Since government spending is a long, complex process and not an event that occurs at a single point in time,
its timing depends on its measurement. In this paper, we argue that national income accounting (NIPA) measures
government spending too late in the process to fully capture its economic effects, resulting in a systematic
downward bias in aggregate time series estimates of the fiscal multiplier. This bias occurs because new
government purchases initially show up in GDP as inventory investment rather than government purchases. We
combine budget and contract data to produce an alternate measure of government spending based on authorizations.
This new measure anticipates NIPA G by 3–4 quarters and allows for cleaner, more precise identification of
fiscal shocks. We show that our new measure produces time series multiplier estimates of approximately 1 or
higher at all time horizons using both linear projection (LP-IV) and structural VAR methods.
Draft available in August!
This paper puts together three studies:
We find that the early impact of defense news shocks on GDP is due to a rise in business inventories, as contractors ramp up production for new defense contracts. These contracts do not affect government spending (G) until payment-on-delivery, which occurs 23 quarters later. Novel data on defense procurement obligations reveals that contract awards Grangercause shocks to G identified via Cholesky decomposition, but not defense news shocks. We show that Cholesky shocks to G miss early changes in inventories, and thus result in lower multiplier estimates relative to defense news shocks
I use novel data on defense contracts to study the effects of government purchases in the US and develop new stylized facts about their transmission mechanism. My methodology leverages the construction of a new quarterly series of US military prime contracts, available from 1947:1. Defense contracts: (i) are exogenous to output fluctuations; (ii) retain statistical power and robustness across various samples; (iii) accurately measure the timing of the shocks; and (iv) obviate the need for narrative analysis. My findings indicate that a positive shock to defense contracts, ordered first in a VAR, bolsters output, inventories, non-durable-plus-service consumption, hours worked, employment, labor earnings, disposable income, the price-cost markup, the product-wage, and labor productivity. I argue that the observed gains in labor productivity stem from `learning-by-doing," a feature particularly relevant to the production of military items. Further, leveraging a two-sector RBC model, I demonstrate that the learning-by-doing induced productivity enhancements in the manufacturing sector suffice to increase aggregate consumption, rationalizing the VAR evidence.
When Does Government Spending Matter? Evidence from a New Measure of Defense Spending (2022), by Gillian Brunet.
We investigate the effects of fiscal consolidations in the United States and their propagation through the
production network. Using a narrative approach, we identify exogenous fiscal adjustments and employ a spatial
autoregression (SAR) model to separate the total effects of these adjustments into direct and network components.
Our analysis reveals that tax-based consolidations have a more pronounced recessionary impact than
expenditure-based ones, with approximately 27% of the total effect of tax-based consolidations attributable
to network spillovers, compared to 11% for expenditure-based plans. A quarter of this difference in their
total output effect is attributable to the stronger network propagation of tax increases compared to
government spending cuts.
We use restricted data from the Quarterly Census of Employment and Wages to link the universe of US establishments
with the universe of contractors in the Federal Procurement Data System. Leveraging detailed institutional
knowledge of federal acquisitions, we construct a new dataset of unanticipated contracts and examine their
effects on employment growth. We find positive, significant, and persistent effects on firms with fewer than
150 employees. Using loan data from the US Federal Reserve (Y14-Q), we show that small firms expand their credit
and experience lower interest rates after winning unanticipated contracts. At the regional level, we estimate
a cost-per-job of $57,000 per year using unanticipated contracts—ten times smaller than previous estimates
based on all defense contracts. Lastly, we leverage the restricted census data to decompose the employment multiplier
into a direct effect on contractors of 55% and an indirect effect on non-contractors of 45%.
This study proposes a novel two-step method to identify and quantify news shocks, applicable to many
macroeconomic contexts. Enhancing narrative identification with Large Language Model searches, we compile
the set of events (2001–2023) that altered the expected path of U.S. defense expenditure. Using cross-sectional
regressions of defense contractors’ stock returns on their pre-event reliance on military revenues, we estimate
market-implied measures of expected defense spending changes. This method validates statistically each event,
and quantifies each shock in a simple, model-consistent fashion. Employing these shocks in a shift-share analysis
of fiscal policy yields a one-year MSA-level GDP multiplier of 1.3 for U.S. military build-ups.
We construct regional income distributions using "Generalized Pareto Interpolation" and use them to build
region-specific personal income tax (PIT) shocks. We examine the regional effects of the tax cuts enacted
during the Bush and Trump administrations, as well as the tax increase on top-income earners during the
Obama administration.