Banking-Credit | Macro Paper Warehouse

Banking with Inside Money: An Efficiency Analysis

Mon, 01 Jan 0001 00:00:00 +0000

Layer 1: Overview

This paper demonstrates that the canonical efficiency result of Diamond and Dybvig (1983) — that banks using maturity transformation can decentralize the first-best risk-sharing allocation — breaks down when banking is conducted with inside money rather than real contracts. The paper constructs a minimal modification of the Diamond-Dybvig (DD) model in which output requires combining labor (supplied by workers) and technology (owned by entrepreneurs), so that bank deposits arise as inside money created ex nihilo when loans are extended, and shows three results: (1) non-contingent nominal demand deposits cannot reproduce the first-best allocation, because the constraint that nominal deposits earn the same real return as the productive technology prevents banks from providing state-contingent real payoffs; (2) state-contingent deposit rate contracts, which are proposed as an efficiency fix in the DD tradition, also fail to reach the first best — Proposition 2 establishes that contingent deposit rates produce a consumption allocation inconsistent with efficiency (specifically, aggregate consumption at each date cannot satisfy the efficiency ratio required by equation 8), and the allocation under contingent contracts is no better in welfare terms than the non-contingent baseline; (3) allowing entrepreneurs to liquidate loans before maturity (Proposition 3) likewise leaves the equilibrium inefficient, because competition equalizes deposit and lending rates in a way that prevents supply of goods from matching the efficient schedule across periods. The paper then characterizes when central bank intervention can improve welfare and shows that outside money is not demanded in the baseline economy, limiting the central bank’s leverage, and that the lender-of-last-resort function can prevent bank runs even when efficiency is unachievable.

In depth

Q1. What is the core model and how does inside money arise?

The paper adds a single departure from the original DD real model: output requires labor from workers and technology from entrepreneurs, which introduces a motive for money to be valued — entrepreneurs borrow units of account (inside money/deposits) from banks at date 0 to pay workers’ wages, and these deposits then circulate as a means of payment for consumption goods at dates 1 and 2. Unlike the outside-money models in Allen and Gale (1998), Skeie (2008), and Allen et al. (2014), inside money is created ex nihilo on the bank’s balance sheet when loans are extended — deposits do not represent a transfer of pre-existing funds but are liabilities created through lending. Banks in this model are price-takers and cannot take direct decisions on real investments or liquidations, which are the responsibility of entrepreneurs. This is the key distinction from the DD and subsequent literature: it is the production of deposits in the provision of loans that generates inside money, and it is the impossibility of making these nominal claims produce state-contingent real payoffs that prevents efficiency.

Q2. Why can’t non-contingent nominal deposits achieve the first best?

In any competitive equilibrium with valued deposits, the no-arbitrage condition requires that the real return on deposits equals the real return on the productive technology R in each period, so the ratio of patient-to-impatient consumption (c₂/c₁) for workers must equal R — but the first-best allocation requires c₁ and c₂ to satisfy the planner’s Euler equation u′(c₁) = Ru′(c₂), which for coefficient of relative risk aversion greater than 1 implies 1 < c₁*/c₂* < R, not c₂/c₁ = R.** This is formalized by comparing the equilibrium allocation (Proposition 1 and the Corollary) — where workers’ consumption satisfies cᵢW(1) = 1/p₁ and cᵢW(2) = R/p₁ with p₁ ∈ (0.5, ∞) — against the efficiency condition (equation 8). Because the real value of deposits is pinned by the price level in the goods market, and competitive banks have no power to engineer the price adjustments needed to create state-contingency, the nominal deposit contract is generically inefficient. This contrasts with Allen and Gale (1998) and Skeie (2008), where central bank control over either prices or real investment liquidation allows efficient outcomes.

Q3. What is the formal result on state-contingent deposit contracts?

Proposition 2 establishes that introducing contingent deposit rates (paying a higher rate to impatient depositors, id₂(1) > id₂(2)) yields an aggregate allocation in which total consumption at date 1 is at most 2 (the liquidation value) and total consumption at date 2 is at least 2R — the same aggregate feasibility constraints as the non-contingent case — and this allocation is incompatible with efficiency and no better in welfare terms than the baseline. The reason is structural: for goods to be supplied at both dates 1 and 2, the rate id₂(2) must satisfy id₂(2)·(P₁/P₂) < R ≤ id₂(1)·(P₁/P₂), but this means only impatient entrepreneurs supply goods at date 1, leaving the aggregate supply schedule identical to the non-contingent case. Even if banks had perfect information about depositor types and could implement contingent contracts without incentive compatibility concerns, the first-best allocation would remain outside the consumption possibility set of the competitive equilibrium.

Q4. What is the result on early loan liquidation?

Proposition 3 shows that allowing entrepreneurs to choose how much of their loan to repay early (at date 1 versus date 2) produces a unique equilibrium in which entrepreneurs are indifferent about when to liquidate, equilibrium deposit and loan rates satisfy id₁ = ib₁ = 0 and (1 + ib₂)(P₁/P₂) = (1 + id₂)(P₁/P₂) = R, and the resulting allocation remains inefficient. The key constraint is unchanged: competition across banks drives both deposit and lending rates to equalize in real terms, so the supply of goods at each date is still not controlled by the bank and cannot reproduce the first-best schedule. Allowing borrowers to prepay their loans does not alter the fundamental tension between fixed nominal contracts and state-contingent real outcomes.

Q5. When can banks be welfare-dominated by bilateral trade?

In the symmetric equilibrium (P₁ = D₁), the banking allocation gives E(uB) = λu(1) + (1−λ)u(R), which is welfare-dominated by the bilateral labor market allocation E(uLM) whenever the coefficient of relative risk aversion and/or the technology return R exceed a threshold — specifically, when agents are risk averse enough that the midpoint consumption available under bilateral bargaining (2R/(R+1)) is preferred to the lottery {1 with probability λ, R with probability 1−λ} — contradicting the presumption that bank intermediation is necessarily superior to direct contracting. This result, formalized by condition (41), implies that the social value of banking as an institution depends on the degree of risk aversion and the illiquidity premium R: the banking allocation is preferred when agents are relatively risk tolerant and/or R is large (so the lottery’s spread is attractive), but bilateral trade may dominate when agents are risk-averse and R is modest.

Q6. What role can central banks play and what is the lender-of-last-resort result?

The paper shows that in the baseline nominal economy, outside money is not demanded by any agent — deposits dominate cash in rate of return and the interbank payment flows net to zero — so the central bank has no leverage to affect real allocations through open-market operations; efficiency is out of reach even for a central bank. However, the paper identifies a limited but important role for central bank intervention: the lender-of-last-resort function can prevent bank runs that would otherwise be self-fulfilling equilibria in the model, even though the central bank cannot restore the first-best allocation. This is because the existence of an emergency liquidity backstop eliminates the coordination failure that makes runs self-fulfilling, without requiring the central bank to replicate the state-contingent real payoffs needed for efficiency. A central bank could potentially be incorporated into an extended model with an uneven distribution of payment flows across banks (creating a demand for reserves), but the paper argues that even then, competition across banks would still prevent contingent deposit rates from achieving efficiency.

Key Concepts

inside money : bank-created deposits that arise ex nihilo when loans are extended to borrowers and circulate as means of payment between agents; the paper’s key departure from the prior banking literature, which modeled deposits as outside money (central-bank-issued fiat money) intermediated by banks rather than money created through lending.

consumption possibility set : the set of feasible allocations achievable by the competitive equilibrium with inside-money banking; the paper’s central result is that the efficient first-best allocation — satisfying u′(c₁*) = Ru′(c₂*) — lies outside this set, so the inefficiency is not correctable by improving incentive design within the existing contract space.

nominal deposit contract : a demandable deposit that specifies a fixed nominal interest rate independent of the realization of individual liquidity preference shocks; the paper’s analysis shows that such contracts cannot produce the state-contingent real payoffs required for efficient risk-sharing in an inside-money economy, even when supplemented with contingent rates or early loan liquidation.

lender of last resort : the central bank’s capacity to provide emergency liquidity to banks facing runs by coordinating expectations away from the bank-run equilibrium; the paper’s limited positive result for central bank policy — it can prevent runs even when it cannot achieve efficiency.

Central Bank Digital Currency with Collateral-Constrained Banks

Mon, 01 Jan 0001 00:00:00 +0000

The paper analyzes the implications of introducing a retail central bank digital currency (CBDC) that competes with commercial bank deposits for household liquidity, in a model where banks must post government bonds as collateral to access central bank lending. The authors revisit Niepelt’s (2022) “equivalence of payment systems” result and find that equivalence survives even under a collateral constraint: the central bank can still offer loans to banks that replicate the no-CBDC equilibrium allocation, but at a lending rate lower than Niepelt’s unconstrained rate, because tighter terms are needed to incentivize sufficient loan uptake when banks must redirect portfolio holdings toward government bonds to qualify. A structural cost remains: banks must hold government bonds as collateral at the expense of extending credit to firms, so equivalence in allocation does not imply full neutrality — banks’ business models and the government’s intermediation role change even when aggregate output and prices are unchanged. In the dynamic extension where the central bank does not sterilize the CBDC introduction, banks respond by narrowing deposit spreads to attract inflows, with the result that a CBDC ramp-up to 5 percent of steady-state output expands rather than contracts bank credit to firms.

In depth

Q1. What is the equivalence of payment systems result and how does the collateral constraint change it?

Brunnermeier and Niepelt (2019) and Niepelt (2022) established that the central bank can neutralize the real effects of CBDC introduction by lending to banks at an appropriate rate to replace lost deposit funding, a result the present paper revisits by adding a collateral requirement on central bank lending — specifically, that banks must hold eligible government bonds up to a fraction θb of their central bank loan value. Under this constraint, Proposition 1 shows that equivalence survives: there exists a central bank lending rate that replicates the no-CBDC equilibrium allocation and price system. However, this lending rate is lower than Niepelt’s unconstrained rate by a factor increasing in the restrictiveness of the constraint (lower θb requires a lower lending rate), because when banks are collateral-constrained, cheaper terms are needed to induce them to borrow enough from the central bank to offset deposit outflows.

Q2. What is Corollary 1 and why does “full neutrality” fail?

Corollary 1 states that even when the central bank achieves allocation equivalence by setting the appropriate lending rate, banks must redirect portfolio holdings from firm loans to government bonds to meet the collateral requirement — crowding out bank credit to firms by an amount equal to the bond uptake, with the crowding-out diminishing as the collateral constraint becomes less restrictive (higher θb). This is the sense in which “full neutrality” fails under the collateral constraint: aggregate output and prices are unchanged, but the composition of credit changes — banks extend less to firms and hold more government bonds — and the government or household sector must absorb the gap in firm financing. In the limiting case where CBDC and deposits are equally valuable to households (λ = 1), the government alone compensates for the reduction in bank loans, effectively expanding its own intermediation role.

Q3. What does the dynamic extension show about bank disintermediation?

Simulating a gradual and near-permanent increase in CBDC to 5 percent of steady-state output without central bank sterilization, the paper finds that banks respond by narrowing their deposit interest spread to attract deposit inflows, such that total deposits do not fall and bank loans to firms expand rather than contract — the opposite of the disintermediation hypothesis. The mechanism relies on the assumption that banks have market power in their regional deposit markets (each bank is a monopsonist): in response to CBDC competition, the bank voluntarily reduces the rent it extracts on deposits (the spread between the risk-free rate and the deposit rate), attracting more deposit inflows. This deposit inflow, combined with central bank loan uptake, expands the bank’s balance sheet and increases credit extension to firms. The result stands in contrast to models with competitive deposit markets, where banks cannot respond to CBDC competition through deposit pricing.

Q4. What changes even if credit is not reduced?

Even when the dynamic model shows credit expansion rather than contraction, the paper establishes that CBDC introduction alters banks’ balance sheet composition and business model: banks shift toward holding more government bonds and away from firm loans, the government assumes a larger credit intermediation role, and the aggregate distribution of capital ownership changes — constituting the form of non-neutrality that survives even when total credit is unchanged. This is what Corollary 1 calls the failure of “full neutrality”: the real allocation equivalence holds at the aggregate level, but the sectoral distribution of who provides credit to firms shifts from the banking sector toward the public sector. The paper interprets this as a structural consequence of the collateral requirement on central bank lending that is absent in the frictionless equivalence benchmark.

Key concepts

equivalence of payment systems : the theoretical result (from Brunnermeier-Niepelt 2019 and Niepelt 2022) that the central bank can ensure the same equilibrium allocation whether or not CBDC exists, by adjusting its lending terms to banks; this paper revisits and extends the result to environments with a collateral constraint.

collateral constraint (θb) : the requirement in this model that banks hold eligible government bonds as a fraction of the central bank loans they take on; adding this friction to Niepelt’s framework preserves equivalence in allocation but requires a lower central bank lending rate and crowds out bank loans to firms.

disintermediation : the concern that CBDC adoption would cause households to shift en masse from bank deposits to CBDC, reducing bank funding and contracting bank credit; the paper finds this does not occur in either the equivalence analysis or the dynamic extension.

monopsony in deposits : the market structure assumption that each regional bank is the sole deposit provider in its region, giving it pricing power over deposit rates; this is what enables banks in the dynamic model to narrow the deposit spread in response to CBDC competition, generating deposit inflows rather than outflows.

full neutrality : a stronger invariance result requiring that not only the equilibrium allocation but also banks’ balance sheet composition and business model are unchanged by CBDC introduction; the paper shows this fails under the collateral constraint even when allocation equivalence holds.

Cyberattacks on Small Banks and the Impact on Local Banking Markets

Mon, 01 Jan 0001 00:00:00 +0000

This paper studies what happens to local banking markets when a small bank suffers a successful cyberattack, using a stacked difference-in-differences design on 16 cyber incidents at small U.S. banks drawn from the Privacy Rights Clearinghouse database over 2005–2017. Attacked small banks experience a deposit growth rate roughly 22 percentage points lower than matched control banks in the two years following a breach, reflecting depositors’ loss of confidence in the targeted institution’s cybersecurity capacity. The deposit attrition is sharply stronger in counties with lower digital literacy, consistent with less-informed depositors placing disproportionate weight on a visible security failure. Deposit losses do not flow evenly to all competitors: positive spillovers accrue only to the dominant or largest banks in the local market, not to other small banks, concentrating market share toward large incumbents. Affected small banks subsequently attract riskier mortgage borrowers — proxied by higher loan-to-value ratios and lower FICO scores — suggesting that the deposit-cost pressure from a cyberattack induces yield-seeking behavior. The aggregate effect is a reduction in credit access for informationally opaque small borrowers that slows local small-establishment growth.

In depth

Q1. What is the identification strategy and what variation does it exploit?

The paper uses a stacked difference-in-differences design that stacks sub-experiments around each of the 16 cyberattack events, comparing the attacked small bank against a matched set of control banks in the same local market that did not experience a breach, with the event window centered on the quarter of the reported breach. The primary data source for cyber incidents is the Privacy Rights Clearinghouse (PRC) database, which records data breaches across industries; the paper restricts attention to incidents at U.S. commercial banks with total assets below a size threshold that classifies them as small. The stacking design allows each attack event to contribute its own two-by-two (pre/post, treated/control) comparison while controlling for time fixed effects across all events, which is important because cyberattacks cluster in certain periods. Identification relies on the parallel-trends assumption: absent the cyberattack, the deposit growth trajectory of the attacked bank would have evolved like that of matched local competitors. The paper validates this assumption with pre-trend tests and provides a battery of robustness checks including alternative matching procedures and excluding events that coincide with other bank-specific news.

Q2. How large is the deposit effect at attacked small banks and what is the direction of deposit flows?

Attacked small banks see deposit growth rates approximately 22 percentage points lower than control banks over the two years following a breach, a decline that is economically large relative to the unconditional mean deposit growth rate in the sample. The market-share impact is of the order of 1 percentage point lower for the attacked bank. Crucially, the deposit outflows do not disperse evenly to all rivals: the paper finds positive and statistically significant deposit spillovers only at the dominant large bank (or banks) in the local market, with no measurable increase at competing small banks. This asymmetric spillover is consistent with depositors fleeing to scale — perceiving large banks as having the technological resources and regulatory scrutiny to maintain cybersecurity — rather than simply seeking any alternative.

Q3. What role does digital literacy play in moderating the deposit response?

The deposit effect is significantly stronger in counties with below-median digital literacy, measured using population-weighted indices of internet connectivity and self-reported computer use from the American Community Survey, suggesting that less-digitally-literate depositors rely more heavily on observable security signals — such as a publicized breach — when assessing bank safety. In high-digital-literacy counties, the average customer may already have some prior belief about cyber risk across institutions and may discount a single breach as less informative, dampening the flight-to-quality response. In low-digital-literacy counties, the breach is a more salient and credibility-destroying event. The heterogeneity is quantitatively meaningful and survives controlling for MSA-level income, education, and urbanization.

Q4. How does the competitive position of large banks in the local market moderate the spillover?

When a large bank holds a dominant market position prior to the attack — measured by having a market share above the 75th percentile of the local deposit distribution — the positive deposit spillover to that large bank is more than 30 percentage points larger than in markets where large banks hold a weaker position, pointing to a flight-to-incumbency effect that operates on top of the flight-to-scale effect. This finding implies that cyberattacks on small banks are particularly concentrating in markets where large banks are already dominant: the attack accelerates an existing market-share gradient rather than creating a new one. The result has policy relevance for local banking market competition: communities that already have concentrated banking sectors are more exposed to structural concentration following cyber events.

Q5. Do deposit rates at attacked small banks rise or fall, and does this signal a funding-cost channel?

Deposit rate evidence in the working paper suggests that attacked small banks do not uniformly raise deposit rates to retain customers, which is consistent with the deposit outflows being driven by non-price concerns about security rather than competitive pricing, and which rules out a simple funding-cost-through-repricing mechanism. The absence of a strong deposit-rate increase at the attacked bank indicates that depositors are responding to a qualitative signal about the bank’s cybersecurity capacity rather than being price-insensitive. This matters for the economic interpretation: the mechanism is loss of depositor confidence rather than increased funding costs passed through from the attack’s direct remediation expenses.

Q6. What happens to the loan portfolio and borrower risk profile of attacked small banks after a breach?

In the post-attack period, affected small banks shift their mortgage originations toward riskier borrowers, with originations showing higher average loan-to-value ratios and lower average FICO scores relative to the pre-attack period and relative to control banks, consistent with yield-seeking behavior driven by the deposit-funding squeeze. This borrower-quality deterioration implies a second-order financial stability concern beyond the immediate deposit loss: attacked banks may take on more risk in the loan book at precisely the moment when their funding base is weakening. The evidence is thus consistent with a mechanism in which the cyberattack triggers a cascade — deposit loss → funding pressure → reach-for-yield → loan-quality deterioration.

Q7. What are the real-economy consequences at the local market level?

Counties that experience a cyberattack on a local small bank show lower subsequent small-establishment growth relative to control counties, measured using County Business Patterns data on establishments with fewer than 20 employees, consistent with reduced small-business credit availability as small banks contract lending. Large banks that absorb deposit inflows from the attacked institution do not offset this credit reduction: the deposit inflows do not translate into proportionate increases in small-business or small-mortgage lending, reflecting the well-documented diseconomy of scale in relationship lending by large institutions. The real-economy effect is concentrated in counties where small banks had a larger pre-attack share of local deposits and credit, consistent with the mechanism that the effect operates through credit-supply disruption rather than demand shocks.

Q8. How does this paper relate to the literature on bank runs and financial contagion?

Unlike classic bank-run models in which depositor withdrawals are self-fulfilling or triggered by sunspot-like coordination failures, this paper’s results suggest that cyberattacks constitute an information event that rationally updates depositors’ beliefs about the attacked bank’s technological competence, generating a fundamentals-based run on the specific institution rather than systemic panic. The results complement the emerging literature on cyber risk in financial institutions (e.g., Kashyap and Wetherilt 2019, Eisenbach et al. 2022) by documenting market-level spillovers and real effects beyond the attacked institution. The finding that large banks absorb deposits following attacks on small banks also connects to the “too-big-to-fail” literature by showing that size confers a competitive advantage in moments of localized financial stress.

Key Concepts

stacked difference-in-differences : an event-study design in which multiple treatment events are each assigned their own pre/post comparison window, the sub-experiments are then stacked into a single dataset, and pooled regressions with event-by-period fixed effects estimate the average treatment effect; used in this paper to exploit variation across 16 separate cyberattack events at small banks.

Privacy Rights Clearinghouse (PRC) database : a publicly available database of data-breach incidents across industries in the United States, which the paper uses as the primary source for identifying confirmed cyberattacks on commercial banks; the paper restricts to incidents classified as hacking or skimming rather than physical theft or accidental exposure.

deposit spillover : the increase in deposit inflows to competitor banks in the same local market following a cyberattack on a rival institution; in this paper, measured as the change in deposit growth at non-attacked banks relative to their own pre-attack trends.

flight-to-scale : the pattern in which depositors shift funds from smaller to larger banks following a cyber incident, driven by the belief that larger banks have superior cybersecurity resources; the paper documents that this flight benefits only the largest local bank rather than all large banks.

digital literacy : a county-level index measuring residents’ familiarity with digital technologies, internet access, and computer use; used in the paper to test whether depositor reactions to cyberattacks are stronger where depositors have less prior information about cyber risk.

reach-for-yield : the tendency of a bank with a weakened funding base to shift its loan portfolio toward higher-yielding, riskier borrowers to maintain net interest margins; documented in this paper as a behavioral response of attacked small banks in the post-breach period.

Financial Intermediation and Aggregate Demand: A Sufficient Statistics Approach

Mon, 01 Jan 0001 00:00:00 +0000

This paper develops a sufficient statistics approach to measuring the aggregate demand effects of financial intermediation disturbances — shocks to the ability of financial intermediaries to supply credit. The central contribution is characterizing, in a general class of models with heterogeneous firms and financial frictions, the aggregate demand impact of a disruption to intermediary balance sheets as a function of a small set of sufficient statistics observable from data: the elasticity of investment to intermediary net worth, the share of investment financed through intermediaries, and the sensitivity of asset prices to intermediary capacity. The approach does not require full model estimation, allowing model-free measurement of the aggregate demand loss from identified intermediary distress episodes. Applied to the 2008–2009 financial crisis, the paper estimates that the shock to financial intermediary balance sheets generated an aggregate demand reduction of 3–4 percentage points of GDP — substantially larger than estimates from reduced-form regressions that do not account for general equilibrium propagation.

In depth

Q1. What are the key sufficient statistics?

The three sufficient statistics are: (1) the elasticity of investment to intermediary net worth — how much investment falls per dollar of balance sheet loss; (2) the share of investment financed through intermediaries — how broadly the balance sheet shock propagates; (3) the sensitivity of asset prices to intermediary capacity — how much collateral values fall when intermediaries are distressed. Together these three moments summarize the aggregate demand impact of a balance sheet shock without requiring the researcher to specify the full structural model.

Q2. Why does the sufficient statistics approach give larger estimates than reduced-form regressions?

Reduced-form regressions typically compare investment of firms exposed to distressed versus healthy intermediaries, capturing the partial equilibrium direct effect of credit supply reduction; the sufficient statistics approach accounts for the general equilibrium propagation — the fall in asset prices and investment that affects even firms not directly borrowing from distressed intermediaries. The 3–4 percentage point estimate includes these spillovers; the reduced-form estimate misses them.

Q3. What is the policy implication?

The larger aggregate demand estimate implies that recapitalizing intermediaries during financial crises generates larger macroeconomic benefits than direct-effect estimates would suggest, strengthening the case for bank bailouts, TARP-style capital injections, and central bank emergency lending as counter-recessionary tools. The sufficient statistics framework also provides a natural way to compare intervention magnitudes: a policy that restores $X of intermediary capital generates an aggregate demand boost proportional to the measured elasticity.

Key concepts

sufficient statistics for financial intermediation : the small set of model-free moments (investment elasticity to net worth, intermediary financing share, asset price sensitivity) that summarize the aggregate demand impact of intermediary distress, derived in this paper from a general class of heterogeneous-firm models.

general equilibrium propagation : the amplification of an intermediary balance sheet shock through asset price declines and economy-wide investment responses, which the sufficient statistics approach captures and reduced-form regressions miss; the source of the larger 3–4 pp GDP estimate relative to partial equilibrium benchmarks.

How Bad Are Weather Disasters for Banks?

Mon, 01 Jan 0001 00:00:00 +0000

Using FEMA disaster declarations matched to SHELDUS property-damage estimates and Call Report data for 1995–2018, this paper finds that weather disasters — even at their most severe — have had modest effects on U.S. bank safety over the last quarter century. For single-county banks exposed to 95th-percentile disasters, Z-scores decline by roughly 9 percent at a five-year horizon under the panel estimates; reaching failure thresholds from sample mean Z-score levels would require a disaster approximately 6.7 standard deviations more destructive than a 95th-percentile event. Federal disaster aid does not appear to be the primary driver of this resilience, since banks exposed to weather events without FEMA declarations exhibit similar stability. Instead, the paper points to a loan demand channel — multi-county bank lending increases roughly 0.25 percentage points per standard deviation of damage at five years without an accompanying interest-rate increase — and to local banks’ apparent avoidance of mortgage lending in flood-prone areas beyond what official flood maps predict, consistent with local information about true flood risk limiting exposure before disasters strike.

In depth

Q1. How severe are weather disaster effects on bank safety?

The paper finds that weather disasters at any severity level produce small and often statistically insignificant effects on the key bank safety measures — charge-offs, capital ratios, return-on-assets volatility, and Z-scores — at single-county banks, with the largest measured effect being roughly a 9 percent decline in Z-scores at the 95th percentile of disaster damage at a five-year horizon. The regression framework uses bank and state-year fixed effects, with SHELDUS damage as the continuous severity measure and FEMA disaster declarations as a binary indicator. For multi-county banks, charge-offs increase by roughly 10 percent at five years, but net income also rises, suggesting disaster-area loan demand partially offsets credit losses. The paper’s calculation is that pushing a typical bank from its mean Z-score of 135.9 to the failure threshold would require a Z-score decline of 127.9 — far exceeding the estimated −9 percent impact of a 95th-percentile disaster, which would need to be approximately 6.7 standard deviations more destructive to close that gap.

Q2. Is bank resilience an artifact of federal disaster aid?

The paper presents evidence that federal disaster aid is not the primary source of bank resilience, since banks exposed to weather events that did not receive FEMA disaster declarations exhibit similarly modest effects on bank safety measures. The test is designed to separate the insurance mechanism (FEMA aid replacing household income and debt service capacity) from intrinsic bank resilience. The fact that non-FEMA disasters produce comparable stability redirects attention to the demand-side and local-knowledge channels as the more fundamental explanations for the resilience finding.

Q3. What is the loan demand channel and how large is it?

Multi-county banks experience an increase in lending of roughly 0.25 percentage points per standard deviation of SHELDUS damage at a five-year horizon, and the authors find no accompanying increase in loan interest rates, which is consistent with a demand-side shift rather than a tightening of lending standards. The demand interpretation is that disasters create a wave of borrowing demand as households and firms repair or replace damaged assets, and the increased loan volume helps offset the increase in charge-offs. The pattern is found at multi-county banks — which can serve affected and unaffected areas simultaneously — but not at single-county banks, consistent with lending capacity mattering for capturing the demand increase.

Q4. What does “local knowledge” mean in this context?

Local banks originate approximately 6.4 percent fewer log mortgage dollars per application in FEMA flood zones than would be predicted by the official flood map classifications alone, with the gap widening to 7–8 percent in areas that have experienced more than five FEMA flood declarations compared to areas with fewer than three, which is consistent with local lenders holding information about true flood risk not captured in official maps. The finding is consistent with local banks having access to community-level information — observed flooding history, property-level characteristics, local drainage and elevation — that is not incorporated into official FEMA flood zone classifications. This pre-disaster selectivity limits mortgage accumulation in the highest-risk areas before disasters occur.

Q5. What are the implications for climate risk assessment?

The paper explicitly frames the historical resilience documented for 1995–2018 as informing rather than settling assessments of physical risk to banks from future climate change, since more frequent or more severe disasters could overwhelm the demand-offset and local-knowledge mechanisms that the paper identifies as sustaining bank performance. The key qualification is temporal scope: the demand-side recovery effect requires that affected areas have the income and economic capacity to service new loans, and the local-knowledge effect requires that banks have experienced enough repeated flooding to develop accurate private flood risk assessments. Both conditions could become less reliable as climate change alters the frequency, geography, and severity of weather events relative to the historical distribution.

Key concepts

Z-score : a bank-level distance-to-insolvency measure equal to (return on assets + capital ratio) divided by return-on-assets volatility; higher values indicate greater distance from failure; used here as the primary measure of disaster impact on bank safety.

SHELDUS : the Spatial Hazard Events and Losses Database for the United States, providing county-level property damage estimates for weather events; used in this paper as the continuous measure of disaster severity in panel regressions.

single-county bank : a bank whose entire depositor base is drawn from one county, making it fully exposed to local disaster effects with no geographic diversification across other counties.

loan demand channel : the mechanism by which disasters increase demand for credit from households and firms repairing or replacing damaged assets, generating new loan volume that partially offsets credit losses at banks serving affected areas.

local knowledge : the paper’s label for the informational advantage that local banks appear to have about true flood risk beyond what official FEMA flood zone classifications capture, inferred from lower mortgage originations in areas with a history of repeated flooding.

Income Inequality and Job Creation

Mon, 01 Jan 0001 00:00:00 +0000

The paper establishes a causal link from rising top income shares to reduced net job creation at small firms, working through a bank funding channel rooted in non-homothetic household portfolio allocation: because high-income households hold a smaller fraction of financial wealth in bank deposits (less than one-fifth for the top decile versus two-thirds for the bottom quintile, per the Survey of Consumer Finance), a redistribution of income toward top earners shifts aggregate saving away from deposits toward stocks and bonds. Banks must raise deposit rates to retain funding, which passes through to loan rates; since small, informationally-opaque firms depend disproportionately on bank credit while large firms have direct capital-market access, higher loan rates compress small firms’ net job creation relative to large firms. Using U.S. state-level panel data from 1981 to 2015, a shift-share instrumental variable, and a quantitative general equilibrium model, the paper documents this channel and finds it accounts for 13% of the 4.97 percentage-point rise in large-firm employment share and between 7.5% and 15% of the decline in the labor share since 1980.

Motivating facts (Section 2):

The U.S. net job creation rate of small firms (1–499 employees) declined from roughly +4% in 1980 to near 0% by 2015 and co-moves strongly with the top 10% income share (Figure 1a), suggesting a systematic relationship
SCF data show that the deposit share of financial wealth falls monotonically with income: bottom quintile (Q1) ≈ 65–70%; middle quintile ≈ 45%; top decile < 20% (Figure 2a). Non-financial wealth and stocks/bonds rise sharply with income
FDIC data show deposits account for 93% of total liabilities for the average bank and 75% of total liabilities on aggregate (Figure 2b); average bank raises 98% of deposits in its headquarters state (capital-weighted: 89%), so local deposit supply directly constrains local bank credit

Empirical specification (Section 3): Panel regression at the state–firm-size–year level, 47 states, 1981–2015, 16,435 observations. Dependent variable: net job creation rate (JCR − JDR). Key regressor: interaction of the top 10% income share with a “small firm” dummy (firms 1–499 vs. 500+). Regression includes state–firm-size fixed effects and state–time fixed effects, the latter absorbing all time-varying unobservable state-level factors common to firms of different sizes (e.g., globalization, technology). Identification via a pre-determined share IV: each state’s top 10% income share in 1970 (ten years before the sample) interacted with the leave-one-out national trend in top income shares — exploiting cross-state variation in sensitivity to the aggregate national trend while isolating it from local cyclical conditions.

Empirical results (Table 1, Table 2):

IV estimate: a 10 percentage-point rise in the top 10% income share reduces the relative net job creation rate of small firms by 1.2 percentage points (Table 1, col. 3)
Extensive margin (entry, exit, private-to-public transitions): accounts for approximately 20% of the 1.2pp effect (Table 1, col. 4)
One standard deviation higher top income share (5.4pp) → 0.7pp lower small-firm net JCR (Figure 1b, binned scatter OLS preview)
Counterfactual: had the U.S. top 10% income share remained at its 1980 level (instead of rising ~16pp from 34.5% to 50.5%), small firms’ net job creation rate would be 1.9 percentage points higher — more than 50% above its 2015 level
Bank-level regressions (Table 2): rising top income shares in a bank’s headquarters state lead to higher deposit rates and lower total deposit volumes — consistent with banks raising rates to retain a declining deposit supply

Model (Section 4): General equilibrium model with two types of households and two types of firms. Households differ by income group (high, H, and low, L), each endowed with heterogeneous productivities {si,χ}; households choose consumption, labor supply, and portfolio allocation between bank deposits (providing liquidity services captured by a CES deposit utility term ψd·η) and direct capital investment in public firms. Non-homotheticity: the deposit utility weight is calibrated so high-income households hold fewer deposits per unit of wealth. Firms are either public (large, direct capital-market access, production function with capital share θ and returns to scale γ) or private (small, bank-dependent; labor-only production with bank working capital constraint ϕ̃ governing the loan demand; entry/exit governed by stochastic fixed cost f̃ ~ U[0,f̃max] and a cost of going public κ ~ U[0,κ̃max]). Banks intermediate deposits into loans at a fixed cost, implying a zero-profit loan rate above the deposit rate.

Calibration (Table 3): Two panels:

Panel (a) externally fixed: capital depreciation rate (NIPA), mean US stock market return = 1.08, top 10% income share target = 34.6% (initial, Frank 2009 data), deposit rate = 4% (national average)
Panel (b) internally calibrated to BDS and SCF (early 1980s):
- Labor supply to public firms = 46.9%; private firms = 53.1% (BDS baseline)
- Labor demand to public firms = 46.9%; private firms = 53.1% (matched exactly)
- Deposit share of Q3 household = 0.45; top 10% deposit share = 0.22 (SCF)
- Household discount factor β = 0.9182; deposit utility scale ψd = 0.0632; deposit utility elasticity η = 2.6096
- Capital share in public firms θ; returns to scale γ set to match labor demand targets
- Firm productivity SD σz = 0.0315; bank dependence ϕ̃ and fixed cost bound f̃max matched to Table 1 empirical estimates (intensive and extensive margin); public-share cost bound κ̃max matched to share of firms >500 employees (BDS)

GE experiment (Section 6): Top 10% income share raised permanently from 34.5% to 50.5%, matching Frank (2009) data evolution, via lump-sum transfers from low- to high-income households (holding average income constant to isolate the portfolio reallocation channel). Key aggregate outcomes (Figure 3):

Aggregate deposits fall by more than 2%; savings flow into public firm capital, which rises 2% — the portfolio reallocation effect in levels
Deposit rate rises 0.4pp; loan rate rises 0.7pp; public firm capital return falls 0.14pp — consistent with bank-level empirical estimates
Private firm employment falls ~2%; public firm employment rises ~1%; aggregate employment falls modestly
Private firm employment share falls 0.64 percentage points — the channel explains 13% of the actual 4.97pp BDS decline in employment at firms below 500 employees (1980–2015)
Around one-fifth of the employment share decline comes from the extensive margin (private firm exit and transitions to public status), matching the empirical ratio
Labor share falls 0.3pp, explained by public firms growing relatively larger and being more capital-intensive; this accounts for 7.5% to 15% of the observed 2–4pp decline in the US labor share
Aggregate output falls 0.3%, driven by resource reallocation: private firms have marginal product of labor roughly one-sixth higher than public firms (consistent with the higher small-firm net JCR coefficient), so shifting employment to public firms suppresses aggregate productivity

Welfare effects (Section 6.2, Figure 4): The top 10% experience an increase in consumption-equivalent welfare; bottom 90% experience a decrease. The full model amplifies both effects relative to a counterfactual model with fixed portfolio shares: portfolio reallocation raises top-earner welfare by an additional ~1% (consumption equivalent) relative to the fixed-share benchmark and lowers bottom-earner welfare by ~1% — because in the full model, private firm wages fall (loan rate rise reduces labor demand) while in the fixed-share benchmark private firm wages rise (tops save more deposits, lowering loan rates). Ignoring portfolio heterogeneity thus significantly understates the welfare consequences of income redistribution.

Scope conditions: The mechanism operates through portfolio reallocation only; the paper holds average income constant (lump-sum redistribution) to isolate the channel, abstracting from any direct effects of rising incomes on aggregate savings rates. The IV exploits state-level variation in top income shares; cross-state spillovers in bank credit markets would attenuate estimated coefficients. The model assumes banks cannot replace lost deposits one-for-one with non-deposit liabilities, consistent with institutional frictions documented in the banking literature (Stein, 1998; Hanson et al., 2015). The analysis covers pre-tax income shares; post-tax redistribution through the tax code would dampen the mechanism.

In depth

Q1. Why does the portfolio composition of saving matter more than the aggregate savings rate?

The key non-homotheticity is in the composition of saving, not the level: high-income households allocate less than one-fifth of financial wealth to bank deposits while low-income households allocate two-thirds; as income shifts to the top, total deposits decline even if aggregate saving rises modestly. Banks cannot substitute deposit funding with non-deposit liabilities without cost — deposits provide cheap, stable funding because of their unique liquidity and monitoring properties (Stein, 1998; Hanson et al., 2015). An increase in the deposit rate is thus the equilibrating mechanism: banks must bid deposits back from higher-return assets, and the higher funding cost passes through to loan rates.

Q2. Why are small firms disproportionately harmed by higher loan rates?

Small, informationally-opaque firms rely on bank credit for external finance — 92% of small firms in the 1993 National Survey of Small Business Finances use bank loans — while large public firms can raise equity and bonds directly, bypassing banks entirely. When loan rates rise, small firms face a tighter credit constraint on their working capital and fixed costs of operation; the higher loan rate simultaneously reduces their demand for bank credit and raises the value of exiting or transitioning to public status (reducing the private-firm fixed cost burden). Large firms, by contrast, experience lower financing costs as the capital return falls and equity markets absorb more saving — amplifying the relative job creation gap.

The IV uses each state’s top 10% income share in 1970 — ten years before the sample begins, when income shares were flat nationally — interacted with the leave-one-out national trend; any factor driving both job creation outcomes and income inequality in a state would need to have affected firms of different sizes within that state in the same direction as the national trend, while also having had no such effect in all other states. The instrument’s validity rests on: (i) national income share trends after 1980 being driven by aggregate forces (technology, globalization) exogenous to any single state’s labor market; (ii) the pre-1980 period showing no systematic co-movement between state income shares and subsequent employment trends; and (iii) robustness to excluding industries that account for a large share of a state’s employment (Table OA4).

Q4. What explains the aggregate output decline when private firms have higher marginal products?

The output decline of 0.3% arises because the reallocation from private (higher marginal product) to public (lower marginal product) firms outweighs the positive capital accumulation effect: as more saving flows into public firm equity/capital, output would rise, all else equal — but the capital stock increase is modest and aggregate savings rise only slightly, so the dominant effect is misallocation. The marginal product gap between private and public firms is not an assumption of the model but a calibration consequence: matching the empirical estimate that small firms’ net JCR responds more to loan rate changes (Table 1) requires their marginal product to be higher, generating the misallocation loss when resources shift toward large firms.

Q5. How does rising inequality amplify its own effect through welfare and further portfolio reallocation?

In the full model with heterogeneous portfolios, the redistribution from low- to high-income households directly reduces aggregate deposits (because the recipients hold fewer deposits per dollar), which raises deposit and loan rates, which lowers wages at private firms, which further reduces low-income households’ labor income. This GE feedback loop — portfolio composition → bank rates → wages → income distribution → portfolio composition — amplifies the initial redistribution effect by approximately 1 percentage point of consumption-equivalent welfare compared to a model in which households are forced to hold fixed portfolio shares. In the fixed-portfolio model, tops invest more in deposits when they receive transfers, partially offsetting the deposit supply decline, and private firm wages rise — the opposite of the full model.

Q6. What fraction of US macroeconomic trends since 1980 can the channel explain?

The channel accounts for 13% of the 4.97pp rise in large-firm employment share, 7.5–15% of the 2–4pp fall in the aggregate labor share, and a 0.3% output loss from resource misallocation — meaningful but partial contributions to trends that are multi-causal. The partial contributions reflect that rising income inequality is one of several forces driving these trends (technology adoption, trade, market concentration, capital-skill complementarity); the paper explicitly abstracts from these other forces by using lump-sum transfers that hold average income constant, isolating the portfolio reallocation channel alone.

Q7. What happens to firm entry and exit under rising inequality?

A higher loan rate raises the effective cost of operating as a private firm (working capital is more expensive), reducing the threshold productivity level below which private firms exit and raising the threshold above which private firms find it worthwhile to incur the IPO-type cost of going public; both margins reduce the number of private firms in equilibrium, consistent with declining business dynamism. The model implies approximately one-fifth of the employment share decline at small firms comes from this extensive margin — closely matching the data decomposition from the BDS — and the public firm share rises by 0.003pp, consistent with the small but positive trend in the share of large-firm establishments observed in the data.

FDIC data show deposits represent 93% of average bank liabilities and 75% of aggregate bank liabilities; banks rely on their headquarters-state deposit base for the vast majority of funding because regulatory and institutional frictions constrain inter-state deposit gathering — even the four largest US banks (JP Morgan, Citi, Wells Fargo, Bank of America) raise over 70% of deposits in their headquarters state. The literature (Stein, 1998; Jakab and Kumhof, 2015) establishes that deposits provide uniquely stable, cheap funding that cannot be replaced at equivalent cost by wholesale liabilities or interbank borrowing; any substitution requires costly premium over the deposit rate, implying the attenuation bias if anything understates the true causal effect on loan rates.

Key concepts

non-homothetic deposit preference : the empirical regularity that the share of financial wealth allocated to bank deposits declines with income — two-thirds for the bottom quintile, under one-fifth for the top decile; this non-homotheticity means that a mean-preserving income redistribution toward top earners reduces the aggregate deposit supply relative to total saving, the paper’s foundational portfolio channel.

pre-determined share IV : the paper’s instrumental variable for state-level top income shares: each state’s 1970 top 10% income share interacted with the leave-one-out national trend in top 10% shares; identifies causal effects by exploiting differential state sensitivity to national inequality trends, purged of local cyclical factors and large-firm wage premia.

private versus public firm : the model’s key firm heterogeneity; private firms are small, bank-dependent (working capital constrained), and pay fixed operating costs; public firms are large, equity-financed, and face no bank credit constraint. The intensive-margin effect of higher inequality (rising loan rates) and extensive-margin effect (higher exit rates, more IPO transitions) both compress the private firm employment share.

deposit rate pass-through : the mechanism by which a decline in aggregate deposit supply forces banks to raise deposit rates to retain funds; the higher deposit rate is passed through to loan rates via the bank’s zero-profit condition, raising the cost of credit for bank-dependent private firms by approximately twice the deposit rate increase (0.7pp loan rate rise for 0.4pp deposit rate rise in the model).

business dynamism channel : the extensive margin of the paper’s mechanism — rising top income shares increase loan rates, which increase private firm exit rates and the rate of private-to-public firm transitions, reducing firm entry and contributing to documented trends of falling startup rates and declining business dynamism in the US since 1980.

Outsourcing bank loan screening: The economics of third-party loan guarantees

Mon, 01 Jan 0001 00:00:00 +0000

Third-party loan guarantees—in which a fee-charging guarantor formally guarantees a bank loan and conducts its own due diligence on the borrower—are present in approximately 10% of Chinese bank loans and are required for most small and medium enterprise (SME) financing. This paper investigates their economic function using proprietary data from a large private loan guarantee firm and interviews with market participants. The paper systematically tests and rejects two leading alternative hypotheses: that guarantees circumvent interest rate caps via regulatory arbitrage (rejected because total loan payments never approach the cap in the data), and that guarantees primarily induce borrowers to self-select based on creditworthiness. The positive evidence points instead to a “second level of delegation of loan evaluation”: guarantors have private information about borrower quality beyond hard accounting data and collateral, screen bad loans effectively, and their pricing is consistent with the outsourced screening interpretation. This framework is analogous to Diamond’s (1984) delegated monitoring, but prior to loan origination rather than after.

In depth

Q1. Why are third-party loan guarantees prevalent in China’s SME lending market?

During the paper’s sample period, third-party loan guarantees are essentially a prerequisite for most SME loans in China, arising because banks face high costs of evaluating small borrowers with limited collateral and opaque financials; guarantors specialize in gathering soft information through site visits and examination of borrower books. The loan guarantee industry consists of a few large firms and many smaller ones; in exchange for a fee paid by the borrower and a pledge of collateral to the guarantor, the guarantor provides a formal credit guarantee to the lending bank. This is a transactional (not relationship-based) business.

Q2. How do the data reject the regulatory arbitrage and self-selection hypotheses?

The regulatory arbitrage hypothesis (that guarantees allow total payments to exceed the interest rate cap) is rejected cleanly because none of the loans in the sample have a total payment—interest plus guarantee fee—at or near the interest rate cap. The self-selection hypothesis (that guarantees induce only high-quality borrowers to apply, as in Thakor 1982) is rejected because: (i) only a small fraction of applications are accepted, suggesting the guarantor and bank do most of the selection rather than borrowers self-selecting; and (ii) the data show guarantors successfully screen out bad loans using private information, inconsistent with the borrower being the primary information-bearing party.

Q3. What is the positive evidence for the outsourced screening interpretation?

The guarantor has information about borrowers beyond hard accounting data and collateral—gathered from site visits and business-record examinations—and this private information is reflected in its risk assessments, which predict loan performance independently. Pricing of loans and guarantees in the data is consistent with a model in which the guarantor’s fee reflects its risk assessment (its private signal about borrower quality), and the bank’s interest rate reflects the residual risk after conditioning on the guarantor’s approval. A key empirical finding is that the correlation between bank loan rates and guarantor risk assessment is negative—when rates were higher in the economy, banks lent to safer credits, because high rates correlate with scarce credit in the sample—a pattern consistent with screening rather than adverse selection.

Q4. What are the broader implications for SME finance and financial intermediation theory?

The paper interprets third-party loan guarantees as a “second level of delegation of loan evaluation”—analogous to Diamond’s (1984) delegated monitoring (which occurs after loan origination) but occurring before—suggesting that the guarantor occupies a specialized information-gathering role that banks cannot efficiently internalize. This outsourcing of screening is potentially a more efficient organizational form than either direct bank screening (if banks face higher per-borrower costs) or government guarantees (which lack the performance incentives of private guarantors). The contrast with the CDS market (where AIG-style guarantors performed no serious checking or hedging) underscores that private guarantors with proper incentives can perform meaningful screening.

Key concepts

third-party loan guarantee : a contractual arrangement in which a fee-charging private guarantor formally guarantees a bank loan and bears the credit risk if the borrower defaults, having conducted independent due diligence on the borrower; the paper shows this functions as outsourced screening.

delegated screening (pre-loan) : the paper’s interpretation of the guarantor’s role as a second layer of delegation of loan evaluation before origination, analogous to Diamond’s (1984) delegated monitoring after origination; the guarantor has a comparative advantage in gathering borrower-specific soft information.

Private Information and Price Regulation in the US Credit Card Market

Mon, 01 Jan 0001 00:00:00 +0000

The 2009 Credit Card Accountability Responsibility and Disclosure (CARD) Act barred credit card lenders from discretionarily raising borrowers’ interest rates in response to new information. Using the near-universe of US credit card account data (covering roughly 90% of outstanding general-purpose balances across 17–19 large and midsize issuers) together with a large panel of consumer credit reports, the paper documents that the class of rate increases restricted by the Act affected over 50% of borrowing accounts annually before the Act; incidence dropped to nearly zero afterward, and the interquartile range of interest rates on newly-mature accounts compressed immediately by one-third. The paper then estimates a structural model of the credit card market featuring differentiated lenders competing à la Bertrand, consumers with dynamic discrete choice over lenders and borrowing status, private information types identified from equilibrium pricing, and flexible correlation between demand and risk. Imposing the Act’s restrictions in the estimated model reveals that consumer surplus rises at all credit scores — by roughly $600 for subprime and over $1,000 for prime and superprime consumers — despite partial market unraveling among the deepest subprime accounts. The net surplus gains are driven by two forces: (1) a fall in lender markups (pre-CARD-Act median-risk subprime markups exceeded 40 percent), and (2) the insurance value of rate lock-in for borrowers whose credit risk deteriorates over time. Counterfactual analysis shows that if pre-CARD-Act markets had been perfectly competitive (zero markups), the Act would have induced complete market unraveling and consumer surplus would have fallen by $100–$600 per consumer.

In depth

Q1. What did the CARD Act restrict, and how large was the pre-Act practice it curtailed?

Before the CARD Act, lenders raised borrowers’ interest rates in response to new information about risk and demand on 48–54% of borrowing accounts at least once per year; the Act’s repricing restrictions drove this incidence to nearly zero and immediately compressed the interquartile range of interest rates across accounts within a credit-score tier from about 7.5 percentage points to about 5 percentage points — a one-third reduction in price dispersion. The pricing of emergent risk (risk revealed after account opening) was nearly indistinguishable from the pricing of origination risk before the Act (both approximately 30 basis points per 10-point FICO difference); after the Act, emergent risk was priced at only about 7 basis points per 10 FICO points — less than one-third as much — while origination risk remained priced at 26 basis points.

Q2. How are private information types identified and what do they reveal about adverse selection?

Private information types ψ are identified from the equilibrium pricing of mature accounts: the paper exploits portfolio-wide repricing events (where a lender raises rates on all accounts in a segment simultaneously) as quasi-experimental price variation to estimate price sensitivities γ via 2SLS, then inverts the lender first-order conditions to recover residual default-risk types orthogonal to observed credit scores. The Chiappori–Salanié bivariate probit test on simulated borrowing choices and default outcomes confirms adverse selection on new accounts: the estimated correlation of unobservables is 0.104, and within each credit-score group the correlation between private type and borrowing utility is at least 0.4 — meaning the highest-risk private types also have the strongest demand for credit.

Q3. What does the structural model say about markups and their source?

Estimated pre-CARD-Act markups exceed 40% for the median-risk subprime consumer; the high markups reflect a combination of switching costs (setup costs κ averaging $86.5–$89.7 per account for the lowest FICO groups), high borrowing utility δ among high-risk types (averaging 22.9 at FICO 580–599, falling to 3.3 at FICO 780–799), and lenders’ exploitation of private information about which borrowers have low price sensitivity. Setup costs are substantially larger than liquidity costs (costs of paying off existing balances), consistent with prior findings on adjustment costs in financial markets; new-account acquisition costs are increasing in credit score, consistent with lenders making larger offers to attract higher-quality borrowers.

Q4. What happens to prices under the CARD Act restrictions, and why does partial unraveling occur only for deep subprime?

Among deep subprime (FICO 580–599), the Act induces near-complete pooling at roughly 50% APR annualized for all private types; the safest private types exit the market as they are pooled with riskier peers, whose higher costs push prices further up — a textbook Akerlof unraveling spiral — and over 30% of the privately safest subprime borrowers face prices that newly exceed their willingness to pay. At higher credit scores (e.g., FICO 680–699), nearly all private types face lower prices because the mark-up compression dominates; at FICO 780+, all private types face lower or unchanged prices. Average traded prices fall at all credit score levels because: (1) consumers who exit were paying lower prices than those who stay, and (2) borrowers locked into favorable rates retain them as their types worsen.

Q5. What are the consumer surplus gains, and what drives them?

Consumer surplus rises at all credit scores — by approximately $600 for subprime consumers and by over $1,000 for prime and superprime consumers — with the gain coming from two sources of roughly equal magnitude: markup compression (a transfer from lender profits to consumer surplus) and the insurance value of rate lock-in for consumers whose default risk deteriorates. The insurance channel is most important for superprime borrowers, who are most likely to lock in favorable pricing and to experience type migration over the life of a relationship; the direct pecuniary markup gains dominate for subprime borrowers. Even absent any insurance value, the surplus gains from markup compression alone (a few hundred dollars) are comparable to prior reduced-form estimates of the Act’s price effects.

Q6. Why were high pre-CARD-Act markups necessary for the surplus gains?

In a counterfactual where pre-CARD-Act markets are perfectly competitive (marginal-cost pricing), imposing the CARD Act restrictions causes complete market unraveling: all prices exceed 150% APR, virtually no consumers borrow, and surplus per consumer falls by $100–$600 depending on credit score. With only modestly higher price sensitivity (one bootstrapped standard error larger than the point estimate), unraveling occurs at all credit score tiers and total surplus falls throughout the market. The intuition is that with zero markups, lenders have no cushion to absorb adverse selection costs; restricting risk-based repricing immediately makes lending unprofitable for any pooled price, triggering exit cascades that the pre-CARD-Act markup buffer prevented.

Q7. What is the adverse retention finding and its magnitude?

After the Act, lenders face adverse retention on mature accounts: for every 100 basis points by which emergent risk is priced below origination risk, the quarterly hazard of attrition from borrowing falls by 0.7 percentage points — meaning newly risky borrowers are less likely to leave while newly safe borrowers are more likely to leave. This is precisely the dynamics consistent with Akerlof adverse selection: the Act’s restriction on emergent-risk pricing reduces the signal lenders can use to retain safe borrowers selectively, degrading the quality of each lender’s continuing portfolio.

Q8. What private information rents existed before the Act?

Before the CARD Act, lenders exploited private information about borrowers’ demand characteristics: accounts that engaged in over-limit transactions or brief delinquencies (less than 30 days late) received median price increases of 6.9 and 15.5 percentage points annualized respectively, and 12-month revenue yields on these accounts rose by over 50% relative to baseline accounts, generating sustained higher returns despite higher charge-off risk. The revenue yield increase is not transitory; it persists for at least 12 months, and the returns figures confirm these behaviors reveal not just higher costs but also lower price sensitivity — enabling lenders to extract information rents.

Key concepts

private information type (ψ) : in the model’s notation, a consumer’s residual risk and demand characteristic that is known to the consumer and revealed to the incumbent lender over time through account behavior, but not observed by competing lenders or at the time of account opening; identified from equilibrium pricing using the assumption of price-invariant default.

emergent risk : default risk that becomes observable to the lender after account origination, in contrast to origination risk visible at account opening; the CARD Act restricted repricing in response to emergent risk while leaving origination-risk pricing unrestricted.

adverse retention : the post-CARD-Act phenomenon in which borrowers who become higher-risk are less likely to attrite (because their pricing is not raised) while borrowers who become lower-risk are more likely to leave (because their favorable pricing is no longer reinforced); the paper estimates a 0.7 percentage point drop in quarterly attrition hazard per 100 basis points of under-pricing of emergent risk.

insurance value of rate lock-in : the welfare benefit to consumers from knowing that an adverse draw of their risk type will not trigger a price increase; quantified by comparing actual surplus gains with gains in a counterfactual where types are perfectly persistent (eliminating the demand for insurance); roughly equal in magnitude to the direct markup compression gains.

market unraveling : the Akerlof-style exit spiral in which pooling raises prices, inducing exit by low-risk types, which raises the average cost of the remaining pool, which raises prices further; in the paper’s estimates this is severe only among deep subprime (FICO below 620) and would have been universal had pre-CARD-Act markups been zero.

Production and Financial Networks in Interplay

Mon, 01 Jan 0001 00:00:00 +0000

This paper provides the first integrated empirical analysis of how bank credit supply shocks propagate through both the production network and the financial network simultaneously, using the universe of firm-to-firm VAT transactions and bank-firm credit register data for Spain during the 2008-09 global financial crisis. The theoretical framework, following Bigio and La’O (2016), links credit supply shocks to price distortions in the real economy and derives network-mediated propagation effects. The central empirical finding is that propagation through the production network triples the impact of direct bank credit shocks: a negative bank shock induces a 0.98 percentage point reduction in the directly affected firm’s purchases and sales growth, while first-order network effects add another 0.91 pp and higher-order network effects add 1.07 pp, for a combined indirect effect equal to twice the direct effect. Both upstream and downstream propagation are economically significant and of similar magnitude at the first-order level. Market concentration amplifies all propagation effects, and firms that are simultaneously central in both the production and financial networks generate disproportionately large aggregate contractions.

Layer 1: Overview

Huremovic, Jimenez, Moral-Benito, Peydro, and Vega-Redondo study how financial shocks originating in the banking sector propagate through interlinked production and financial networks, exploiting Spain’s administrative registers covering essentially the complete production and credit networks of the Spanish economy during the 2008-09 crisis. The Spanish data are unique: approximately 4.3 million VAT firm-to-firm transactions (above a €3,005 threshold) covering 245,000 firms, matched with 1.68 million bank-firm loans from 206 active banks. Bank credit supply shocks are identified using the Khwaja-Mian (2008) / Amiti-Weinstein (2018) approach — isolating bank-level credit supply variation by conditioning on firm-time fixed effects across firms with multiple bank relationships — and cross-validated using banks’ pre-crisis interbank market exposure. The paper’s main contribution is to show that treating production and financial networks separately understates the real effects of financial shocks by a factor of three: the combined direct and indirect (network-mediated) effects are three times the direct bank shock effect alone. First-order and higher-order downstream effects are both quantitatively significant, while upstream propagation is strong at first order but attenuates at higher orders.

In depth

Q1. How does the paper identify bank credit supply shocks, and what makes Spain’s administrative data unusual?

Bank credit supply shocks are identified using within-firm variation across bank relationships — the Khwaja-Mian/Amiti-Weinstein approach — which partials out all firm-level credit demand variation by including firm-time fixed effects, isolating the supply component of each bank’s credit change during the 2008-09 crisis. Spain is particularly suited for this analysis for two reasons. First, it is a bank-dominated economy with minimal shadow banking, so bank credit is the primary external financing channel and the credit register is comprehensive (capturing all loans above €6,000). Second, around 75% of credit comes from firms with at least two banking relationships, enabling the within-firm identification. A complementary shock measure based on banks’ pre-crisis reliance on interbank funding — a market sharply disrupted by the Lehman failure — yields similar results and does not require multi-bank relationships. Crucially, both shock measures show effects that are significant during the 2008-09 crisis but not in the pre-crisis year 2007, consistent with the shocks being crisis-specific supply disruptions rather than pre-existing trends.

Q2. What are the direct effects of bank credit supply shocks on firm-level real outcomes?

At the link (firm-to-firm) level, a direct negative bank credit supply shock to a supplier reduces the purchasing firm’s growth in purchases from that supplier by 3.7 percentage points (29% of the median purchase growth), while a shock to a customer reduces the supplier’s sales growth to that customer by 5.1 percentage points (37% of median sales growth). At the firm level, aggregating across all suppliers and customers, direct bank shocks reduce employment growth by 0.41 percentage points (41% of the median) and investment growth by 0.55 percentage points (9% of the median), consistent with the existing bank lending channel literature. Negative bank shocks also affect total credit availability at the firm level, including trade credit, indicating that the transmission operates through multiple channels and not only through the reduction in direct bank credit.

Q3. How large are the first-order and higher-order network propagation effects, and how do they compare to direct effects?

The first-order indirect effects — propagation from direct customers and suppliers — are of comparable magnitude to the direct bank shock effects: a negative bank shock to all direct suppliers generates a 2.3 pp reduction in firm purchases, while a shock to all direct customers generates a 1.9 pp reduction in sales, both comparable to the 0.98 pp direct effect on purchases and sales combined. Higher-order downstream effects (shocks to suppliers of suppliers) are also quantitatively important at approximately 2.0 pp, similar in magnitude to first-order downstream effects. In contrast, higher-order upstream propagation is weak — only first-order customer shocks matter for upstream transmission. This asymmetry is consistent with the theoretical model’s prediction that upstream propagation is non-linear in shock magnitude, attenuating more rapidly at higher orders than downstream propagation. In aggregate, the combined direct plus first-order plus higher-order effects triple the direct effect: the overall reduction in purchases and sales growth is approximately three times the direct bank shock effect alone.

Q4. What is the symmetric finding on upstream versus downstream propagation, and why does it matter?

Upstream and downstream propagation at the first-order level are of similar magnitude — a negative bank shock induces a 3.7 pp contraction in purchases (downstream, from the shocked supplier to the buying firm) and a 5.1 pp contraction in sales (upstream, from the shocked customer to the selling firm) — challenging the prior literature’s assumption that production network propagation is predominantly downstream. The comparable magnitudes of upstream and downstream propagation imply that financial shocks hitting customers matter for suppliers almost as much as financial shocks hitting suppliers matter for customers. The model provides the analytical basis for this result: downstream propagation is linear in shock magnitude (input supply contraction is passed through proportionally), while upstream propagation is non-linear (demand shortfalls at the customer do not fully translate into supply contraction from the supplier if the customer can be substituted). The near-symmetry at first order, however, means that both channels must be modeled for accurate aggregate impact assessment.

Q5. How does market concentration amplify financial shock propagation?

Firms operating in more concentrated markets — proxied by sectoral market concentration — experience stronger propagation both upstream and downstream; firm-to-firm propagation is also amplified when the two connected firms are mutual trading partners (both buyer and seller of each other), and for downstream propagation specifically when firms are geographically distant and share no common bank. The market concentration amplification is consistent with the theory: concentrated markets have fewer substitution possibilities for inputs and outputs, so firms cannot easily re-route around a shocked partner, forcing the shock to transmit more fully along the existing network link. The amplification from mutual trading ties reflects that the combined demand-and-supply shock through a reciprocal link creates compound effects. The attenuation of downstream propagation when firms share a common bank is consistent with the bank internalizing the financial interdependence of borrowers connected in a supply chain.

Q6. What is the contribution of combining production and financial network analysis jointly, beyond studying either separately?

The joint analysis reveals that the real effects of financial shocks are massively understated when production and financial networks are studied in isolation: the overall impact triples the direct bank shock effect, a result that only emerges when both network structures are mapped and their interaction is quantified. The paper also shows that aggregating to the firm level — rather than analyzing only link-level effects — is essential: firms minimize shocks from individual connections by adjusting across multiple suppliers or customers, so link-level estimates do not translate directly to firm-level outcomes. The joint network analysis further reveals a “dual centrality” amplification: firms that are central both in the production network (high customer-supplier centrality) and in the financial network (large credit relationships with strongly-shocked banks) generate disproportionately large aggregate output contractions. A standard deviation increase in a firm’s customer centrality is associated with a 3 pp decrease in its purchase growth, while the same increase in supplier centrality is associated with a 0.6 pp decrease in sales growth.

Key Concepts

upstream propagation : the transmission of a bank credit supply shock from a directly shocked customer to that customer’s suppliers, operating through the demand channel — a customer facing tighter credit reduces its purchases, contracting the supplier’s sales; the paper shows first-order upstream effects (5.1 pp reduction in sales growth) are of similar magnitude to first-order downstream effects.

downstream propagation : the transmission of a bank credit supply shock from a directly shocked supplier to that supplier’s customers, operating through the supply channel — a supplier facing tighter credit reduces its output, contracting the availability of inputs to customers; both first-order (2.3 pp) and higher-order (2.0 pp) downstream effects are quantitatively large.

dual centrality amplification : the finding that firms simultaneously central in the production network (many supplier-customer relationships) and in the financial network (large credit from banks that receive large supply shocks) generate disproportionately large aggregate output contractions when hit by financial shocks, because the shock propagates through both network channels simultaneously.

Khwaja-Mian identification : the strategy of isolating bank credit supply shocks by exploiting within-firm variation across banks — conditional on firm-time fixed effects, changes in credit from different banks to the same firm reflect supply rather than demand — originally proposed by Khwaja and Mian (2008) and extended by Amiti and Weinstein (2018).

credit network shock : a bank-level credit supply shock derived from the Khwaja-Mian/Amiti-Weinstein methodology, capturing the component of each bank’s credit contraction attributable to bank-level supply factors rather than firm-level demand; the paper uses both this measure and an interbank-market-exposure measure to cross-validate identification.

Real Credit Cycles

Mon, 01 Jan 0001 00:00:00 +0000

This paper incorporates diagnostic expectations — beliefs that overweight the representativeness of recent data, formalized as $E_t^\theta(A_{t+1}) = E_t(A_{t+1}) + \theta[E_t(A_{t+1}) - E_{t-1}(A_{t+1})]$ with θ > 0 — into a workhorse real business cycle model with heterogeneous firms and risky defaultable debt, to assess whether non-rational belief overreaction can account for boom-bust credit cycles without requiring large fundamental shocks. The diagnosticity parameter θ is structurally estimated via simulated method of moments, targeting moments including forecast-error predictability from the IBES manager guidance database, and yields θ ≈ 0.991, consistent with prior estimates from financial analysts and professional forecasters. The estimated DE model generates several untargeted results that the rational-expectations (RE) benchmark cannot: countercyclical credit spreads, predictable firm-level bond returns, and investment fragility in good times — specifically, a one-standard-deviation negative TFP shock causes a much larger investment decline when the previous period had good TFP news than in normal times. The model also shows that the 2008-09 spread increase can be generated by mere disappointment of overoptimistic beliefs, not requiring a large negative TFP shock. These findings establish diagnostic expectations as a parsimonious and empirically disciplined mechanism for producing financial reversals in business cycle models.

Layer 1: Overview

Bordalo, Gennaioli, Shleifer, and Terry modify a standard heterogeneous-firm RBC model with risky defaultable debt by a single behavioral parameter — the diagnosticity θ governing belief overreaction to TFP news — to assess whether non-rational beliefs can quantitatively account for boom-bust credit cycles. The model departs from the rational expectations (RE) benchmark only in that firms and lenders form expectations diagnostically: after good TFP news, agents become excessively optimistic about future TFP, causing too much investment and debt issuance; when TFP growth disappoints relative to those optimistic expectations (even without an outright TFP decline), agents sharply revise down their beliefs, causing credit spreads to spike and investment to collapse. The diagnosticity parameter θ ≈ 0.991 is estimated by structural SMM targeting 16 moments — including 3 moments from IBES manager guidance data directly measuring the predictability of forecast errors — and is consistent with independent estimates from analyst forecasts (θ ≈ 0.9, Bordalo et al. 2019), professional macroeconomic forecasters (θ ≈ 0.5, Bordalo et al. 2020), and bond-price-implied beliefs (θ = 1.0, D’Arienzo 2020). The paper shows that the estimated DE model, unlike the RE benchmark, delivers countercyclical spreads, predictable firm-level bond returns, investment nonlinearity (fragility in good times), and an account of the 2008-09 spread episode requiring only a modest TFP disappointment.

In depth

Q1. What are diagnostic expectations, and how does the single parameter θ govern their departure from rational expectations?

Diagnostic expectations (DE) are beliefs that overweight outcomes that are representative of recent news relative to their true base rate, formalized as $E_t^\theta(A_{t+1}) = E_t(A_{t+1}) + \theta[E_t(A_{t+1}) - E_{t-1}(A_{t+1})]$, where $\theta \geq 0$ is the diagnosticity parameter: when $\theta = 0$ beliefs are rational, and when $\theta > 0$ agents exaggerate the persistence of current news shocks. The mechanism is grounded in the psychology of selective recall: good news makes good future outcomes top-of-mind and thus overweighted. In the context of an AR(1) TFP process, DE agents effectively behave as if TFP follows an ARMA(1,1) with an additional moving-average term that boosts the perceived response to current shocks. The parameter θ has a clean measurement interpretation: θ ≈ 1 means that for every unit of incoming news, agents’ beliefs overshoot by approximately one additional unit (forecast errors are roughly equal in magnitude to the news that generated them). DE are forward-looking (unlike adaptive expectations) and hence not mechanically subject to the Lucas critique, since agents’ beliefs respond to news in a structured way.

Q2. How is θ identified and estimated, and what disciplines the model’s departure from rationality?

The diagnosticity parameter θ is identified from three moments that directly exploit the predictability of future forecast errors from current firm-level investment and debt issuance growth — moments that are positive under DE and exactly zero under RE — drawn from the IBES manager guidance database covering 1999-2018. The key identification equation is: $\text{cov}(\Delta \text{Forecast Error}{t+1}, \Delta x_t) = a\pi a_x \rho \theta (1+\theta)$ where $x$ is investment or debt, positive if and only if θ > 0. In the data, a one-standard-deviation increase in the firm’s investment rate predicts approximately 10 percentage points stronger disappointment in next-year earnings, and a one-standard-deviation increase in debt issuance predicts about 5 percentage points stronger disappointment — robust to within-firm estimation that controls for heterogeneity in optimism across firms. The estimated θ ≈ 0.991 (s.e. 0.074) is precisely estimated and falls well within the range [0.5, 1.5] implied by independent estimates from other datasets. The RE model (constrained to θ = 0) cannot generate any comovement between future forecast error growth and current firm fundamentals, offering a falsifiable restriction that the data reject.

Q3. What is the investment fragility finding, and why can the RE model not replicate it?

The DE model generates a strong nonlinearity in investment: the same one-standard-deviation negative TFP shock causes a much larger investment decline when it follows a period of good TFP news (good times) than when it follows average or bad news; the RE model produces essentially no such nonlinearity, with investment responses roughly flat across initial conditions. The mechanism is as follows: after a positive TFP shock, firms and lenders become overoptimistic, driving high investment and low credit spreads. The aggregate investment response to the subsequent negative shock is therefore large — overoptimism has boosted the capital stock and the debt level beyond what fundamentals warrant, so the negative shock both lowers true productivity and triggers a sharp correction in beliefs. Under RE, agents correctly anticipate mean reversion of TFP and do not overbuild, so the same negative shock hits a less-leveraged economy and generates a smaller correction. This fragility-in-good-times mechanism is consistent with empirical evidence from Bachmann et al. (2013), Winberry (2017), and Bloom et al. (2018) that investment is more sensitive to shocks during booms.

Q4. How does the DE model account for countercyclical spreads, and why does the RE model predict the wrong sign?

Under DE, credit spreads are countercyclical because lenders become excessively optimistic about future TFP in good times, driving down perceived default risk and hence spreads below their rational counterpart; when optimism wanes, spreads spike beyond what fundamental deterioration alone would warrant. Under RE with constant required returns (as modeled), the supply of capital tracks fundamentals; in good times with high TFP, default risk is genuinely lower, so spreads fall — a qualitatively correct prediction. But the RE model also generates a positive correlation between spreads and investment in the cross-section of firms, while the data show a strong negative correlation (Column 10 of Table 5: Corr(Investment, Spread) = -0.057 in data, -0.054 in DE model, +0.083 in RE model). The DE mechanism driving this: overoptimistic lenders simultaneously over-supply credit (reducing spreads) and firms over-invest, creating the negative comovement. The paper links this formally to the concept of “financial shocks” in Jermann and Quadrini (2012) and Gilchrist and Zakrajšek (2012): in the DE framework, waning optimism produces inward shifts in the supply of capital that appear as exogenous financial shocks in reduced-form analyses.

Q5. How does the model account for the 2008-09 spread episode, and what shock size is required?

The DE model generates a spread increase consistent in magnitude with the 2008-09 episode from a modest moderation in TFP growth — not an outright TFP decline, but merely disappointment relative to the optimistic expectations formed during the preceding boom — while the RE model requires a large negative TFP shock of implausible size. During 2005-2007, a sequence of positive TFP shocks made firms and lenders excessively optimistic; when TFP growth merely slowed in 2007-08 (below the high level agents had been projecting), their beliefs corrected sharply, spreading up and investment down. In the DE model, the deceleration of TFP growth is sufficient to produce spread increases matching the observed magnitude during 2008-09, along with quantitatively consistent declines in aggregate investment, credit, and earnings forecast revisions. The RE model cannot match this because rational agents, correctly anticipating mean reversion, would not have built up the overoptimistic base to correct from.

Q6. How do the microeconomic boom-bust predictions of the model perform out-of-sample?

The model-simulated data replicate the firm-level boom-bust cycles documented in the paper’s Section 2: current overoptimism (proxied by high investment or debt issuance) predicts next-year spread increases, lower realized bond returns, and subsequent investment declines, with magnitudes that quantitatively match the data regressions; the RE model generates none of these predicted cycles. Specifically, in model-simulated firm-level regressions: higher current investment predicts 1-year-ahead spread increases; current spread increases predict negative future bond returns (the diagnostic model implies bonds are overpriced during booms, consistent with predictable low returns); and current high investment predicts future investment declines (mean reversion amplified by DE correction). All three predictions are also confirmed in the data and at the sectoral level, providing multiple out-of-sample validation tests. The diagnosticity parameter θ = 1 estimated from forecast errors simultaneously fits these untargeted dynamics.

Key Concepts

diagnostic expectations : beliefs that overweight outcomes representative of recent news, with the single deparature parameter θ ≥ 0 governing the degree of overreaction; in the AR(1) TFP context, agents act as if TFP follows an ARMA(1,1) with over-weighted current shocks; estimated at θ ≈ 1 from firm-level forecast error data, consistent with independent estimates from multiple other datasets.

fragility in good times : the paper’s key qualitative finding that the investment response to a given negative TFP shock is much larger when the shock follows a period of positive TFP news; arises because DE agents have built up excessive optimism, inflated capital stocks, and stretched leverage during the boom, making the correction larger; absent in the RE model.

diagnosticity parameter (θ) : the single behavioral parameter governing the degree to which agents overweight representative recent outcomes; θ = 0 is rational expectations; θ ≈ 1 is the structural SMM estimate, implying that forecast errors are roughly as large as the news that generated them; identified from the covariance between future forecast-error growth and current investment/debt changes.

financial shocks as waning optimism : the paper’s interpretation of “financial shocks” — inward shifts in the supply of capital generating spread spikes — as the endogenous waning of previously excessive diagnostic optimism, rather than exogenous disturbances to lender preferences or required returns; provides microfoundations for the Jermann-Quadrini and Gilchrist-Zakrajšek empirical findings.

countercyclical credit spreads : the empirical regularity that credit spreads fall in good times and rise in bad times, a moment the DE model matches (through overoptimistic lenders compressing spreads in booms) but the RE model with constant required returns fails to match in the cross-section (predicting a positive correlation between investment and spreads).

The Cost of Consumer Collateral: Evidence From Bunching

Mon, 01 Jan 0001 00:00:00 +0000

Layer 1: Overview

This paper estimates the shadow cost that consumers assign to pledging their primary residence as collateral, using administrative loan application and performance data from the U.S. Federal Disaster Loan (FDL) Program, which offers low-interest loans to households following natural disasters. A loan amount threshold — set at $10,000 from 2005–2007, $14,000 from 2008–2013, and $25,000 from 2014–2018 — separates uncollateralized from collateralized borrowing, with no other loan terms changing at the threshold; this sharp, discontinuous design allows the paper to use bunching estimation to identify collateral aversion. Roughly one-third of all program borrowers, and 38% of those with losses above the threshold, choose exactly the maximum uncollateralized loan amount, generating sharp mass at the threshold. Traditional bunching estimates, corroborated by two alternative approaches using household-level damage data and originally requested loan amounts, consistently find that the median borrower is willing to forgo 40–47% of their potential loan amount to avoid pledging their home as collateral, equivalent in demand terms to a 200 basis point interest rate increase. The paper also exploits threshold variation over time as an instrument for collateralization and finds that posting collateral causally reduces default rates by approximately 35%, an effect comparable in magnitude to a 100-point increase in borrower credit score, establishing that collateral substantially mitigates moral hazard in consumer lending.

In depth

Q1. What is the setting and why does it cleanly identify the collateral shadow cost?

The Federal Disaster Loan Program creates a quasi-experimental threshold because collateral is the only loan term that changes at the $25,000 boundary — interest rate, maturity, approval probability, and all other terms remain identical on both sides. Households with uninsured disaster damages (median $51,000) can borrow up to their loss amount at a fixed 2.5% rate; those requesting above the threshold must post their home as collateral. Unlike mortgage or auto loan markets, which always require collateral, and credit card markets, which never do, this program generates a setting where the binary collateral requirement is the borrower’s own choice subject only to the threshold, eliminating the standard endogeneity between contract terms and borrower risk. The authors use administrative data covering over 1 million applications from 2005 to 2018 across all 50 states.

Q2. How do the bunching estimates identify the distribution of collateral aversion?

The traditional bunching estimator fits a polynomial to the distribution of loan amounts below the threshold, extrapolates it above the threshold to construct a counterfactual without the collateral requirement, and attributes the excess mass at the threshold (the “missing” mass above) to collateral aversion. The bunching region spans from the threshold to the upper loan amount beyond which essentially no borrower would give up to avoid collateral; for the $25,000 threshold, this region extends to $49,900. Across all three threshold regimes, 73–78% of borrowers within the bunching region move to the threshold, and the estimated mean private value of collateral ranges from $7,944 (at the $10,000 threshold) to $18,268 (at the $25,000 threshold), representing 37–44% of ideal loan amounts. The median borrower’s collateral aversion — 39–47% depending on the threshold — is robust across all three estimation methods.

Q3. What alternative bunching methods does the paper develop and what do they find?

Two alternative estimation approaches — a difference-in-bunching (DiB) estimator that compares borrowers with identical damage levels across different threshold regimes, and an originally-requested-loan estimator that uses the amount households requested before collateral salience increased — both yield median collateral aversion estimates consistent with the traditional method (40–47%), while suggesting considerably wider heterogeneity in the tails. The DiB approach exploits the fact that for a household with $20,000 in damages, the same loan amount was uncollateralized under the $25,000 regime but required collateral under the $10,000 regime, enabling consumer-level identification. The originally-requested-loan method shows that 70% of eventual bunchers initially requested an amount above the threshold, with the majority of the shift occurring after meeting with a loan officer when the collateral requirement became salient. Both methods are immune to the standard counterfactual mis-specification concern of the traditional bunching approach, and they suggest the traditional estimator substantially under-predicts the proportion of highly collateral-averse borrowers at the upper end of the distribution.

Q4. What is the mechanism behind collateral aversion and what does heterogeneity reveal?

Collateral aversion in this setting is driven by both financial incentives and behavioral/preference factors, as evidenced by the finding that roughly 30% of borrowers already underwater on existing mortgages — who have no real equity to lose — still bunch at the threshold to avoid adding a lien on their home. More creditworthy borrowers (higher credit scores, higher incomes) are actually more likely to bunch, consistent with an “advantageous selection” interpretation in which borrowers who are confident in their repayment capacity are especially averse to the stigma and risk of pledging their home. Interest rates also matter: borrowers bunch more when program interest rates are higher, suggesting that financial incentives amplify the existing aversion. The magnitude of bunching — giving up thousands of dollars of subsidized low-interest disaster recovery loans — indicates that the perceived cost of a lien on the primary residence extends far beyond the financial value of potential foreclosure.

Q5. How does collateral causally reduce default rates?

Using time variation in the collateral threshold as an instrument for whether a borrower’s loan is collateralized — borrowers are more likely to collateralize when the threshold is low (so their ideal loan amount exceeds the low threshold) than when it is high — the paper estimates that collateral causally reduces default rates by about 35%. This local average treatment effect applies to borrowers who would collateralize under the $10,000 threshold but not under the $25,000 threshold, and the magnitude is comparable to a 100-point FICO score improvement. The finding implies that collateral requirements address genuine moral hazard in consumer lending: when a primary residence is pledged, borrowers take repayment obligations substantially more seriously, reducing the probability of strategic or precautionary default. This provides causal evidence for the classic prediction of models like Bester (1985) and Chan and Thakor (1987) that collateral mitigates information asymmetries and expands efficient credit access.

Q6. What are the aggregate implications and contribution to methodology?

In aggregate, borrowers in the program have given up more than $1.1 billion in disaster recovery loans to avoid posting collateral, indicating that collateral requirements — standard in most large consumer credit markets — impose large implicit costs that are not captured in stated interest rates. The methodological contribution is threefold: the paper is among the first to apply bunching to consumer (rather than corporate) collateral; it develops two consumer-level alternative estimators that relax assumptions of the standard method and reveal greater heterogeneity in collateral aversion; and it separately identifies the moral hazard effect of collateral from adverse selection by exploiting threshold variation, extending bunching beyond tax compliance and into household finance.

Key Concepts

collateral shadow cost : the implicit cost a borrower assigns to pledging collateral beyond the direct financial cost; measured in this paper as the maximum loan amount a borrower forgoes to avoid posting their home, identified from bunching mass at the collateral threshold in the FDL program.

bunching estimator : an estimation strategy that infers a structural parameter — here collateral aversion — from the excess density of agents at a policy threshold, using a polynomial-extrapolated counterfactual distribution to identify the missing mass above the threshold.

difference-in-bunching (DiB) estimator : a consumer-level alternative to traditional bunching estimation that uses borrowers with identical damage levels but facing different threshold regimes over time to construct a within-person counterfactual for the ideal loan amount, avoiding assumptions about the counterfactual distribution shape.

advantageous selection : the pattern in which more creditworthy, higher-income borrowers are the ones most likely to avoid collateral requirements, the reverse of the adverse selection typically assumed in collateral models; consistent with these borrowers having strong repayment intent independent of the lien.

The Effects of Regulatory Office Closures on Bank Behavior

Mon, 01 Jan 0001 00:00:00 +0000

Using closures of U.S. bank regulatory offices between 2002 and 2013 as difference-in-differences shocks to the physical proximity between supervisors and the community banks they oversee, the paper asks whether a decentralized network of local supervisory offices produces safer banks. The authors first show closures are not predicted by the risk or performance of the supervised banks — offices near a regional main office with falling workload are the ones shut — which supports treating closures as plausibly exogenous to affected banks. Following a closure, banks previously supervised by the closed office increase total lending by about 6-10% and tilt toward riskier loans (e.g., commercial real estate), and overall risk-taking as measured by the Z-Score rises by roughly 19-32% of the sample mean, with larger increases in distance to the new office associated with riskier policies. Banks affected before the 2008-09 financial crisis subsequently exhibited more bad loans, higher charge-offs, and higher failure rates during the crisis. Examining channels, the authors find affected banks report lower and less timely loan-loss provisions (and more income-increasing provisions, making balance sheets more opaque), increase dividend payouts, and see lower risk-adjusted returns on assets — which they read as evidence that proximity lets supervisors enforce timelier provisioning, restrain payouts, and share expertise. On balance the authors interpret the results as implying that geographical proximity reduces informational frictions in supervisory monitoring and leads to more stable banks — that is, the monitoring-benefit view dominates the regulatory-capture view on average.

In depth

Q1. What is the paper’s research design and identification strategy?

The paper uses a difference-in-differences design built on closures of FDIC, Federal Reserve, and OCC regulatory offices from 2002 to 2013, comparing banks that lost their supervising office to nearby banks in the same county supervised by a different office. Because U.S. banks in the same geographic area may be supervised by one of three federal regulators, within a county where an office closed only banks supervised by that closed office should be affected, while similarly located banks supervised by another office serve as controls exposed to the same local economic conditions. The analysis draws on a hand-collected data set mapping regulatory office locations and focuses on community banks, which are tied to local markets and served by traveling rather than in-house examiners. The tightest specifications include county-quarter, regulatory-office-quarter, and bank fixed effects, and the authors report parallel pre-trends and, using a timing-effects model, effects that appear only after closures and not before.

Q2. Are office closures plausibly exogenous to the affected banks?

The authors report that office closures are unrelated to the risk, performance, assets, or loans of the banks the office supervised; instead, offices closest to a regional main office experiencing a falling workload are the ones closed. They read this as the reasons for closure residing with banks outside the closed office’s immediate vicinity and reflecting a rebalancing of supervisory resources within regions, which they argue alleviates reverse-causality concerns that poor bank performance could drive both office closures and subsequent higher risk-taking.

Q3. What happens to bank lending and risk-taking after a regulatory office closes?

Following a closure, affected banks increase total lending by 6-10% and increase overall risk-taking as measured by the Z-Score by 19-32% of the sample mean, directing new lending toward riskier loan categories such as commercial real estate. In addition, larger increases in physical distance to the new supervising office are associated with riskier policies, which the authors interpret as evidence that proximity alleviates informational frictions in collecting information from and communicating with banks. The paper reports that its analysis does not provide clear evidence that treated banks hold more capital after office closures.

Q4. Do these changes have consequences for bank fragility?

Banks affected by office closures prior to the 2008-09 financial crisis subsequently exhibited more bad loans, higher charge-offs, and were more likely to fail during the crisis. The authors present this as evidence that the additional lending and risk-taking following closures was not benign, and read it, collectively, as support for the view that a decentralized supervisory structure — by keeping supervisors proximate — leaves banks less fragile.

Q5. Through what channels does proximity appear to operate?

The authors examine three nonmutually exclusive channels — provisioning practices, payouts, and supervisory expertise — and report evidence consistent with each. On provisioning, affected banks report both lower and less timely loan-loss provisions and make greater use of income-increasing provisions, which leads to more opaque balance sheets. On payouts, affected banks significantly increase their dividend payouts to shareholders. On expertise, risk-adjusted returns on assets for affected banks decrease after closures, which the authors read as consistent with proximate supervisors advising banks toward more efficient risk-taking. They interpret the combined evidence as proximity reducing informational frictions in supervisory monitoring.

Q6. How do the authors position their contribution and interpret the findings overall?

The authors state that, to the best of their knowledge, they are the first to use regulatory office closures to study how geographical networks of offices matter for bank supervision, and they interpret their results as indicating that the monitoring-benefit view dominates the regulatory-capture view on average. They emphasize that both views — that proximity aids monitoring and that proximity risks capture — can operate simultaneously, so the empirical question is which dominates; the finding of riskier, more fragile banks after supervisors move farther away implies proximity’s monitoring benefits dominate. The paper frames the policy-relevant implication as: geographical proximity reduces informational frictions in supervisory monitoring and leads to more stable banks.

Key concepts

Regulatory office closure : the shutting of a local supervisory office of the FDIC, Fed, or OCC, used here as a shock that increases the physical distance between a community bank and its supervisor while leaving the bank’s regulator and the applicable rules unchanged.

Decentralized supervisory structure : the arrangement whereby a unified body of banking regulation is enforced through geographically dispersed networks of local supervisory offices, intended to give supervisors easier access to local (especially soft) information about banks.

Monitoring-benefit (proximity) view : the hypothesis that physical proximity lowers the cost of collecting soft information and communicating supervisory expectations, enabling supervisors to enforce safer bank policies.

Regulatory capture view : the alternative hypothesis that proximity to supervised banks can undermine monitoring, because closer contact fosters social/communal ties or career concerns that lead supervisors to cater to bank interests.

Loan loss provisions (LLPs) : accounting charges intended to reflect the expected future losses on a bank’s loan portfolio; under-provisioning can flatter short-term liquidity and performance while masking inadequate capital, and the paper finds affected banks report lower and less timely LLPs.

Z-Score : the accounting-based measure of bank risk the paper uses; the paper reports that overall risk-taking, as measured by the Z-Score, increases by 19-32% of the sample mean for banks affected by office closures.

Community banks : smaller banks tied to local markets and served by traveling rather than in-house examiners — the sample the paper focuses on.

Key concepts

Community banks : smaller banks tied to local markets and served by traveling rather than in-house examiners — the sample the paper focuses on.

Unequal and Unstable: Income Inequality and Bank Risk

Mon, 01 Jan 0001 00:00:00 +0000

This paper documents that U.S. metropolitan statistical areas with higher income inequality have a larger share of failed banks, higher average bank default probabilities, and greater dispersion of bank risk, using cross-sectional regressions across 178 MSAs and 5,543 banks over 2000–2019 with the Gini coefficient measured from the 2006 American Community Survey. A move from the 25th to the 75th percentile of the Gini distribution (0.429 to 0.460) is associated with a 0.124 percentage point higher share of failed banks, a large effect relative to the 0.3 percent mean failure rate in the sample. To account for these patterns, the paper builds a general equilibrium model in the Allen and Gale (2000) tradition in which banks compete to lend to households that differ by income and finance housing purchases with mortgages; competition and deposit insurance together induce some banks to lend to low-income (subprime) households at rates that carry negative expected present value, creating a segment of endogenously risky banks that fail with positive probability in the bad state. Income inequality expands the subprime borrower pool both directly — by shifting more households below the endogenous income cutoff — and indirectly — by raising the equilibrium cutoff itself via higher housing prices — leading to a larger share of risky banks. A key counterfactual result is that if deposit insurance premiums fully reflected bank-specific risk (eliminating risk-shifting), all banks would be safe regardless of the income distribution, isolating risk-shifting as the necessary friction.

In depth

Q1. What is the empirical design, and what does the data show?

The paper uses a cross-sectional dataset at the MSA level, covering 178 MSAs with 5,543 commercial and savings bank headquarters over 2000–2019, regressing several measures of bank risk on the Gini coefficient measured from the 2006 ACS (one-year survey) while controlling for MSA-level income, population, urbanization, and year fixed effects in panel extensions. Bank failure is the primary risk measure: the share of bank headquarters that failed (FDIC-confirmed failure dates, excluding 2008–2009 TARP years to avoid ambiguity about government support) is regressed on the Gini coefficient. Additional risk measures — banks’ predicted probabilities of default (from a logit model trained on financial ratios) and z-scores — are used to confirm that the Gini result is not driven entirely by crisis-period observations. The paper finds significant positive relationships between the Gini and: (i) the share of failed banks (Panel A), (ii) the risk of the most-risky banks per MSA (Panel B), (iii) average bank risk (Panel C), and (iv) the dispersion of bank risk across banks in the MSA (Panel D). Robustness checks use 3-year survey Gini coefficients, the income share of the top 5 percent as an alternative inequality measure, and panel regressions with MSA-level clustering; results are qualitatively unchanged. Poverty (share of households below the poverty line) is not significantly associated with bank risk, distinguishing inequality from the level of the lower tail.

Q2. What is the theoretical framework and what agents populate the model?

The model is a two-date (0 and 1) general equilibrium model with a continuum of households heterogeneous in income and a continuum of ex-ante identical, risk-neutral bankers; households finance housing purchases at date 0 with mortgage loans that are repaid at date 1, and bankers can choose at date 0 to operate a safe bank (solvent in both states) or a risky bank (insolvent in the bad state with probability q). Housing is produced by competitive firms with increasing marginal cost, so the equilibrium housing price P_0 is an increasing function of aggregate housing demand. Deposits are insured (explicitly or implicitly), and banks are subject to a minimum capital requirement (maximum leverage ratio ρ). The cost of bank capital exceeds the cost of deposits, so all banks lever to the maximum. Each bank’s cost of managing its balance sheet is quadratic in balance sheet size, which pins down individual bank size and allows a clean characterization of how many risky vs. safe banks exist in equilibrium.

Q3. What is the key sorting result between banks and borrowers (Proposition 1 and 2)?

In equilibrium, there is an endogenous income cutoff y such that households with income above y are prime borrowers served by safe banks at undistorted interest rates r_u(y), and households with income below y* are subprime borrowers served by risky banks at a uniform risk-shifting interest rate r_rs; crucially, subprime loans carry negative expected net present value because competition among risky banks drives r_rs below the break-even rate for a safe bank (Corollary 1).** The risk-shifting interest rate r_rs is lower than the undistorted rate for low-income borrowers because a risky bank only internalizes the loan payoff in the good state (where it is solvent) and benefits from the deposit insurance subsidy in the bad state (where it defaults and the fund covers depositor losses). Risky banks are therefore willing to lend at below-NPV rates, and competition among them drives r_rs to equality with their marginal cost conditional on survival. Safe banks rationally refuse to enter the subprime segment because they internalize the expected loss in the bad state. The clientele of safe and risky banks do not overlap in equilibrium.

Q4. How does income inequality affect the proportion of risky banks?

Income inequality raises the share of risky banks through two reinforcing channels: a direct channel that shifts a larger mass of households below the fixed cutoff y, expanding the subprime borrower pool, and an indirect channel that moves the cutoff y upward (because inequality raises the equilibrium housing price P_0, which in turn raises the default rate among any given income level, making more households effectively subprime) — under convex housing demand (plausible if n(y) is concave below a poverty-line income ymin), both channels reinforce each other.** Numerically, with a log-normal income distribution calibrated to the observed Gini range of 0.35–0.55, the model generates a monotone positive relationship between the Gini coefficient and (i) the proportion of risky banks, (ii) average bank default probability, and (iii) dispersion of bank default probabilities — matching the empirical patterns from Section 2. A Pareto income distribution produces a steeper relationship, suggesting the result is robust to distributional assumptions. The proportion of risky banks in Proposition 2 equals the subprime housing demand divided by the sum of subprime and weighted prime demand, and is therefore a smooth function of the income distribution H.

Q5. Why is risk-shifting — not borrower riskiness — the necessary friction?

Proposition 3 shows that if deposit insurance premiums fully reflected each bank’s individual default probability (i.e., if risk-shifting were not feasible), all banks would choose to be safe even when the income distribution places many households with high default rates below y, because a risky bank would have to pay higher deposit rates to attract insured deposits and would be unable to extract a competitive advantage from subprime lending.* The proposition isolates risk-shifting as a necessary condition: without it, the income distribution has no effect on bank risk. This result directly connects to Keeley’s (1990) observation that bank risk reflects the option value of limited liability plus deposit insurance. It also implies that policies that make deposit insurance premiums bank-risk-sensitive (such as risk-based FDIC premiums) could substantially mitigate the inequality–bank-risk nexus, though the paper notes that empirical evidence suggests current risk-based premiums do not fully internalize bank-specific risk.

Q6. How does housing supply elasticity interact with the inequality–bank-risk relationship?

When housing supply is inelastic (high marginal cost c_1), a mean-preserving spread in income raises the equilibrium housing price P_0 substantially, which raises the subprime cutoff y sharply via the indirect channel — and for very high values of c_1, this can actually push the cutoff above the top of the income distribution, putting all households into the prime segment and reducing the share of risky banks.* This asymmetry means that in regions with very inelastic housing supply (e.g., coastal urban areas with strict zoning), higher inequality may be associated with fewer risky banks because the high housing price forces even low-income households to borrow at rates where a safe bank is marginally competitive. Conversely, in regions with elastic housing supply, the indirect channel is weak and the direct channel dominates, so higher inequality unambiguously raises bank risk. The paper characterizes this interaction numerically using Figure 4, noting that the perverse (negative) relationship is theoretically possible but considered less empirically relevant in practice.

Q7. What are the model’s extensions and robustness?

The paper discusses four extensions that leave the central mechanism intact: (i) ex-ante heterogeneous banks, in which risky banks specialize in subprime mortgages and also finance risky firms; (ii) risk-weighted capital requirements, which permit risk-shifting as long as risk weights are not fully calibrated to bank-specific risk; (iii) a firm sector, in which risky banks serve risky small firms in addition to subprime households; and (iv) housing speculation by high-income households, which amplifies the risky-bank segment by creating additional demand for negative-NPV mortgages. In extension (iv), the high-income speculator demands a risky mortgage even though speculator income is above y*, creating an additional channel through which inequality can generate bank risk beyond the subprime-borrower mechanism. The discussion also addresses the baseline model’s assumptions about flat deposit rates and homogeneous bankers, arguing that relaxing either would introduce quantitative but not qualitative changes to the central sorting result.

Q8. What does the paper contribute relative to the literature on bank risk and inequality?

The paper’s empirical contribution is to document a robust cross-sectional positive relationship between income inequality and bank failure rates at the MSA level, a relationship that is not driven by poverty (the lower tail per se), is present for multiple bank-risk measures, and survives including the crisis years 2008–2009 in the bank-risk measures (though significance weakens). On the theory side, the contribution relative to the Cairo-Sim (2018) monetary policy and inequality work and the Allen-Gale (2000) rational bubbles framework is to endogenize the sorting of banks and borrowers into safe/risky pairs in response to the income distribution, and to show that this sorting — not borrower riskiness per se — generates the empirical bank-risk gradient. The model is purposefully simple (one period, no dynamics, no aggregate shock heterogeneity) to make the mechanism transparent; the authors acknowledge that a dynamic model with time-varying inequality might generate additional predictions about the timing of bank failures relative to inequality trends.

Key Concepts

subprime cutoff y* : the endogenous income level that separates prime (income above y*) from subprime (income below y*) borrowers; determined in equilibrium as the income level at which the undistorted mortgage rate for a safe bank equals the risk-shifting rate charged by a risky bank; shifts in response to the equilibrium housing price.

risk-shifting interest rate (r_rs) : the mortgage rate that a risky bank is willing to accept on a subprime loan, determined by the condition that the bank earns zero profit conditional on the good state (survival), without internalizing the loss imposed on the deposit insurance fund in the bad state; in the paper’s equilibrium, r_rs is uniform across all subprime borrowers.

undistorted interest rate (r_u(y)) : the mortgage rate that a safe bank requires from a household with income y, determined by the full expected return on the loan across both good and bad states; increasing in y because lower-income households have higher default rates in the bad state.

negative NPV subprime loans : mortgage loans to households with income below y* that carry a negative expected present value when the deposit insurance cost is internalized; attractive only to risky banks that do not internalize the bad-state payoff, and not to safe banks that must break even in expectation.

Allen-Gale (2000) rational bubbles framework : a one-period general equilibrium model in which banks lend to asset purchasers using deposit insurance, creating a wedge between private and social returns on risky assets; this paper adapts that framework to a mortgage/housing market with a continuous income distribution to generate endogenous bank sorting and an inequality channel.

direct vs. indirect channel of inequality : the direct channel (region A in Figure 2) operates by shifting more households below the existing cutoff y* as the income distribution spreads; the indirect channel (region B) operates by raising y* itself through higher equilibrium housing prices; both channels reinforce each other when housing demand is convex in income (n(y) convex below the poverty line).

deposit insurance subsidy : the implicit transfer from the deposit insurance fund to risky banks in the bad state; risky banks pay the same deposit rate as safe banks despite imposing expected losses on the fund, creating the wedge that makes subprime lending attractive to risky banks and not to safe banks.

Banking-Credit | Macro Paper Warehouse

Banking with Inside Money: An Efficiency Analysis

Layer 1: Overview

In depth

Q1. What is the core model and how does inside money arise?

Q2. Why can’t non-contingent nominal deposits achieve the first best?

Q3. What is the formal result on state-contingent deposit contracts?

Q4. What is the result on early loan liquidation?

Q5. When can banks be welfare-dominated by bilateral trade?

Q6. What role can central banks play and what is the lender-of-last-resort result?

Key Concepts

Central Bank Digital Currency with Collateral-Constrained Banks

In depth

Q1. What is the equivalence of payment systems result and how does the collateral constraint change it?

Q2. What is Corollary 1 and why does “full neutrality” fail?

Q3. What does the dynamic extension show about bank disintermediation?

Q4. What changes even if credit is not reduced?

Key concepts

Cyberattacks on Small Banks and the Impact on Local Banking Markets

In depth

Q1. What is the identification strategy and what variation does it exploit?

Q2. How large is the deposit effect at attacked small banks and what is the direction of deposit flows?

Q3. What role does digital literacy play in moderating the deposit response?

Q4. How does the competitive position of large banks in the local market moderate the spillover?

Q5. Do deposit rates at attacked small banks rise or fall, and does this signal a funding-cost channel?

Q6. What happens to the loan portfolio and borrower risk profile of attacked small banks after a breach?

Q7. What are the real-economy consequences at the local market level?

Q8. How does this paper relate to the literature on bank runs and financial contagion?

Key Concepts

Financial Intermediation and Aggregate Demand: A Sufficient Statistics Approach

In depth

Q1. What are the key sufficient statistics?

Q2. Why does the sufficient statistics approach give larger estimates than reduced-form regressions?

Q3. What is the policy implication?

Key concepts

How Bad Are Weather Disasters for Banks?

In depth

Q1. How severe are weather disaster effects on bank safety?

Q2. Is bank resilience an artifact of federal disaster aid?

Q3. What is the loan demand channel and how large is it?

Q4. What does “local knowledge” mean in this context?

Q5. What are the implications for climate risk assessment?

Key concepts

Income Inequality and Job Creation

In depth

Q1. Why does the portfolio composition of saving matter more than the aggregate savings rate?

Q2. Why are small firms disproportionately harmed by higher loan rates?

Q3. How is the pre-determined share IV constructed and why does it satisfy the exclusion restriction?

Q4. What explains the aggregate output decline when private firms have higher marginal products?

Q5. How does rising inequality amplify its own effect through welfare and further portfolio reallocation?

Q6. What fraction of US macroeconomic trends since 1980 can the channel explain?

Q7. What happens to firm entry and exit under rising inequality?

Q8. Why do deposits account for such a large share of bank liabilities and why can’t banks substitute easily?

Key concepts

Outsourcing bank loan screening: The economics of third-party loan guarantees

In depth

Q1. Why are third-party loan guarantees prevalent in China’s SME lending market?

Q2. How do the data reject the regulatory arbitrage and self-selection hypotheses?

Q3. What is the positive evidence for the outsourced screening interpretation?

Q4. What are the broader implications for SME finance and financial intermediation theory?

Key concepts

Private Information and Price Regulation in the US Credit Card Market

In depth

Q1. What did the CARD Act restrict, and how large was the pre-Act practice it curtailed?

Q2. How are private information types identified and what do they reveal about adverse selection?

Q3. What does the structural model say about markups and their source?

Q4. What happens to prices under the CARD Act restrictions, and why does partial unraveling occur only for deep subprime?

Q5. What are the consumer surplus gains, and what drives them?

Q6. Why were high pre-CARD-Act markups necessary for the surplus gains?

Q7. What is the adverse retention finding and its magnitude?

Q8. What private information rents existed before the Act?

Key concepts

Production and Financial Networks in Interplay

Layer 1: Overview

In depth

Q1. How does the paper identify bank credit supply shocks, and what makes Spain’s administrative data unusual?

Q2. What are the direct effects of bank credit supply shocks on firm-level real outcomes?

Q3. How large are the first-order and higher-order network propagation effects, and how do they compare to direct effects?

Q4. What is the symmetric finding on upstream versus downstream propagation, and why does it matter?

Q5. How does market concentration amplify financial shock propagation?