The latest census in England and Wales was carried out in 2021. Since then, the data have been made available the ONS’s interactive website. In addition, the data is also available via the ONS API, which is still in development. Using an exhaustive search on ONS’s website (with the application of a web scraper), over 100 datasets. In this article, we will explore the distance travelled to work dataset (TS058) and the employment by sector (TS060). The analysis below simply reflects the thought process and is intended to inspire further study to establish any causal links.
Looking across the geographical level of Low Tier Local Authorities (LTLAs), it appears that LTLAs with a higher share of the population that are employed in the servicing sector tend to have a lower share of the population who have a long commute (greater than 20km) for work.
Compared to employers in the production, manufacturing and construction industries, tertiary sector workers have greater liberty in choosing their workplace. Factory workers do not have as many options. In the UK’s economy which is heavily based on the service sector, job sites for manufacturing and production jobs are relatively far and few between. Workers in this sector may simply have fewer options when it comes to job location.
Aggregated on the regional level, however, this relationship between the employment sector and distance travelled becomes more spurious. The relationship observed on the country level seems to be dominated by the trend found in the metropolitan area. Namely, there is a strong correlation in London and similarly in North West of England. London has the highest share of the population employed in the service sector and the lowest share of the population with long commutes for work. This result should come without many surprises.
Interestingly though, the trend is also observed in regions that are home to commuter towns, in the East of England and South East of England. Perhaps, at the end of the day, not a lot of people living in these commuter towns are too keen on taking the long journey to work as we traditionally have thought! But we still should not rule out other factors such as the rise of remote work coming into play here until we have carefully identified the causal link.
Author’s Note:
For detailed methodology on visaulisation and data collection, please navigate to the respective GitHub repository. Some datapoints (Low Tier Local Authority) have been omitted from the data visualisation due to the constraint of the ONS API.