe and has been on a rise since then. It seems that youth unemployment was already high even before the pandemic began , and increased substantially since the pandemic. As visible in the plot, the growth has for youth unemployment has been exponentially higher than that of total unemployment rate. France is the only country which seems to returning to its normal unemployment rate but had seen at a surge at the beginning of the pandemic. But the unemployment rate for their youth in other countries like Spain, Portugal, Italy and the others have been impacted severely by this pandemic.