1 Discussion of Schuh & Stavins Jon Zinman Dartmouth College 26 Oct 2007

2 Acknowledgement FRBBOS great provider of public goods re: research on consumer payment choice –Conferences –AEA session

3 Main Contribution “Beyond Demographics” –Adding payment chars reduces sig of demos –Adding payment chars dramatically increases fit Can do more: –Show regression results with just demos –Tighten critique of what (little) we learn from demo correls High-frequency, not life-cycle, choice here Economic and policy modeling: need primitives

4 Tweaks to Existing Setup Motivation: Paper is primarily interested in what’s driving declining check volume –Don’t we know that action is on intensive margin? (85% of SCF hhs had checking account in 1995; 89% in 2004) –Conclusion says that many hh’s have not reduced check use Sounds plausible, but what’s data source?

5 Tweaks to Existing Setup Econometric implementation: more outcomes? Intensive margin: why is share right outcome variable? –Skewed? –Denominator endogenous? –Try some specs with level or log, conditional on income and other proxies for total spending? Rejection (hazard out) on extensive margin? –Any data on this? –Interesting re: response to low prob. events (fraud) –Interesting re: spending control on credit cards?

6 Tweaks to Existing Setup Econometric Implementation: Reorganize/reclassify payment characteristics to provide tighter links to model based on primitives? –Show survey questions –What’s in “cost”? (fixed, marginal, overdraft risk….?) –What’s in “convenience” (time cost? Portability?) –Where is acceptance?

7 Tweaks to Existing Setup Representativeness issue: Get “old” data from consulting/market research firms, and compare?

8 Tweaks to Existing Setup Exposition/interpretation Showing that perceived attributes correlate with choices sensible, but…. Acknowledge possible justification bias

9 Blue Sky for Future Surveys Issues with existing survey data: Too heavy on introspection (vs. revealed preference). Worried about: –Noise –Justification bias Not enough latitude for heterogeneity in “payment chars” –over time (even at high frequencies) Credit revolving status Overdraft risk –across transaction types Cash back POS vs. billpay

10 Blue Sky Solutions for future surveys Ask about recent transactions –Whats not whys Including acceptance –Compare this elicitation method to subjective attribute perceptions: would be nice methodological contribution Could also tweak subjective questions to allow for heterogeneity in payment chars: –Do ever find [media] most [attribute/char]? –How often?

11 Blue Sky Exercise for future surveys: What’s in the 70% unexplained variation? How can we collect data on these factors?