FYI: Comparison to Stanford's California Poverty Measure about c-tam

[MaxGhenis]

@MattHJensen indicated interest in this on today's call.

[Amy-Xu]

Does this mean

1. we could compare the California portion of data to see how the distributions different under two methodologies?

2. if the differences are significant and sensible, we should expand the model for other states?

What are the plans you have in mind?

@MaxGhenis @MattHJensen

[MaxGhenis]

Columbia's Center on Poverty and Social Policy also does a lot of antipoverty work involving recreating the Supplemental Poverty Measure, and part of this is imputing benefits. In particular, Jane Waldfogel and Christopher Wimer have authored many SPM-related papers, such as an anchored SPM (2013) and an evaluation of 2020 candidates' antipoverty bills for Vox (Stanford's Sara Kimberlin, who also works on CPM, also contributed to this). The anchored SPM paper goes through their imputation procedure.

In terms of your questions, I think it could be worth reaching out to Stanford and/or Columbia to let them know about this project, and see if they have suggestions on what to prioritize, since they seem to be experts in assistance programs. Who knows, they might be interested in sharing their code as a new PSL project, or even working toward a unified poverty model with C-TAM/taxdata in the long run.

I suggested bringing in SPM to taxdata/taxcalc last year (PSLmodels/Tax-Calculator#1896) and it was decided not to be a fit at that time. But I think at some point this would still be a great feature, and the fact that other poverty researchers do it--albeit imperfectly compared to official Census data--suggests there's opportunity to unify around open-source models as the project has done for tax analysis.