policyengine-us
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PolicyEngine
The PolicyEngine US Python package contains a rules engine of the US tax-benefit system, and microdata generation for microsimulation analysis.
The legal [code](https://code.dccouncil.gov/us/dc/council/code/sections/4-205.52) describes amounts for up 19 household members but previous and subsequent [handbooks](https://dhs.dc.gov/service/tanf-district-families) only display amounts for up to 10 members. We only have the values for size...
The [legal code](https://code.dccouncil.gov/us/dc/council/code/sections/4-205.24#(a)) outlines individual eligibility requirements without elaborating on situations with mixed couples
https://www.law.cornell.edu/uscode/text/42/9902#2
We currently use `is_tax_unit_head` in spm unit level programs which could lead to inaccuracies for households with multiple tax units / families in an spmu. Same for `is_spm_unit_spouse` and `is_spm_unit_head_or_spouse`
I think the parameters that were filled in with state enrollment data were intended to be future projections from cbo https://www.cbo.gov/data/baseline-projections-selected-programs#9 @MaxGhenis @PavelMakarchuk
[Factsheet](https://dhs.dc.gov/sites/default/files/dc/sites/dhs/publication/attachments/Power%20Program%20Fact%20Sheet.pdf)
Per commentary from Pavel: We can use `pd.merge()` with `how=left` by creating two dataframes and joining them at the index - the benefit is more explicit handling of missing values...
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The PolicyEngine US Python package contains a rules engine of the US tax-benefit system, and microdata generation for microsimulation analysis.