Macroprudential, Monetary Policy Synergies and Credit Supply: Evidence from Matched Bank-Firm Loan-Level Data in Brazil
What this paper finds — and why it matters
Layer 1: Overview
Research question and motivation: Reserve requirements (RRs) were largely abandoned as a monetary tool in advanced economies after inflation targeting, but emerging markets (EMs) — especially Brazil — kept using them countercyclically before, during and after the GFC and COVID-19 (53 EMs eased RRs during the pandemic). Despite their wide use, there was scarce loan-level evidence on whether RRs actually manage domestic credit cycles through credit supply, and on whether they have synergies with the short-term policy rate. The paper fills this gap.
Data and strategy: The authors use quarterly matched bank-firm loan-level data from Brazil’s credit registry (SCR), augmented with bank controls and firm employment data from RAIS, covering 2008Q1-2015Q2 (30 quarters). After cleaning and a 10% random firm sample, the working sample is 2,595,398 observations spanning 90,440 firms and 83 commercial banks. Identification rests on three moves: (1) firm-quarter fixed effects on multiple-bank-relationship firms (Khwaja-Mian/Jimenez approach) to absorb credit demand; (2) a bank-level counterfactual exposure variable, ΔResReq (the Camors et al. 2019 construction), measuring how much each bank is differentially “taxed” by RR rule changes given its ex-ante deposit mix, holding policy fixed at pre-September-2008 rules; ΔResReq averages -1.64 (sd 2.61) at bank level. (3) High-frequency monetary policy surprises (Kuttner 2001) from 30-day interest-rate swaps around Copom announcements, interacted with ΔResReq to identify policy synergies.
Main findings (signs, magnitudes, scope): A 1 pp tightening of RRs reduces a bank’s credit to a firm by 0.52-0.56 pp next quarter (no firm-quarter FE), and -0.67 pp with firm-quarter FE — coefficient stability across saturations suggests exposure is orthogonal to demand. Private domestic banks are roughly twice as responsive: -1.39 pp (Table IV) and -1.68 pp in the synergies specification (Table V). With a simultaneous one-standard-deviation surprise policy-rate tightening, the response rises to -1.90 pp — evidence of monetary-macroprudential synergy. A comparable interest-rate surprise alone contracts credit 0.63 pp; a 1 pp Selic increase, 0.71 pp. Bank capital matters: a private domestic bank one sd above mean capital/assets cuts credit only 0.85 pp (vs 1.68 pp), implying capital-liquidity substitution — but only during tightening, not loosening. After controlling for heterogeneity, there is no significant tightening-vs-loosening asymmetry for private domestic banks; the asymmetry found in cross-country work is driven by less-responsive government and foreign banks (foreign banks fully mitigate loosening). Economic policy uncertainty (EPU, Baker-Bloom-Davis) weakens transmission: a 1 pp loosening raises credit 1.50 pp, but only 1.22 pp when EPU is one sd (71 points) higher — about 19% mitigation. Using an aggregate macroprudential index instead of bank exposure yields qualitatively similar but weaker effects (a 1 sd index move gives -1.43 pp vs -2.02 pp for the intensity-sensitive aggregate counterfactual), so cross-country index studies underestimate RR effects and overestimate asymmetries. At the firm level, firms do not insulate themselves (no leakage). Real effects on employment are modest and not economically significant: no significant hiring effect; a 1 pp RR loosening reduces firings by ~1.6% (all banks) / ~2% (private domestic), requiring an 8.33 pp loosening to prevent one additional firing.
Implications: RRs are an effective state-contingent (Pigouvian) tax to manage domestic credit booms and busts via credit supply, can stimulate credit even with the policy rate unchanged (useful at the ELB or under “fear of floating”), and should be eased more aggressively when EPU is high.
Layer 2: Deep Dive
What is the identification strategy and what are the main threats to it?
Three layers. First, firm-quarter fixed effects on firms with multiple bank relationships absorb firm-level credit demand (Khwaja-Mian/Jimenez et al. 2014), so the within-firm-quarter comparison isolates supply. Second, a bank-level counterfactual exposure variable, ΔResReq, measures differential RR ’taxation’ from each bank’s ex-ante deposit mix relative to pre-September-2008 rules, holding policy fixed — this separates RR supply effects from the policy rate and from aggregate credit-cycle dynamics. Third, high-frequency monetary policy surprises (one-day swap changes after Copom) provide exogenous variation in the policy rate for the synergy interaction. Main threats: (a) banks could shift their liability mix toward less-affected deposits (evasion) — addressed in Appendix A.3 (no significant deposit reallocation); (b) more-exposed banks could be differentially exposed to other macro shocks — addressed via ‘horserace’ interactions with local and global variables (Tables VI-VII); (c) policy-rate endogeneity — addressed by using surprises; (d) excess/voluntary reserves as omitted variable — addressed in A.8-A.9 (insignificant). Coefficient stability when adding firm-quarter FE (Oster 2019) supports exogeneity of ΔResReq to demand.
What are the main mechanisms and how are they distinguished empirically?
The core mechanism is RRs acting as a countercyclical Pigouvian tax that withdraws liquid funds during tightening (constraining supply) and injects cash during loosening (stimulating supply). The synergy mechanism is that simultaneous policy-rate tightening amplifies the RR credit-supply contraction (-1.68 to -1.90 pp for private domestic banks). The EPU mechanism is that high policy uncertainty makes banks more cautious, reducing the amplification of stimulus policy (loosening becomes ~19% less effective). These are distinguished by interacting ΔResReq separately with policy-rate surprises, with EPU, and with bank characteristics, all within the saturated firm-quarter FE model, and by running separate loosening vs tightening subsamples (16 loosening quarters, 14 tightening quarters).
What heterogeneity is documented?
By bank ownership: government and foreign banks are less sensitive to RRs (government banks lend countercyclically; foreign banks respond to home-country policy and fully mitigate loosening effects), while private domestic banks are about twice as responsive as the average bank. By capital: higher-capital private domestic banks are insulated from RR tightening (one sd above mean capital cuts the response from -1.68 to -0.85 pp), consistent with capital-liquidity substitution (Acosta-Smith et al. 2019); this insulation appears only during tightening, not loosening. By state of EPU: transmission is weaker when economic policy uncertainty is high. NPL share is not associated with lower credit growth during tightening as it is during loosening.
What robustness checks are run?
(A.3) Bank-level panel regressing changes in savings/demand/time deposits on lagged exposure — no significant reallocation, so banks are not evading the policy. (A.4) Replicating Table V with the actual Selic change instead of surprises — a 1 pp RR tightening plus 1 sd (0.97) Selic tightening gives -2.02 pp (vs -1.9 pp with surprises). (A.5) Dropping influential policy quarters (2008Q4, 2009Q1, 2010Q1-Q2, 2010Q4, 2011Q1) — results unchanged. (A.6-A.7) Adding controls for ex-ante liability structure (shares of savings/time/demand deposits) — baseline qualitatively and quantitatively unchanged. (A.8-A.9) Controlling for / interacting with excess voluntary reserves (averaging 0.08% of liabilities) — insignificant and leaves estimates unchanged. Tables VI-VII horserace against local (inflation, GDP, current account, EPU) and global (Fed funds, US shadow rate, VIX, commodity prices, other macropru policies) variables — estimates stable.
How does this paper relate to and differ from closely related prior work?
It uses the same counterfactual exposure variable as Camors et al. (2019), who studied RRs as a tax on dollar deposits in Uruguay; and relates to Epure et al. (2018) on Romania and the global financial cycle. Unlike that literature, which focuses on FX/dollar-denominated deposits and global-cycle spillovers, Brazil’s low foreign-debt banking sector lets the authors isolate RRs targeting the DOMESTIC credit cycle. They claim to be the first loan-level paper to estimate RR effects on domestic credit cycles while disentangling and documenting monetary-policy synergies, the first to link higher EPU to lower macroprudential effectiveness, and the first to assess bank capital’s mitigating role for RR tightening. Against the cross-country macroprudential-index literature (Cerutti-Claessens-Laeven 2017, Akinci-Olmstead-Rumsey 2018, Alam et al. 2019), which finds borrower-targeted tools stronger than bank-targeted RRs and tightening more effective than loosening, this paper shows the index approach ignores policy intensity and bank exposure, thereby underestimating RR effects and overestimating asymmetries. On real effects, modest employment results echo Richter, Schularick, and Shim (2019).
What are the policy implications and their scope conditions?
RRs are effective for managing domestic credit booms and busts through credit supply, and can stimulate credit even when the policy rate is unchanged — relevant for EMs at the effective lower bound or constrained by ‘fear of floating’ from using the policy rate countercyclically. Synergies with the policy rate are relevant and significant mainly during tightening (statistically weaker, for firms, during loosening). Because high EPU mutes the stimulus, policymakers trying to unfreeze credit (e.g., COVID-19) must ease RRs more aggressively when policy uncertainty is high. Scope conditions: results are estimated on Brazil 2008-2015, on multiple-bank-relationship firms, for credit in local currency, with the strongest responses concentrated in lower-capital private domestic banks; real effects on employment are modest and not economically significant in either direction.
Are there leakage or general-equilibrium concerns at the firm level?
The authors test whether firms insulate themselves by substituting toward less-affected banks (Jimenez et al. 2017 found full insulation for Spanish dynamic provisions). Using firm-level regressions (equation 10), they find firms associated with more-exposed banks are NOT insulated from either loosening or tightening — strong effects survive at the firm level — so the transmission channel does not ’leak,’ confirming RRs are effective at dampening credit booms in aggregate.
What is the relationship between the policy variables and the credit cycle in the raw data?
Changes in RRs track aggregate bank credit countercyclically: the correlation between the system-wide counterfactual RR variable and aggregate credit is 0.50, far above the 0.14 correlation between credit growth and CPI inflation, supporting the financial-stability (not inflation) motivation. The correlation between RR changes and the Selic policy rate is 0.31, motivating the need to disentangle the two instruments.