Credit-Policy | 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.

Summary of a forthcoming paper, AI-assisted and human-reviewed. See the linked original for the authoritative claims and full conditions.

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.

A Tale of Two Bailouts and Their Impact on Subprime Consumer Debt

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

This paper examines the effects of the Troubled Asset Relief Program (TARP) and the Paycheck Protection Program (PPP)—two government bailout programs during the Global Financial Crisis and the COVID-19 crisis, respectively—on subprime consumer debt, using over 11 million credit bureau observations of individual consumer debt combined with banking, bailout, and local market data. TARP and PPP are found to have opposite effects: subprime consumers in markets with more TARP institutions experienced significantly increased debt burdens following the bailouts, while PPP was associated with reduced subprime consumer debt. Both programs are treated as quasi-natural experiments due to their rapid, largely unanticipated assembly. The findings yield policy implications regarding bailout structures and the conditions attached to bailout funds.

Summary based on a working paper version, AI-assisted and human-reviewed. See the linked published article for the authoritative version.

In depth

Q1. What are the two bailout programs studied and why are they treated as natural experiments?

TARP (2008) and PPP (2020) are treated as quasi-natural experiments because they were assembled quickly during crisis conditions and were largely unanticipated, providing relatively exogenous financial shocks to markets based on the presence of eligible institutions, rather than on prior local demand for credit. Both programs had distinct structures and intended targets—TARP aimed at stabilizing financial institutions directly, while PPP aimed at supporting small business payrolls to prevent employment losses—making their differential effects on subprime consumer debt informative about the channels through which bailout design matters.

Q2. How did TARP affect subprime consumer debt and why?

Subprime consumers in markets with more TARP institutions had significantly increased debt burdens following TARP, consistent with a channel in which bank stabilization via TARP relaxed credit supply conditions (especially for lower-quality borrowers) or with a moral hazard channel in which TARP-recipient banks extended credit more aggressively knowing they had government backing. Subprime mortgages played a central role in the buildup to the GFC, growing from 2.5% to 8.4% of mortgage balances outstanding between 2001 and 2007; the finding that TARP increased rather than reduced subprime debt burdens raises concerns about whether bank stabilization programs sufficiently constrain the subsequent lending behavior of recipient institutions.

Q3. How did PPP affect subprime consumer debt and why?

PPP was associated with reduced subprime consumer debt, consistent with a channel in which the payroll support prevented the expected wave of unemployment-driven debt distress and credit score deterioration that would otherwise have converted prime consumers into subprime borrowers during the COVID-19 crisis. Prior to PPP, the COVID-19 recession—with unemployment peaking at 14.7% in April 2020—was expected to cause a ballooning of subprime consumer debt; the failure of this ballooning to materialize and the actual decline in subprime debt is attributed in part to PPP’s employment and income support function.

Q4. What are the policy implications for bailout design?

The opposite effects of TARP (which increased subprime debt) and PPP (which reduced it) yield policy implications for bailout structures and the conditions attached to bailout funds: bailouts directed at banks without explicit restrictions on subsequent lending behavior may inadvertently stimulate the accumulation of high-risk household debt, while bailouts directed at supporting household incomes and employment may reduce systemic credit risk. These findings suggest that the distribution channel of bailout funds (through banks vs. directly to households and employers) has first-order effects on the resulting debt accumulation and credit risk in the household sector.

Key concepts

TARP (Troubled Asset Relief Program) : the 2008 U.S. government program that provided capital injections to financial institutions during the Global Financial Crisis; found in this paper to be associated with increased subprime consumer debt burdens in affected markets. PPP (Paycheck Protection Program) : the 2020 U.S. government program that provided small business loans/grants to support payrolls during the COVID-19 crisis; found in this paper to be associated with reduced subprime consumer debt, opposite to TARP’s effect. subprime consumer debt : obligations of consumers with low credit scores; the paper’s key outcome measure; elevated levels associated with systemic credit risk (as seen in the buildup to the GFC) and used as a barometer of financial vulnerability in the household sector.

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.

Summary of a forthcoming paper, AI-assisted and human-reviewed. See the linked original for the authoritative claims and full conditions.

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.

Regulating Credit Lines in the Presence of Fire‐Sale Externalities

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

This paper provides a contract-theoretic rationale for the special liquidity regulation of bank credit lines—a form of lending that has received little attention in the regulatory literature despite being the most important source of firm liquidity risk management. In the model, banks choose pre-arranged funding (committed before drawdowns accumulate) and ex-post funding (raised as drawdowns occur) to finance firms’ liquidity needs through credit lines. In states with high liquidity needs, banks cannot raise sufficient ex-post funding to meet all drawdowns and renege on some credit lines, forcing liquidations. Because each additional liquidation depresses the equilibrium liquidation value for all liquidated firms—a pecuniary externality—competitive banks choose insufficient pre-arranged funding in the private equilibrium. A minimum requirement on bank pre-arranged funding per committed (undrawn) funds in credit lines restores constrained efficiency, despite making credit lines more costly; welfare improves because more firms receive funding in high-liquidity states. The optimal regulatory ratio is increasing in the frequency of high-liquidity-need states, the value lost in liquidation, and the sensitivity of liquidation values to forced sales, and decreasing in the premium on pre-arranged funding.

Summary of a forthcoming paper, AI-assisted and human-reviewed. See the linked original for the authoritative claims and full conditions.

In depth

Q1. Why can banks not fully meet credit line drawdowns in high liquidity need states?

In high liquidity need states, where many firms simultaneously draw on their credit lines, the revenues that banks receive from credit lines (interest payments and fees from the small share of firms that need no drawdown) shrink relative to the total drawdown demand, and the resulting shortfall cannot be fully met through ex-post funding raised from new investors because bank revenues are the collateral for such funding. The model captures the systemic nature of correlated liquidity shocks: when drawdowns are idiosyncratic, banks can cross-subsidize from non-drawing firms and raise ex-post funding easily; when drawdowns are highly correlated, these cross-subsidy revenues vanish and ex-post funding is insufficient, making pre-arranged funding essential for maintaining credit line insurance.

Q2. What is the pecuniary externality and why does it lead to under-provision of pre-arranged funding?

When a bank reneges on a credit line and the borrowing firm is liquidated, the forced sale of the firm’s assets depresses the equilibrium liquidation value—a fire-sale externality that reduces the payoff for all other firms being liquidated simultaneously; competitive banks do not internalize this negative spillover because, individually, each bank takes liquidation prices as given, leading the private equilibrium to feature too little pre-arranged funding and too frequent reneging relative to the constrained social optimum. This is a classic pecuniary externality (Lorenzoni 2008): the externality does not operate through a technological channel but through prices (liquidation values), so it is invisible to competitive agents who treat prices as parametric.

Q3. How does the minimum liquidity requirement on credit lines restore efficiency?

A minimum requirement mandating that banks hold a specified amount of pre-arranged funding per committed (undrawn) credit line funds induces competitive banks to internalize the social value of additional pre-arranged funding—namely, that more pre-arranged funding reduces the number of liquidated firms and raises equilibrium liquidation values—and thereby implements the constrained planner’s solution. This regulatory tool resembles the Basel III LCR (which requires banks to hold liquid assets equal to 5%-30% of undrawn credit lines, depending on the type of credit facility) and the NSFR (which requires stable funding equal to at least 5% of undrawn credit lines); the paper provides the first theoretical justification for precisely this type of regulation for credit lines and characterizes how the optimal ratio depends on economic fundamentals.

Q4. What are the determinants of the optimal regulatory ratio?

The optimal minimum pre-arranged funding requirement per committed funds in credit lines is higher when: (1) the premium on pre-arranged over ex-post funding is lower (making additional pre-arranged funding less costly at the margin); (2) high-liquidity-need states are more frequent (making the insurance value of pre-arranged funding higher in expectation); (3) liquidations are more costly (larger welfare losses per uninsured firm); and (4) liquidation values are more sensitive to the number of liquidations (a steeper fire-sale externality). This comparative statics result is policy-relevant: it implies that the Basel III framework’s one-size-fits-all approach to credit line liquidity ratios cannot be optimal across jurisdictions with different economic fundamentals, and national authorities should calibrate requirements to local conditions.

Key concepts

credit line pre-arranged funding : bank funding committed before credit line drawdowns accumulate; provides insurance against high-liquidity-need states by ensuring the bank can meet drawdowns even when ex-post funding is insufficient; corresponds to equity-like stable funding in Basel III terminology. fire-sale pecuniary externality on liquidation values : the depression of equilibrium firm liquidation values caused by simultaneous forced sales when many firms are liquidated after banks renege on credit lines; not internalized by competitive banks, leading to under-provision of pre-arranged funding in the private equilibrium. optimal credit line liquidity requirement : a minimum ratio of pre-arranged funding to committed (undrawn) credit line funds that restores constrained efficiency by internalizing the fire-sale externality; shown to be an increasing function of the frequency of high-liquidity-need states, liquidation costs, and liquidation-value sensitivity.