Motivating banks to lend? Credit spillover effects of the Main Street Lending Program

Overview

Research Question. Minoiu, Zarutskie, and Zlate ask whether participation in the Main Street Lending Program (MSLP)—a Federal Reserve emergency facility launched in mid-2020 to channel credit to small and mid-sized firms during the COVID-19 pandemic—caused banks to lend more outside the program. The authors focus on credit spillover effects: did MSLP-participating banks ease standards and expand volumes on their general commercial and industrial (C&I) loan books, beyond the direct loans originated under the program itself?

Institutional Context. The MSLP opened for lender registration on June 15, 2020 and began accepting loan submissions on July 6, 2020, expiring December 31, 2020. Of $600 billion in available SPV capacity, only $16.05 billion was actually deployed, making overall take-up approximately 2.7% of capacity. Despite this, the program required participating banks to retain 5% of each loan’s credit risk while offloading 95% to the SPV, and charged borrowers LIBOR plus 300 bps. Registration rate among all Call Report banks was 11.7% (614 out of 5,242 banks), with participation rising steeply with bank size: from 6.5% of banks in the below-$1-billion asset group to 63.8% of banks with assets above $50 billion.

Data and Methodology. The analysis draws on multiple data sources: (a) supervisory Y-14Q H1 loan-level data covering C&I loans above $1 million commitments, reported by 32 bank holding companies (BHCs) that account for roughly three-quarters of total U.S. C&I loans; (b) Y-14Q A9 loan portfolio segment data for small business C&I loans (below $1 million commitments) from 22 BHCs; (c) quarterly Senior Loan Officer Opinion Survey (SLOOS) microdata for April, July, and October 2020, providing bank-level assessments of lending standard changes, loan terms, demand shifts, and stated reasons for tightening; (d) Dealscan syndicated loan originations for 262 banks (51 MSLP participants); and (e) bank balance sheet data from Call Reports, including the Ellul-Yerramilli risk management index (RMI) for 16 BHCs. The core empirical design is a difference-in-differences (DiD) comparing MSLP-participating vs. non-participating banks before (2020:Q1–Q2) and after (2020:Q3) program implementation. To address nonrandom selection, the authors instrument MSLP participation with three variables: (i) a dummy for banks that cited registration as “too burdensome” in the September 2020 supplementary SLOOS; (ii) a dummy for banks with prior experience pledging loan collateral at the Fed’s discount window; and (iii) a dummy for banks with prior experience pledging securities collateral at the discount window. Firm×quarter fixed effects absorb time-varying credit demand at the borrower level (Khwaja-Mian design), and bank×borrower fixed effects further control for relationship-specific lending patterns.

Main Findings — Extensive Margin (Large Business Loans). In the Y-14Q H1 data, MSLP banks were 30–32% more likely to renew existing loans than non-MSLP banks in 2020:Q3, with the probability of renewal 1.6–1.7 percentage points higher (against a sample average renewal rate of 5.3%). New loan originations were 22–27% more likely at MSLP banks, or 1.1–1.4 percentage points higher (against a sample average origination rate of 5.1%). 2SLS estimates are similar in magnitude to OLS, indicating selection bias is modest.

Main Findings — Extensive Margin (Small Business Loans and Survey Data). In the A9 small business segment data, MSLP lenders had 17.3% more small business loan accounts outstanding in 2020:Q3 than non-MSLP banks. In SLOOS microdata, MSLP banks were approximately 15 percentage points less likely to report tightening C&I lending standards in 2020:Q3 (conditional on demand controls), compared to an actual tightening rate of 37.5%. This effect is larger for small (more financially constrained) firms (16–17 percentage points) than for large firms (13–14 percentage points).

Main Findings — Intensive Margin. On loan terms, MSLP banks charged spreads that were approximately 9 basis points lower on renewed/originated C&I loans in the Y-14Q data, and 13.5 basis points lower in the Dealscan syndicated loan sample, compared to non-MSLP banks in 2020:Q3. 2SLS estimates are somewhat larger (19–30 bps). In the Dealscan sample, MSLP banks also extended syndicated loans that were 11.2% larger (about $2.4 million more given a $22 million average loan size). Survey data confirm MSLP banks were less likely to tighten most individual loan terms.

Aggregate Magnitude. The authors estimate that, in the absence of the MSLP, total loan renewals and originations at Y-14Q reporting banks in 2020:Q3 would have been approximately 10% lower. Scaling to the broader banking sector, the estimated credit spillover effect is approximately $44.8 billion in C&I lending—nearly three times the $16.05 billion in direct MSLP loan purchases.

Mechanism. Survey and objective evidence both point to reduced risk aversion as the primary channel, rather than immediate balance sheet constraint relief. MSLP banks were significantly less likely to cite “reduced tolerance for risk” as a reason for tightening lending standards after the program’s introduction, while showing no differential propensity to cite capital or liquidity deterioration. Banks with higher risk management index scores (more risk-averse institutions) exhibited larger spillover effects on two of three lending margins. Indicators of immediate balance sheet tightness (excess capital cushions, cost of capital, core deposit reliance) do not predict larger spillovers, with a partial exception for lower excess capital and higher loan loss reserves — suggesting future rather than current balance sheet constraints may have played some role.

Scope Conditions and Robustness. The backstop mechanism is explicitly tied to the program’s credibility period: the spillover effects are smaller in 2020:Q4, consistent with the Treasury’s November 19, 2020 announcement that the program would not be extended, which diminished its backstop role. Placebo regressions using 2018 and 2019 data find no differential lending behavior between MSLP and non-MSLP banks before the program, supporting parallel trends. Results are robust to controls for PPP participation, credit line drawdown exposure, loan loss provisioning, and bank-level loan portfolio cyclicality.

Q&A

Q1: What precisely is the “spillover effect” that the paper measures, and how does it differ from the direct effect of the MSLP? A: The direct effect is the $16.05 billion in MSLP loans purchased by the SPV — credit extended specifically through the program. The spillover effect refers to changes in banks’ general C&I lending behavior outside the program: renewals and originations of non-MSLP loans, changes in lending standards and terms for all business borrowers, and changes in small business loan volumes. The sample in the Y-14Q regression explicitly excludes MSLP loans themselves, so the estimates reflect only the indirect, broader credit effects.

Q2: What instruments does the paper use for MSLP participation, and why are they plausibly exogenous? A: Three IVs are employed: (1) a dummy for banks that cited program registration as “too burdensome” as a very important reason for not joining (from the September 2020 supplementary SLOOS); (2) a dummy for banks that pledged loan collateral at the Fed’s discount window in December 2019; and (3) a dummy for banks that pledged securities collateral at the discount window in the same period. The exclusion restriction argument is that (1) reflects banks’ administrative capacity and prior Fed engagement rather than underlying balance sheet strength or lending appetite, and that (2) and (3) reflect familiarity with Fed collateral processes in ways that made a loan-based program easier to understand and join — without independently affecting lending standards or volumes in 2020:Q3.

Q3: How large are the spillover effects on the extensive margin of large corporate lending? A: In the Y-14Q H1 data across 32 BHCs, MSLP banks renewed loans 1.6–1.7 percentage points more frequently and originated new loans 1.1–1.4 percentage points more frequently in 2020:Q3, relative to non-MSLP banks. Against sample averages of 5.3% renewal rate and 5.1% origination rate, these translate to MSLP banks being 30–32% more likely to renew and 22–27% more likely to originate loans. The 2SLS estimates are broadly similar in magnitude, suggesting that self-selection bias in OLS is limited.

Q4: What are the estimated aggregate dollar spillovers from the MSLP? A: The paper calculates that, in the absence of the program, total loan renewals and originations at Y-14Q H1 MSLP banks in 2020:Q3 would have been lower by approximately $33.6 billion (derived from 44,274 bank-borrower pairs × 1.38 existing loans per pair × 3.06 percentage points of extra loan activity × $17.98 million average loan size). Scaling to all Y-14Q banks (MSLP and non-MSLP alike), the shortfall would represent roughly a 10% reduction in total 2020:Q3 loan renewals and originations. Extrapolating to the full banking sector (since Y-14Q banks cover about 75% of total C&I lending), and assuming similar spillover magnitudes for banks outside the sample, total MSLP spillovers amount to roughly $44.8 billion — approximately three times the $16.05 billion in direct MSLP loan purchases.

Q5: What is the estimated effect on C&I lending standards using survey data? A: Using SLOOS microdata, the paper estimates that MSLP banks were approximately 15 percentage points less likely to tighten C&I lending standards in 2020:Q3 compared to non-MSLP banks, after controlling for demand conditions. The actual tightening rate in 2020:Q3 was 37.5%, meaning the counterfactual tightening rate absent the program would have been approximately 5 percentage points higher. In a further hypothetical where all SLOOS sample banks had participated, the counterfactual tightening rate would have been nearly 10 percentage points higher than actual.

Q6: Are spillover effects larger for small or large borrowers, and what does this imply? A: The SLOOS-based estimates show that MSLP banks were 16–17 percentage points less likely to tighten lending standards for small firms (annual sales below $50 million), compared to 13–14 percentage points less likely for large and middle-market firms — a statistically significant difference. The authors interpret this as consistent with the MSLP reducing risk aversion broadly, with the largest effect on borrowers facing greater credit constraints where uncertainty about creditworthiness was highest.

Q7: What evidence supports the risk aversion (psychological backstop) mechanism over the balance sheet constraint mechanism? A: From SLOOS data, MSLP banks were significantly less likely (at the 1% level) to cite “reduced tolerance for risk” as a reason for tightening lending standards after the program’s introduction, while showing no differential likelihood of citing deteriorating capital or liquidity positions as reasons. Furthermore, splitting banks by the risk management index (RMI), the spillover effects are stronger for high-RMI (more risk-averse) banks on two of three lending outcomes. Conversely, proxies for immediate balance sheet constraints — excess capital cushions, core deposit ratios, equity issuance, and cost of capital — do not yield consistently stronger spillover effects for more constrained banks. The only partial exception is lower excess capital and higher loan loss reserves, which are associated with more loan renewals, suggesting future rather than current balance sheet constraints may have contributed.

Q8: What is the risk management index (RMI), and how is it used here? A: The RMI is an index developed by Ellul and Yerramilli (2013) that captures the strength of a bank’s internal risk management function, constructed from variables including whether the bank has a chief risk officer (CRO), the CRO’s executive status and relative compensation, risk committee member experience, and meeting frequency. Available for 61 BHCs over 2011–2013, it is matched to 16 BHCs in the Y-14Q H1 sample and used as a pre-COVID proxy for institutional risk aversion. Banks above the median RMI show larger MSLP spillover effects on loan renewals and tightening standards, consistent with the interpretation that the MSLP reduced effective risk aversion more for banks that had higher baseline risk-consciousness.

Q9: How do the authors address the concern that PPP participation — not MSLP participation — might drive the results? A: First, they test directly that MSLP participation does not predict outstanding PPP/federally-guaranteed loan balances (in Q2 or Q3 2020) in the A9 loan segment data, finding no correlation. Second, they add an interaction of PPP loan balances (divided by total assets) × Post to the baseline regression in Table A10 and find that while PPP lending is positively associated with loan renewals and originations, the MSLP bank × Post coefficient remains statistically significant and similar in magnitude to the baseline, ruling out PPP participation as the driver of the baseline results.

Q10: What explains the low take-up of the MSLP despite its large designed capacity? A: Survey responses from the September 2020 supplementary SLOOS indicate several demand- and supply-side constraints: banks reported they could generally meet credit demand outside the program; borrower leverage limits (capped at 4–6× EBITDA depending on facility) were seen as too restrictive; the LIBOR plus 300 bps interest rate was high relative to historical pricing for eligible firms; and registration and loss-sharing arrangements were viewed as burdensome and uncertain. The paper interprets these findings as consistent with banks treating the MSLP primarily as a backstop — a facility they would activate only if economic conditions deteriorated significantly — rather than a primary lending channel.

Q11: How does the paper address the threat that MSLP participation reflects bank-level cyclicality in loan portfolios? A: Table 10 controls for bank-specific C&I loan portfolio cyclicality, measured as the correlation between each bank’s C&I loan growth and aggregate banking-sector C&I loan growth estimated over 1985:Q1–2021:Q2 using two functional forms. The MSLP bank × Post coefficient estimates remain very similar to the baseline after including these controls, ruling out the concern that MSLP participants were simply banks with naturally more procyclical or countercyclical lending patterns.

Q12: What happens to the estimated spillover effects in 2020:Q4, and what does this reveal? A: The paper shows (Table A6) that extending the sample to include 2020:Q4 yields somewhat smaller estimated spillover effects than in the baseline 2020:Q3 period. The authors attribute this to the November 19, 2020 announcement by Treasury Secretary Mnuchin that the MSLP would not be extended beyond year-end, which effectively ended the program’s backstop role and — consistent with the psychological backstop mechanism — reduced banks’ confidence in the program’s future availability and thus the spillover motivation.

Q13: Does the paper find spillover effects on intensive margin loan terms, and how large are they? A: On loan spreads, MSLP banks charged approximately 9 basis points lower spreads on floating-rate C&I loans renewed or originated in 2020:Q3 in the Y-14Q data (2SLS: 19 bps), and approximately 13.5 bps lower spreads in the Dealscan syndicated loan sample (2SLS: 30 bps). The 9 bps OLS estimate implies the average spread across all LIBOR-indexed C&I loans in 2020:Q3 would have been approximately 4 bps higher absent the program (i.e., 0.43 × 9 bps), relative to an actual average spread of 235 bps — an effect the authors characterize as economically small. On loan size, the Dealscan evidence indicates MSLP banks extended syndicated loans that were 11.2% larger (2SLS: 25% larger).

Key Concepts

Credit Spillover Effects: As used in this paper, spillover effects refer to the impact of MSLP participation on participating banks’ lending behavior outside and beyond the program itself — specifically, changes in loan renewal rates, new loan origination rates, lending standards, and loan terms for non-MSLP C&I loans. This is distinct from the direct effect (i.e., loans originated through the MSLP proper).

Psychological Backstop: The paper’s term for the mechanism by which the MSLP reduced participating banks’ effective risk aversion without necessarily easing their immediate balance sheet constraints. By committing to provide lending support if conditions deteriorated, the program built banks’ confidence to lend ex ante, functioning as “insurance” against bad outcomes rather than a direct funding facility. The mechanism is distinguished from balance sheet easing by the fact that constrained and unconstrained banks exhibited similar spillover effects.

Extensive Margin of Lending: The binary dimension of lending activity — specifically, whether a bank renews an existing loan or originates a new loan within a bank-borrower pair. In this paper, measured as the share of existing loan commitments within each bank-borrower pair that are renewed or newly originated each quarter. Contrasted with the intensive margin.

Intensive Margin of Lending: The quantitative dimension of existing lending relationships — specifically, the average loan size and average spread on loans renewed or originated in a given period, conditional on a loan being extended.

Senior Loan Officer Opinion Survey (SLOOS): A quarterly Federal Reserve survey of senior lending officers at large U.S. banks covering self-reported changes in C&I lending standards, terms (including spreads, maximum loan size, maturity, covenants, collateral requirements), demand conditions, and — in supplementary editions — reasons for changing standards. Used in this paper both as an outcome variable (tightening standards) and as a control variable (changes in loan demand) and as a source of IV variation (burden of MSLP registration).

Risk Management Index (RMI): An index developed by Ellul and Yerramilli (2013) measuring the strength of a bank’s internal risk management function, combining information on the presence and compensation of a chief risk officer, risk committee composition, and meeting frequency. Used in this paper as a pre-pandemic proxy for institutional risk aversion to test whether the MSLP disproportionately reduced risk aversion in banks with stronger risk controls.

Difference-in-Differences with Granular Fixed Effects: The primary identification strategy, comparing changes in lending outcomes between MSLP-participating and non-participating banks before (2020:Q1–Q2) and after (2020:Q3) program implementation. The paper uses firm×quarter fixed effects following Khwaja and Mian (2008) to absorb borrower-level credit demand, and bank×borrower fixed effects following Chodorow-Reich (2013) to absorb relationship-specific supply factors — isolating the bank credit supply effect attributable to MSLP participation.

Originate-and-Distribute Feature (of MSLP): The MSLP’s design in which banks originate MSLP loans but sell 95% of the credit exposure to the SPV, retaining only 5%. This feature was intended to free up balance sheet capacity for further lending. The paper tests whether this channel (easing current balance sheet constraints) explains the observed spillovers, finding limited support relative to the risk aversion reduction channel.