Financial-Frictions | Macro Paper Warehouse

Diversion Risk, Markups, and the Financing Cost Advantage of Trade Credit

Thu, 01 Jan 2026 00:00:00 +0000

This paper provides a theory and evidence for why firms with higher markups extend more trade credit, focusing on a financing cost channel that is distinct from existing competition-based explanations. In the model, diversion risk creates a wedge between the bank borrowing rate and the deposit rate. Under cash in advance, the buyer must borrow the full invoice amount (production cost times markup); under trade credit, the seller instead borrows only her production costs. Since higher markups amplify the difference in borrowing needs between these two payment forms, they make trade credit more attractive—and this advantage strengthens with the buyer’s borrowing rate, generating a unique interaction prediction. Empirical tests using detailed Chilean export transactions matched with firm-product markup estimates (De Loecker et al. 2016 methodology) find that a one standard deviation rise in upstream markups increases trade credit by 13 days, with the extensive and intensive margins contributing roughly equally; this effect strengthens with the destination country’s borrowing costs. Results are robust to instrumenting markups with plant-product level physical productivity and replicate in U.S. Compustat data with the real Effective Fed Funds Rate as the borrowing cost proxy.

In depth

Q1. Why does a higher markup make trade credit more attractive?

Under cash in advance, the buyer must pre-pay the full invoice price (production cost times markup), requiring borrowing equal to the markup times production cost; under trade credit, the seller instead borrows only her production costs to finance production while the buyer pays later from sales revenues, requiring no pre-payment borrowing at all. Because diversion risk causes banks to charge more than the deposit rate for loans, a higher markup amplifies the savings in financing costs from using trade credit rather than cash in advance, making trade credit strictly preferred whenever the markup and interest rate spread are both positive. This mechanism is operative even if the seller and buyer face identical borrowing rates and even if goods are no harder to divert than cash (distinguishing it from Burkart and Ellingsen 2004, where trade credit dominates because goods are harder to divert).

Q2. What is the unique empirical prediction that distinguishes the financing cost channel?

The model uniquely predicts that the positive effect of upstream markups on trade credit should increase with the buyer’s borrowing rate: when borrowing is expensive, the relative financing cost advantage of trade credit (which reduces total borrowing) is larger, so higher markups generate even more trade credit use. This interaction prediction distinguishes the financing cost channel from competition-based theories (Demir and Javorcik 2018; Giannetti et al. 2021) which predict higher upstream bargaining power (lower markups) → more trade credit, and allows identification even with a rich set of fixed effects because the interaction term is residual to seller, buyer, and destination fixed effects.

Q3. What do the Chilean export data show?

A one standard deviation rise in upstream markups increases trade credit by 13 days on average, with the extensive margin (probability of using trade credit) and intensive margin (trade credit maturity conditional on use) contributing roughly equally; crucially, the effect of markups on trade credit strengthens with the destination country’s borrowing costs, consistent with the unique interaction prediction of the financing cost channel. Markup estimates are constructed at the firm-product level using the De Loecker, Eeckhout, and Unger (2016) methodology applied to Chilean manufacturing survey data, which requires quantity-based information on inputs and outputs to avoid revenue-based measurement confounds; the extensive fixed effects structure (seller × product, buyer-country × product, and seller × buyer-country-year fixed effects) addresses omitted variable concerns.

Q4. How does the paper handle endogeneity of markups?

The paper instruments for firm-product markups using plant-product level physical productivity, which is a supply-side technological variable that affects markups through the cost side (more productive firms have lower marginal costs and thus higher markups for a given price) but is unlikely to directly affect payment choice; the IV results are quantitatively similar to OLS, supporting the causal interpretation of the markup effect on trade credit. Because markups estimated with revenue data can conflate productivity with demand shocks (the ‘De Loecker critique’), the Chilean quantity-based data are particularly valuable: firm-product quantities and input prices are directly observed in the manufacturing survey, enabling markup estimates that are free of revenue confounds.

Key concepts

financing cost channel of trade credit : the mechanism by which trade credit reduces the total bank borrowing needed for a transaction—because the seller borrows only production costs rather than the buyer borrowing the full invoice price—thereby lowering financing costs when diversion risk creates a borrowing-deposit rate wedge; the paper’s central contribution, distinct from competition-based explanations of trade credit provision. diversion risk and borrowing-deposit rate wedge : the risk that borrowers divert borrowed funds, which causes banks to charge a borrowing rate above the deposit rate; the spread between these rates determines the per-dollar financing cost saved by switching from cash in advance to trade credit, amplifying the role of markups in payment choice. De Loecker et al. (2016) markup estimation : a methodology for estimating markups at the firm-product level using quantity-based production data (physical inputs and outputs) rather than revenue data, avoiding the confound between productivity and demand shocks; used here to obtain the Chilean firm-product markup estimates.

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.

Business, Liquidity, and Information Cycles

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

The paper studies how the two roles of stock markets — revealing information about firms’ fundamentals (which guides capital allocation) and providing liquidity — interact, arguing that when stocks are used more intensively for liquidity, their prices reveal less information about fundamentals. The authors build a Grossman-Stiglitz-style trading model with two types of rational traders (‘day’ traders who value liquidity and ’night’ traders who value fundamentals) that generates endogenous noise in prices, derive an analytical measure of price informativeness (PI), and structurally estimate PI from firm-level panel data for 16 countries over 1984-2022, finding that PI declines in periods of insufficient funding liquidity (such as the Great Recession and the COVID-19 pandemic) and that these fluctuations are explained mostly by changes in trading activity rather than information quality. Integrating the trading module into a real business cycle model with heterogeneous firms calibrated to the United States, they simulate recessions: a stand-alone recession is ‘cleansing’ — prices become more informative and allocation improves, mitigating output losses by 4.4% — whereas a recession coinciding with banking distress is ‘sullying’ — agents rely more on stocks for liquidity, prices become less informative, and worsened misallocation magnifies output losses by 22%. A counterfactual with exogenous (rather than endogenous) information implies output would fall about 43% more than in the benchmark, which the authors read as evidence that endogenous information acquisition lets stock markets ’lean against the wind’ in recessions. All magnitudes are model-based and specific to the U.S. calibration.

In depth

Q1. What interaction between stock-market roles does the paper study?

The paper studies how the liquidity role of stock markets affects their information role: if stocks are used more intensively for liquidity, prices reveal less information about firms’ fundamentals. While the information and liquidity roles of stock markets are each well studied, their interaction is less understood; the authors ask whether using stocks for liquidity enhances or weakens their information role, how distress in other liquidity sources (such as banks) affects price informativeness, and how this contributes to the depth of recessions.

Q2. How does the trading model generate the information-liquidity tradeoff?

The authors extend Grossman and Stiglitz (1980) by replacing noise traders with two types of rational traders — ‘day’ traders interested in liquidity and ’night’ traders interested in fundamentals — so that each type’s trades act as endogenous noise for the other. In equilibrium a linear pricing function exists in which price informativeness depends on the relative weights of fundamental versus liquidity information in prices, and those weights are determined by how many day and night traders operate, their information choices, and how aggressively they trade. When funding markets malfunction, the economy relies more on stocks for liquidity, there are more day traders, and price informativeness declines.

Q3. What is Price Informativeness (PI), and how is it estimated?

Price Informativeness (PI) is defined analytically as a function of the dispersion of firm productivity, the dispersion of stock-price fluctuations, and their respective price loadings; in a high-PI market, a firm’s high relative stock price is a strong signal of positive information about its fundamentals. The authors estimate PI structurally using firm-level panel data from 16 countries spanning 1984 to 2022. The linear relationship among stock prices, earnings, and stock liquidity holds independently of general-equilibrium considerations, which is what makes the structural estimation tractable.

Q4. What are the empirical cyclical properties of PI?

PI exhibits cyclicality and, more importantly, declines in periods of insufficient funding liquidity, such as the Great Recession and the COVID-19 pandemic. Decomposing PI into its four components, the authors show its fluctuations are mostly explained by changes in trading activity rather than by changes in information quality or the amount of information acquired.

Q5. How is the trading module embedded in a general-equilibrium model and disciplined?

The trading module is integrated into a real business cycle model with heterogeneous firms in which stock prices guide capital allocation, calibrated to the United States with two possibly correlated aggregate shocks — one to aggregate productivity and one to funding liquidity — to capture recessions with and without banking distress. The calibrated model replicates the cyclical properties of the empirical PI measure without targeting them. The authors also discipline how much new information prices convey using price-investment correlations across firms and over time, concluding that new stock-price information is roughly as important as what decision makers already know.

Q6. What are the quantitative real effects in recessions?

In a stand-alone recession, increased uncertainty induces all traders to acquire more information, raising price informativeness and improving allocation, which mitigates output losses by 4.4% (‘cleansing’); when a recession coincides with funding-market distress, heightened liquidity-driven trading makes prices less informative and worsens allocation, magnifying output losses by 22% (‘sullying’). The authors interpret the 22% figure as a sizable real effect of banking problems operating through a novel channel: the weakening of the information and allocative role of stock markets.

Q7. What do the information-structure counterfactuals show?

If information were exogenous rather than endogenously acquired, liquidity distress would reduce PI by more and output would decline about 43% more than in the benchmark — implying endogenous information acquisition lets stock markets ’lean against the wind’ during recessions. The authors further find that halving the cost of information about fundamentals would make output declines about 5% smaller, whereas halving the cost of information about a stock’s liquidity would make declines about 2% larger, leading them to conclude that the welfare effect of transparency is nuanced — easier access to one type of information can make it harder to infer another.

Q8. What are the main limitations and scope conditions?

The authors flag two limitations: the framework assumes no feedback from the real economy back to financial markets (prices affect investment, but investment does not affect prices), and the counterfactuals focus on how the information environment affects price informativeness, abstracting from other channels through which information affects production. Adding two-way feedback would sacrifice the tractability of linear pricing but could introduce additional magnification forces. All quantitative magnitudes are specific to the U.S. calibration.

Key concepts

price informativeness (PI) : the extent to which stock prices reveal to an outside observer the information that informed traders hold about firms’ fundamentals; defined in the paper as an analytical function of productivity dispersion, price-fluctuation dispersion, and their price loadings, and estimated structurally.

day traders vs. night traders : the paper’s two types of rational traders — day traders trade to satisfy liquidity needs, night traders trade on information about fundamentals — whose trades act as endogenous noise for one another, replacing the exogenous noise traders of Grossman-Stiglitz.

funding liquidity vs. market liquidity : funding liquidity is liquidity provided by intermediaries through credit; market liquidity is the ability to trade stocks to meet liquidity needs; when funding liquidity is scarce, agents substitute toward market liquidity, raising liquidity-driven trading.

cleansing vs. sullying recessions : in the paper’s usage, a cleansing recession improves allocation (here via more informative prices), while a sullying recession worsens it; a recession is cleansing without banking distress and sullying when it coincides with funding-market distress.

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.

Climate Policies, Macroprudential Regulation, and the Welfare Cost of Business Cycles

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

This paper embeds a carbon pricing sector into an extended DSGE model with a financial accelerator (E-DSGE) featuring heterogeneous firms, bank monitoring, and a borrowing-constraint amplification mechanism, then compares the welfare cost of business cycles under a cap-and-trade (CAT) scheme versus a carbon tax. The central result is that, in the presence of financial frictions, CAT generates lower welfare costs than a carbon tax: under TFP and risk shocks calibrated to US quarterly data, the baseline welfare cost of business cycles is 0.6178 percent of consumption under CAT versus 1.5231 percent under a carbon tax — roughly 2.5 times larger under a tax. The mechanism is that permit prices under CAT are procyclical (they fall in downturns, reducing firms’ carbon compliance burden precisely when balance sheets are most stressed), acting as an automatic stabilizer for financial amplification, while the carbon tax holds a fixed price and provides no such buffer. A countercyclical optimal carbon tax rule that reacts vigorously to output (optimal sensitivity parameter τ = 52.2245) can mimic CAT’s stabilizing behavior, but even optimized environmental rules leave a significant welfare gap between regimes. Reserve requirement macroprudential regulation narrows this gap substantially: a static 2 percent reserve requirement brings CAT welfare costs to 0.1957 and carbon tax costs to 0.3863; an optimal dynamic rule keyed to credit growth or asset price growth brings both regimes below 0.20, effectively aligning them. A deposit interest rate subsidy can also narrow the gap when combined with a dynamic subsidy rule, but a static subsidy actually worsens welfare costs because it raises leverage and amplifies shocks around a more fragile steady state.

In depth

Q1. What is the model structure and how does the environmental policy sector integrate with financial frictions?

The model is an E-DSGE built on the Christiano, Motto, and Rostagno (2014) financial accelerator framework, extended to include a carbon price instrument and heterogeneous firms that face both standard borrowing constraints and carbon compliance costs. There is a representative household and three firm sectors: a continuum of capital-producing entrepreneurs, retailers, and a goods sector. Banks extend loans to entrepreneurs at a spread over the risk-free rate; the external finance premium is endogenous because bank monitoring is costly and borrowers face costly state verification (as in Bernanke, Gertler, and Gilchrist, 1999). Environmental policy is introduced through a carbon permit or tax that enters firms’ marginal cost, so the carbon price affects both production decisions and the entrepreneur’s net worth, which in turn feeds back into the spread through the financial accelerator. Calibration uses US quarterly data (Table 1 in the paper), and the model is solved by log-linearizing around a deterministic steady state.

Q2. Why do financial frictions create a welfare advantage for cap-and-trade over carbon taxes?

Under a carbon tax, the tax rate is fixed by the regulator regardless of macroeconomic conditions; when a TFP or risk shock contracts output and reduces firm net worth, the fixed carbon cost amplifies the contraction by reducing the entrepreneur’s retained earnings, worsening the external finance premium, and deepening the financial accelerator loop. Under a CAT scheme, the equilibrium permit price is endogenous: it falls when aggregate activity and emissions decline, automatically lowering the compliance cost burden for firms at exactly the moment when balance sheets are most constrained. This procyclicality of permit prices functions as an automatic stabilizer, partially offsetting the financial accelerator’s amplification. The paper shows this via impulse response functions (Figures 1 and 2 in the paper) to TFP and risk shocks: under CAT, the responses of investment, bankruptcy, spread, and output are systematically more muted than under a carbon tax. Quantitatively, the baseline welfare cost of business cycles is 0.6178 percent of consumption under CAT versus 1.5231 percent under a carbon tax — a gap of nearly 0.91 percentage points of consumption.

Q3. How do optimal environmental policy rules affect welfare costs, and do they close the gap between regimes?

An optimal flexible CAT rule that allows permit prices to respond countercyclically to a macroeconomic indicator (net output) reduces welfare costs from 0.6178 to 0.4528 percent; an optimal flexible carbon tax rule reduces costs from 1.5231 to 1.1811 percent. In both cases, the optimal rule specifies vigorous countercyclical response: the optimal sensitivity parameter for the carbon tax rule is τ = 52.2245, meaning the tax rate must decrease sharply in recessions to mimic the automatic procyclicality of permit prices under CAT. Despite these improvements, the welfare gap between the two regimes persists even under optimal environmental rules: the optimized CAT still generates roughly 0.73 percentage points lower welfare costs than the optimized carbon tax. The paper concludes that countercyclical environmental policy can reduce but not eliminate the inherent stabilization advantage of CAT in the presence of financial frictions — because the fundamental mechanism (endogenous permit prices vs. fixed tax rate) cannot be fully replicated by a tax rule with a single output-gap indicator.

Q4. How do reserve requirement macroprudential regulations interact with the carbon pricing choice?

Introducing a static 2 percent reserve requirement (banks can loan out only 98 percent of deposits) already strongly reduces welfare costs and partially aligns the two regimes: CAT welfare costs fall from 0.6178 to 0.1957, and carbon tax costs fall from 1.5231 to 0.3863 (Table 5 in the paper). The mechanism is that reserve requirements limit bank credit expansion, lowering equilibrium leverage and reducing the severity of the financial accelerator — when firms’ balance sheets are less leveraged, adverse shocks cause smaller spirals in net worth and spreads. Dynamic reserve requirement rules — keyed to credit growth (optimal ψ_B ≈ 1.047) or asset price growth (optimal ψ_Q ≈ 0.722) — reduce welfare costs further, to 0.1207 under CAT and 0.2300 under a carbon tax with a credit-growth rule, effectively narrowing the gap to around 0.10 percentage points. The optimal policy mix (jointly optimizing both the macroprudential and environmental rules) achieves minimal additional improvement beyond the macroprudential optimum alone, suggesting the dominant stabilizing role is played by financial regulation rather than the choice of carbon pricing instrument when both are available and optimally calibrated.

Q5. How does macroprudential regulation affect the volatility of emissions and permit prices under each regime?

Table 6 in the paper reports coefficients of variation (CVE for emissions volatility, CVP_E for permit price volatility) across policy combinations. Under baseline CAT with no macroprudential regulation, CVE = 0 (the cap fixes aggregate emissions by construction) and CVP_E = 8.3578 — permit prices are very volatile. Adding a static reserve requirement reduces CVP_E to 2.5125; an optimal credit-growth rule reduces it to 1.0935, a reduction of nearly 87 percent from baseline. Under baseline carbon tax, CVP_E = 0 (the tax price is fixed by regulation) but CVE = 0.0574 — emissions are volatile. Adding a static reserve requirement reduces CVE to 0.0273; an optimal credit-growth rule reduces it to 0.0153. The paper interprets this as macroprudential regulation fostering convergence between the two instruments in their business cycle properties: it substantially stabilizes permit prices under CAT and substantially stabilizes emissions under a carbon tax, reducing the distinguishing uncertainty of each pricing approach. The optimal policy mix under a carbon tax with a dynamic subsidy achieves CVE = 0.0090 and CVP_E = 0.4956, showing that well-designed financial regulation can make a carbon tax nearly as emissions-stable as a CAT while also reducing permit price volatility.

Q6. What happens under an interest rate subsidy to depositors as an alternative macroprudential tool?

A static deposit interest rate subsidy of the welfare-maximizing level (1 percent) worsens welfare costs of business cycles — from 0.6178 to 1.1028 under CAT and from 1.5231 to 3.5597 under a carbon tax — because the subsidy moves the economy to a higher-leverage steady state, around which financial amplification is more severe (Table 7 in the paper). The intuition is that the subsidy encourages saving by raising the return on deposits, which raises equilibrium loan supply, which raises leverage; a more leveraged economy is more sensitive to adverse shocks. A dynamic subsidy rule that responds countercyclically to credit growth (optimal κ ≈ 1.319) mitigates this problem: it discourages saving when credit is expanding and encourages it when credit is contracting, partially stabilizing leverage dynamics. The dynamic subsidy reduces welfare costs substantially — to 0.2506 under CAT and 0.4706 under a carbon tax — and a joint optimization of the subsidy and the carbon pricing rule achieves 0.1926 under CAT and 0.4366 under a carbon tax with a dynamic subsidy. The authors note that the static subsidy result illustrates a general principle: macroprudential policies that move the steady state toward higher leverage can amplify cycle costs even while achieving efficiency gains around the steady state, and that distinguishing between steady-state and fluctuation welfare effects is essential when comparing such policies.

Q7. What are the main welfare and policy conclusions?

The paper establishes three conclusions. First, climate policy instrument choice has macroeconomic stabilization consequences in financially frictionous economies: CAT dominates a carbon tax for welfare when financial frictions are operative and macroprudential policy is absent or limited. Second, macroprudential regulation — particularly dynamic reserve requirement rules — is the more powerful tool for reducing the welfare cost of business cycles under both carbon pricing regimes, and can largely align the two regimes, making the instrument choice less consequential when macroprudential policy is well-calibrated. Third, the interaction between financial regulation and carbon pricing is non-trivial: the optimal sensitivity parameters for macroprudential rules differ depending on whether the economy uses CAT or a carbon tax, because the endogenous procyclicality of permit prices changes how financial shocks propagate through the economy.

Key concepts

financial accelerator: the mechanism by which adverse shocks to entrepreneurial net worth raise the external finance premium (the spread between the loan rate and the risk-free rate), reduce investment and output, further depress net worth, and generate amplified cycles; the core friction in the E-DSGE model and the channel through which carbon pricing affects welfare costs.

procyclical permit prices: the endogenous tendency of permit prices under a CAT scheme to fall when aggregate economic activity and emissions decline; the paper’s central mechanism through which CAT acts as an automatic stabilizer for the financial accelerator — permit prices fall precisely when firms’ balance sheets are most stressed, reducing compliance costs and partially offsetting amplification.

welfare cost of business cycles (Lucas measure): the percentage of consumption that a representative household would be willing to give up to move from a world with business cycle fluctuations to one without, evaluated relative to the deterministic steady state; in the paper’s baseline calibration, this is 0.6178 percent under CAT and 1.5231 percent under a carbon tax.

reserve requirement macroprudential regulation: a regulatory constraint requiring banks to hold a fraction of deposits in reserves, limiting loan supply; implemented in the model as Φ_t ∈ (0,1] where lower Φ_t requires banks to hold more reserves; a static 2 percent reserve requirement already substantially narrows the welfare gap between carbon pricing regimes, and an optimal dynamic rule nearly closes it.

E-DSGE (Environmental DSGE): the paper’s model class — a DSGE with financial frictions (Christiano-Motto-Rostagno financial accelerator) and a carbon pricing sector; used to analyze the interaction between environmental policy instruments and macroprudential regulation in an economy with both climate and financial externalities.

coefficient of variation of emissions (CVE) / permit prices (CVP_E): volatility measures used to assess how macroprudential regulation affects the business-cycle properties of each carbon pricing instrument; macroprudential regulation substantially reduces CVP_E under CAT and CVE under a carbon tax, making each instrument’s uncertainty properties more symmetric.

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.

Firm Quality Dynamics and the Slippery Slope of Credit Intervention

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

Crises have cleansing effects—low-quality firms face greater financial shortfalls and invest less than high-quality firms—but public credit support dampens these effects by reducing financing cost differentials, distorting the firm quality distribution downward and reducing total productivity. This trade-off between preserving output capacity and distorting quality determines the optimal size of intervention. The distortionary effects are self-perpetuating: a downward bias in quality necessitates interventions of greater scale in future crises, implying further distortions—a “slippery slope.” The distortions are amplified by expectations: because low-quality firms expect underpriced government funding in future crises, their Tobin’s q is biased upward, leading them to overinvest even in normal times, while high-quality firms may underinvest. A low interest rate environment exacerbates the distortionary effects because the low yield on savings discourages firms from accumulating precautionary internal liquidity against crises.

In depth

Q1. What are the cleansing effects of crises and how does credit intervention dampen them?

Crises have cleansing effects because low-quality firms face tighter financial constraints and have lower Tobin’s q, causing them to invest less than high-quality firms; public credit support reduces this differential, preserving overall production capacity but distorting the quality distribution downward. The model follows the limited-commitment literature (Kehoe-Levine, Kiyotaki-Moore, Rampini-Viswanathan): firms differ in productive capital quality that also serves as collateral. Government intervention is valued because the government has superior enforcement ability compared to private investors, but its credit support cannot be perfectly priced by quality—due to informational limits or political constraints—so it pulls financing costs of high- and low-quality firms closer together, dampening the cleansing mechanism.

Q2. What is the “slippery slope” mechanism?

The slippery slope arises because the downward bias in the quality distribution induced by one intervention necessitates larger interventions in future crises, generating a ratchet toward ever-larger public credit support. After intervention, high-quality firms accumulate capital less rapidly than they would absent intervention, while low-quality firms’ capital shares remain higher than in the laissez-faire equilibrium. The resulting lower aggregate productivity means that future crises are more severe in terms of output loss, requiring a larger optimal intervention, which in turn further distorts the quality distribution.

Q3. How do expectations of future intervention amplify the distortions?

Because low-quality firms expect underpriced credit support in future crises, their Tobin’s q is biased upward, motivating them to overinvest even in normal times; simultaneously, high-quality firms may underinvest because their Tobin’s q may fall below the first-best level. The self-perpetuating distortion thus operates through both the crisis-time reallocation channel and the pre-crisis investment channel, amplifying the divergence from the efficient allocation relative to a setting with no anticipation effects.

Q4. Why does a low interest rate environment exacerbate the distortionary effects?

A low interest rate environment exacerbates the distortionary effects of credit intervention because the low yield on savings discourages high-quality firms from accumulating precautionary internal liquidity against crises, causing them to invest less in crises and requiring a greater scale of credit support. Low-quality firms, expecting underpriced government funding, have even less incentive to self-insure through savings when interest rates are low, further worsening the quality distribution. The paper’s findings echo cautions against ultra-low interest rates (Brunnermeier and Koby, 2018; Quadrini, 2020) by providing a distinct mechanism operating through firm quality dynamics.

Q5. Can intervention be welfare-improving despite the distortions?

The paper shows that when carefully designed, intervention can improve welfare even though it generates distortionary effects on the firm quality distribution—the trade-off between preserving production capacity and distorting quality determines the optimal size of intervention. This framing does not suggest intervention should be avoided, but that its optimal scale requires balancing the quantity-preserving benefit against the quality-distorting cost. The paper previously circulated as “The Distortionary Effects of Central Bank Direct Lending on Firm Quality Dynamics.”

Key concepts

cleansing effect of crises : the tendency for crises to reduce the investment of low-quality firms relative to high-quality firms through tighter financial constraints, reallocating capital toward higher-productivity uses; credit intervention dampens this by reducing the financing cost differential. slippery slope of intervention : the self-perpetuating dynamic in which intervention-induced downward distortion of the quality distribution necessitates larger interventions in future crises, generating a ratchet toward ever-larger public credit support. credit mispricing : the inability of public credit support to differentiate financing costs by firm quality, arising from informational limits or political constraints on discriminatory treatment; the proximate source of the quality-distribution distortion.

Illiquid Lemon Markets and the Macroeconomy

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

The paper develops a quantitative capital-accumulation model in which capital trades in illiquid markets with asymmetric information — sellers know the quality of their capital but buyers do not. It combines this model with microdata on nonresidential capital units listed for trade to measure the degree of information asymmetry and quantify its macroeconomic effects.

Model: The economy features heterogeneous capital units characterized by observed quality ω (e.g., size, location, age — observable to both buyers and sellers) and unobserved quality a (known only to the seller). Capital trades in directed-search markets: sellers post a price and a target submarket; buyers direct their search; a matching function determines trade probabilities. Buyers observe announced quality and have an inspection technology that reveals true quality with probability ψ (“lemon detection probability”); with probability 1−ψ a low-quality unit goes undetected. In equilibrium, sellers of high-quality capital signal their type by listing at higher prices and accepting lower trading probabilities (the Guerrieri-Shimer-Wright 2010 competitive search separating equilibrium, adapted to the capital accumulation setting). The key model prediction is that the residual price — the component of a listed price orthogonal to observed characteristics — is positively correlated with duration on the market, with the slope increasing as the degree of asymmetric information (1−ψ) rises.

Data: Idealista, Spain’s largest online real estate platform, provides monthly listings for all nonresidential structures (retail, office, and industrial space) listed for sale from 2005 to 2018 — approximately 8.9 million property-month observations from over 1.15 million distinct capital units. The average listed price per square foot is $162 (2017 dollars); the average duration on the market is 10.5 months; each listing receives on average 800 views, 45 clicks, and 3 emails per month from prospective buyers.

Empirical facts (Section 4): Two cross-sectional regularities confirm the model’s predictions:

Predicted price (from a hedonic regression on observable characteristics) is negatively correlated with duration — units with better observable characteristics sell faster, consistent with full-information competitive search (higher buyer valuation → higher matching rate)
Residual price (orthogonal to observables) is positively correlated with duration — estimated slope coefficient ŷq ≈ 0.148 — consistent with asymmetric-information signaling (high-quality capital sellers post high residual prices to separate from low-quality sellers, accepting lower trading probabilities)
The residual-price/duration slope exhibits strong countercyclical variation, roughly doubling during the Euro crisis (peak slope ≈ 0.38, compared to baseline ≈ 0.148), consistent with asymmetric information worsening during downturns

Calibration (monthly frequency, Table 4 fixed; Table 5 fitted):

Fixed parameters: β = 0.9966 (annual rate of time preference 4%), α = 0.35 (capital share), δ = 0.0074/month (8.5% annual nonresidential depreciation), γ = 1.004 (1.6% annual TFP growth), γn = 1.0027 (1% annual population growth), ϕ = 0.0027 (3.2% annual firm exit rate), η = 0.8 (matching curvature), φ = 0.5 (seller bargaining power)
Fitted to four data moments (slope ŷq, SD of predicted prices, SD of residual prices, mean duration): ψ = 0.9795 (probability a lemon goes unnoticed = 2% per inspection); σω = 0.72 (SD observed quality); σa = 0.58 (SD unobserved quality); m̄ = 0.267 (matching efficiency)
Model-simulated moments match targets essentially exactly (Table 5); untargeted relationship between duration and predicted prices is also well-matched (Table 6)

Steady-state output effects (Table 7, relative to full-information benchmark):

Total output: −1.22% in baseline (ψ = 0.9795)
Effective capital input: −2.55% (main driver of output loss)
Capital stock: −1.12% (32% of output effect — reduced returns to producing new capital)
Capital unemployment rate: +1.0 pp above full-information rate of 5% (25% contribution — high-quality capital remains listed longer)
Allocation channel: 16% contribution — information asymmetries disproportionately reduce trading of high-quality capital, lowering average quality of employed capital
Labor input: −0.5% (26% contribution — reduced capital input lowers labor demand)
Moving to full information (ψ → 1): output gain of +1.5% — modest at baseline, indicating the baseline economy is not far from full information
Moving to Euro-crisis level (ψ = 0.96): output decline of ~2% — large response because the economy’s output elasticity to ψ is high

Crisis experiment (Section 5.3): An unexpected 2 percentage-point decline in ψ (to 0.96, calibrated to match the observed increase in the residual-price/duration slope during the Euro crisis), lasting 3 years and reverting with persistence ρψ = 0.94:

Output contraction on impact: 2%
Time to recover half the output decline: more than 5 years (slow recovery driven by persistent capital underinvestment)
Primary mechanism: lower inspection accuracy → high-quality capital sellers reduce trading probability to signal quality → capital unemployment rate rises (especially for high-quality units) → expected return to producing new capital falls → investment contracts → capital input declines persistently
Secondary interaction: at higher steady-state asymmetric information (ψ = 0.96), other shocks (TFP, exit rate, discount factor) are amplified — e.g., the cumulative output response to an exit rate shock is 26% larger than in a full-information economy

Scope conditions: The model abstracts from aggregate uncertainty (the baseline is steady-state analysis), financial intermediaries, and endogenous information technology. The dataset covers Spain’s nonresidential real estate market 2005–2018; the measurement of ψ from listed prices and duration assumes that residual prices fully reflect unobserved capital quality (Proposition 5’s small-search-cost approximation). The quantitative results are robust to alternative bargaining protocols (TIOLI), higher firm exit rates, inelastic labor supply, and narrower observable-characteristic sets.

In depth

Q1. Why does asymmetric information generate a positive correlation between residual prices and duration?

In the model’s separating equilibrium, sellers of high-quality capital choose prices and targeting strategies that prevent low-quality sellers from mimicking them; since low-quality sellers have a lower marginal cost of accepting lower trading probabilities (their capital is worth less to them in continued use), high-quality sellers can separate by listing at higher residual prices paired with lower market tightness and lower matching rates. The correlation between residual price and duration is therefore a direct measure of the degree of asymmetric information: the slope coefficient ŷq increases monotonically as ψ decreases (Proposition 5 and Figure 4), allowing the researcher to back out ψ from the micro data.

Q2. Why is the residual-price/duration slope countercyclical?

The data show that the slope roughly doubled during Spain’s 2008–2013 downturn and euro crisis, consistent with the model’s prediction that asymmetric information (1−ψ) worsens during economic contractions. The paper interprets this as evidence that buyers’ ability to evaluate capital quality deteriorates when economic uncertainty rises — for example, during crises it is harder to assess the profitability of retail or office space based on observable characteristics alone. This countercyclical pattern motivates the crisis experiment in Section 5.3, where a 2pp increase in 1−ψ (the degree of information asymmetry) replicates the observed slope dynamics.

Q3. Why is the 2% crisis output contraction slow to recover?

The sluggishness of recovery operates through the investment channel: when high-quality capital sellers reduce trading probabilities to signal their type, they slow the transfer of used capital from sellers (firms that exit) to buyers (firms that expand), reducing the effective capital input; this lower capital input reduces the expected marginal return to producing new capital, depressing investment; because capital accumulates gradually, the output recovery inherits the slow pace of investment recovery. The persistence parameter ρψ = 0.94 (monthly) adds further sluggishness from the slow normalization of the information environment itself.

Q4. Why are the steady-state output losses modest while the crisis response is large?

The economy features a moderate baseline degree of asymmetric information (ψ = 0.9795 — only 2% lemon-detection failure), so the steady-state distortion is small (−1.22% output relative to full information); however, the economy has a large elasticity of output to ψ, so even a small deterioration in information quality (2pp) generates large output effects (−2%). This high sensitivity arises because the effects of asymmetric information are highly nonlinear: at low levels of information frictions, small increases in the lemon probability generate proportionally large increases in the required signaling by high-quality sellers, sharply reducing their trading probabilities.

Q5. How does asymmetric information interact with other shocks?

At the baseline degree of asymmetric information (ψ = 0.9795), the aggregate responses to standard shocks (TFP, discount factor, exit rate) are similar to an economy with full information; however, at the Euro-crisis level (ψ = 0.96), the cumulative output response to an exit rate shock is 26% larger than under full information. The mechanism is that asymmetric information taxes the reallocation of capital: when more capital must be reallocated (due to higher firm exit), more of it passes through the illiquid, distorted lemon market, amplifying the output effect of the underlying shock.

Q6. What policies can reduce the distortions from asymmetric information?

The paper notes two broad policy directions: (1) policies that improve information transparency — making previously private capital characteristics public, e.g., mandatory disclosure or standardized quality certification — directly raise ψ and shift the economy toward full information, eliminating the signaling distortion; (2) policies that reduce the incentive for mimicking — for example, by allowing post-transaction renegotiation after quality is revealed (the TIOLI bargaining extension in Table 8) — have similar quantitative effects to the baseline. The paper leaves the welfare analysis of specific information-provision policies for future research.

Q7. What is the role of the data in identifying the model parameters?

The four targeted moments — slope of duration on residual prices, standard deviation of predicted prices, standard deviation of residual prices, and mean duration — jointly identify the four structural parameters {ψ, σω, σa, m̄} (Proposition 5); the key insight is that ψ and m̄ are separately identified because ŷq and mean duration respond differently to each: ψ and m̄ both affect ŷq positively, but m̄ reduces mean duration while ψ increases it, providing orthogonal variation. The calibration achieves an essentially exact match of the four targeted moments (Table 5) and also matches the untargeted negative slope between duration and predicted prices (Table 6), providing an overidentification check.

Key concepts

lemon market : a secondary market for heterogeneous assets in which sellers have private information about quality; following Akerlof (1970), lemons (low-quality assets) crowd out high-quality assets unless high-quality sellers can credibly signal their type; in the paper, signaling takes the form of higher listed prices paired with lower trading probabilities.

residual price : the component of a capital unit’s listed price orthogonal to its observable characteristics (the residual from a hedonic regression); the paper’s key empirical variable, theoretically shown to be positively correlated with unobserved capital quality and with duration under asymmetric information.

inspection technology : a buyer’s technology that reveals the true quality of a capital unit with probability ψ before (or after) purchase; the accuracy ψ governs the degree of asymmetric information in the economy — lower ψ implies worse information, requiring more costly signaling by high-quality sellers.

countercyclical asymmetric information : the empirical finding that the slope between residual prices and duration roughly doubles during the Euro crisis, interpreted as deterioration in buyers’ ability to evaluate capital quality during economic downturns; motivates the crisis experiment.

three channels of output loss : the three mechanisms through which asymmetric information reduces output: (i) lower capital stock (reduced investment incentives); (ii) higher capital unemployment rate (high-quality capital remains listed longer); (iii) adverse allocation effect (high-quality capital trades less frequently, lowering average quality of employed capital).

Money Markets, Collateral and Monetary Policy

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

The paper studies the euro area interbank money markets during the global financial crisis (2007–09) and sovereign debt crisis (2010–15), documenting four empirical regularities and building a quantitative general equilibrium model to evaluate their macroeconomic impact and the role of central bank policy. The central finding is that the ECB’s collateral policy — lending to banks at haircuts more favorable than private markets — prevented output and investment from falling roughly twice as much as they would have under a passive constant-balance-sheet policy.

Four empirical observations (Section 2, 2003–2015):

The share of unsecured interbank borrowing declined throughout the euro area; banks substituted toward secured (repo) transactions — the secured share rose from roughly 42% to 90% of turnover
Private market haircuts on Southern sovereign bonds (IT, ES, PT) rose dramatically during the sovereign debt crisis, peaking at 25.16% in 2012–2013 (vs 3% in 2010) — while the ECB kept its haircuts nearly unchanged, creating a “haircut gap”
Bank borrowing from the ECB increased eight-fold in Southern regions as the haircut gap widened
Household deposits at banks remained stable throughout

Model architecture (Section 3): Two regions (North: DE/FR; South: IT/ES/PT) share a common central bank. Each period is divided into a morning and afternoon. In the morning, banks choose portfolios subject to a Gertler-Karadi (2011) leverage constraint (fraction λ of assets can be diverted by the manager) and a central bank collateral constraint (CB loans require bonds pledged at CB haircut η). In the afternoon, banks face idiosyncratic liquidity shocks ω~iid F(ω) on deposits. Connected banks (fraction ξ) can borrow unsecured in the afternoon interbank market. Unconnected banks (fraction 1−ξ) must cover their maximum possible payment outflow ωmaxD by holding reserves or pledging bonds as collateral in the private secured market (at haircut 1−η̃^γ). Five inequality constraints — the morning leverage constraint, a CB collateral constraint, and three short-sale constraints (bonds, deposits, capital) — can each switch between binding and slack; the model requires a non-linear solution (Dynare Levenberg-Marquardt mixed complementarity solver).

Calibration (Table 2, quarterly frequency):

Standard: capital share θ = 0.33, depreciation δ = 0.02, discount factor β = 0.994, Frisch inverse ε = 0.40, government spending g = 0.566
Bond maturity 1/κ = 5.952 years; dividend fraction φ = 0.025; leverage constraint λ = 0.701
Pre-crisis interbank structure: ξ = 0.42 (42% connected), haircuts η̃ = η = 0.97 (3%)
Maximum liquidity shock ωmax = 0.10; foreign sector bond demand elasticity ρ = 1.757
6 targeted moments (Table 3, exact fit): Govt/GDP = 0.20; bank leverage = 6; annual bond spread = 0.2%; bank share of bond holdings = 23%; foreign sector share = 64%; annual inflation = 2%
Non-targeted moments broadly matched: central bank bond holdings/GDP, government debt/GDP

Two shock processes (Section 5.2):

ξ shock (permanent, onset t=1 corresponding to 2009 Q1): connected share log(ξt) transitions from ξ−1 = 0.42 to ξ∞ = 0.10 with AR(1) persistence ρξ = 0.95
η̃S shock (temporary-persistent, onset t=13 corresponding to 2012 Q1): Southern private haircut recovery factor follows AR(2) with ρη1 = 1.65, ρη2 = −0.70 and an initial impulse ε13 = −0.11; model haircuts peak at 25%, matching the data peak of 25.16%

Comparative statics (Section 6.1):

ξ shock alone: As the share of unconnected banks rises from 0.58 to 0.89 (pre- to post-2008 average), the capital stock falls 10% on aggregate and output declines 1.8% in the new steady state; no CB intervention occurs because CB and private haircuts are equal — banks have no incentive to use CB funding
η̃S shock alone (without prior ξ shift): Output falls only 0.15% even as private haircuts reach 40% in comparative statics; the muted effect arises because collateral markets are segmented in the baseline — Northern banks hold only Northern bonds (unaffected haircuts), fully counteracting Southern banks’ investment decline

Dynamic analysis (Section 6.2): In the full simulation combining both shocks:

The ξ shock causes an immediate output and investment overshoot below the new steady-state: anticipating future crowding-out of capital (unconnected banks hold bonds/reserves rather than investing), bank net worth falls immediately and leverage declines, pushing output below the eventual new steady state before gradual recovery
The η̃S shock (at t=13) additionally tightens collateral constraints for unconnected banks in the South; they endogenously switch to holding money as collateral, which integrates money markets across regions and creates a pecuniary externality on Northern banks (all banks now face the same higher collateral price for money) — a sharp contrast to the segmented-market comparative statics where Northern banks were unaffected
CB take-up peaks at 2.5% of total bank assets under CO policy, closely matching the data

CO policy vs CB policy counterfactual (Section 6.2.3, Figure 10):

Under the CO policy (benchmark: ECB keeps CB haircut at 3% while private market haircuts rise to 25%), unconnected banks in the South substitute expensive deposit funding for cheaper CB funding, reducing the collateral premium for money and directly benefiting Northern unconnected banks (pecuniary externality channel)
Under the CB policy (counterfactual: constant balance sheet, CB haircut = 100%), this substitution is impossible; collateral scarcity is unmitigated; the Northern banks’ spillover is larger
Main result: output and investment fall around twice as much on impact under the CB policy; the CB policy also produces a stronger post-crisis rebound as higher initial capital returns raise bank leverage
Conclusion: the ECB’s collateralized lending operations were crucial in containing the crisis, working through a haircut-gap channel that reduced the premium on collateral and attenuated the pecuniary externality between North and South

Scope conditions: Sovereign default risk on government bonds is treated as exogenous (the model does not endogenize default); the paper notes this would require a separate analysis linking haircuts to default probabilities. Prices are set one period in advance (not a full NK model), which disciplines inflation dynamics but is not a full monetary policy analysis. The model abstracts from the ECB’s Securities Markets Programme (sterilized asset purchases, not in scope). The two-region framework aggregates heterogeneous countries into North and South. Results depend on the perfect-foresight assumption; uncertainty about the path of shocks would introduce additional precautionary effects.

In depth

Q1. Why did the decline in unsecured interbank lending harm the real economy?

Unsecured interbank borrowing allows banks to pool idiosyncratic liquidity shocks without holding any liquid buffer; when unconnected banks (unable to borrow unsecured) must instead cover their maximum possible afternoon deposit outflow ωmaxD by holding bonds or reserves, they divert balance sheet capacity away from capital investment, crowding it out. As the share of unconnected banks rises from 42% to 90%, this crowding-out effect operates through two channels: (i) direct diversion of assets from productive capital to unproductive liquidity buffers; (ii) higher demand for collateral raises the collateral premium on bonds, increasing the effective cost of deposit funding and inducing all banks — even connected ones — to downsize their balance sheets through the leverage constraint.

Q2. Why was the steady-state impact of Southern haircuts muted while the dynamic impact was large?

In the baseline steady-state, collateral markets are segmented: Northern unconnected banks hold only Northern bonds (unaffected by Southern haircuts) and Southern unconnected banks hold only Southern bonds; in comparative statics, Northern banks absorb the capital freed by Southern banks’ disinvestment and the aggregate effect is small (−0.15% output for haircuts rising to 40%). In the dynamic model, however, the prior ξ shock has already pushed Northern unconnected banks to hold money as collateral (since high bond demand from all unconnected banks raises bond prices until money becomes the cheaper alternative); when Southern haircuts then spike, Southern banks also switch to money as collateral — and since money is a non-regional collateral, its price spike affects all unconnected banks simultaneously, integrating the previously segmented collateral markets and transmitting the Southern shock to the North.

Q3. How does the CO policy’s “haircut gap” channel work?

Under CO policy, the ECB maintains its haircut at 3% while private markets charge 25%; for each unit of collateral, a bank can access (1−0.03)=0.97 units from the ECB but only (1−0.25)=0.75 units from the private repo market — a 22-percentage-point haircut gap that makes ECB funding more efficient per unit of collateral pledged. When private haircuts rise, unconnected Southern banks face a collateral scarcity that makes deposit funding more expensive (higher afternoon constraint tightening); under CO policy, they optimally substitute toward CB funding, reducing their dependence on expensive deposits and mitigating the collateral premium spike. This directly benefits Northern unconnected banks because the reduced collateral premium for money (driven by Southern banks switching out of money as collateral) relaxes their own afternoon constraints without any direct exposure to Southern bonds.

Q4. Why does the CB policy produce a stronger post-crisis rebound?

The CB policy’s larger initial output and investment decline implies a larger undershoot below the new (post-ξ) steady state; during the recovery phase, banks face elevated returns on capital investment because capital is below its steady-state level; these higher returns raise bank net worth and allow more aggressive leverage, producing a steeper rebound than under the CO policy where the downturn was mitigated. This “larger crisis, faster recovery” tradeoff means the CB policy does not necessarily produce lower total welfare than the CO policy over the full cycle — the welfare comparison requires integrating the entire path, not just comparing the initial impact.

Q5. What makes the model require a non-linear solution?

The model features five inequality constraints that each can switch between binding and slack as parameters change: the morning leverage constraint, a collateral constraint on CB loans, and three short-sale constraints (kt,i ≥ 0, Bt,i ≥ 0, Dt,i ≥ 0). Standard linearized DSGE methods assume constraints are either always binding or always slack; here, for instance, connected banks begin holding positive money balances only when the share of unconnected banks rises past a threshold (0.61 in comparative statics), at which point the collateral premium rises enough to equalize returns on bonds and money — a kink that requires tracking which constraints are active. The Dynare Levenberg-Marquardt mixed complementarity solver handles these transitions, with T=400 periods imposed to ensure convergence to steady state.

Q6. What is the role of the leverage constraint in transmitting interbank frictions to the real economy?

The leverage constraint (Gertler-Karadi 2011) limits each bank’s total assets to Vt,i/λ; when money market frictions reduce the bank’s value Vt,i — either directly (collateral premia reduce bond prices and thus net worth) or through lower expected future net worth — the binding leverage constraint forces a proportional reduction in all assets including capital. This is the channel through which a purely financial friction in interbank markets (collateral scarcity) translates into a real investment decline: the leverage constraint links bank net worth to lending capacity, and interbank frictions that depress net worth also shrink investment. The result that “output and investment fall around twice as much” under CB policy is quantitatively driven by this chain: CB policy mitigates the collateral premium, preserving net worth and thus the lending capacity of banks.

Q7. Why do household deposits remain stable even as interbank markets are disrupted?

The model’s equilibrium has banks absorbing shocks through their balance sheet structure (switching between deposit funding, CB funding, bonds, and money) rather than through deposit supply; household deposits Dt,i are determined by households’ intertemporal optimization and the deposit rate, both of which are relatively insulated from the interbank friction. The friction operates within the banking system (between banks, or between banks and the CB), not in the retail deposit market; the afternoon liquidity shocks are interbank in nature (payment flows between banks) and are settled without household involvement. This matches Observation 4 from the data (stable household deposits) and is consistent with the mechanism: banks’ portfolio recomposition toward CB funding or bonds is a liability-side substitution that leaves retail deposits intact.

Key concepts

haircut gap channel : the mechanism through which the ECB’s policy of maintaining favorable haircuts (3%) on collateral while private market haircuts spike (to 25%) provides effective relief from collateral scarcity; banks can access more liquidity per unit of pledged collateral from the ECB than from the private repo market, inducing substitution from deposit funding to CB funding when the private haircut gap widens.

connected vs. unconnected banks : the model’s key bank heterogeneity; connected banks (fraction ξ) can borrow unsecured in the afternoon interbank market and therefore need no liquidity buffer; unconnected banks must cover their maximum afternoon payment outflow ωmaxD with reserves or pledged bond collateral, crowding out capital investment — the shift from ξ = 0.42 to ξ = 0.10 is the model’s representation of the euro area secured-market shift.

pecuniary externality (North-South spillover) : the channel through which a rise in Southern bond haircuts affects Northern banks even though Northern bonds are not repriced; when Southern banks switch to holding money as collateral, the demand for money rises, pushing up its collateral price; Northern unconnected banks (already holding money after the ξ shock) pay the higher price, tightening their afternoon constraint and reducing their capital investment indirectly.

collateral premium : the shadow price on bonds arising from their dual role as investment assets (in the morning) and collateral for afternoon liquidity (in the private repo or CB markets); when the afternoon constraint is binding, the collateral premium is positive — bonds are valued above their pure investment return — and determines how much of a bank’s balance sheet is diverted from capital to liquidity buffers.

CO policy vs CB policy : the paper’s two scenarios for the ECB’s response; CO policy (benchmark) maintains collateralized lending at a fixed (favorable) CB haircut, allowing CB balance sheet expansion as private haircuts rise; CB policy (counterfactual) keeps the balance sheet constant (CB haircut = 100%, no CB lending), forcing all liquidity needs to be met through private markets — the comparison isolates the macroeconomic value of the ECB’s lender-of-last-resort function.

Oil price fluctuations, US banks, and macroprudential policy

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

This paper estimates the effect of oil price fluctuations on US banking variables using a Bayesian SVAR with sign restrictions following Baumeister and Hamilton (2019). Oil market shocks that lead to a contraction in world economic activity are found to unambiguously lower the amount of bank credit to the US economy, tend to decrease US banks’ net worth, and tend to increase the US credit spread. The effects can be strong and long-lasting or more modest and short-lived, depending on the source of the oil price fluctuation. The effects are found to be stronger for smaller and lower-leveraged banks.

In depth

Q1. What is the empirical strategy?

The paper extends the state-of-the-art oil market SVAR of Baumeister and Hamilton (2019) to incorporate three US banking variables—banks’ net worth, the US credit spread, and the amount of bank credit extended—estimated with monthly data over January 1974 through December 2019. An agnostic approach is taken on sign restrictions for the US banking block: no restrictions are imposed on banking variables beyond those already imposed by Baumeister and Hamilton (2019) on the oil block, so the results for banking variables are driven primarily by data rather than prior restrictions. This extends earlier work that studied oil prices and credit spreads (Abbritti et al., 2020) or oil prices and stock markets (Kilian and Park, 2009) in isolation.

Q2. What is the main finding regarding the effect of oil shocks on banks?

Oil market shocks that lead to a contraction in world economic activity are found to unambiguously lower the amount of bank credit to the US economy, tend to decrease US banks’ net worth, and tend to increase the US credit spread. “Unambiguously” reflects that the sign restrictions impose no prior on the direction of credit’s response, so the finding that credit falls is driven entirely by data. The paper is the first to characterize the effect of oil market shocks on banks’ net worth and to estimate the credit effect within the SVAR framework.

Q3. How do the effects differ by the source of oil price fluctuations?

The effects on banking variables can be strong and long-lasting or more modest and short-lived, depending on the underlying source of the oil price change—reflecting the SVAR framework’s decomposition of oil price movements into distinct structural shocks. The distinction between oil supply shocks, demand shocks driven by global activity, and demand shocks driven by speculative factors implies that shocks of the same sign in the oil price may have different magnitudes and durations of effects on banks, consistent with Kilian (2009)’s decomposition.

Q4. Which banks are most affected?

The effects of oil market shocks on banking variables are found to be stronger for smaller and lower-leveraged banks. Smaller banks may be more exposed to oil-related regional economic downturns through concentrated loan portfolios, while lower-leveraged banks may face different collateral and risk dynamics relative to more highly leveraged peers.

Key concepts

oil market Bayesian SVAR : a structural vector autoregression that uses a Bayesian prior over sign restrictions to identify oil supply shocks, oil demand shocks related to global real activity, and oil-specific demand shocks, following Baumeister and Hamilton (2019); extended here to include US banking variables. credit spread : the difference between yields on corporate bonds or loans and a risk-free reference rate; used as a measure of the credit risk premium and financial conditions in US credit markets.

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.

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).

Stock market participation and macro-financial trends

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

This paper documents a puzzle for canonical limited-participation models: when U.S. stock market participation rose from 31.6% to 53% between 1989 and 2007—a period also characterized by the Great Moderation—the equity premium and stock return volatility increased rather than fell as those models would predict. The paper resolves this puzzle using an RBC model with concentrated capital ownership in which capitalists have external habit utility with a habit stock that depends on aggregate per capita consumption. As participation rises, the representative capitalist’s consumption converges to aggregate consumption, shrinking the surplus-consumption ratio and raising endogenous average risk-aversion; this risk-aversion channel dominates the conventional risk-sharing channel (which predicts a lower equity premium under higher participation). The model implies that higher participation generates a sizeable rise in both the equity premium and stock return volatility while reducing the risk-free rate and aggregate consumption volatility—jointly explaining the observed U.S. macro-financial patterns. Household-level data from the Consumption Expenditure Survey (1984–2017) and cross-state variation support the model’s mechanism.

In depth

Q1. What is the puzzle the paper addresses?

Existing limited-participation models predict that higher stock market participation should reduce the equity premium (by improving risk-sharing), yet the U.S. experienced a rising equity premium and higher stock return volatility precisely during the period of sharp participation growth (1989–2007), at the same time as the Great Moderation. The standard channel predicts that as more households access financial markets, the representative capitalist’s risk burden falls and the covariance between capitalists’ consumption and equity returns declines, lowering the equity premium. The data contradict this prediction, motivating the paper’s novel mechanism.

Q2. What is the novel risk-aversion channel?

As stock market participation rises, the representative capitalist’s consumption converges toward aggregate per capita consumption, shrinking the surplus-consumption ratio and thereby raising the endogenous effective risk-aversion of the economy—this risk-aversion channel dominates the conventional risk-sharing channel. The key assumption is that capitalists’ habit stock depends on aggregate per capita consumption. The surplus-consumption ratio (the gap between the capitalist’s consumption and the habit level) determines risk-aversion in the external habit utility framework. As participation rises, the capitalist’s consumption approaches the habit level, increasing risk-aversion and the equity premium, even as aggregate consumption volatility falls.

Q3. What are the model’s predictions for macro-financial variables?

In the model economy, an increase in stock market participation generates a sizeable rise in both the equity premium and the volatility of stock returns, a moderate increase in the price-dividend ratio, and a fall in the average risk-free rate and aggregate consumption volatility—jointly accounting for the U.S. macro-financial experience since the 1980s. The rise in equity premium and stock volatility produced by higher participation substantially counteracts the shrinking effect due to lower aggregate uncertainty from the Great Moderation, providing a unified explanation for the co-movement of these macro-financial trends.

Q4. What is the empirical evidence supporting the mechanism?

Household-level data from the U.S. Consumption Expenditure Survey (1984–2017) show that the model-implied average risk-aversion for the representative stockholder trended upward over time closely tracking the rate of participation, while the stockholder-to-aggregate consumption ratio trended downward; cross-state data document a negative relationship between participation and the stockholder-to-aggregate consumption ratio. Both the time-series and cross-sectional patterns are consistent with the model’s prediction that higher participation compresses the gap between stockholder and aggregate consumption, the key driver of the risk-aversion channel.

Key concepts

participation puzzle : the empirical regularity that only a fraction of the population participates in the stock market; exploited in asset pricing models to explain the equity premium with plausible average risk-aversion; this paper studies the consequences of the upward trend in participation since the 1980s. surplus-consumption ratio : the gap between the capitalist’s consumption and their habit level, normalized by consumption; the key state variable in external habit utility models; determines endogenous risk-aversion so that a shrinking surplus-consumption ratio raises risk-aversion. risk-aversion channel : the novel mechanism introduced in this paper: as stock market participation rises, the capitalist’s consumption converges to aggregate consumption, shrinking the surplus-consumption ratio and raising endogenous risk-aversion and thus the equity premium; dominates the conventional risk-sharing channel in the model. risk-sharing channel : the conventional channel in limited-participation models: higher participation improves risk-sharing, reducing the covariance between stockholder consumption and equity returns and tending to depress the equity premium; present in the model but dominated by the risk-aversion channel.

Turbulent business cycles

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

Firm-level evidence shows that recessions are characterized not just by aggregate downturns but by a sharp rise in turbulence—a reshuffling of firms’ productivity rankings in which high-productivity firms are less likely to maintain their relative standing. This paper documents four stylized facts about the macroeconomic and cross-sectional effects of turbulence (measured as one minus the Spearman rank correlation of firm-level TFP between adjacent years in Compustat data): turbulence is countercyclical; increases in turbulence reallocate labor and capital from high- to low-productivity firms; turbulence is negatively correlated with aggregate manufacturing TFP and the aggregate stock market; and an increase in turbulence is associated with persistent declines in real GDP, consumption, investment, and employment. To explain the mechanism, the authors build a real business cycle model with heterogeneous firms and financial frictions: when turbulence rises, high-productivity firms’ expected equity values fall because their productivity is less likely to persist, which tightens their borrowing constraints relative to low-productivity firms, inducing reallocation that reduces aggregate TFP. Crucially, turbulence differs from uncertainty shocks because it changes both the conditional mean and variance of the firm productivity distribution, enabling it to generate synchronized recessions with declining aggregate activity.

In depth

Q1. How is turbulence measured and how does it differ from uncertainty?

Turbulence is measured as one minus the Spearman rank correlation (ρₜ) of firm-level total factor productivity between adjacent years using Compustat data; a low correlation indicates more churning of productivity rankings, so 1 − ρₜ rises in recessions. The authors use an instrumental variable approach to correct for attenuation bias from measurement error in firm-level TFP, following Bloom et al. (2018) for the baseline construction. The conceptual distinction from uncertainty is that uncertainty shocks only raise the conditional variance of the productivity distribution while leaving the conditional mean unchanged. A turbulence shock changes both: it makes the conditional mean of future productivity lower for currently high-productivity firms and higher for currently low-productivity firms, thereby inducing reallocation from high to low producers and generating first-moment effects on aggregate output that pure uncertainty shocks cannot produce.

Q2. What are the empirical facts about turbulence, and how are they established?

The paper documents four facts using a vector autoregression with turbulence orthogonalized against uncertainty and other aggregate shocks: (1) turbulence is countercyclical, rising sharply in recessions; (2) an increase in turbulence reallocates labor and capital from high- to low-productivity firms, an effect that is amplified by financing constraints; (3) turbulence is negatively correlated with aggregate manufacturing TFP and aggregate stock market value; and (4) turbulence shocks generate persistent declines in GDP, consumption, investment, and employment. The reallocation effects in fact (2) remain significant after controlling for the confounding effects of recessions and uncertainty, and the amplification by financing constraints is separately identified.

Q3. What is the model mechanism through which turbulence drives recessions?

In the model, firms produce using capital and labor subject to idiosyncratic productivity and borrowing constraints tied to expected equity value; when turbulence rises, high-productivity firms are less likely to remain productive, reducing their expected equity value and tightening their borrowing constraints relative to low-productivity firms. This differential tightening induces reallocation of labor and capital toward low-productivity firms, reducing aggregate TFP. The feedback through equity values and collateral constraints amplifies the reallocation and generates aggregate-level recessions with synchronized declines in activity. The mechanism is distinct from models in which all firms face symmetric uncertainty shocks: turbulence creates differential effects by firm productivity level.

Q4. How does the model match the observed macroeconomic dynamics?

The calibrated model replicates the empirical dynamics: it generates the observed reallocation from high- to low-productivity firms, declines in aggregate TFP and stock market value, and persistent contractions in GDP, consumption, investment, and employment following a turbulence shock. The financial frictions play a quantitatively important role in amplifying the reallocation effects, consistent with the empirical finding that financing constraints amplify the cross-sectional reallocation documented in fact (2).

Key concepts

turbulence : the rate of churning in firms’ productivity rankings, measured as one minus the Spearman rank correlation of firm-level TFP between adjacent years; distinct from uncertainty in that it changes both the conditional mean and variance of the productivity distribution.

reallocation channel : the mechanism through which turbulence depresses aggregate TFP by shifting labor and capital from high- to low-productivity firms, amplified by tighter credit constraints on high-productivity firms whose expected equity value falls when productivity persistence declines.