- Reading time: 3 minutes
- Price: Free download
- Published: 3rd October 2026
- Word count: 648 words
- File format: Text
Personal statement example
Every April the payroll system at the bakery chain where I work part-time needs updating for the new National Living Wage. The first year I helped with this, the change itself was simple: about a third of our shop and production staff moved up to the new legal minimum. What took longer was answering the people who had been earning a few pence above the old floor and now found themselves level with new starters. Several asked whether they would get a rise too. Some did, after a discussion between the area managers; others did not. That small decision, repeated across thousands of employers, is the question I spent my final undergraduate year trying to measure, and it is why I now want to study economics at an advanced level.
My dissertation asked whether increases in the UK wage floor raise pay for workers just above it. Using published percentile tables from the Annual Survey of Hours and Earnings, I compared changes at the lower percentiles in occupations and regions where the floor bites hard with those where it barely binds, in a difference-in-differences framework. I found that the lowest percentiles moved closely with the floor, with smaller movements further up, but I was careful about what my design could support. Aggregated percentiles cannot follow individual workers, and my parallel trends assumption was weakest for exactly the low-paid service occupations I cared most about. My supervisor's main comment was that I had written an honest limitations section for a question that needed microdata. I agree, and learning to work with that kind of data properly is one of my reasons for applying.
The reading behind the project changed how I think about labour markets. Card and Krueger's study of fast-food employment in New Jersey and Pennsylvania showed me how a well-chosen comparison can challenge a textbook prediction, and the debate it provoked showed how much rides on data quality. Alan Manning's Monopsony in Motion gave me a framework in which employers have some wage-setting power, which made the area managers' discretion at work look less like an accident and more like the thing to be modelled. Working through parts of Angrist and Pischke's Mostly Harmless Econometrics, I could follow the logic of instrumental variables and regression discontinuity, but I want the formal training in probability theory and asymptotics that would let me judge when those methods fail, rather than just apply them.
My job has given me habits that I expect to be useful. Payroll errors are noticed immediately by the people affected, so I check reconciliations line by line and document every change to a pay rule. I have also built a spreadsheet that flags staff whose hourly rate falls within a small margin of the statutory minimum before each uplift, which the managers now use when planning budgets. It is modest work, but it taught me that clean definitions matter more than clever analysis.
Outside work I am a volunteer timekeeper at my local parkrun most Saturdays, which involves standing at the finish with a stopwatch and reconciling times with barcode scans afterwards; mismatches are surprisingly common when a large group finishes together. I also play board one for a chess club in a county league. Chess has made me comfortable spending a long time on a single position and accepting that a promising line can turn out to be unsound.
I am applying for postgraduate study because my undergraduate degree took me as far as asking a good question with imperfect tools. I want to deepen my microeconomic theory and econometrics to a level where I could design a credible study of wage-setting with individual-level data, and to test whether research is the right longer-term path for me. I bring careful working habits, real familiarity with how pay decisions are made, and a question I have not finished with.