Introduction

Occupational position is recoded from multiple micro-census questions that capture employment status and job position to identify groups that are actively involved in the labour market (as a clerk, worker, civil servant, contract agent or self-employed worker) and those that are inactive (pensioner, unemployed).

Employment status

Employment status is captured by the following categories:

table(WaveOne$SD10) %>%
  kable("html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", 
                                      "condensed", "responsive"),
                full_width = F, position = "center")

Job position

Job position is measured on the following scale:

table(WaveOne$SD8) %>%
  as.data.frame() %>%
  setNames(c("Response", "Frequency")) %>%
  kable("html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", 
                                      "condensed", "responsive"),
                full_width = F, position = "center")

Now the variable will be recoded into Clerks, Worker, Civil servants, Contract agents, Pensioner, Unemployed (inactive) and Self-employed

Combine job positon and employment status

An empty variable is created:

WaveOne$job <- NA

First, the information on the categories Clerk, Worker, Civil servants and Contract agents are copied to the new variable:

WaveOne$job[WaveOne$SD10 %in% levels(WaveOne$SD10)[3:6]] <- 
  WaveOne$SD10[WaveOne$SD10 %in% levels(WaveOne$SD10)[3:6]]

Afterwards, the self-employed, freelancers and farmers are merged to the new category Self-employed:

WaveOne$job[WaveOne$SD10 %in% levels(WaveOne$SD10)[7:10]] <- "Self-employed"

Now, the information from the employment status for pensioners is introduced:

WaveOne$job[WaveOne$SD8 %in% levels(WaveOne$SD8)[2]] <- "Pensioner"

All categories other than Pensioner and Employed from the employment status are merged into the category Unemployed (inactive):

WaveOne$job[WaveOne$SD8 %in% levels(WaveOne$SD8)[4:10]] <- "Unemployed (inactive)"

And finally, format the new variable as factor and transfer the labels from the old variables:

WaveOne$job <- factor(WaveOne$job)
levels(WaveOne$job)[1:4] <- c("Clerk",
                              "Worker",
                              "Civil servant",
                              "Contract agent")
WaveOne$job <- factor(WaveOne$job, 
       levels = c("Clerk",
                  "Worker",
                  "Civil servant",
                  "Contract agent",
                  "Pensioner",
                  "Unemployed (inactive)",
                  "Self-employed")) 

Distribution of new variable

The new variable has the following distribution: