1. WP1: Web scraping Job Vacancies- ELSTAT
Hellenic Statistical Authority
WP1: Web scraping Job Vacancies- ELSTAT
Christina Pierrakou – Eleni Bisioti
Essnet Big Data: WP1:Webscraping/Job Vacancies

2. Overview Introduction Web Scraping Tools Deduplication
Web scraping experiment
Matching Results
Next Steps

3. Introduction
Scrape ads directly from two job portals using a web scraping tool
Collect key variables:
job title
job description
Location
Company name
Posted date
Salary and job type (full time/temporary)

4. Web Scraping Tools Import.io
“point and click” tool for general scraping purposes
Content Grabber
setting scraping agents,
waiting for selectors
handling error cases

5. Deduplication
In the structure of a job portal there is a specific “point” from where one could scrape data and produce data sets without duplicates.
This approach worked well for each portal
More work is needed to examine the removal of duplicates in the joint data set.

6. Web Scraping Experiment
Matching advertising businesses on job portals with enterprises contained in the:
Sample of Job Vacancy Survey
Statistical Business Register.

7. Matching Results (1/3)
Sample: 3060 deduplicated ads
Period:
55% of ads, the employing enterprises were identified
45% of ads, the employing enterprises were not identified

8. Matching Results (2/3) No company name available for 77% of ads
Systematic way of starting such as “Leading Company…”; or “Well Known Firm…” etc.
Probably “ghost vacancies”

9. Matching Results (3/3) 256 enterprises were identified
9% of enterprises matched  sample of Job Vacancy Survey
30% of enterprises were matched  Statistical Business Register

10. Classification of Companies by Economic Activities (NACE rev.2)

11. Classification of Ads by Economic Activities (NACE rev.2)

12. Next Steps
Main focus is continuing the work on matching the enterprises names from job portals with
JV survey data and
Statistical Business Register
to understand the job portal coverage.

13. Thank you very much for your attention