Pull request to integrate data for employment criteria into main.
Since the unemployment criteria for the IRA requires both unemployment and employment data for qualification and is also at the MSA and non-MSA level, there are extractions and transformations for various data sources.
Here's a quick overview/explainer of each of the sources and the role they play in creating a final list of qualifying MSAs and Non-MSAs for the IRA fossil employment criteria.
QCEW: Quarterly Census of Employment and Wages — data for every 2-6 digit NAICS industry where we identify fossil employment
LAU: Local Area Unemployment — data on unemployment rates at the county level
CPS: Current Population Survey – national unemployment rate data
Still missing: testing! welcome suggestions since I'm not as familiar.
Notebook that pulls it all together for final output with list of all qualifying areas:
notebooks/create_unemployment.ipynb
Let me know if you have any questions / want additional materials on how modules interact since there are a lot of interdepencies.
Pull request to integrate data for employment criteria into main.
Since the unemployment criteria for the IRA requires both unemployment and employment data for qualification and is also at the MSA and non-MSA level, there are extractions and transformations for various data sources.
Here's a quick overview/explainer of each of the sources and the role they play in creating a final list of qualifying MSAs and Non-MSAs for the IRA fossil employment criteria.
QCEW: Quarterly Census of Employment and Wages — data for every 2-6 digit NAICS industry where we identify fossil employment
LAU: Local Area Unemployment — data on unemployment rates at the county level
CPS: Current Population Survey – national unemployment rate data
Still missing: testing! welcome suggestions since I'm not as familiar.
Notebook that pulls it all together for final output with list of all qualifying areas:
Let me know if you have any questions / want additional materials on how modules interact since there are a lot of interdepencies.