An Overview of the Singapore Hiring Landscape

Dataset

The job posting data is collected on a MySQL database and I will be using R along with the dbplyr package to explore the dataset. To interface with the backend database, I am using DBI to create a connection to it. For this post I created a slightly modified copy of the data in a table called sg_jobs_tbl.

For reference, the main fields that were scraped are the job title, posting date, company, post type (sponsored or not), occupation (called tag in the database), job description, experience required, company’s address, company’s industry and a brief overview of the company.

Data Cleaning

Let us begin by importing the libraries we require and setting up the database connection:

library(DBI)
library(tidyverse)

con <- dbConnect(RMariaDB::MariaDB(),
                 default.file   = paste0(path, '/.my.cnf'),
                 groups = 'jobs_db')

To query the database, we can use the dbSendQuery() function and pass in a standard sql statement for it to evaluate. dbFetch() is used to get the results back in a dataframe. Let’s take a look at how many postings we have collected over the month of October:

q <- dbSendQuery(con, "SELECT count(*) FROM sg_jobs_tbl")
dbFetch(q)
##   count(*)
## 1    55456
dbClearResult(q)

We have more than 50,000 postings in a month, but how many of them are from recruiting agencies or direct job openings? We can filter out firms with ‘recruitment firm’ as part of their company description to derive the count of postings by actual companies.

q <- dbSendQuery(con, "SELECT count(*) FROM sg_jobs_tbl where company_snapshot not like '%RECRUITMENT FIRM%'")
dbFetch(q)
##   count(*)
## 1    19327
dbClearResult(q)

Apparently more than 50% of all job posts are by recruitment firms. This means that we have to be a little careful of the data quality since firms might spam the job board with multiple posts across the month for a single job opening.

One of the nice features of R is the tidyverse ecosystem. It provides a consistent syntax to manipulate data regardless of the backend source. So we can just use our favourite dplyr verbs and the package will automatically convert it to sql syntax.² In the code below, I extract the most frequent company, title combinations across the entire month:

q <- tbl(con, "sg_jobs_tbl") %>%
  filter(!is.na(company), !is.na(title)) %>%
  group_by(company, title) %>%
  count() %>%
  arrange(desc(n))

A nice aspect of writing sql statements in dplyr syntax is that it is lazily evaluated i.e. the code will only be run when it is explicitly required, such as a print statement. Given a tbl object with a DBI connection, dplyr will use dbplyr to generate the sql translation which we can preview by calling the show_query() function. Even when we ask it to print the results of the query, it only returns the first 10 observations. To return the entire query as a data frame we have to use the collect() function. We can take advantage of this feature by just printing the query and getting the top 10 observations:

q
## # Source:     lazy query [?? x 3]
## # Database:   mysql 8.0.12 [root@localhost:/jobs_db]
## # Groups:     company, title
## # Ordered by: desc(n)
##    company                  title                                n        
##    <chr>                    <chr>                                <S3: int>
##  1 ScienTec Personnel       ""                                   32       
##  2 ST Electronics (Info-co~ Carpark Patrolling Officer     Ref ~ 24       
##  3 Talentvis Singapore Pte~ Recruitment Consultant x2 (No exper~ 22       
##  4 Dynamic Human Capital P~ Patient Service Associate x 20 ( Va~ 21       
##  5 Achieve Career Consulta~ Wealth Manager x 5 / Top Foreign Ba~ 21       
##  6 PRIMESTAFF MANAGEMENT S~ **Technology Assistant (up to $2300~ 18       
##  7 JOBSTUDIO PTE LTD        Assistant Teachers x 10 (Childcare ~ 18       
##  8 ST Electronics (e-Servi~ Audit Associate [W]                  16       
##  9 ST Electronics (e-Servi~ Audit Associate (SDL VP Call) [W]    16       
## 10 ST Electronics (e-Servi~ Customer Service Officer [CCAS - Bu~ 16       
## # ... with more rows

Does ST Electronics really need 32 carpark officers? Let’s take a look at the date which these offers are posted.

q <- tbl(con, "sg_jobs_tbl") %>%
  filter(company == 'ST Electronics (Info-comm Systems) Pte Ltd') %>%
  filter(title %like% '%Carpark Patrolling Officer%') %>%
  arrange(posted_dt) %>%
  select(posted_dt)

q
## # Source:     lazy query [?? x 1]
## # Database:   mysql 8.0.12 [root@localhost:/jobs_db]
## # Ordered by: posted_dt
##    posted_dt          
##    <dttm>             
##  1 2018-10-04 00:27:56
##  2 2018-10-13 00:12:24
##  3 2018-10-13 00:12:27
##  4 2018-10-13 00:12:27
##  5 2018-10-13 00:12:29
##  6 2018-10-13 00:12:29
##  7 2018-10-13 00:12:30
##  8 2018-10-13 00:12:31
##  9 2018-10-13 00:12:33
## 10 2018-10-13 00:12:36
## # ... with more rows

There appears to be numerous duplicated postings around midnight. This could possibly be a system issue. It is harder to tell whether jobs posted in different days across the month are actually for the same position or a different opening. For this analysis, we will just consider unique job postings in a single day. Again, we will make use of the lazy evaluation feature by creating an intermediate table called ‘view’ which all subsequent queries will be based off. Let’s take another look at the most popular company, title combinations:

view <- tbl(con, "sg_jobs_tbl") %>%
  mutate(posted_dt = sql('date(posted_dt)')) %>%
  select(title, posted_dt, company, post_type, tag, jd, experience, address,
         company_industry, company_snapshot, company_overview) %>%
  distinct() %>%
  filter(!is.na(company), !is.na(title), title!="") %>%
  rename(occupation = tag)

q <- view %>%
  group_by(company, title) %>%
  count() %>%
  arrange(desc(n))

q
## # Source:     lazy query [?? x 3]
## # Database:   mysql 8.0.12 [root@localhost:/jobs_db]
## # Groups:     company, title
## # Ordered by: desc(n)
##    company                title                                  n        
##    <chr>                  <chr>                                  <S3: int>
##  1 Dynamic Human Capital~ Patient Service Associate x 20 ( Vari~ 21       
##  2 Achieve Career Consul~ Wealth Manager x 5 / Top Foreign Bank~ 21       
##  3 Talentvis Singapore P~ Recruitment Consultant x2 (No experie~ 20       
##  4 JOBSTUDIO PTE LTD      Assistant Teachers x 10 (Childcare / ~ 18       
##  5 PRIMESTAFF MANAGEMENT~ **Technology Assistant (up to $2300 /~ 18       
##  6 ST Electronics (e-Ser~ Claims Associate [W] (1 Year Contract) 15       
##  7 ST Electronics (e-Ser~ Audit Associate [W]                    15       
##  8 ST Electronics (e-Ser~ Audit Associate (SDL VP Call) [W]      15       
##  9 ST Electronics (e-Ser~ Customer Service Officer [CCAS - Buki~ 15       
## 10 ST Electronics (e-Ser~ Contact Centre Officer [W]             15       
## # ... with more rows

The first 5 entries are recruitment firms with ST Electronics making up the rest of the top 10.

Exploratory Analysis

q <- view %>%
  filter(!(company_snapshot %like% '%RECRUITMENT FIRM SNAPSHOT%')) %>%
  group_by(company) %>%
  count() %>%
  arrange(desc(n))

top_hiring_companies <- collect(q)
head(top_hiring_companies, 15) %>% knitr::kable()

q <- view %>%
  filter(!(company_snapshot %like% '%RECRUITMENT FIRM SNAPSHOT%')) %>%
  group_by(company_industry) %>%
  count() %>%
  arrange(desc(n))

top_hiring_industries <- collect(q)

top_hiring_industries <- top_hiring_industries %>%
  mutate(industry = strsplit(company_industry, split='/')[[1]][1])

top_hiring_industries %>%
  head(10) %>%
  ggplot(aes(x=industry, y=as.numeric(n), fill=as.factor(industry))) +
  geom_bar(stat = 'identity') +
  theme(axis.text.x=element_text(angle=45,hjust=1,vjust=1),
        axis.title.x=element_blank()) +
  theme_bw() +
  coord_flip() +
  scale_fill_brewer(guide=FALSE, palette ="Set3") + 
  scale_x_discrete(limits = top_hiring_industries[['industry']][10:1]) +
  labs(y = 'Number of Job Postings', x = 'Industry',
       title = 'Number of Job Postings by Industry')

top_hiring_companies %>% 
  mutate(posts = case_when(
    n >= 200 ~ '>= 200',
    n >= 100 ~ '100 - 200',
    n >= 50 ~ '50 - 100',
    n >= 25 ~ '25 - 50',
    n >= 10 ~ '10 - 25',
    n >= 5 ~ '5 - 10',
    n >= 3 ~ '2 - 5',
    n >= 1 ~ '1'
  )) %>%
  group_by(posts) %>% 
  summarise(count = sum(n) / 1000) %>%
  arrange(desc(count)) %>%
  ggplot(aes(x=posts, y=as.numeric(count), fill = as.factor(count))) +
  geom_bar(stat = 'identity') +
  theme_bw() +
  scale_fill_brewer(guide=FALSE, palette ="Set3") +
  scale_x_discrete(limits = c('1', '2 - 5', '5 - 10', '10 - 25', '25 - 50',
                              '50 - 100', '100 - 200', '>= 200')) +
  labs(y = 'Sum of posts (thousands)', x = 'Firm Size (using posts as proxy)',
       title = 'Job Postings by Firm Size')

q <- view %>%
  filter(!(company_snapshot %like% '%RECRUITMENT FIRM SNAPSHOT%'),
         !is.na(occupation)) %>%
  group_by(occupation) %>%
  count() %>%
  arrange(desc(n))

top_vacancies <- collect(q)
nrow(top_vacancies)
## [1] 6163
head(top_vacancies, 15) %>% knitr::kable()

q <- view %>%
  mutate(recruiter = ifelse(company_snapshot %like% '%RECRUITMENT FIRM SNAPSHOT%', 1, 0)) %>%
  group_by(occupation, recruiter)  %>%
  count()

jobs_source_count <- collect(q)

q <- view %>%
  group_by(occupation) %>%
  summarise(count_total = n()) %>%
  filter(count_total >= 5)

jobs_count <- collect(q)

direct_jobs <- jobs_count %>%
  inner_join(jobs_source_count, by = 'occupation') %>%
  mutate(frac = n / count_total) %>%
  filter(recruiter == 0) %>%
  arrange(desc(frac), desc(count_total))

indirect_jobs <- jobs_count %>%
  inner_join(jobs_source_count, by = 'occupation') %>%
  mutate(frac = n / count_total) %>%
  filter(recruiter == 1) %>%
  arrange(desc(frac), desc(count_total))

compare_jobs <- jobs_count %>%
  inner_join(jobs_source_count, by = 'occupation') %>%
  mutate(frac = n / count_total) %>%
  filter(recruiter == 1, frac > 0.1, frac < 0.9, count_total > 50) %>%
  arrange(desc(frac)) %>%
  select(occupation, count_total, frac)

head(compare_jobs, 10) %>% knitr::kable()

tail(compare_jobs, 10) %>% knitr::kable()

1. There is a lot of cross post across job portals so collecting postings from one site would mostly suffice.↩

2. There are still some limitations with this approach such as working with window functions but it will suffice for our purposes.↩