Stimulus: Job Impact by Congressional District

February 27, 2009 by Emsi Burning Glass

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Earlier this month, the White House released job creation estimates tied to the $787 billion stimulus package. By now, the total estimated figure — 3.5 million jobs — has been well circulated around the media. What’s less well known, however, is the White House’s impact numbers by congressional district (for a PDF version of the report, click here).

For states like Alaska, which has just one congressional district, the estimated creation is straightforward — 8,000 jobs across the board. But for states with multiple districts, the estimate for each was based on the original job impact assessment and multipliers. The theoretical jobs were then distributed based on Census data regarding the working-age population and industrial composition by district. That explanation comes from the self-described independent, non-profit ProPublica web site.

ProPublica, like many sources, is skeptical of the estimates. And clearly the congressional figures are politically driven. Congressional districts are not typically included with usual geographical regions (e.g. ZIP codes, metropolitan statistical areas, counties, states) because the boundaries often change, sometimes more than once per Census.

For more on how the White House derived its multipliers, here’s an explanation from the Job Impact report:

For the output effects of the recovery package, we started by averaging the multipliers for increases in government spending and tax cuts from a leading private forecasting firm and the Federal Reserve’s FRB/US model.  The two sets of multipliers are similar and are broadly in line with other estimates.  We considered multipliers for the case where the federal funds rate remains constant, rather than the usual case where the Federal Reserve raises the funds rate in response to fiscal expansion, on the grounds that the funds rate is likely to be at or near its lower bound of zero for the foreseeable future.

We applied these multipliers directly to the straightforward elements of the package, but made some adjustments for elements that take the form of transfers to the states and tax-based investment incentives.  For transfers to the states, we assumed that 60% is used to prevent spending reductions, 30% is used to avoid tax increases, and the remainder is used to reduce the amount that states dip into rainy day funds.  We assumed that these effects occur with a one quarter lag.  For tax-based investment incentives, we used the rule of thumb that the output effects correspond to one-fourth of the effects of an increase in government spending with the same immediate revenue effects.  This implies a fairly small effect from a given short-term revenue cost of the incentives.  But, because much of the lost revenue is recovered in the long run, it implies a fairly substantial short-run impact for a given long-run revenue loss.  We confess to considerable uncertainty about our choice of multipliers for this element of the package.

EMSI will soon release a comprehensive Input-Output Guidebook, which includes a chapter on multipliers. One thing to keep in mind is that it’s difficult to take a national model and apply it regionally in such a broad way. Multipliers are generated largely from regional purchase coefficients (RPCs) and the national direct input technical requirements matrix (also known as the “A” Matrix). RPCs represent the percentage of local demand that is satisfied by local supply. High RPCs are an indication of higher multiplier effects since money spent on input requirements are being retained locally.

Therefore, keeping a regional focus is vital when it comes to multipliers.