Will Matcham

Made it to sunny Valencia and excited to attend @earie-news.bsky.social 2025 today and to present my research on credit card lenders tomorrow!

Prof Mark Schankerman and I are hiring two full-time research assistants to work with us at LSE on research projects relating to productivity, innovation, and intellectual property rights.

Apply here: jobs.lse.ac.uk/Vacancies/I/...

Deadline 30th September 11:59 UK

Almost two years at @royalholloway.bsky.social now, and still nowhere near over how gorgeous this campus is.

Before I submit any writing, I find and remove all instances of "It is worth mentioning that" (if it's not worth mentioning, why am I writing it??) and "It is important to note that" (convince the reader it's important through the substance, not empty words..). Pure academic throat clearing, gone.

You can see the details of the revision here:
papers.ssrn.com/sol3/papers....

Let me know if you have any comments or questions and I'll be happy to answer! (10/10)

I am really looking forward to presenting and submitting this paper going forward, knowing that I have addressed this frequent comment. I can finally be at peace with the paper, and now just need to find a home for it. Wish me luck! (9/10)

plot showing two features. First, under EU regulation, credit card interest rates are flat along customers' interest rate elasticity, for both high- and low-risk types. Second, when regulation is relaxed, lenders give higher interest rates for inelastic borrowers, and do this in the same way for high- and low-risk types.

All other findings in the paper go through, including my favourite figure from the paper, included here. It shows that lenders use interest rate tailoring in the counterfactual to price discriminate, rather than control default risk (which is done separately through tailored credit limits). (8/10)

My estimated costs to lenders of individualizing interest rates are substantial, are higher at lenders known to have more sophisticated customers and correlate positively with individuals’ incomes. So, this change was not just to patch up my machinery, but generated new insights (7/10)

Then, the counterfactual could be operationalized by turning off the regulatory constraint and setting these costs to zero. Now I am even more confident that the model and counterfactuals are internally consistent. (6/10)

I finally cracked how to model lenders’ interest rate and credit limit choices subject to the regulation. The key trick was to include linear costs to lenders of individualizing interest rates, which I could estimate off the few cases where they did choose to deviate from the rate advertised. (5/10)

While I sympathized with these comments, it was never obvious to me how I could give lenders the potential to individualize interest rates and credit limits in the baseline, subject to the regulation.

That all changed at the start of this year, however.. (4/10)

Prior to this revision, I was often confronted with the comment that my baseline model did not explain interest rate choices and was therefore not appropriate for a counterfactual in which I take away the regulation limiting lenders in individualizing interest rates. (3/10)

In the paper I use statement-level data to show that UK lenders tailor credit limits, but NOT interest rates, to customers’ risk. They are inhibited by the costs of individualizing interest rates that come from EU-wide regulations requiring them to advertise an interest rate for each product (2/10)

I have just posted a noteworthy revision of my paper “Risk-Based Borrowing Limits in Credit Card Markets.” The main novelty of the paper and its findings are the same. Why is the revision of interest, then? Read on to find out: (1/10)

Cute detail in @jeffgortmaker.com's JMP: in a paper about open source software, pyBLP is used for the demand estimation, which is Jeff's own open source software!

It's a great paper - you should take a look at it.

Ok let's give this a go then!