The Credit Channel of Public Procurement
What this paper finds — and why it matters
Layer 1: Overview
Research question and motivation. Public procurement accounts for roughly one-third of government spending (12.6% of GDP and 30% of total government expenditures in OECD countries in 2019). The standard view is that procurement helps firms grow by raising their revenues. Gabriel asks whether procurement also operates through a previously underexplored credit channel: if a procurement contract is a secure future cash-flow stream, firms can pledge it as collateral to obtain more credit. This matters especially in bank-dependent economies (in Portugal and several OECD countries, >80% of nonfinancial corporate debt is bank loans; <1% of Portuguese firms access capital markets), and for small/financially constrained firms.
Data and strategy. The author web-scrapes >1 million Portuguese electronic procurement contracts (2009-2019) from the official BASE registry, matching winners’ tax IDs to firm balance-sheet/income data (IES via BPLIM) and to the monthly Credit Registry (CRC) with loan-level collateral types. Focusing on contracts awarded via public contests (a silent sealed-bid first-price-auction-like setting) for quasi-exogenous variation yields 138,561 contract-winner pairings and 35,675 unique winner-year observations. Average contract award is ~€202,170 (median ~€33,762-34,762), average duration ~297 days, ~3.6 contestants. Identification uses Jordà (2005) local projections (Eq. 1) regressing credit growth (scaled by lagged assets) on the award amount (scaled by lagged assets), with firm and industry×year fixed effects, SEs clustered at the firm level. The identifying assumption is that winning via public contest is not systematically correlated with firm characteristics; conditional on fixed effects, winner/non-winner differences largely disappear (except total assets, which is controlled).
Main findings (with magnitudes). Winning an additional €1 of procurement raises total firm credit by up to €0.07 (3.3 cents drawn credit on impact, plus ~4 cents in potential/undrawn credit lines; total ~7 cents in the award year), and raises cash and bank deposits by ~6 cents. Interest rates fall by over 0.3 percentage points on impact, indicating the increase is supply-driven (winners’ average implicit rate ~6.9%, median ~5.1%). A back-of-envelope calculation gives ~2.5 pp credit growth one year out (vs. ~5 pp in Spain per di Giovanni et al. 2024). The credit increase is almost entirely collateralized; in monthly data, firm personal guarantees (which include future procurement cash flows) account for >66% of the credit increase at month 4, and adding state guarantees, cash-flow-based lending explains ~75%. On the real side: +6 cents of non-current assets/investment (mostly PPE) per euro, persistent employment gains, ~70% rise in sales income one year post-award, positive net income of ~5 cents per euro. cash-flow-based lending is ~44% of firm credit in the sample.
Heterogeneity and aggregate. Investment responses are concentrated in small/constrained firms (β ≈ €7.3 for small/micro vs. −€1.2 for big firms 2 years out; difference significant at 1%); credit responses do not differ significantly by size. Regionally (Eq. 2, NUTS-III, region+year FE, clustered at region), €1 of procurement raises regional GVA by ~€1.3 (€1.32 on impact), implying ~€0.32 crowding-in of private production; the credit channel accounts for ~5% (5.5%) of this. Procurement boosts private R&D but not TFP, with only modest, short-lived inflation and no broad regional credit expansion (suggesting credit redistribution toward winners).
Layer 2: Deep Dive
What is the identification strategy and what are the main threats to it?
The author exploits public contests, which resemble a silent sealed-bid first-price auction with a costly single bid: the hiring entity does not know who bids and firms do not know their competitors or how many there are, so the winner is not ex-ante predictable. He estimates Jordà (2005) local projections (Eq. 1) of credit growth on the award amount, both scaled by lagged total assets, with firm and industry×year fixed effects and firm-clustered SEs. The key identifying assumption is that winning via public contest is not systematically correlated with other firm characteristics. Threats: (i) selection if contracts go to more productive firms (would overstate effects) or displace private opportunities (would understate); (ii) anticipation, if firms foresee winning and adjust early. He addresses anticipation by including pre-event horizons h=-2, h=-3 (annual) and pre-months (monthly), finding no significant pre-trends, and by focusing on contests (where outcomes are unknown, unlike direct awards) and using yearly aggregation (the announce-to-decision gap was ~4 months in 2020). Figure C.1 shows unconditional winner/non-winner differences mostly vanish once fixed effects are included, except total assets (which is controlled). Appendix C.1 adds a local-projections difference-in-differences robustness check following Dube et al. (2023).
What is the credit channel mechanism and how is it distinguished from a demand story?
The mechanism is cash-flow-based lending: procurement contracts represent secure future cash flows that firms pledge as collateral (personal/firm guarantees), easing borrowing constraints. It is distinguished from a credit-demand story by the price of credit: a demand-driven increase would raise interest rates, but rates fall by >0.3 pp on impact, consistent with a supply-driven expansion. Two micro-mechanisms raise perceived creditworthiness: (i) collateral value of the contract itself, and (ii) a signaling/certification effect where government endorsement reduces bank information asymmetry. Monthly collateral decomposition (Figure 5) shows the credit increase is overwhelmingly backed by firm personal guarantees (>66% at month 4; ~75% including state guarantees), with asset-based collateral mostly insignificant, directly supporting the cash-flow collateral channel.
How is the signaling/certification mechanism tested separately?
In Appendix Table C.3 (discussed in Section 3.5) the author compares first-time award recipients to firms with previous awards. First-time winners enjoy significantly higher and more persistent responses in credit, employment, and investment, which he interprets as a reputation/certification effect that partially resolves a banking information-asymmetry problem (banks learn the firm has government demand). This is distinct from the pure collateral mechanism, which is tested with the monthly collateral-type decomposition.
What heterogeneity is documented?
By firm size (Commission Recommendation 2003/361/CE: small = headcount <50 and turnover/balance-sheet <€10m): credit responses do not differ significantly between small and big firms, but investment and employment responses are much larger and more persistent for small/constrained firms (investment β ≈ €7.3 small vs. −€1.2 big at 2 years, difference significant at 1% and growing with horizon; HAC p-values for employment differences are 0.05 at 1yr and 0.00 at 2yr). This is rationalized via the financial-accelerator hypothesis (Bernanke et al. 1999) and investment-cash-flow sensitivity literature (Fazzari et al. 1988). Employment heterogeneity mirrors Giroud and Mueller (2017). By sector: Construction and Medical Equipment (~60% of 2019 procurement value) account for much of the credit response but show no significant persistent differences in investment/employment. By award history: first-time winners respond more strongly (reputation effect).
What does the monthly analysis add over the annual analysis?
Using monthly credit/collateral data within the first year (relevant since the median contract lasts <1 year), the credit increase begins at award inception, rises sharply in the first month, and peaks ~3 months after the award (aligning with the annual ~3+ cents/euro). The increase is almost entirely collateralized (unsecured credit shows a muted response) and of sound quality (non-performing credit barely moves). Both long- and short-maturity credit rise, with long-term credit responding more strongly. Crucially, no significant credit movement appears up to three months before signing, reinforcing the no-anticipation conclusion.
What are the aggregate/regional results and how are they estimated?
The author aggregates procurement by spending location to NUTS-III regions and estimates local-projection multipliers (Eq. 2) with region and year fixed effects, SEs clustered at region, sample matched 2010-2016 (25 regions × 6 years), procurement winsorized at the 95th percentile. A €1 increase in regional procurement raises GVA by ~€1.3 (€1.32 on impact, interpreted as an open-economy relative multiplier à la Nakamura-Steinsson 2014), implying €0.32 crowding-in of private production. Eq. 3 interacts procurement with winners’ credit (following Basso and Rachedi 2021): the positive significant interaction means credit amplifies the multiplier; a 1% credit-to-GVA increase raises the multiplier by 11% on impact, and since winners’ credit is ~0.5% of GVA, the credit channel adds ~(0.11×0.5)% ≈ 5.5% (5%). National-accounts regressions (Table 4) show procurement raises private value added (€1.2 on impact), private investment, private R&D (innovation), and modest short-lived inflation, but not TFP; aggregate nonfinancial-firm credit is subdued, suggesting credit redistribution toward winners rather than broad expansion.
What robustness checks and caveats are noted?
Robustness: anticipation tests at multiple pre-horizons (annual and monthly); a local-projections diff-in-diff specification (Dube et al. 2023) in Appendix C.1; fixed-effects conditioning that removes most winner/non-winner differences; winsorizing the regional regressor at the 95th percentile (results sensitive to outliers). Caveats explicitly acknowledged: (i) no loan-level data, so the implicit interest rate is total interest expense / lagged effective credit, and financial covenants cannot be observed (if present, estimates would be conservative); (ii) under Portugal’s Public Procurement Code (Ch. IX), contracts above ~€500k may require a guarantee up to 5% of value, often a bank guarantee that appears as firm-guaranteed credit—but the central message still holds; (iii) procurement coverage is incomplete (web-scraped data ≈ one-third of total procurement, ~3% of GDP), so regional coefficients should be read with caution; (iv) the regional credit measure may not capture the full cumulative credit response and credit increases could partly reflect non-procurement factors; (v) collateral values are not market-adjusted and are often capped at the loan amount.
How does this paper relate to and differ from closely related prior work?
It contributes to three literatures. (1) Firm-level effects of fiscal policy/procurement (Barrot-Nanda 2020; Goldman 2020; Cox et al. 2024; Ferraz et al. 2021; Lee 2021): prior work emphasizes revenues as the driver; Gabriel adds a new credit/collateral transmission mechanism across all industries. The closest contemporaneous work is di Giovanni et al. (2024) for Spain, who document a positive procurement-credit correlation; relative to them, this paper provides detailed evidence on the credit-supply channel and its investment implications, measures contract heterogeneity, and—unlike their welfare/allocation-system focus—provides the first local procurement multiplier estimates with the credit channel’s share. (2) Government spending and fiscal multipliers, including stronger fiscal effects under tight credit (Ferraresi et al. 2015; Aghion et al. 2014). (3) Financial frictions and collateral type, shifting from asset/liquidation-value collateral (Kiyotaki-Moore 1997) to cash-flow-based collateral (Lian-Ma 2021; Ivashina et al. 2022; Drechsel 2022; Caglio et al. 2022); the novelty is cash flows from sales to the government as collateral. Notably his investment elasticity for small firms (~5 cents/euro cumulative at one year) is smaller than Hebous and Zimmermann’s (2021) ~13 cents.
What are the policy implications and their scope conditions?
Two implications: (1) Targeting design—because small/financially constrained firms respond more strongly and persistently in investment and employment, targeting procurement to such firms (as pushed by the European Commission/Parliament for SMEs) likely raises aggregate investment and employment, not just efficiency. (2) Financial stability—letting firms pledge procurement contracts as collateral diversifies collateral away from real-estate/asset-based booms (which deplete project information and lead to deep downturns, Asriyan et al. 2022), so procurement could temper collateral-induced financial fluctuations. Scope conditions: external validity is greatest for countries where procurement is a large GDP share and firms rely heavily on bank credit (true for many developed and developing economies, e.g., Portugal where <1% of firms access capital markets); the effect grows more important the more bank-dependent firms are. The interest-rate decline is a firm-level result and should not be read as procurement lowering equilibrium interest rates economy-wide; a procurement shock can be a reallocation of spending rather than higher total spending/deficit.
What is the nature of the real-side response and why is the sales response not larger?
Winning raises non-current assets by ~6 cents per euro (mostly PPE/tangible, not intangibles or financial investments), comparable to Hebous-Zimmermann’s ~10 cents and to real-estate-collateral elasticities (~6 cents, Chaney et al. 2012; Catherine et al. 2022). Employment rises persistently beyond the first year (Ferraz et al. 2021), though without a matching rise in value added. Sales income rises ~70% one year post-award—less than a one-for-one mapping of public demand to sales—for two reasons: a ‘duration effect’ (contracts spread revenue over years; some last up to a decade) and a ‘capacity constraint effect’ (firms prioritize government contracts, diverting other business to competitors, which also shows up in regional GVA), potentially mitigated by sub-contracting. Despite higher costs of goods sold, net income stays positive at ~5 cents per euro, so contracts are profitable.