The 2009 CARD Act limited credit card lenders' ability to raise borrowers' interest rates on the basis of new information. Pricing became less responsive to public and private signals of borrowers' risk and demand characteristics, and price dispersion fell by one‐third. I estimate the efficiency and distributional effects of this shift toward more pooled pricing. Prices fell for high‐risk and price‐inelastic consumers, but prices rose elsewhere in the market and newly exceeded willingness to pay for over 30% of the safest subprime borrowers. On net, average traded prices fell and consumer surplus rose at all credit scores. Higher consumer surplus was partly driven by a fall in lender profits, and partly by the Act's insurance value to borrowers who could retain favorable pricing after adverse changes to their default risk. The relatively high level of pre‐CARD‐Act markups was crucial for realizing these surplus gains.

We study the removal of information from a market, such as a job-applicant screening tool. We characterize how removal harms groups with relative advantage in that information: typically those for whom the banned information is most precise relative to alternative signals. We illustrate this using recent bans on employers’ use of credit report data. Bans decrease job-finding rates for Black job-seekers by 3 percentage points and increase involuntary separations for Black new hires by 4 percentage points, primarily because other screening tools, such as interviews, have around 60% higher standard deviation of signal noise for Black relative to white job-seekers.

Since the 2000s, economists across fields have increasingly used consumer credit reporting data for research. We introduce readers to the economics and institutional details of these data. Using examples from the literature, we provide practical guidance on how to use these data to construct economic measures of borrowing, consumption, credit access, financial distress, and geographic mobility. We explain what credit scores measure and why. We highlight how researchers can access credit reporting data via existing datasets or by creating new datasets, including by linking credit reporting data with surveys and external datasets.