Lloyds Bank offers 1 opportunity to only one student to work with them on one of these topics. Please indicate which topic you would like to work on in your application formLink opens in a new window.

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Topic 1: Regional Productivity

Internal supervisor: Ao Wang (warwick.ac.uk)

External supervisor: a member from Lloyds Bank

The main driver of regional inequality in the UK is no longer labour market outcomes, but differences in productivity. Even after accounting for different industries, UK regions underperform compared to London and the Southeast. Declining productivity is not unique to the UK, but the difference between the regions is much larger here than in comparable countries (like Germany and France). We would like to understand what drives this regional variation in productivity in the UK through one of these two questions:

• How do social attitudes affect regional productivity in the UK (with potential comparison to France/Germany)?

After years of living in stagnant economic conditions, did people’s views around work/creativity/life purpose change as well? Does this change according to their location, and does it alter their productivity at work? How do the UK regions compare to each other when it comes to those social attitudes, and how do they compare to places like France and Germany? Does this have any effect on productivity? The students can use data from the British Attitudes Survey /World Value Survey as well as national accounts on regional productivity to answer this question.

• How does industrial concentration affect regional productivity in the UK (also with potential comparison to France/Germany)?

The same type of business (in terms of sector) is more productive in London than anywhere else in the country. So what is it about London that drives up this productivity? Particularly, does London’s ‘initial condition’ of having strong industry concentration matter? How much can we attribute London’s productivity to an ‘agglomeration effect’? Taking aside London and the Southeast, can we explain variation in regional productivity through how concentrated specific industries are? The students can use data from the Business Register & Employment Survey or the Inter Departmental Business Register along with national accounts on regional productivity to answer this question.

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Topic 2: Labour market

Internal supervisor: tbc

External supervisor: a member from Lloyds Bank

In the past few years, we’ve seen a rise in economic inactivity among older workers (50+). At the same time, the number of people inactive because of long-term illness reached a record higher.

Some research show that illness does not account for the increase in inactivity rate. They found that the people who are reporting health as a reason for not working were already out of the labour force or unemployed. Instead, the increase in inactivity from employment is attributed to a lifestyle change triggered by the pandemic. Others cast doubt on this, citing that people have complex reasons for not working that may not be capture by simple direct questions. For example, those saying that retirement was their main reason for leaving might also have underlying health issues that pushed them to early retirement. We would like to understand why older people are dropping out of the labour force through this question:

• How do workers’ job history affect the time at which they drop out of the labour force? (with potential comparison to a previous time period)

The purpose of this question is to build a profile of the people who dropped out of the labour force post-pandemic. Were they particularly concentrated in one type of job/sector/occupation? Did they drop out because they were in low-quality work that negatively impacted their health? Or did they drop out of jobs/sectors/occupations that tend to pay well and afford them early retirement? How does this compare to previous time periods? The student will need to use longitudinal data to answer this question, either from the Labour Force Survey or from Understanding Society.

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Required Skills

Required:

• Knowledge of applied microeconomic techniques.
• Experience with statistical software (R, Stata, or Python).

Optional:

• Experience using the UK Data Service to get access to datasets.
• Knowledge of spatial economics and econometrics techniques (for the 2^nd project).