Microdata case study: UK labour market transformation over the last ten years
This is the third blog in a case study series that explores the merits of microdata in policy analysis.
Using Labour Force Survey (LFS) and Census 2011 data, Senior Economist Eve Xinru Lin looks at the transformation of the UK labour market between 2011 and 2019 and provides insight on whether the historical trend will still fit into the context of the current policy environment going forward.
From the first two blogs in this series (Over-indebtedness in the UK and Changing retirement patterns in the UK) , we learned that microdata allows us to go beneath the surface of traditional aggregate statistics to shed light on the circumstances of different groups. They are an increasingly important source of insight for our work here at Cambridge Econometrics.
Case study: UK labour market transformation using the Labour Force Survey and Census 2011
While the immediate response to the impact of Covid has quite rightly been the priority, it is also important to understand the long-term trends and structural changes in our economy, if we are to equip people with the skills they need in the labour market.
As a result of that and with 2021 being a Census year, I chose to use a combination of the Census 2011 and the quarterly LFS to look at the sectoral and occupational changes in the UK between 2011 and 2019.
Using the rich data provided by the Census 2011 and the LFS, I identified three key findings from my analysis:
1. General trend towards professionalisation across sectors
There has been a general trend of professionalisation in the UK labour market, with increasing numbers of jobs concentrated in professional and associate professional occupations across sectors (Major Groups 2 and 3 in the Standard Occupational Classification). Also, as an occupational group, professional occupations account for the highest share of jobs in 2019 (22%).
2. Greater demand for higher and lower-skilled workers
The change in the occupational profile of employment reflects, to some extent, job polarisation in the UK (Chart 1). Employment has increased in occupations that are traditionally thought of as being highly skilled (managerial, professional and associate professional occupations) and also in relatively lower-skilled occupations like elementary, caring and leisure occupations. There has been a net decline in employment in administrative and secretarial and skilled trades occupations, with the latter in particular usually requiring intermediate-level technical skills. This trend is more prominent in the business and professional service sectors, but also in manufacturing, construction and transport.
3. Shifts in the occupational mix of the agriculture, mining, and utilities sectors
Chart 2 illustrates how employment has changed within broad sectors and occupations between 2011 and 2019. Microdata makes it possible to explore some of these trends in greater detail. For example:
Agriculture: the share of employment in skilled agricultural and related trades fell by 23 percentage points over 2011-19, whilst the share of employment in plant and machine operative occupations increased by 7 percentage points. It would be interesting to see how this trend emerges as we navigate through Brexit and the 4th agricultural revolution.
Mining and utilities: the sector is changing to support the transition to a low-carbon economy, which has affected the types of skills demanded in the workforce. In the period 2011-19, there has been increasing demand for the more highly skilled (e.g. managers and directors, science, research and technology professionals) and those with customer service skills (e.g. business and public service associated occupations). On the other hand, demand for process, plant and machine operatives dropped quite significantly.
Explore more trends in the heatmap below.
So how has this case study illustrated the benefits of microdata?
This particular case study demonstrates the merit of microdata in policy analysis. Given its richness in data, not only does it allow us to identify trends at individual level, but also enables us to explore relationships between wide range of socioeconomic indicators in a way that is impossible in the published data.
The upside of microdata is that, because it contains extensive set of individual characteristics, it can be used to examine behaviour for a target group in society and help to inform more targeted policy intervention.
We are facing a very different crisis – Covid-19 has already made disruptive changes to the UK labour market and will continue to have an impact. As a response to Covid-19, the government have already put in place an unprecedented economic package to limit the economic damage and will continue to support the economy as it recovers. In ‘Build Back Better: our plan for growth’, we see that the government aims to ‘level up’ the country, to reskill and upskill the workforce and to support the transition to net zero.
It will be interesting to see what the long-term implications are of covid-related policies on the labour market. Will the trends identified in this article continue in light of Covid-19 and Brexit?
Some commentators have suggested that firms may accelerate their adoption of AI and automation as a result of Covid 19, which if it happens, could have implications for those in low-skilled jobs. It is also still unclear as to whether sectors such as retail may be permanently affected by the pandemic.
The latest statistics suggest that although shoppers are returning to stores, the proportion of spending online is still significantly higher than before the pandemic. Should this shift in our shopping habits become permanent, this will have implications for the mix of jobs and skills required in that sector in the future.
The use of microdata will be particularly useful to take these questions forward, especially around how the demand for skills will change, and how disproportionate the job interruption will be for occupations that require different level of skills.